In [1]:
# Data
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import impl.layer as l
# Dataset preparation and pre-processing
mnist = input_data.read_data_sets('data/MNIST_data/', one_hot=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_val, y_val = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mnist.test.labels
X_train.shape, X_val.shape, X_test.shape
Extracting data/MNIST_data/train-images-idx3-ubyte.gz
Extracting data/MNIST_data/train-labels-idx1-ubyte.gz
Extracting data/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting data/MNIST_data/t10k-labels-idx1-ubyte.gz
Out[1]:
((55000, 784), (5000, 784), (10000, 784))
In [2]:
# Pre-processing: normalizing
def normalize(X):
# max scale for images 255= 2**8= 8 bit grayscale for each channel
return (X - X.mean(axis=0)) #/ X.std(axis=0)
X_train, X_val, X_test = normalize(X=X_train), normalize(X=X_val), normalize(X=X_test)
In [3]:
# Model
import impl.layer as l # or from impl.layer import *
from impl.loss import * # import all functions from impl.loss file # import impl.loss as loss_func
from sklearn.utils import shuffle as skshuffle
class FFNN:
def __init__(self, D, C, H, L):
self.L = L # layers
self.C = C # classes
self.losses = {'train':[], 'train_acc':[],
'valid':[], 'valid_acc':[],
'test':[], 'test_acc':[]}
self.model = []
self.W_fixed = []
self.grads = []
self.dy_prev = np.zeros((1, C))
self.y_prev = np.zeros((1, C))
low, high = -1, 1
# Input layer: weights/ biases
m = dict(W=np.random.uniform(size=(D, H), low=low, high=high) / np.sqrt(D / 2.), b=np.zeros((1, H)))
self.model.append(m)
# Fixed feedback weight
m = np.random.uniform(size=(D, H), low=low, high=high) / np.sqrt(D / 2.)
self.W_fixed.append(m)
# Input layer: gradients
self.grads.append({key: np.zeros_like(val) for key, val in self.model[0].items()})
# Hidden layers: weights/ biases
m_L = []
for _ in range(L):
m = dict(W=np.random.uniform(size=(H, H), low=low, high=high) / np.sqrt(H / 2.), b=np.zeros((1, H)))
m_L.append(m)
self.model.append(m_L)
# Fixed feedback weight
m_L = []
for _ in range(L):
m = np.random.uniform(size=(H, H), low=low, high=high) / np.sqrt(H / 2.)
m_L.append(m)
self.W_fixed.append(m_L)
# Hidden layer: gradients
grad_L = []
for _ in range(L):
grad_L.append({key: np.zeros_like(val) for key, val in self.model[1][0].items()})
self.grads.append(grad_L)
# Output layer: weights/ biases
m = dict(W=np.random.uniform(size=(H, C), low=low, high=high) / np.sqrt(H / 2.), b=np.zeros((1, C)))
self.model.append(m)
# Fixed feedback weight
m = np.random.uniform(size=(H, C), low=low, high=high) / np.sqrt(H / 2.)
self.W_fixed.append(m)
# Output layer: gradients
self.grads.append({key: np.zeros_like(val) for key, val in self.model[2].items()})
def fc_forward(self, X, W, b):
out = (X @ W) + b
cache = (W, X)
return out, cache
def fc_backward(self, dout, cache, W_fixed):
W, X = cache
dW = X.T @ dout
db = np.sum(dout, axis=0).reshape(1, -1) # db_1xn
# dX = dout @ W.T # vanilla Backprop
dX = dout @ W_fixed.T # fba backprop
return dX, dW, db
def train_forward(self, X, train):
caches, ys = [], []
# Input layer
y, fc_cache = self.fc_forward(X=X, W=self.model[0]['W'], b=self.model[0]['b']) # X_1xD, y_1xc
y, nl_cache = l.tanh_forward(X=y)
# y, nl_cache = l.sigmoid_forward(X=y) # non-linearity/ activation
# y -= l.sigmoid(0.0) # zero-centered/ mean
# y *= 2.0 # uni-var/ std
if train:
caches.append((fc_cache, nl_cache))
X = y.copy() # pass to the next layer
# Hidden layers
fc_caches, nl_caches = [], []
for layer in range(self.L):
y, fc_cache = self.fc_forward(X=X, W=self.model[1][layer]['W'], b=self.model[1][layer]['b'])
y, nl_cache = l.tanh_forward(X=y)
# y, nl_cache = l.sigmoid_forward(X=y) # non-linearity/ activation
# y -= l.sigmoid(0.0) # zero-centered/ mean
# y *= 2.0 # uni-var/ std
X = y.copy() # pass to next layer
if train:
fc_caches.append(fc_cache)
nl_caches.append(nl_cache)
if train:
caches.append((fc_caches, nl_caches)) # caches[1]
# Output layer
y, fc_cache = self.fc_forward(X=X, W=self.model[2]['W'], b=self.model[2]['b'])
y_prob = l.softmax(X=y)
if train:
caches.append(fc_cache)
return y_prob, caches # for backpropating the error
def cross_entropy(self, y_prob, y_train):
m = y_prob.shape[0]
# prob = l.softmax(y_pred)
log_like = -np.log(y_prob[range(m), y_train] + l.eps) # to avoid the devision by zero
data_loss = np.sum(log_like) / m
return data_loss
def dcross_entropy(self, y_prob, y_train): # this is equal for both since the reg_loss (noise) derivative is ZERO.
m = y_prob.shape[0]
# grad_y = l.softmax(y_pred)
grad_y = y_prob
grad_y[range(m), y_train] -= 1.
grad_y /= m
return grad_y
def loss_function(self, y_prob, y_train):
loss = self.cross_entropy(y_prob, y_train) # softmax is included
dy = self.dcross_entropy(y_prob, y_train) # dsoftmax is included
return loss, dy
def train_backward(self, dy, caches, y):
grads = self.grads.copy() # initialized by Zero in every iteration/epoch
dy_prev = self.dy_prev.copy() # for temporal differencing
self.dy_prev = dy.copy() # next iteration/ epoch
# y_prev = self.y_prev.copy() # for temporal differencing
# self.y_prev = y.copy() # next iteration/ epoch
# Output layer
fc_cache = caches[2]
# softmax_backward is included in dcross_entropy.
dX, dW, db = self.fc_backward(dout=dy, cache=fc_cache, W_fixed=self.W_fixed[2])
dy = dX.copy()
# dy = dy @ self.W_fixed[2].T # done
dy_prev = dy_prev @ self.W_fixed[2].T
# y = y @ self.W_fixed[2].T # done
# y_prev = y_prev @ self.W_fixed[2].T
grads[2]['W'] = dW
grads[2]['b'] = db
# Hidden layer
fc_caches, nl_caches = caches[1]
for layer in reversed(range(self.L)):
# dy = l.tanh_backward(cache=nl_caches[layer], dout=dy) # diffable function
# dy = l.sigmoid_backward(cache=nl_caches[layer], dout=dy) # diffable function
dy *= dy - dy_prev # temporal diff instead of differentiable function
# dy *= y - y_prev # temporal diff instead of differentiable function
dX, dW, db = self.fc_backward(dout=dy, cache=fc_caches[layer], W_fixed=self.W_fixed[1][layer])
dy = dX.copy()
# dy = dy @ self.W_fixed[2].T # done
dy_prev = dy_prev @ self.W_fixed[1][layer].T
# y = y @ self.W_fixed[1][layer].T # done
# y_prev = y_prev @ self.W_fixed[1][layer].T
grads[1][layer]['W'] = dW
grads[1][layer]['b'] = db
# Input layer
fc_cache, nl_cache = caches[0]
# dy = l.tanh_backward(cache=nl_cache, dout=dy) # diffable function
# dy = l.sigmoid_backward(cache=nl_caches[layer], dout=dy) # diffable function
dy *= dy - dy_prev # temporal diff instead of differentiable function
# dy *= y - y_prev # temporal diff instead of differentiable function
dX, dW, db = self.fc_backward(dout=dy, cache=fc_cache, W_fixed=self.W_fixed[0])
grads[0]['W'] = dW
grads[0]['b'] = db
return dX, grads
def test(self, X):
y_prob, _ = self.train_forward(X, train=False)
# if self.mode == 'classification':
y_pred = np.argmax(y_prob, axis=1) # for loss ==err
return y_pred, y_prob
def get_minibatch(self, X, y, minibatch_size, shuffle):
minibatches = []
if shuffle:
X, y = skshuffle(X, y)
for i in range(0, X.shape[0], minibatch_size):
X_mini = X[i:i + minibatch_size]
y_mini = y[i:i + minibatch_size]
minibatches.append((X_mini, y_mini))
return minibatches
def sgd(self, train_set, val_set, alpha, mb_size, n_iter, print_after):
X_train, y_train = train_set
X_val, y_val = val_set
# Epochs
for iter in range(1, n_iter + 1):
# Minibatches
minibatches = self.get_minibatch(X_train, y_train, mb_size, shuffle=True)
idx = np.random.randint(0, len(minibatches))
X_mini, y_mini = minibatches[idx]
# Train the model
y_prob, caches = self.train_forward(X_mini, train=True)
_, dy = self.loss_function(y_prob, y_mini)
_, grads = self.train_backward(dy, caches, y_prob)
# Update the model for input layer
for key in grads[0].keys():
self.model[0][key] -= alpha * grads[0][key]
# Update the model for the hidden layers
for layer in range(self.L):
for key in grads[1][layer].keys():
self.model[1][layer][key] -= alpha * grads[1][layer][key]
# Update the model for output layer
for key in grads[2].keys():
self.model[2][key] -= alpha * grads[2][key]
# Training accuracy
y_pred, y_prob = self.test(X_mini)
loss, _ = self.loss_function(y_prob, y_mini) # softmax is included in entropy loss function
self.losses['train'].append(loss)
acc = np.mean(y_pred == y_mini) # confusion matrix
self.losses['train_acc'].append(acc)
# Validate the updated model
y_pred, y_prob = self.test(X_val)
valid_loss, _ = self.loss_function(y_prob, y_val) # softmax is included in entropy loss function
self.losses['valid'].append(valid_loss)
valid_acc = np.mean(y_pred == y_val) # confusion matrix
self.losses['valid_acc'].append(valid_acc)
# Test the final model
y_pred, y_prob = nn.test(X_test)
test_loss, _ = self.loss_function(y_prob, y_test) # softmax is included in entropy loss function
self.losses['test'].append(test_loss)
test_acc = np.mean(y_pred == y_test)
self.losses['test_acc'].append(test_acc)
# print('Test accuracy mean: {:.4f}, std: {:.4f}, loss: {:.4f}'.
# format(acc.mean(), acc.std(), loss))
# Print the model info: loss & accuracy or err & acc
if iter % print_after == 0:
print('Iter-{}, train loss-{:.4f}, acc-{:.4f}, valid loss-{:.4f}, acc-{:.4f}, test loss-{:.4f}, acc-{:.4f}'.format(
iter, loss, acc, valid_loss, valid_acc, test_loss, test_acc))
In [4]:
# Hyper-parameters
n_iter = 100000 # number of epochs
alpha = 1e-3 # learning_rate
mb_size = 50 # 2**10==1024 # width, timestep for sequential data or minibatch size
print_after = 10 # n_iter//10 # print loss for train, valid, and test
num_hidden_units = 32 # number of kernels/ filters in each layer
num_input_units = X_train.shape[1] # noise added at the input lavel as input noise we can use dX or for more improvement
num_output_units = y_train.max() + 1 # number of classes in this classification problem
num_layers = 2 # depth
# Build the model/NN and learn it: running session.
nn = FFNN(C=num_output_units, D=num_input_units, H=num_hidden_units, L=num_layers)
nn.sgd(train_set=(X_train, y_train), val_set=(X_val, y_val), mb_size=mb_size, alpha=alpha,
n_iter=n_iter, print_after=print_after)
Iter-10, train loss-2.3219, acc-0.1200, valid loss-2.3191, acc-0.1034, test loss-2.3200, acc-0.0937
Iter-20, train loss-2.3418, acc-0.1000, valid loss-2.3190, acc-0.1040, test loss-2.3199, acc-0.0940
Iter-30, train loss-2.3174, acc-0.0600, valid loss-2.3188, acc-0.1044, test loss-2.3198, acc-0.0942
Iter-40, train loss-2.3151, acc-0.1800, valid loss-2.3187, acc-0.1046, test loss-2.3196, acc-0.0946
Iter-50, train loss-2.3148, acc-0.0800, valid loss-2.3185, acc-0.1046, test loss-2.3195, acc-0.0950
Iter-60, train loss-2.3034, acc-0.1400, valid loss-2.3184, acc-0.1052, test loss-2.3193, acc-0.0953
Iter-70, train loss-2.3074, acc-0.1200, valid loss-2.3183, acc-0.1052, test loss-2.3192, acc-0.0954
Iter-80, train loss-2.3214, acc-0.0800, valid loss-2.3181, acc-0.1056, test loss-2.3190, acc-0.0955
Iter-90, train loss-2.3169, acc-0.1200, valid loss-2.3180, acc-0.1058, test loss-2.3189, acc-0.0957
Iter-100, train loss-2.3270, acc-0.1200, valid loss-2.3178, acc-0.1058, test loss-2.3188, acc-0.0957
Iter-110, train loss-2.3057, acc-0.1400, valid loss-2.3177, acc-0.1056, test loss-2.3186, acc-0.0961
Iter-120, train loss-2.3192, acc-0.0600, valid loss-2.3176, acc-0.1060, test loss-2.3185, acc-0.0962
Iter-130, train loss-2.3221, acc-0.0800, valid loss-2.3174, acc-0.1058, test loss-2.3183, acc-0.0962
Iter-140, train loss-2.3185, acc-0.1000, valid loss-2.3173, acc-0.1060, test loss-2.3182, acc-0.0965
Iter-150, train loss-2.3348, acc-0.0600, valid loss-2.3171, acc-0.1060, test loss-2.3181, acc-0.0969
Iter-160, train loss-2.3342, acc-0.0400, valid loss-2.3170, acc-0.1062, test loss-2.3179, acc-0.0971
Iter-170, train loss-2.3173, acc-0.0800, valid loss-2.3168, acc-0.1066, test loss-2.3178, acc-0.0972
Iter-180, train loss-2.3258, acc-0.0800, valid loss-2.3167, acc-0.1070, test loss-2.3176, acc-0.0975
Iter-190, train loss-2.3474, acc-0.1000, valid loss-2.3166, acc-0.1070, test loss-2.3175, acc-0.0977
Iter-200, train loss-2.3291, acc-0.1000, valid loss-2.3164, acc-0.1072, test loss-2.3174, acc-0.0980
Iter-210, train loss-2.2914, acc-0.1200, valid loss-2.3163, acc-0.1072, test loss-2.3172, acc-0.0979
Iter-220, train loss-2.3322, acc-0.0600, valid loss-2.3162, acc-0.1074, test loss-2.3171, acc-0.0982
Iter-230, train loss-2.3330, acc-0.0800, valid loss-2.3160, acc-0.1072, test loss-2.3169, acc-0.0980
Iter-240, train loss-2.3173, acc-0.0400, valid loss-2.3159, acc-0.1074, test loss-2.3168, acc-0.0986
Iter-250, train loss-2.2968, acc-0.1600, valid loss-2.3157, acc-0.1076, test loss-2.3166, acc-0.0987
Iter-260, train loss-2.2801, acc-0.1600, valid loss-2.3156, acc-0.1076, test loss-2.3165, acc-0.0992
Iter-270, train loss-2.3240, acc-0.1000, valid loss-2.3155, acc-0.1076, test loss-2.3164, acc-0.0995
Iter-280, train loss-2.3100, acc-0.0800, valid loss-2.3153, acc-0.1078, test loss-2.3162, acc-0.0998
Iter-290, train loss-2.3204, acc-0.1000, valid loss-2.3152, acc-0.1080, test loss-2.3161, acc-0.1000
Iter-300, train loss-2.2978, acc-0.1200, valid loss-2.3150, acc-0.1082, test loss-2.3160, acc-0.1002
Iter-310, train loss-2.3246, acc-0.0800, valid loss-2.3149, acc-0.1082, test loss-2.3158, acc-0.1006
Iter-320, train loss-2.3086, acc-0.1000, valid loss-2.3148, acc-0.1084, test loss-2.3157, acc-0.1007
Iter-330, train loss-2.3302, acc-0.0800, valid loss-2.3146, acc-0.1082, test loss-2.3155, acc-0.1008
Iter-340, train loss-2.3000, acc-0.1400, valid loss-2.3145, acc-0.1084, test loss-2.3154, acc-0.1012
Iter-350, train loss-2.3133, acc-0.0600, valid loss-2.3143, acc-0.1084, test loss-2.3153, acc-0.1014
Iter-360, train loss-2.3136, acc-0.1000, valid loss-2.3142, acc-0.1080, test loss-2.3151, acc-0.1013
Iter-370, train loss-2.3010, acc-0.0600, valid loss-2.3141, acc-0.1084, test loss-2.3150, acc-0.1014
Iter-380, train loss-2.3056, acc-0.1000, valid loss-2.3139, acc-0.1082, test loss-2.3148, acc-0.1015
Iter-390, train loss-2.3180, acc-0.0800, valid loss-2.3138, acc-0.1082, test loss-2.3147, acc-0.1017
Iter-400, train loss-2.3154, acc-0.0800, valid loss-2.3136, acc-0.1086, test loss-2.3146, acc-0.1017
Iter-410, train loss-2.3165, acc-0.1200, valid loss-2.3135, acc-0.1092, test loss-2.3144, acc-0.1018
Iter-420, train loss-2.3303, acc-0.0200, valid loss-2.3134, acc-0.1096, test loss-2.3143, acc-0.1021
Iter-430, train loss-2.3424, acc-0.0600, valid loss-2.3132, acc-0.1098, test loss-2.3141, acc-0.1022
Iter-440, train loss-2.3156, acc-0.1200, valid loss-2.3131, acc-0.1102, test loss-2.3140, acc-0.1025
Iter-450, train loss-2.3035, acc-0.0800, valid loss-2.3129, acc-0.1106, test loss-2.3139, acc-0.1027
Iter-460, train loss-2.2933, acc-0.1400, valid loss-2.3128, acc-0.1106, test loss-2.3137, acc-0.1029
Iter-470, train loss-2.3088, acc-0.0800, valid loss-2.3127, acc-0.1106, test loss-2.3136, acc-0.1032
Iter-480, train loss-2.3002, acc-0.1800, valid loss-2.3125, acc-0.1106, test loss-2.3134, acc-0.1033
Iter-490, train loss-2.2852, acc-0.1400, valid loss-2.3124, acc-0.1110, test loss-2.3133, acc-0.1036
Iter-500, train loss-2.3130, acc-0.1000, valid loss-2.3122, acc-0.1114, test loss-2.3132, acc-0.1038
Iter-510, train loss-2.3124, acc-0.0600, valid loss-2.3121, acc-0.1118, test loss-2.3130, acc-0.1042
Iter-520, train loss-2.3264, acc-0.0200, valid loss-2.3119, acc-0.1118, test loss-2.3129, acc-0.1047
Iter-530, train loss-2.3308, acc-0.0400, valid loss-2.3118, acc-0.1118, test loss-2.3127, acc-0.1048
Iter-540, train loss-2.2752, acc-0.2000, valid loss-2.3117, acc-0.1118, test loss-2.3126, acc-0.1050
Iter-550, train loss-2.3098, acc-0.1000, valid loss-2.3115, acc-0.1120, test loss-2.3124, acc-0.1055
Iter-560, train loss-2.3098, acc-0.0800, valid loss-2.3114, acc-0.1120, test loss-2.3123, acc-0.1057
Iter-570, train loss-2.3088, acc-0.1000, valid loss-2.3112, acc-0.1120, test loss-2.3122, acc-0.1059
Iter-580, train loss-2.2872, acc-0.2200, valid loss-2.3111, acc-0.1124, test loss-2.3120, acc-0.1064
Iter-590, train loss-2.3083, acc-0.0800, valid loss-2.3110, acc-0.1126, test loss-2.3119, acc-0.1064
Iter-600, train loss-2.3175, acc-0.1000, valid loss-2.3109, acc-0.1126, test loss-2.3118, acc-0.1063
Iter-610, train loss-2.3211, acc-0.0600, valid loss-2.3107, acc-0.1126, test loss-2.3116, acc-0.1064
Iter-620, train loss-2.3166, acc-0.0800, valid loss-2.3106, acc-0.1128, test loss-2.3115, acc-0.1067
Iter-630, train loss-2.3104, acc-0.1200, valid loss-2.3104, acc-0.1136, test loss-2.3114, acc-0.1072
Iter-640, train loss-2.2848, acc-0.1800, valid loss-2.3103, acc-0.1138, test loss-2.3112, acc-0.1075
Iter-650, train loss-2.3043, acc-0.1600, valid loss-2.3101, acc-0.1138, test loss-2.3111, acc-0.1077
Iter-660, train loss-2.2987, acc-0.1600, valid loss-2.3100, acc-0.1142, test loss-2.3109, acc-0.1079
Iter-670, train loss-2.3234, acc-0.0600, valid loss-2.3099, acc-0.1144, test loss-2.3108, acc-0.1085
Iter-680, train loss-2.3257, acc-0.0600, valid loss-2.3097, acc-0.1148, test loss-2.3106, acc-0.1089
Iter-690, train loss-2.3154, acc-0.0400, valid loss-2.3096, acc-0.1152, test loss-2.3105, acc-0.1088
Iter-700, train loss-2.2867, acc-0.1800, valid loss-2.3094, acc-0.1148, test loss-2.3104, acc-0.1091
Iter-710, train loss-2.3062, acc-0.1200, valid loss-2.3093, acc-0.1150, test loss-2.3102, acc-0.1096
Iter-720, train loss-2.3376, acc-0.0800, valid loss-2.3092, acc-0.1152, test loss-2.3101, acc-0.1095
Iter-730, train loss-2.3078, acc-0.0600, valid loss-2.3090, acc-0.1156, test loss-2.3099, acc-0.1099
Iter-740, train loss-2.2891, acc-0.2600, valid loss-2.3089, acc-0.1160, test loss-2.3098, acc-0.1100
Iter-750, train loss-2.3344, acc-0.0800, valid loss-2.3087, acc-0.1160, test loss-2.3096, acc-0.1100
Iter-760, train loss-2.3107, acc-0.1200, valid loss-2.3086, acc-0.1160, test loss-2.3095, acc-0.1101
Iter-770, train loss-2.3279, acc-0.1000, valid loss-2.3085, acc-0.1164, test loss-2.3094, acc-0.1102
Iter-780, train loss-2.3214, acc-0.0600, valid loss-2.3083, acc-0.1166, test loss-2.3092, acc-0.1103
Iter-790, train loss-2.3312, acc-0.0800, valid loss-2.3082, acc-0.1168, test loss-2.3091, acc-0.1109
Iter-800, train loss-2.3271, acc-0.0800, valid loss-2.3081, acc-0.1172, test loss-2.3090, acc-0.1111
Iter-810, train loss-2.3079, acc-0.1200, valid loss-2.3079, acc-0.1176, test loss-2.3088, acc-0.1112
Iter-820, train loss-2.3179, acc-0.0600, valid loss-2.3078, acc-0.1178, test loss-2.3087, acc-0.1113
Iter-830, train loss-2.3097, acc-0.1000, valid loss-2.3076, acc-0.1178, test loss-2.3085, acc-0.1113
Iter-840, train loss-2.3053, acc-0.1400, valid loss-2.3075, acc-0.1184, test loss-2.3084, acc-0.1118
Iter-850, train loss-2.3083, acc-0.0600, valid loss-2.3073, acc-0.1184, test loss-2.3083, acc-0.1117
Iter-860, train loss-2.3190, acc-0.1000, valid loss-2.3072, acc-0.1188, test loss-2.3081, acc-0.1119
Iter-870, train loss-2.3070, acc-0.0800, valid loss-2.3071, acc-0.1190, test loss-2.3080, acc-0.1121
Iter-880, train loss-2.3010, acc-0.0800, valid loss-2.3069, acc-0.1188, test loss-2.3078, acc-0.1122
Iter-890, train loss-2.2944, acc-0.1000, valid loss-2.3068, acc-0.1192, test loss-2.3077, acc-0.1122
Iter-900, train loss-2.3009, acc-0.1600, valid loss-2.3066, acc-0.1196, test loss-2.3075, acc-0.1124
Iter-910, train loss-2.3344, acc-0.1000, valid loss-2.3065, acc-0.1202, test loss-2.3074, acc-0.1124
Iter-920, train loss-2.2926, acc-0.1200, valid loss-2.3063, acc-0.1200, test loss-2.3073, acc-0.1124
Iter-930, train loss-2.2958, acc-0.1600, valid loss-2.3062, acc-0.1200, test loss-2.3071, acc-0.1127
Iter-940, train loss-2.3034, acc-0.1600, valid loss-2.3061, acc-0.1210, test loss-2.3070, acc-0.1130
Iter-950, train loss-2.2937, acc-0.1000, valid loss-2.3059, acc-0.1212, test loss-2.3068, acc-0.1135
Iter-960, train loss-2.2912, acc-0.2000, valid loss-2.3058, acc-0.1216, test loss-2.3067, acc-0.1137
Iter-970, train loss-2.2894, acc-0.1600, valid loss-2.3056, acc-0.1214, test loss-2.3065, acc-0.1141
Iter-980, train loss-2.3025, acc-0.1000, valid loss-2.3055, acc-0.1216, test loss-2.3064, acc-0.1146
Iter-990, train loss-2.2935, acc-0.1000, valid loss-2.3053, acc-0.1216, test loss-2.3063, acc-0.1145
Iter-1000, train loss-2.3347, acc-0.0400, valid loss-2.3052, acc-0.1222, test loss-2.3061, acc-0.1147
Iter-1010, train loss-2.2848, acc-0.1600, valid loss-2.3051, acc-0.1224, test loss-2.3060, acc-0.1151
Iter-1020, train loss-2.2927, acc-0.1600, valid loss-2.3049, acc-0.1228, test loss-2.3058, acc-0.1151
Iter-1030, train loss-2.3042, acc-0.1000, valid loss-2.3048, acc-0.1228, test loss-2.3057, acc-0.1155
Iter-1040, train loss-2.3136, acc-0.0800, valid loss-2.3046, acc-0.1228, test loss-2.3055, acc-0.1158
Iter-1050, train loss-2.2857, acc-0.1800, valid loss-2.3045, acc-0.1228, test loss-2.3054, acc-0.1162
Iter-1060, train loss-2.3042, acc-0.1200, valid loss-2.3044, acc-0.1230, test loss-2.3053, acc-0.1164
Iter-1070, train loss-2.2942, acc-0.0800, valid loss-2.3042, acc-0.1234, test loss-2.3051, acc-0.1165
Iter-1080, train loss-2.2734, acc-0.2400, valid loss-2.3040, acc-0.1236, test loss-2.3050, acc-0.1166
Iter-1090, train loss-2.2992, acc-0.1400, valid loss-2.3039, acc-0.1242, test loss-2.3048, acc-0.1168
Iter-1100, train loss-2.2998, acc-0.1400, valid loss-2.3038, acc-0.1242, test loss-2.3047, acc-0.1170
Iter-1110, train loss-2.3121, acc-0.0800, valid loss-2.3036, acc-0.1244, test loss-2.3045, acc-0.1174
Iter-1120, train loss-2.3125, acc-0.0800, valid loss-2.3035, acc-0.1248, test loss-2.3044, acc-0.1176
Iter-1130, train loss-2.2955, acc-0.2200, valid loss-2.3033, acc-0.1252, test loss-2.3042, acc-0.1179
Iter-1140, train loss-2.3062, acc-0.1000, valid loss-2.3032, acc-0.1250, test loss-2.3041, acc-0.1177
Iter-1150, train loss-2.3099, acc-0.1200, valid loss-2.3031, acc-0.1254, test loss-2.3040, acc-0.1183
Iter-1160, train loss-2.3109, acc-0.0600, valid loss-2.3029, acc-0.1256, test loss-2.3038, acc-0.1186
Iter-1170, train loss-2.2797, acc-0.1200, valid loss-2.3028, acc-0.1262, test loss-2.3037, acc-0.1189
Iter-1180, train loss-2.3078, acc-0.1600, valid loss-2.3026, acc-0.1264, test loss-2.3036, acc-0.1195
Iter-1190, train loss-2.2849, acc-0.1000, valid loss-2.3025, acc-0.1264, test loss-2.3034, acc-0.1200
Iter-1200, train loss-2.3184, acc-0.0400, valid loss-2.3024, acc-0.1272, test loss-2.3033, acc-0.1202
Iter-1210, train loss-2.3158, acc-0.0800, valid loss-2.3022, acc-0.1276, test loss-2.3031, acc-0.1206
Iter-1220, train loss-2.2928, acc-0.1400, valid loss-2.3021, acc-0.1282, test loss-2.3030, acc-0.1211
Iter-1230, train loss-2.2991, acc-0.1400, valid loss-2.3019, acc-0.1282, test loss-2.3029, acc-0.1215
Iter-1240, train loss-2.2908, acc-0.1800, valid loss-2.3018, acc-0.1286, test loss-2.3027, acc-0.1217
Iter-1250, train loss-2.2726, acc-0.1200, valid loss-2.3017, acc-0.1292, test loss-2.3026, acc-0.1222
Iter-1260, train loss-2.3067, acc-0.1000, valid loss-2.3015, acc-0.1290, test loss-2.3024, acc-0.1226
Iter-1270, train loss-2.2918, acc-0.2400, valid loss-2.3014, acc-0.1294, test loss-2.3023, acc-0.1228
Iter-1280, train loss-2.2986, acc-0.0400, valid loss-2.3013, acc-0.1300, test loss-2.3022, acc-0.1229
Iter-1290, train loss-2.2934, acc-0.1600, valid loss-2.3011, acc-0.1300, test loss-2.3020, acc-0.1235
Iter-1300, train loss-2.2987, acc-0.1200, valid loss-2.3010, acc-0.1304, test loss-2.3019, acc-0.1239
Iter-1310, train loss-2.3227, acc-0.0800, valid loss-2.3009, acc-0.1306, test loss-2.3018, acc-0.1240
Iter-1320, train loss-2.3221, acc-0.0800, valid loss-2.3007, acc-0.1308, test loss-2.3016, acc-0.1242
Iter-1330, train loss-2.2955, acc-0.0600, valid loss-2.3006, acc-0.1308, test loss-2.3015, acc-0.1244
Iter-1340, train loss-2.2929, acc-0.0800, valid loss-2.3004, acc-0.1312, test loss-2.3013, acc-0.1245
Iter-1350, train loss-2.2827, acc-0.1600, valid loss-2.3003, acc-0.1314, test loss-2.3012, acc-0.1250
Iter-1360, train loss-2.2873, acc-0.1600, valid loss-2.3002, acc-0.1318, test loss-2.3011, acc-0.1252
Iter-1370, train loss-2.2927, acc-0.1200, valid loss-2.3000, acc-0.1320, test loss-2.3009, acc-0.1254
Iter-1380, train loss-2.2807, acc-0.1600, valid loss-2.2999, acc-0.1324, test loss-2.3008, acc-0.1258
Iter-1390, train loss-2.2848, acc-0.1200, valid loss-2.2997, acc-0.1324, test loss-2.3007, acc-0.1263
Iter-1400, train loss-2.3125, acc-0.1000, valid loss-2.2996, acc-0.1326, test loss-2.3005, acc-0.1268
Iter-1410, train loss-2.3072, acc-0.1000, valid loss-2.2995, acc-0.1330, test loss-2.3004, acc-0.1272
Iter-1420, train loss-2.2969, acc-0.1600, valid loss-2.2993, acc-0.1334, test loss-2.3002, acc-0.1274
Iter-1430, train loss-2.3173, acc-0.0600, valid loss-2.2992, acc-0.1334, test loss-2.3001, acc-0.1277
Iter-1440, train loss-2.2847, acc-0.1400, valid loss-2.2990, acc-0.1340, test loss-2.2999, acc-0.1283
Iter-1450, train loss-2.3038, acc-0.1200, valid loss-2.2989, acc-0.1340, test loss-2.2998, acc-0.1286
Iter-1460, train loss-2.2820, acc-0.1200, valid loss-2.2988, acc-0.1340, test loss-2.2997, acc-0.1291
Iter-1470, train loss-2.2902, acc-0.1600, valid loss-2.2986, acc-0.1348, test loss-2.2995, acc-0.1292
Iter-1480, train loss-2.3004, acc-0.1800, valid loss-2.2985, acc-0.1350, test loss-2.2994, acc-0.1287
Iter-1490, train loss-2.3359, acc-0.0200, valid loss-2.2983, acc-0.1350, test loss-2.2993, acc-0.1291
Iter-1500, train loss-2.3001, acc-0.1200, valid loss-2.2982, acc-0.1352, test loss-2.2991, acc-0.1290
Iter-1510, train loss-2.2922, acc-0.1400, valid loss-2.2981, acc-0.1358, test loss-2.2990, acc-0.1297
Iter-1520, train loss-2.2962, acc-0.1400, valid loss-2.2979, acc-0.1362, test loss-2.2988, acc-0.1300
Iter-1530, train loss-2.2841, acc-0.1400, valid loss-2.2978, acc-0.1362, test loss-2.2987, acc-0.1303
Iter-1540, train loss-2.3130, acc-0.0800, valid loss-2.2976, acc-0.1366, test loss-2.2986, acc-0.1305
Iter-1550, train loss-2.2723, acc-0.2200, valid loss-2.2975, acc-0.1372, test loss-2.2984, acc-0.1306
Iter-1560, train loss-2.3107, acc-0.1200, valid loss-2.2974, acc-0.1376, test loss-2.2983, acc-0.1305
Iter-1570, train loss-2.2871, acc-0.1400, valid loss-2.2972, acc-0.1384, test loss-2.2981, acc-0.1311
Iter-1580, train loss-2.3007, acc-0.2000, valid loss-2.2971, acc-0.1384, test loss-2.2980, acc-0.1313
Iter-1590, train loss-2.3288, acc-0.0800, valid loss-2.2969, acc-0.1382, test loss-2.2979, acc-0.1318
Iter-1600, train loss-2.2965, acc-0.0800, valid loss-2.2968, acc-0.1384, test loss-2.2977, acc-0.1322
Iter-1610, train loss-2.3034, acc-0.1600, valid loss-2.2967, acc-0.1388, test loss-2.2976, acc-0.1324
Iter-1620, train loss-2.3064, acc-0.1000, valid loss-2.2965, acc-0.1398, test loss-2.2975, acc-0.1323
Iter-1630, train loss-2.2986, acc-0.1200, valid loss-2.2964, acc-0.1398, test loss-2.2973, acc-0.1323
Iter-1640, train loss-2.3018, acc-0.1200, valid loss-2.2962, acc-0.1404, test loss-2.2972, acc-0.1326
Iter-1650, train loss-2.3215, acc-0.0400, valid loss-2.2961, acc-0.1406, test loss-2.2970, acc-0.1332
Iter-1660, train loss-2.2978, acc-0.1200, valid loss-2.2960, acc-0.1406, test loss-2.2969, acc-0.1330
Iter-1670, train loss-2.2979, acc-0.1400, valid loss-2.2958, acc-0.1408, test loss-2.2967, acc-0.1333
Iter-1680, train loss-2.2788, acc-0.1200, valid loss-2.2957, acc-0.1410, test loss-2.2966, acc-0.1334
Iter-1690, train loss-2.3103, acc-0.0800, valid loss-2.2955, acc-0.1412, test loss-2.2965, acc-0.1335
Iter-1700, train loss-2.2888, acc-0.1000, valid loss-2.2954, acc-0.1416, test loss-2.2963, acc-0.1338
Iter-1710, train loss-2.3112, acc-0.1000, valid loss-2.2953, acc-0.1414, test loss-2.2962, acc-0.1339
Iter-1720, train loss-2.2933, acc-0.1400, valid loss-2.2951, acc-0.1416, test loss-2.2960, acc-0.1346
Iter-1730, train loss-2.3068, acc-0.1000, valid loss-2.2950, acc-0.1422, test loss-2.2959, acc-0.1349
Iter-1740, train loss-2.2976, acc-0.1200, valid loss-2.2948, acc-0.1426, test loss-2.2957, acc-0.1353
Iter-1750, train loss-2.2804, acc-0.1600, valid loss-2.2947, acc-0.1428, test loss-2.2956, acc-0.1355
Iter-1760, train loss-2.3089, acc-0.0600, valid loss-2.2946, acc-0.1434, test loss-2.2955, acc-0.1359
Iter-1770, train loss-2.2834, acc-0.1800, valid loss-2.2944, acc-0.1436, test loss-2.2953, acc-0.1361
Iter-1780, train loss-2.2974, acc-0.0800, valid loss-2.2943, acc-0.1442, test loss-2.2952, acc-0.1362
Iter-1790, train loss-2.2827, acc-0.2000, valid loss-2.2941, acc-0.1448, test loss-2.2951, acc-0.1367
Iter-1800, train loss-2.2855, acc-0.1000, valid loss-2.2940, acc-0.1448, test loss-2.2949, acc-0.1372
Iter-1810, train loss-2.3002, acc-0.1000, valid loss-2.2938, acc-0.1448, test loss-2.2948, acc-0.1374
Iter-1820, train loss-2.2842, acc-0.1000, valid loss-2.2937, acc-0.1452, test loss-2.2946, acc-0.1380
Iter-1830, train loss-2.2843, acc-0.1600, valid loss-2.2936, acc-0.1454, test loss-2.2945, acc-0.1380
Iter-1840, train loss-2.2822, acc-0.1400, valid loss-2.2934, acc-0.1452, test loss-2.2944, acc-0.1384
Iter-1850, train loss-2.3122, acc-0.1400, valid loss-2.2933, acc-0.1460, test loss-2.2942, acc-0.1389
Iter-1860, train loss-2.2842, acc-0.1600, valid loss-2.2932, acc-0.1468, test loss-2.2941, acc-0.1388
Iter-1870, train loss-2.2824, acc-0.1400, valid loss-2.2930, acc-0.1472, test loss-2.2940, acc-0.1397
Iter-1880, train loss-2.2951, acc-0.1000, valid loss-2.2929, acc-0.1474, test loss-2.2938, acc-0.1401
Iter-1890, train loss-2.3060, acc-0.1600, valid loss-2.2927, acc-0.1474, test loss-2.2937, acc-0.1405
Iter-1900, train loss-2.3121, acc-0.0600, valid loss-2.2926, acc-0.1480, test loss-2.2935, acc-0.1411
Iter-1910, train loss-2.2912, acc-0.0800, valid loss-2.2925, acc-0.1480, test loss-2.2934, acc-0.1414
Iter-1920, train loss-2.2864, acc-0.2200, valid loss-2.2923, acc-0.1482, test loss-2.2933, acc-0.1413
Iter-1930, train loss-2.3043, acc-0.1600, valid loss-2.2922, acc-0.1484, test loss-2.2932, acc-0.1420
Iter-1940, train loss-2.2897, acc-0.1600, valid loss-2.2921, acc-0.1488, test loss-2.2930, acc-0.1424
Iter-1950, train loss-2.2842, acc-0.1400, valid loss-2.2919, acc-0.1492, test loss-2.2929, acc-0.1423
Iter-1960, train loss-2.2750, acc-0.2400, valid loss-2.2918, acc-0.1494, test loss-2.2927, acc-0.1427
Iter-1970, train loss-2.3068, acc-0.0800, valid loss-2.2917, acc-0.1494, test loss-2.2926, acc-0.1432
Iter-1980, train loss-2.3044, acc-0.1200, valid loss-2.2916, acc-0.1496, test loss-2.2925, acc-0.1438
Iter-1990, train loss-2.3048, acc-0.1200, valid loss-2.2914, acc-0.1496, test loss-2.2924, acc-0.1439
Iter-2000, train loss-2.3010, acc-0.1600, valid loss-2.2913, acc-0.1498, test loss-2.2922, acc-0.1441
Iter-2010, train loss-2.3038, acc-0.0800, valid loss-2.2911, acc-0.1496, test loss-2.2921, acc-0.1445
Iter-2020, train loss-2.3003, acc-0.0600, valid loss-2.2910, acc-0.1496, test loss-2.2920, acc-0.1448
Iter-2030, train loss-2.2723, acc-0.1400, valid loss-2.2909, acc-0.1496, test loss-2.2918, acc-0.1449
Iter-2040, train loss-2.3039, acc-0.1600, valid loss-2.2908, acc-0.1496, test loss-2.2917, acc-0.1448
Iter-2050, train loss-2.2747, acc-0.2200, valid loss-2.2906, acc-0.1494, test loss-2.2915, acc-0.1449
Iter-2060, train loss-2.3135, acc-0.0600, valid loss-2.2905, acc-0.1498, test loss-2.2914, acc-0.1456
Iter-2070, train loss-2.2960, acc-0.1200, valid loss-2.2903, acc-0.1496, test loss-2.2913, acc-0.1456
Iter-2080, train loss-2.2848, acc-0.2400, valid loss-2.2902, acc-0.1500, test loss-2.2911, acc-0.1463
Iter-2090, train loss-2.3201, acc-0.1200, valid loss-2.2901, acc-0.1504, test loss-2.2910, acc-0.1464
Iter-2100, train loss-2.2843, acc-0.2200, valid loss-2.2899, acc-0.1504, test loss-2.2909, acc-0.1465
Iter-2110, train loss-2.2818, acc-0.1600, valid loss-2.2898, acc-0.1504, test loss-2.2907, acc-0.1468
Iter-2120, train loss-2.2893, acc-0.1000, valid loss-2.2897, acc-0.1504, test loss-2.2906, acc-0.1468
Iter-2130, train loss-2.2696, acc-0.1400, valid loss-2.2895, acc-0.1504, test loss-2.2905, acc-0.1469
Iter-2140, train loss-2.2938, acc-0.1200, valid loss-2.2894, acc-0.1504, test loss-2.2903, acc-0.1468
Iter-2150, train loss-2.2858, acc-0.1600, valid loss-2.2893, acc-0.1504, test loss-2.2902, acc-0.1468
Iter-2160, train loss-2.2881, acc-0.2200, valid loss-2.2891, acc-0.1504, test loss-2.2900, acc-0.1471
Iter-2170, train loss-2.2865, acc-0.0800, valid loss-2.2890, acc-0.1508, test loss-2.2899, acc-0.1473
Iter-2180, train loss-2.2912, acc-0.0800, valid loss-2.2888, acc-0.1506, test loss-2.2898, acc-0.1475
Iter-2190, train loss-2.2793, acc-0.2000, valid loss-2.2887, acc-0.1508, test loss-2.2896, acc-0.1478
Iter-2200, train loss-2.2797, acc-0.1800, valid loss-2.2886, acc-0.1514, test loss-2.2895, acc-0.1480
Iter-2210, train loss-2.3021, acc-0.1600, valid loss-2.2884, acc-0.1510, test loss-2.2893, acc-0.1483
Iter-2220, train loss-2.3207, acc-0.0200, valid loss-2.2883, acc-0.1514, test loss-2.2892, acc-0.1491
Iter-2230, train loss-2.2709, acc-0.2400, valid loss-2.2882, acc-0.1516, test loss-2.2891, acc-0.1494
Iter-2240, train loss-2.2850, acc-0.1400, valid loss-2.2880, acc-0.1522, test loss-2.2889, acc-0.1493
Iter-2250, train loss-2.3011, acc-0.1000, valid loss-2.2879, acc-0.1522, test loss-2.2888, acc-0.1494
Iter-2260, train loss-2.2909, acc-0.1200, valid loss-2.2878, acc-0.1524, test loss-2.2887, acc-0.1500
Iter-2270, train loss-2.2872, acc-0.2000, valid loss-2.2876, acc-0.1524, test loss-2.2885, acc-0.1501
Iter-2280, train loss-2.2702, acc-0.1800, valid loss-2.2875, acc-0.1530, test loss-2.2884, acc-0.1506
Iter-2290, train loss-2.2782, acc-0.1800, valid loss-2.2874, acc-0.1532, test loss-2.2883, acc-0.1510
Iter-2300, train loss-2.2849, acc-0.2000, valid loss-2.2872, acc-0.1536, test loss-2.2881, acc-0.1513
Iter-2310, train loss-2.2940, acc-0.1400, valid loss-2.2871, acc-0.1542, test loss-2.2880, acc-0.1515
Iter-2320, train loss-2.2768, acc-0.1600, valid loss-2.2869, acc-0.1544, test loss-2.2879, acc-0.1519
Iter-2330, train loss-2.2821, acc-0.1600, valid loss-2.2868, acc-0.1550, test loss-2.2877, acc-0.1528
Iter-2340, train loss-2.2735, acc-0.0800, valid loss-2.2867, acc-0.1550, test loss-2.2876, acc-0.1526
Iter-2350, train loss-2.2748, acc-0.1600, valid loss-2.2865, acc-0.1552, test loss-2.2874, acc-0.1527
Iter-2360, train loss-2.3001, acc-0.1000, valid loss-2.2864, acc-0.1550, test loss-2.2873, acc-0.1534
Iter-2370, train loss-2.2949, acc-0.1600, valid loss-2.2863, acc-0.1560, test loss-2.2872, acc-0.1534
Iter-2380, train loss-2.2891, acc-0.1400, valid loss-2.2861, acc-0.1564, test loss-2.2870, acc-0.1539
Iter-2390, train loss-2.2902, acc-0.2000, valid loss-2.2860, acc-0.1564, test loss-2.2869, acc-0.1538
Iter-2400, train loss-2.2602, acc-0.2000, valid loss-2.2859, acc-0.1564, test loss-2.2868, acc-0.1542
Iter-2410, train loss-2.2838, acc-0.1800, valid loss-2.2857, acc-0.1570, test loss-2.2866, acc-0.1545
Iter-2420, train loss-2.2870, acc-0.1400, valid loss-2.2856, acc-0.1574, test loss-2.2865, acc-0.1547
Iter-2430, train loss-2.2852, acc-0.1600, valid loss-2.2854, acc-0.1578, test loss-2.2864, acc-0.1549
Iter-2440, train loss-2.2876, acc-0.1800, valid loss-2.2853, acc-0.1582, test loss-2.2862, acc-0.1551
Iter-2450, train loss-2.3015, acc-0.1000, valid loss-2.2852, acc-0.1582, test loss-2.2861, acc-0.1556
Iter-2460, train loss-2.2904, acc-0.1600, valid loss-2.2850, acc-0.1586, test loss-2.2860, acc-0.1559
Iter-2470, train loss-2.3017, acc-0.0600, valid loss-2.2849, acc-0.1590, test loss-2.2858, acc-0.1560
Iter-2480, train loss-2.3011, acc-0.1200, valid loss-2.2848, acc-0.1590, test loss-2.2857, acc-0.1565
Iter-2490, train loss-2.2801, acc-0.1600, valid loss-2.2846, acc-0.1594, test loss-2.2856, acc-0.1563
Iter-2500, train loss-2.2655, acc-0.1600, valid loss-2.2845, acc-0.1592, test loss-2.2854, acc-0.1568
Iter-2510, train loss-2.2815, acc-0.1800, valid loss-2.2844, acc-0.1600, test loss-2.2853, acc-0.1574
Iter-2520, train loss-2.2906, acc-0.1400, valid loss-2.2842, acc-0.1602, test loss-2.2852, acc-0.1573
Iter-2530, train loss-2.3126, acc-0.1000, valid loss-2.2841, acc-0.1612, test loss-2.2850, acc-0.1576
Iter-2540, train loss-2.2872, acc-0.2000, valid loss-2.2840, acc-0.1616, test loss-2.2849, acc-0.1581
Iter-2550, train loss-2.2954, acc-0.0800, valid loss-2.2838, acc-0.1616, test loss-2.2848, acc-0.1585
Iter-2560, train loss-2.2884, acc-0.1400, valid loss-2.2837, acc-0.1622, test loss-2.2846, acc-0.1588
Iter-2570, train loss-2.2600, acc-0.2400, valid loss-2.2835, acc-0.1622, test loss-2.2845, acc-0.1589
Iter-2580, train loss-2.2774, acc-0.1800, valid loss-2.2834, acc-0.1626, test loss-2.2843, acc-0.1591
Iter-2590, train loss-2.2860, acc-0.1600, valid loss-2.2833, acc-0.1636, test loss-2.2842, acc-0.1591
Iter-2600, train loss-2.2735, acc-0.1600, valid loss-2.2831, acc-0.1638, test loss-2.2841, acc-0.1595
Iter-2610, train loss-2.2766, acc-0.2000, valid loss-2.2830, acc-0.1636, test loss-2.2839, acc-0.1596
Iter-2620, train loss-2.2865, acc-0.1600, valid loss-2.2829, acc-0.1642, test loss-2.2838, acc-0.1599
Iter-2630, train loss-2.2907, acc-0.1400, valid loss-2.2827, acc-0.1644, test loss-2.2837, acc-0.1601
Iter-2640, train loss-2.2778, acc-0.1600, valid loss-2.2826, acc-0.1650, test loss-2.2835, acc-0.1602
Iter-2650, train loss-2.3027, acc-0.1400, valid loss-2.2825, acc-0.1652, test loss-2.2834, acc-0.1601
Iter-2660, train loss-2.2954, acc-0.1400, valid loss-2.2824, acc-0.1658, test loss-2.2833, acc-0.1611
Iter-2670, train loss-2.2647, acc-0.1800, valid loss-2.2822, acc-0.1664, test loss-2.2831, acc-0.1612
Iter-2680, train loss-2.3021, acc-0.0600, valid loss-2.2821, acc-0.1668, test loss-2.2830, acc-0.1619
Iter-2690, train loss-2.2905, acc-0.1600, valid loss-2.2820, acc-0.1668, test loss-2.2829, acc-0.1619
Iter-2700, train loss-2.2639, acc-0.2000, valid loss-2.2818, acc-0.1668, test loss-2.2827, acc-0.1625
Iter-2710, train loss-2.2714, acc-0.2000, valid loss-2.2817, acc-0.1668, test loss-2.2826, acc-0.1622
Iter-2720, train loss-2.2798, acc-0.1000, valid loss-2.2816, acc-0.1672, test loss-2.2825, acc-0.1630
Iter-2730, train loss-2.2843, acc-0.1600, valid loss-2.2814, acc-0.1688, test loss-2.2823, acc-0.1639
Iter-2740, train loss-2.2787, acc-0.1200, valid loss-2.2813, acc-0.1688, test loss-2.2822, acc-0.1641
Iter-2750, train loss-2.2634, acc-0.1200, valid loss-2.2812, acc-0.1694, test loss-2.2821, acc-0.1652
Iter-2760, train loss-2.2771, acc-0.1200, valid loss-2.2810, acc-0.1696, test loss-2.2819, acc-0.1653
Iter-2770, train loss-2.2987, acc-0.1400, valid loss-2.2809, acc-0.1702, test loss-2.2818, acc-0.1656
Iter-2780, train loss-2.2893, acc-0.1800, valid loss-2.2808, acc-0.1704, test loss-2.2817, acc-0.1662
Iter-2790, train loss-2.2827, acc-0.2000, valid loss-2.2806, acc-0.1700, test loss-2.2816, acc-0.1663
Iter-2800, train loss-2.2841, acc-0.1600, valid loss-2.2805, acc-0.1704, test loss-2.2814, acc-0.1664
Iter-2810, train loss-2.2896, acc-0.1400, valid loss-2.2804, acc-0.1708, test loss-2.2813, acc-0.1670
Iter-2820, train loss-2.2869, acc-0.1000, valid loss-2.2803, acc-0.1712, test loss-2.2812, acc-0.1677
Iter-2830, train loss-2.2966, acc-0.1600, valid loss-2.2801, acc-0.1718, test loss-2.2810, acc-0.1679
Iter-2840, train loss-2.2741, acc-0.2200, valid loss-2.2800, acc-0.1720, test loss-2.2809, acc-0.1687
Iter-2850, train loss-2.2788, acc-0.1800, valid loss-2.2799, acc-0.1722, test loss-2.2808, acc-0.1689
Iter-2860, train loss-2.3054, acc-0.1000, valid loss-2.2797, acc-0.1724, test loss-2.2806, acc-0.1691
Iter-2870, train loss-2.2658, acc-0.2000, valid loss-2.2796, acc-0.1724, test loss-2.2805, acc-0.1696
Iter-2880, train loss-2.2917, acc-0.1800, valid loss-2.2795, acc-0.1728, test loss-2.2804, acc-0.1700
Iter-2890, train loss-2.2715, acc-0.2400, valid loss-2.2793, acc-0.1730, test loss-2.2802, acc-0.1699
Iter-2900, train loss-2.2762, acc-0.1200, valid loss-2.2792, acc-0.1732, test loss-2.2801, acc-0.1702
Iter-2910, train loss-2.2874, acc-0.1200, valid loss-2.2791, acc-0.1732, test loss-2.2799, acc-0.1704
Iter-2920, train loss-2.2600, acc-0.1800, valid loss-2.2789, acc-0.1736, test loss-2.2798, acc-0.1708
Iter-2930, train loss-2.2722, acc-0.1600, valid loss-2.2788, acc-0.1742, test loss-2.2797, acc-0.1712
Iter-2940, train loss-2.2742, acc-0.1200, valid loss-2.2787, acc-0.1744, test loss-2.2795, acc-0.1719
Iter-2950, train loss-2.2709, acc-0.2400, valid loss-2.2785, acc-0.1746, test loss-2.2794, acc-0.1722
Iter-2960, train loss-2.2821, acc-0.1800, valid loss-2.2784, acc-0.1750, test loss-2.2793, acc-0.1724
Iter-2970, train loss-2.2954, acc-0.0800, valid loss-2.2783, acc-0.1758, test loss-2.2792, acc-0.1727
Iter-2980, train loss-2.2907, acc-0.1600, valid loss-2.2781, acc-0.1762, test loss-2.2790, acc-0.1732
Iter-2990, train loss-2.2722, acc-0.2200, valid loss-2.2780, acc-0.1764, test loss-2.2789, acc-0.1735
Iter-3000, train loss-2.3042, acc-0.1600, valid loss-2.2779, acc-0.1764, test loss-2.2788, acc-0.1737
Iter-3010, train loss-2.2569, acc-0.2200, valid loss-2.2777, acc-0.1764, test loss-2.2786, acc-0.1739
Iter-3020, train loss-2.2619, acc-0.1600, valid loss-2.2776, acc-0.1768, test loss-2.2785, acc-0.1745
Iter-3030, train loss-2.2771, acc-0.2200, valid loss-2.2775, acc-0.1770, test loss-2.2784, acc-0.1749
Iter-3040, train loss-2.2909, acc-0.1800, valid loss-2.2773, acc-0.1776, test loss-2.2782, acc-0.1755
Iter-3050, train loss-2.2647, acc-0.3000, valid loss-2.2772, acc-0.1778, test loss-2.2781, acc-0.1758
Iter-3060, train loss-2.2805, acc-0.1200, valid loss-2.2771, acc-0.1784, test loss-2.2780, acc-0.1763
Iter-3070, train loss-2.2621, acc-0.1400, valid loss-2.2769, acc-0.1786, test loss-2.2778, acc-0.1763
Iter-3080, train loss-2.2821, acc-0.1000, valid loss-2.2768, acc-0.1796, test loss-2.2777, acc-0.1769
Iter-3090, train loss-2.2605, acc-0.2000, valid loss-2.2767, acc-0.1800, test loss-2.2776, acc-0.1771
Iter-3100, train loss-2.2565, acc-0.2200, valid loss-2.2765, acc-0.1802, test loss-2.2774, acc-0.1774
Iter-3110, train loss-2.2793, acc-0.1600, valid loss-2.2764, acc-0.1806, test loss-2.2773, acc-0.1775
Iter-3120, train loss-2.2575, acc-0.1400, valid loss-2.2763, acc-0.1808, test loss-2.2772, acc-0.1778
Iter-3130, train loss-2.2640, acc-0.1800, valid loss-2.2762, acc-0.1810, test loss-2.2771, acc-0.1780
Iter-3140, train loss-2.2895, acc-0.1200, valid loss-2.2760, acc-0.1812, test loss-2.2769, acc-0.1788
Iter-3150, train loss-2.2956, acc-0.0400, valid loss-2.2759, acc-0.1812, test loss-2.2768, acc-0.1790
Iter-3160, train loss-2.2599, acc-0.2000, valid loss-2.2758, acc-0.1812, test loss-2.2767, acc-0.1795
Iter-3170, train loss-2.2902, acc-0.1400, valid loss-2.2756, acc-0.1818, test loss-2.2765, acc-0.1798
Iter-3180, train loss-2.2357, acc-0.1600, valid loss-2.2755, acc-0.1822, test loss-2.2764, acc-0.1801
Iter-3190, train loss-2.2924, acc-0.2000, valid loss-2.2754, acc-0.1820, test loss-2.2762, acc-0.1801
Iter-3200, train loss-2.2794, acc-0.0800, valid loss-2.2752, acc-0.1820, test loss-2.2761, acc-0.1802
Iter-3210, train loss-2.2577, acc-0.2400, valid loss-2.2751, acc-0.1822, test loss-2.2760, acc-0.1803
Iter-3220, train loss-2.2986, acc-0.1400, valid loss-2.2750, acc-0.1822, test loss-2.2759, acc-0.1805
Iter-3230, train loss-2.2695, acc-0.1200, valid loss-2.2748, acc-0.1820, test loss-2.2757, acc-0.1812
Iter-3240, train loss-2.2779, acc-0.2200, valid loss-2.2747, acc-0.1834, test loss-2.2756, acc-0.1818
Iter-3250, train loss-2.2828, acc-0.1000, valid loss-2.2746, acc-0.1846, test loss-2.2754, acc-0.1820
Iter-3260, train loss-2.2675, acc-0.1800, valid loss-2.2744, acc-0.1844, test loss-2.2753, acc-0.1822
Iter-3270, train loss-2.2804, acc-0.1800, valid loss-2.2743, acc-0.1846, test loss-2.2752, acc-0.1823
Iter-3280, train loss-2.2719, acc-0.1600, valid loss-2.2742, acc-0.1846, test loss-2.2750, acc-0.1827
Iter-3290, train loss-2.2569, acc-0.1800, valid loss-2.2740, acc-0.1850, test loss-2.2749, acc-0.1832
Iter-3300, train loss-2.2748, acc-0.1800, valid loss-2.2739, acc-0.1854, test loss-2.2748, acc-0.1836
Iter-3310, train loss-2.2696, acc-0.1600, valid loss-2.2738, acc-0.1852, test loss-2.2747, acc-0.1840
Iter-3320, train loss-2.2671, acc-0.1600, valid loss-2.2737, acc-0.1858, test loss-2.2745, acc-0.1839
Iter-3330, train loss-2.2549, acc-0.2000, valid loss-2.2735, acc-0.1860, test loss-2.2744, acc-0.1844
Iter-3340, train loss-2.2644, acc-0.2200, valid loss-2.2734, acc-0.1862, test loss-2.2742, acc-0.1848
Iter-3350, train loss-2.2793, acc-0.1800, valid loss-2.2733, acc-0.1864, test loss-2.2741, acc-0.1848
Iter-3360, train loss-2.2673, acc-0.2400, valid loss-2.2731, acc-0.1870, test loss-2.2740, acc-0.1850
Iter-3370, train loss-2.2715, acc-0.2400, valid loss-2.2730, acc-0.1874, test loss-2.2739, acc-0.1852
Iter-3380, train loss-2.2644, acc-0.2800, valid loss-2.2729, acc-0.1880, test loss-2.2737, acc-0.1855
Iter-3390, train loss-2.2733, acc-0.1800, valid loss-2.2727, acc-0.1888, test loss-2.2736, acc-0.1855
Iter-3400, train loss-2.2882, acc-0.1400, valid loss-2.2726, acc-0.1892, test loss-2.2735, acc-0.1858
Iter-3410, train loss-2.2748, acc-0.1600, valid loss-2.2725, acc-0.1896, test loss-2.2733, acc-0.1865
Iter-3420, train loss-2.2926, acc-0.1800, valid loss-2.2723, acc-0.1898, test loss-2.2732, acc-0.1871
Iter-3430, train loss-2.2607, acc-0.1800, valid loss-2.2722, acc-0.1902, test loss-2.2731, acc-0.1875
Iter-3440, train loss-2.2780, acc-0.2200, valid loss-2.2721, acc-0.1910, test loss-2.2729, acc-0.1878
Iter-3450, train loss-2.2532, acc-0.2400, valid loss-2.2719, acc-0.1914, test loss-2.2728, acc-0.1883
Iter-3460, train loss-2.2581, acc-0.1800, valid loss-2.2718, acc-0.1914, test loss-2.2727, acc-0.1884
Iter-3470, train loss-2.2644, acc-0.2800, valid loss-2.2717, acc-0.1920, test loss-2.2725, acc-0.1886
Iter-3480, train loss-2.2826, acc-0.1600, valid loss-2.2715, acc-0.1922, test loss-2.2724, acc-0.1884
Iter-3490, train loss-2.2663, acc-0.1800, valid loss-2.2714, acc-0.1924, test loss-2.2723, acc-0.1886
Iter-3500, train loss-2.3020, acc-0.0400, valid loss-2.2713, acc-0.1928, test loss-2.2722, acc-0.1886
Iter-3510, train loss-2.2781, acc-0.1800, valid loss-2.2712, acc-0.1930, test loss-2.2720, acc-0.1888
Iter-3520, train loss-2.2675, acc-0.2400, valid loss-2.2710, acc-0.1934, test loss-2.2719, acc-0.1894
Iter-3530, train loss-2.2638, acc-0.2200, valid loss-2.2709, acc-0.1936, test loss-2.2718, acc-0.1897
Iter-3540, train loss-2.2658, acc-0.2400, valid loss-2.2708, acc-0.1938, test loss-2.2716, acc-0.1896
Iter-3550, train loss-2.2818, acc-0.1600, valid loss-2.2706, acc-0.1940, test loss-2.2715, acc-0.1900
Iter-3560, train loss-2.2651, acc-0.2400, valid loss-2.2705, acc-0.1942, test loss-2.2714, acc-0.1899
Iter-3570, train loss-2.2897, acc-0.1000, valid loss-2.2704, acc-0.1948, test loss-2.2712, acc-0.1904
Iter-3580, train loss-2.2636, acc-0.2000, valid loss-2.2702, acc-0.1946, test loss-2.2711, acc-0.1905
Iter-3590, train loss-2.2653, acc-0.2400, valid loss-2.2701, acc-0.1950, test loss-2.2710, acc-0.1908
Iter-3600, train loss-2.2757, acc-0.1400, valid loss-2.2700, acc-0.1954, test loss-2.2709, acc-0.1913
Iter-3610, train loss-2.2447, acc-0.2400, valid loss-2.2699, acc-0.1954, test loss-2.2707, acc-0.1917
Iter-3620, train loss-2.3053, acc-0.1400, valid loss-2.2697, acc-0.1960, test loss-2.2706, acc-0.1921
Iter-3630, train loss-2.2610, acc-0.1200, valid loss-2.2696, acc-0.1964, test loss-2.2705, acc-0.1926
Iter-3640, train loss-2.2708, acc-0.2600, valid loss-2.2695, acc-0.1970, test loss-2.2703, acc-0.1931
Iter-3650, train loss-2.2589, acc-0.2200, valid loss-2.2693, acc-0.1970, test loss-2.2702, acc-0.1939
Iter-3660, train loss-2.2757, acc-0.1200, valid loss-2.2692, acc-0.1976, test loss-2.2701, acc-0.1940
Iter-3670, train loss-2.2680, acc-0.0800, valid loss-2.2691, acc-0.1978, test loss-2.2700, acc-0.1940
Iter-3680, train loss-2.2749, acc-0.1800, valid loss-2.2689, acc-0.1982, test loss-2.2698, acc-0.1943
Iter-3690, train loss-2.2775, acc-0.1600, valid loss-2.2688, acc-0.1980, test loss-2.2697, acc-0.1945
Iter-3700, train loss-2.2736, acc-0.0800, valid loss-2.2687, acc-0.1982, test loss-2.2695, acc-0.1945
Iter-3710, train loss-2.2686, acc-0.2200, valid loss-2.2685, acc-0.1988, test loss-2.2694, acc-0.1952
Iter-3720, train loss-2.2756, acc-0.1800, valid loss-2.2684, acc-0.1988, test loss-2.2693, acc-0.1956
Iter-3730, train loss-2.2902, acc-0.1400, valid loss-2.2683, acc-0.1990, test loss-2.2691, acc-0.1965
Iter-3740, train loss-2.2640, acc-0.2200, valid loss-2.2681, acc-0.1994, test loss-2.2690, acc-0.1966
Iter-3750, train loss-2.2620, acc-0.2400, valid loss-2.2680, acc-0.1998, test loss-2.2689, acc-0.1965
Iter-3760, train loss-2.2546, acc-0.2200, valid loss-2.2679, acc-0.2008, test loss-2.2687, acc-0.1976
Iter-3770, train loss-2.2756, acc-0.1200, valid loss-2.2677, acc-0.2010, test loss-2.2686, acc-0.1979
Iter-3780, train loss-2.3081, acc-0.1200, valid loss-2.2676, acc-0.2018, test loss-2.2685, acc-0.1981
Iter-3790, train loss-2.2956, acc-0.1400, valid loss-2.2675, acc-0.2020, test loss-2.2683, acc-0.1984
Iter-3800, train loss-2.2828, acc-0.1600, valid loss-2.2673, acc-0.2022, test loss-2.2682, acc-0.1986
Iter-3810, train loss-2.2683, acc-0.1800, valid loss-2.2672, acc-0.2022, test loss-2.2681, acc-0.1992
Iter-3820, train loss-2.2627, acc-0.1600, valid loss-2.2671, acc-0.2032, test loss-2.2679, acc-0.1995
Iter-3830, train loss-2.2764, acc-0.2400, valid loss-2.2669, acc-0.2030, test loss-2.2678, acc-0.1996
Iter-3840, train loss-2.2683, acc-0.2400, valid loss-2.2668, acc-0.2032, test loss-2.2677, acc-0.2004
Iter-3850, train loss-2.2693, acc-0.2400, valid loss-2.2667, acc-0.2030, test loss-2.2675, acc-0.2009
Iter-3860, train loss-2.2445, acc-0.3200, valid loss-2.2665, acc-0.2028, test loss-2.2674, acc-0.2013
Iter-3870, train loss-2.2560, acc-0.2600, valid loss-2.2664, acc-0.2030, test loss-2.2673, acc-0.2018
Iter-3880, train loss-2.2519, acc-0.3000, valid loss-2.2663, acc-0.2036, test loss-2.2671, acc-0.2022
Iter-3890, train loss-2.2763, acc-0.1600, valid loss-2.2661, acc-0.2038, test loss-2.2670, acc-0.2025
Iter-3900, train loss-2.2826, acc-0.1400, valid loss-2.2660, acc-0.2042, test loss-2.2669, acc-0.2032
Iter-3910, train loss-2.2790, acc-0.2000, valid loss-2.2659, acc-0.2046, test loss-2.2668, acc-0.2038
Iter-3920, train loss-2.2322, acc-0.3000, valid loss-2.2657, acc-0.2046, test loss-2.2666, acc-0.2037
Iter-3930, train loss-2.2480, acc-0.2400, valid loss-2.2656, acc-0.2046, test loss-2.2665, acc-0.2041
Iter-3940, train loss-2.2594, acc-0.3000, valid loss-2.2655, acc-0.2058, test loss-2.2663, acc-0.2043
Iter-3950, train loss-2.2915, acc-0.1200, valid loss-2.2653, acc-0.2060, test loss-2.2662, acc-0.2050
Iter-3960, train loss-2.2649, acc-0.2200, valid loss-2.2652, acc-0.2062, test loss-2.2661, acc-0.2054
Iter-3970, train loss-2.2490, acc-0.2200, valid loss-2.2651, acc-0.2062, test loss-2.2660, acc-0.2058
Iter-3980, train loss-2.2719, acc-0.1800, valid loss-2.2649, acc-0.2068, test loss-2.2658, acc-0.2061
Iter-3990, train loss-2.2944, acc-0.1000, valid loss-2.2648, acc-0.2066, test loss-2.2657, acc-0.2066
Iter-4000, train loss-2.2665, acc-0.2800, valid loss-2.2646, acc-0.2076, test loss-2.2655, acc-0.2071
Iter-4010, train loss-2.2797, acc-0.2200, valid loss-2.2645, acc-0.2080, test loss-2.2654, acc-0.2069
Iter-4020, train loss-2.2647, acc-0.2000, valid loss-2.2644, acc-0.2084, test loss-2.2653, acc-0.2072
Iter-4030, train loss-2.2651, acc-0.2600, valid loss-2.2643, acc-0.2084, test loss-2.2652, acc-0.2076
Iter-4040, train loss-2.2657, acc-0.1200, valid loss-2.2641, acc-0.2084, test loss-2.2650, acc-0.2076
Iter-4050, train loss-2.2612, acc-0.1800, valid loss-2.2640, acc-0.2088, test loss-2.2649, acc-0.2079
Iter-4060, train loss-2.2569, acc-0.1400, valid loss-2.2639, acc-0.2092, test loss-2.2648, acc-0.2083
Iter-4070, train loss-2.2342, acc-0.3400, valid loss-2.2637, acc-0.2106, test loss-2.2646, acc-0.2085
Iter-4080, train loss-2.2435, acc-0.3600, valid loss-2.2636, acc-0.2112, test loss-2.2645, acc-0.2087
Iter-4090, train loss-2.2806, acc-0.1800, valid loss-2.2635, acc-0.2114, test loss-2.2644, acc-0.2089
Iter-4100, train loss-2.2670, acc-0.2000, valid loss-2.2634, acc-0.2118, test loss-2.2643, acc-0.2092
Iter-4110, train loss-2.2644, acc-0.2200, valid loss-2.2632, acc-0.2118, test loss-2.2641, acc-0.2099
Iter-4120, train loss-2.2813, acc-0.1800, valid loss-2.2631, acc-0.2124, test loss-2.2640, acc-0.2099
Iter-4130, train loss-2.2534, acc-0.2800, valid loss-2.2629, acc-0.2126, test loss-2.2639, acc-0.2104
Iter-4140, train loss-2.2799, acc-0.2000, valid loss-2.2628, acc-0.2132, test loss-2.2637, acc-0.2107
Iter-4150, train loss-2.2640, acc-0.2000, valid loss-2.2627, acc-0.2136, test loss-2.2636, acc-0.2107
Iter-4160, train loss-2.2603, acc-0.2400, valid loss-2.2626, acc-0.2134, test loss-2.2635, acc-0.2111
Iter-4170, train loss-2.2713, acc-0.2200, valid loss-2.2624, acc-0.2134, test loss-2.2633, acc-0.2115
Iter-4180, train loss-2.2541, acc-0.2600, valid loss-2.2623, acc-0.2144, test loss-2.2632, acc-0.2117
Iter-4190, train loss-2.2675, acc-0.2200, valid loss-2.2622, acc-0.2144, test loss-2.2631, acc-0.2123
Iter-4200, train loss-2.2628, acc-0.2200, valid loss-2.2620, acc-0.2158, test loss-2.2629, acc-0.2127
Iter-4210, train loss-2.2630, acc-0.1800, valid loss-2.2619, acc-0.2160, test loss-2.2628, acc-0.2128
Iter-4220, train loss-2.2703, acc-0.0800, valid loss-2.2618, acc-0.2166, test loss-2.2627, acc-0.2133
Iter-4230, train loss-2.2863, acc-0.1400, valid loss-2.2616, acc-0.2174, test loss-2.2626, acc-0.2135
Iter-4240, train loss-2.2634, acc-0.2600, valid loss-2.2615, acc-0.2172, test loss-2.2624, acc-0.2143
Iter-4250, train loss-2.2481, acc-0.2800, valid loss-2.2614, acc-0.2174, test loss-2.2623, acc-0.2146
Iter-4260, train loss-2.2760, acc-0.2000, valid loss-2.2613, acc-0.2182, test loss-2.2622, acc-0.2148
Iter-4270, train loss-2.2688, acc-0.1600, valid loss-2.2611, acc-0.2188, test loss-2.2620, acc-0.2153
Iter-4280, train loss-2.2570, acc-0.2000, valid loss-2.2610, acc-0.2190, test loss-2.2619, acc-0.2155
Iter-4290, train loss-2.2623, acc-0.2000, valid loss-2.2609, acc-0.2198, test loss-2.2618, acc-0.2158
Iter-4300, train loss-2.2590, acc-0.2000, valid loss-2.2608, acc-0.2200, test loss-2.2617, acc-0.2159
Iter-4310, train loss-2.2672, acc-0.2200, valid loss-2.2606, acc-0.2200, test loss-2.2615, acc-0.2164
Iter-4320, train loss-2.2549, acc-0.2000, valid loss-2.2605, acc-0.2202, test loss-2.2614, acc-0.2169
Iter-4330, train loss-2.2600, acc-0.3200, valid loss-2.2604, acc-0.2208, test loss-2.2613, acc-0.2174
Iter-4340, train loss-2.2876, acc-0.1000, valid loss-2.2602, acc-0.2208, test loss-2.2611, acc-0.2173
Iter-4350, train loss-2.2625, acc-0.2000, valid loss-2.2601, acc-0.2210, test loss-2.2610, acc-0.2181
Iter-4360, train loss-2.2383, acc-0.2800, valid loss-2.2600, acc-0.2208, test loss-2.2609, acc-0.2181
Iter-4370, train loss-2.2542, acc-0.2200, valid loss-2.2599, acc-0.2206, test loss-2.2608, acc-0.2187
Iter-4380, train loss-2.2521, acc-0.2000, valid loss-2.2597, acc-0.2216, test loss-2.2606, acc-0.2189
Iter-4390, train loss-2.2609, acc-0.2600, valid loss-2.2596, acc-0.2214, test loss-2.2605, acc-0.2188
Iter-4400, train loss-2.2517, acc-0.1800, valid loss-2.2595, acc-0.2224, test loss-2.2604, acc-0.2196
Iter-4410, train loss-2.2617, acc-0.2400, valid loss-2.2593, acc-0.2232, test loss-2.2602, acc-0.2202
Iter-4420, train loss-2.2560, acc-0.1800, valid loss-2.2592, acc-0.2234, test loss-2.2601, acc-0.2208
Iter-4430, train loss-2.2485, acc-0.3400, valid loss-2.2590, acc-0.2238, test loss-2.2600, acc-0.2215
Iter-4440, train loss-2.2275, acc-0.2200, valid loss-2.2589, acc-0.2236, test loss-2.2598, acc-0.2217
Iter-4450, train loss-2.3021, acc-0.2200, valid loss-2.2588, acc-0.2246, test loss-2.2597, acc-0.2222
Iter-4460, train loss-2.2835, acc-0.1600, valid loss-2.2586, acc-0.2248, test loss-2.2596, acc-0.2223
Iter-4470, train loss-2.2558, acc-0.2200, valid loss-2.2585, acc-0.2254, test loss-2.2594, acc-0.2227
Iter-4480, train loss-2.2454, acc-0.2400, valid loss-2.2584, acc-0.2254, test loss-2.2593, acc-0.2228
Iter-4490, train loss-2.2538, acc-0.2000, valid loss-2.2582, acc-0.2260, test loss-2.2592, acc-0.2230
Iter-4500, train loss-2.2585, acc-0.2200, valid loss-2.2581, acc-0.2260, test loss-2.2590, acc-0.2234
Iter-4510, train loss-2.2942, acc-0.1000, valid loss-2.2580, acc-0.2266, test loss-2.2589, acc-0.2233
Iter-4520, train loss-2.2624, acc-0.2600, valid loss-2.2579, acc-0.2266, test loss-2.2588, acc-0.2238
Iter-4530, train loss-2.2355, acc-0.3200, valid loss-2.2577, acc-0.2266, test loss-2.2587, acc-0.2241
Iter-4540, train loss-2.2569, acc-0.2800, valid loss-2.2576, acc-0.2272, test loss-2.2585, acc-0.2243
Iter-4550, train loss-2.2731, acc-0.2000, valid loss-2.2575, acc-0.2274, test loss-2.2584, acc-0.2252
Iter-4560, train loss-2.2603, acc-0.1600, valid loss-2.2573, acc-0.2278, test loss-2.2583, acc-0.2252
Iter-4570, train loss-2.2804, acc-0.1600, valid loss-2.2572, acc-0.2282, test loss-2.2581, acc-0.2255
Iter-4580, train loss-2.2764, acc-0.2600, valid loss-2.2571, acc-0.2286, test loss-2.2580, acc-0.2262
Iter-4590, train loss-2.2537, acc-0.2000, valid loss-2.2569, acc-0.2290, test loss-2.2579, acc-0.2264
Iter-4600, train loss-2.2354, acc-0.2400, valid loss-2.2568, acc-0.2294, test loss-2.2578, acc-0.2269
Iter-4610, train loss-2.2397, acc-0.4000, valid loss-2.2567, acc-0.2294, test loss-2.2576, acc-0.2269
Iter-4620, train loss-2.2638, acc-0.2600, valid loss-2.2566, acc-0.2300, test loss-2.2575, acc-0.2270
Iter-4630, train loss-2.2486, acc-0.2400, valid loss-2.2564, acc-0.2304, test loss-2.2574, acc-0.2275
Iter-4640, train loss-2.2554, acc-0.2200, valid loss-2.2563, acc-0.2306, test loss-2.2572, acc-0.2282
Iter-4650, train loss-2.2370, acc-0.2600, valid loss-2.2562, acc-0.2308, test loss-2.2571, acc-0.2284
Iter-4660, train loss-2.2754, acc-0.2000, valid loss-2.2561, acc-0.2316, test loss-2.2570, acc-0.2288
Iter-4670, train loss-2.2571, acc-0.1400, valid loss-2.2559, acc-0.2318, test loss-2.2569, acc-0.2289
Iter-4680, train loss-2.2492, acc-0.2400, valid loss-2.2558, acc-0.2324, test loss-2.2567, acc-0.2292
Iter-4690, train loss-2.2780, acc-0.1400, valid loss-2.2557, acc-0.2322, test loss-2.2566, acc-0.2292
Iter-4700, train loss-2.2345, acc-0.2600, valid loss-2.2555, acc-0.2326, test loss-2.2565, acc-0.2294
Iter-4710, train loss-2.2655, acc-0.1600, valid loss-2.2554, acc-0.2334, test loss-2.2563, acc-0.2294
Iter-4720, train loss-2.2329, acc-0.3000, valid loss-2.2553, acc-0.2346, test loss-2.2562, acc-0.2303
Iter-4730, train loss-2.2655, acc-0.1600, valid loss-2.2551, acc-0.2350, test loss-2.2561, acc-0.2309
Iter-4740, train loss-2.2435, acc-0.2400, valid loss-2.2550, acc-0.2350, test loss-2.2559, acc-0.2310
Iter-4750, train loss-2.2532, acc-0.2800, valid loss-2.2549, acc-0.2352, test loss-2.2558, acc-0.2314
Iter-4760, train loss-2.2295, acc-0.3600, valid loss-2.2547, acc-0.2358, test loss-2.2557, acc-0.2319
Iter-4770, train loss-2.2261, acc-0.2800, valid loss-2.2546, acc-0.2362, test loss-2.2555, acc-0.2322
Iter-4780, train loss-2.2784, acc-0.1600, valid loss-2.2545, acc-0.2372, test loss-2.2554, acc-0.2324
Iter-4790, train loss-2.2607, acc-0.1800, valid loss-2.2544, acc-0.2374, test loss-2.2553, acc-0.2328
Iter-4800, train loss-2.2552, acc-0.2400, valid loss-2.2542, acc-0.2374, test loss-2.2552, acc-0.2333
Iter-4810, train loss-2.2658, acc-0.2400, valid loss-2.2541, acc-0.2378, test loss-2.2550, acc-0.2335
Iter-4820, train loss-2.2778, acc-0.2600, valid loss-2.2540, acc-0.2376, test loss-2.2549, acc-0.2339
Iter-4830, train loss-2.2882, acc-0.1800, valid loss-2.2538, acc-0.2378, test loss-2.2548, acc-0.2340
Iter-4840, train loss-2.2505, acc-0.3000, valid loss-2.2537, acc-0.2388, test loss-2.2546, acc-0.2344
Iter-4850, train loss-2.2296, acc-0.2400, valid loss-2.2536, acc-0.2390, test loss-2.2545, acc-0.2346
Iter-4860, train loss-2.2236, acc-0.3200, valid loss-2.2534, acc-0.2390, test loss-2.2544, acc-0.2348
Iter-4870, train loss-2.2719, acc-0.1800, valid loss-2.2533, acc-0.2394, test loss-2.2542, acc-0.2349
Iter-4880, train loss-2.2636, acc-0.2400, valid loss-2.2532, acc-0.2402, test loss-2.2541, acc-0.2357
Iter-4890, train loss-2.2576, acc-0.2400, valid loss-2.2531, acc-0.2408, test loss-2.2540, acc-0.2360
Iter-4900, train loss-2.2548, acc-0.2800, valid loss-2.2529, acc-0.2412, test loss-2.2538, acc-0.2363
Iter-4910, train loss-2.2583, acc-0.2400, valid loss-2.2528, acc-0.2416, test loss-2.2537, acc-0.2367
Iter-4920, train loss-2.2528, acc-0.2000, valid loss-2.2527, acc-0.2422, test loss-2.2536, acc-0.2369
Iter-4930, train loss-2.2629, acc-0.2400, valid loss-2.2525, acc-0.2426, test loss-2.2535, acc-0.2375
Iter-4940, train loss-2.2525, acc-0.2200, valid loss-2.2524, acc-0.2428, test loss-2.2533, acc-0.2377
Iter-4950, train loss-2.2339, acc-0.3400, valid loss-2.2523, acc-0.2428, test loss-2.2532, acc-0.2381
Iter-4960, train loss-2.2802, acc-0.1400, valid loss-2.2522, acc-0.2426, test loss-2.2531, acc-0.2388
Iter-4970, train loss-2.2607, acc-0.2800, valid loss-2.2520, acc-0.2432, test loss-2.2530, acc-0.2390
Iter-4980, train loss-2.2464, acc-0.3000, valid loss-2.2519, acc-0.2432, test loss-2.2528, acc-0.2395
Iter-4990, train loss-2.2611, acc-0.1800, valid loss-2.2518, acc-0.2434, test loss-2.2527, acc-0.2397
Iter-5000, train loss-2.2693, acc-0.2000, valid loss-2.2517, acc-0.2438, test loss-2.2526, acc-0.2400
Iter-5010, train loss-2.2209, acc-0.3400, valid loss-2.2515, acc-0.2446, test loss-2.2524, acc-0.2403
Iter-5020, train loss-2.2399, acc-0.2600, valid loss-2.2514, acc-0.2452, test loss-2.2523, acc-0.2405
Iter-5030, train loss-2.2542, acc-0.1800, valid loss-2.2513, acc-0.2458, test loss-2.2522, acc-0.2411
Iter-5040, train loss-2.2534, acc-0.2200, valid loss-2.2511, acc-0.2460, test loss-2.2521, acc-0.2415
Iter-5050, train loss-2.2336, acc-0.3400, valid loss-2.2510, acc-0.2462, test loss-2.2519, acc-0.2421
Iter-5060, train loss-2.2478, acc-0.2000, valid loss-2.2509, acc-0.2464, test loss-2.2518, acc-0.2433
Iter-5070, train loss-2.2339, acc-0.3000, valid loss-2.2508, acc-0.2470, test loss-2.2517, acc-0.2435
Iter-5080, train loss-2.2630, acc-0.2800, valid loss-2.2506, acc-0.2474, test loss-2.2516, acc-0.2441
Iter-5090, train loss-2.2611, acc-0.1800, valid loss-2.2505, acc-0.2480, test loss-2.2514, acc-0.2445
Iter-5100, train loss-2.2340, acc-0.2400, valid loss-2.2504, acc-0.2484, test loss-2.2513, acc-0.2448
Iter-5110, train loss-2.2714, acc-0.2400, valid loss-2.2503, acc-0.2486, test loss-2.2512, acc-0.2455
Iter-5120, train loss-2.2514, acc-0.1800, valid loss-2.2502, acc-0.2490, test loss-2.2511, acc-0.2459
Iter-5130, train loss-2.2583, acc-0.2400, valid loss-2.2500, acc-0.2494, test loss-2.2509, acc-0.2463
Iter-5140, train loss-2.2604, acc-0.1800, valid loss-2.2499, acc-0.2496, test loss-2.2508, acc-0.2467
Iter-5150, train loss-2.2382, acc-0.3800, valid loss-2.2498, acc-0.2500, test loss-2.2507, acc-0.2470
Iter-5160, train loss-2.2471, acc-0.2400, valid loss-2.2496, acc-0.2504, test loss-2.2506, acc-0.2482
Iter-5170, train loss-2.2650, acc-0.2200, valid loss-2.2495, acc-0.2506, test loss-2.2504, acc-0.2489
Iter-5180, train loss-2.2384, acc-0.2400, valid loss-2.2494, acc-0.2508, test loss-2.2503, acc-0.2488
Iter-5190, train loss-2.2510, acc-0.2800, valid loss-2.2492, acc-0.2506, test loss-2.2502, acc-0.2489
Iter-5200, train loss-2.2442, acc-0.3600, valid loss-2.2491, acc-0.2508, test loss-2.2500, acc-0.2492
Iter-5210, train loss-2.2502, acc-0.2400, valid loss-2.2490, acc-0.2512, test loss-2.2499, acc-0.2495
Iter-5220, train loss-2.2649, acc-0.1400, valid loss-2.2489, acc-0.2520, test loss-2.2498, acc-0.2500
Iter-5230, train loss-2.2435, acc-0.1600, valid loss-2.2487, acc-0.2530, test loss-2.2497, acc-0.2502
Iter-5240, train loss-2.2312, acc-0.2200, valid loss-2.2486, acc-0.2532, test loss-2.2495, acc-0.2507
Iter-5250, train loss-2.2668, acc-0.2000, valid loss-2.2485, acc-0.2532, test loss-2.2494, acc-0.2510
Iter-5260, train loss-2.2483, acc-0.2000, valid loss-2.2483, acc-0.2536, test loss-2.2493, acc-0.2513
Iter-5270, train loss-2.2616, acc-0.2000, valid loss-2.2482, acc-0.2544, test loss-2.2492, acc-0.2515
Iter-5280, train loss-2.2414, acc-0.2800, valid loss-2.2481, acc-0.2542, test loss-2.2490, acc-0.2523
Iter-5290, train loss-2.2404, acc-0.2200, valid loss-2.2480, acc-0.2550, test loss-2.2489, acc-0.2527
Iter-5300, train loss-2.2202, acc-0.3200, valid loss-2.2478, acc-0.2552, test loss-2.2488, acc-0.2531
Iter-5310, train loss-2.2623, acc-0.2200, valid loss-2.2477, acc-0.2554, test loss-2.2486, acc-0.2529
Iter-5320, train loss-2.2631, acc-0.2400, valid loss-2.2476, acc-0.2556, test loss-2.2485, acc-0.2539
Iter-5330, train loss-2.2163, acc-0.3600, valid loss-2.2475, acc-0.2566, test loss-2.2484, acc-0.2541
Iter-5340, train loss-2.2449, acc-0.3000, valid loss-2.2473, acc-0.2568, test loss-2.2483, acc-0.2541
Iter-5350, train loss-2.2235, acc-0.1800, valid loss-2.2472, acc-0.2574, test loss-2.2481, acc-0.2544
Iter-5360, train loss-2.2613, acc-0.2000, valid loss-2.2471, acc-0.2578, test loss-2.2480, acc-0.2546
Iter-5370, train loss-2.2269, acc-0.3200, valid loss-2.2470, acc-0.2578, test loss-2.2479, acc-0.2554
Iter-5380, train loss-2.2423, acc-0.2400, valid loss-2.2468, acc-0.2578, test loss-2.2478, acc-0.2559
Iter-5390, train loss-2.2372, acc-0.2800, valid loss-2.2467, acc-0.2578, test loss-2.2476, acc-0.2566
Iter-5400, train loss-2.2472, acc-0.2000, valid loss-2.2466, acc-0.2578, test loss-2.2475, acc-0.2569
Iter-5410, train loss-2.2285, acc-0.2800, valid loss-2.2465, acc-0.2586, test loss-2.2474, acc-0.2573
Iter-5420, train loss-2.2429, acc-0.2400, valid loss-2.2463, acc-0.2588, test loss-2.2473, acc-0.2578
Iter-5430, train loss-2.2316, acc-0.3400, valid loss-2.2462, acc-0.2590, test loss-2.2471, acc-0.2585
Iter-5440, train loss-2.2274, acc-0.2800, valid loss-2.2461, acc-0.2594, test loss-2.2470, acc-0.2589
Iter-5450, train loss-2.2586, acc-0.2600, valid loss-2.2460, acc-0.2600, test loss-2.2469, acc-0.2593
Iter-5460, train loss-2.2545, acc-0.2400, valid loss-2.2458, acc-0.2610, test loss-2.2468, acc-0.2596
Iter-5470, train loss-2.2330, acc-0.2000, valid loss-2.2457, acc-0.2608, test loss-2.2466, acc-0.2599
Iter-5480, train loss-2.2744, acc-0.2000, valid loss-2.2456, acc-0.2614, test loss-2.2465, acc-0.2605
Iter-5490, train loss-2.2508, acc-0.3000, valid loss-2.2454, acc-0.2620, test loss-2.2464, acc-0.2611
Iter-5500, train loss-2.2255, acc-0.2600, valid loss-2.2453, acc-0.2630, test loss-2.2462, acc-0.2615
Iter-5510, train loss-2.2608, acc-0.3400, valid loss-2.2452, acc-0.2626, test loss-2.2461, acc-0.2620
Iter-5520, train loss-2.2839, acc-0.1200, valid loss-2.2451, acc-0.2626, test loss-2.2460, acc-0.2623
Iter-5530, train loss-2.2501, acc-0.2200, valid loss-2.2449, acc-0.2634, test loss-2.2459, acc-0.2625
Iter-5540, train loss-2.2527, acc-0.2200, valid loss-2.2448, acc-0.2642, test loss-2.2457, acc-0.2627
Iter-5550, train loss-2.2192, acc-0.4200, valid loss-2.2447, acc-0.2644, test loss-2.2456, acc-0.2634
Iter-5560, train loss-2.2320, acc-0.3200, valid loss-2.2445, acc-0.2650, test loss-2.2455, acc-0.2635
Iter-5570, train loss-2.2432, acc-0.3200, valid loss-2.2444, acc-0.2652, test loss-2.2453, acc-0.2640
Iter-5580, train loss-2.2486, acc-0.2400, valid loss-2.2443, acc-0.2656, test loss-2.2452, acc-0.2642
Iter-5590, train loss-2.2579, acc-0.2600, valid loss-2.2442, acc-0.2660, test loss-2.2451, acc-0.2649
Iter-5600, train loss-2.2325, acc-0.3000, valid loss-2.2440, acc-0.2658, test loss-2.2450, acc-0.2651
Iter-5610, train loss-2.2115, acc-0.4200, valid loss-2.2439, acc-0.2660, test loss-2.2448, acc-0.2652
Iter-5620, train loss-2.2653, acc-0.2800, valid loss-2.2438, acc-0.2662, test loss-2.2447, acc-0.2657
Iter-5630, train loss-2.2440, acc-0.2400, valid loss-2.2437, acc-0.2666, test loss-2.2446, acc-0.2659
Iter-5640, train loss-2.2473, acc-0.1400, valid loss-2.2436, acc-0.2676, test loss-2.2445, acc-0.2664
Iter-5650, train loss-2.2595, acc-0.2400, valid loss-2.2434, acc-0.2670, test loss-2.2444, acc-0.2665
Iter-5660, train loss-2.2761, acc-0.2400, valid loss-2.2433, acc-0.2674, test loss-2.2442, acc-0.2673
Iter-5670, train loss-2.2522, acc-0.2400, valid loss-2.2432, acc-0.2678, test loss-2.2441, acc-0.2676
Iter-5680, train loss-2.2589, acc-0.2400, valid loss-2.2430, acc-0.2680, test loss-2.2440, acc-0.2678
Iter-5690, train loss-2.2338, acc-0.2400, valid loss-2.2429, acc-0.2684, test loss-2.2438, acc-0.2678
Iter-5700, train loss-2.2148, acc-0.3600, valid loss-2.2428, acc-0.2680, test loss-2.2437, acc-0.2682
Iter-5710, train loss-2.2642, acc-0.2200, valid loss-2.2427, acc-0.2682, test loss-2.2436, acc-0.2688
Iter-5720, train loss-2.2555, acc-0.2800, valid loss-2.2425, acc-0.2684, test loss-2.2435, acc-0.2690
Iter-5730, train loss-2.2203, acc-0.2800, valid loss-2.2424, acc-0.2686, test loss-2.2433, acc-0.2691
Iter-5740, train loss-2.2257, acc-0.2400, valid loss-2.2423, acc-0.2694, test loss-2.2432, acc-0.2694
Iter-5750, train loss-2.2258, acc-0.2800, valid loss-2.2421, acc-0.2696, test loss-2.2431, acc-0.2697
Iter-5760, train loss-2.2047, acc-0.3200, valid loss-2.2420, acc-0.2698, test loss-2.2430, acc-0.2699
Iter-5770, train loss-2.2494, acc-0.2000, valid loss-2.2419, acc-0.2704, test loss-2.2428, acc-0.2702
Iter-5780, train loss-2.2185, acc-0.3400, valid loss-2.2417, acc-0.2708, test loss-2.2427, acc-0.2704
Iter-5790, train loss-2.2403, acc-0.2400, valid loss-2.2416, acc-0.2712, test loss-2.2426, acc-0.2708
Iter-5800, train loss-2.2623, acc-0.1800, valid loss-2.2415, acc-0.2714, test loss-2.2424, acc-0.2711
Iter-5810, train loss-2.2289, acc-0.3800, valid loss-2.2413, acc-0.2720, test loss-2.2423, acc-0.2712
Iter-5820, train loss-2.2377, acc-0.2600, valid loss-2.2412, acc-0.2728, test loss-2.2422, acc-0.2716
Iter-5830, train loss-2.2415, acc-0.2000, valid loss-2.2411, acc-0.2734, test loss-2.2420, acc-0.2720
Iter-5840, train loss-2.2773, acc-0.1800, valid loss-2.2410, acc-0.2738, test loss-2.2419, acc-0.2724
Iter-5850, train loss-2.2275, acc-0.3000, valid loss-2.2408, acc-0.2738, test loss-2.2418, acc-0.2725
Iter-5860, train loss-2.2177, acc-0.3800, valid loss-2.2407, acc-0.2748, test loss-2.2417, acc-0.2727
Iter-5870, train loss-2.2539, acc-0.3000, valid loss-2.2406, acc-0.2748, test loss-2.2415, acc-0.2727
Iter-5880, train loss-2.2415, acc-0.2000, valid loss-2.2404, acc-0.2756, test loss-2.2414, acc-0.2731
Iter-5890, train loss-2.2457, acc-0.2400, valid loss-2.2403, acc-0.2758, test loss-2.2413, acc-0.2738
Iter-5900, train loss-2.2436, acc-0.2600, valid loss-2.2402, acc-0.2758, test loss-2.2412, acc-0.2739
Iter-5910, train loss-2.2407, acc-0.2800, valid loss-2.2401, acc-0.2766, test loss-2.2410, acc-0.2741
Iter-5920, train loss-2.2233, acc-0.3000, valid loss-2.2399, acc-0.2770, test loss-2.2409, acc-0.2743
Iter-5930, train loss-2.2472, acc-0.3200, valid loss-2.2398, acc-0.2776, test loss-2.2408, acc-0.2743
Iter-5940, train loss-2.2290, acc-0.2800, valid loss-2.2397, acc-0.2774, test loss-2.2406, acc-0.2751
Iter-5950, train loss-2.2311, acc-0.3600, valid loss-2.2396, acc-0.2776, test loss-2.2405, acc-0.2749
Iter-5960, train loss-2.2307, acc-0.2800, valid loss-2.2394, acc-0.2778, test loss-2.2404, acc-0.2756
Iter-5970, train loss-2.2685, acc-0.1800, valid loss-2.2393, acc-0.2782, test loss-2.2403, acc-0.2757
Iter-5980, train loss-2.2618, acc-0.2400, valid loss-2.2392, acc-0.2786, test loss-2.2401, acc-0.2762
Iter-5990, train loss-2.2537, acc-0.3200, valid loss-2.2391, acc-0.2788, test loss-2.2400, acc-0.2764
Iter-6000, train loss-2.2333, acc-0.4000, valid loss-2.2389, acc-0.2790, test loss-2.2399, acc-0.2767
Iter-6010, train loss-2.2485, acc-0.3200, valid loss-2.2388, acc-0.2790, test loss-2.2398, acc-0.2772
Iter-6020, train loss-2.2354, acc-0.2000, valid loss-2.2387, acc-0.2796, test loss-2.2396, acc-0.2775
Iter-6030, train loss-2.2668, acc-0.1400, valid loss-2.2386, acc-0.2806, test loss-2.2395, acc-0.2776
Iter-6040, train loss-2.2492, acc-0.2000, valid loss-2.2384, acc-0.2806, test loss-2.2394, acc-0.2777
Iter-6050, train loss-2.2286, acc-0.3000, valid loss-2.2383, acc-0.2810, test loss-2.2393, acc-0.2783
Iter-6060, train loss-2.2575, acc-0.2000, valid loss-2.2382, acc-0.2814, test loss-2.2391, acc-0.2789
Iter-6070, train loss-2.2460, acc-0.2600, valid loss-2.2381, acc-0.2814, test loss-2.2390, acc-0.2794
Iter-6080, train loss-2.2423, acc-0.2000, valid loss-2.2379, acc-0.2816, test loss-2.2389, acc-0.2796
Iter-6090, train loss-2.2258, acc-0.3400, valid loss-2.2378, acc-0.2820, test loss-2.2387, acc-0.2796
Iter-6100, train loss-2.2151, acc-0.3000, valid loss-2.2377, acc-0.2820, test loss-2.2386, acc-0.2800
Iter-6110, train loss-2.2470, acc-0.2200, valid loss-2.2375, acc-0.2818, test loss-2.2385, acc-0.2805
Iter-6120, train loss-2.2272, acc-0.3800, valid loss-2.2374, acc-0.2820, test loss-2.2384, acc-0.2808
Iter-6130, train loss-2.2422, acc-0.2400, valid loss-2.2373, acc-0.2820, test loss-2.2382, acc-0.2814
Iter-6140, train loss-2.2261, acc-0.3000, valid loss-2.2372, acc-0.2822, test loss-2.2381, acc-0.2817
Iter-6150, train loss-2.2224, acc-0.3400, valid loss-2.2370, acc-0.2826, test loss-2.2380, acc-0.2822
Iter-6160, train loss-2.2546, acc-0.2000, valid loss-2.2369, acc-0.2830, test loss-2.2379, acc-0.2825
Iter-6170, train loss-2.2559, acc-0.1400, valid loss-2.2368, acc-0.2838, test loss-2.2377, acc-0.2826
Iter-6180, train loss-2.2073, acc-0.4000, valid loss-2.2366, acc-0.2840, test loss-2.2376, acc-0.2832
Iter-6190, train loss-2.2434, acc-0.3200, valid loss-2.2365, acc-0.2838, test loss-2.2375, acc-0.2836
Iter-6200, train loss-2.2207, acc-0.2600, valid loss-2.2364, acc-0.2842, test loss-2.2373, acc-0.2842
Iter-6210, train loss-2.2306, acc-0.3400, valid loss-2.2363, acc-0.2840, test loss-2.2372, acc-0.2843
Iter-6220, train loss-2.2593, acc-0.1800, valid loss-2.2362, acc-0.2840, test loss-2.2371, acc-0.2845
Iter-6230, train loss-2.2429, acc-0.2000, valid loss-2.2360, acc-0.2844, test loss-2.2370, acc-0.2849
Iter-6240, train loss-2.2391, acc-0.3400, valid loss-2.2359, acc-0.2844, test loss-2.2369, acc-0.2853
Iter-6250, train loss-2.2306, acc-0.3400, valid loss-2.2358, acc-0.2854, test loss-2.2367, acc-0.2855
Iter-6260, train loss-2.2386, acc-0.2200, valid loss-2.2357, acc-0.2858, test loss-2.2366, acc-0.2858
Iter-6270, train loss-2.2390, acc-0.3600, valid loss-2.2355, acc-0.2858, test loss-2.2365, acc-0.2869
Iter-6280, train loss-2.2436, acc-0.2600, valid loss-2.2354, acc-0.2860, test loss-2.2364, acc-0.2872
Iter-6290, train loss-2.2480, acc-0.2000, valid loss-2.2353, acc-0.2862, test loss-2.2362, acc-0.2876
Iter-6300, train loss-2.2382, acc-0.3200, valid loss-2.2352, acc-0.2864, test loss-2.2361, acc-0.2877
Iter-6310, train loss-2.2598, acc-0.2600, valid loss-2.2350, acc-0.2868, test loss-2.2360, acc-0.2879
Iter-6320, train loss-2.2356, acc-0.2400, valid loss-2.2349, acc-0.2872, test loss-2.2359, acc-0.2881
Iter-6330, train loss-2.2355, acc-0.2600, valid loss-2.2348, acc-0.2878, test loss-2.2357, acc-0.2885
Iter-6340, train loss-2.2327, acc-0.3600, valid loss-2.2347, acc-0.2880, test loss-2.2356, acc-0.2887
Iter-6350, train loss-2.2440, acc-0.2400, valid loss-2.2346, acc-0.2882, test loss-2.2355, acc-0.2891
Iter-6360, train loss-2.2264, acc-0.3200, valid loss-2.2344, acc-0.2880, test loss-2.2353, acc-0.2891
Iter-6370, train loss-2.2355, acc-0.2800, valid loss-2.2343, acc-0.2886, test loss-2.2352, acc-0.2894
Iter-6380, train loss-2.2665, acc-0.1400, valid loss-2.2342, acc-0.2886, test loss-2.2351, acc-0.2898
Iter-6390, train loss-2.2091, acc-0.4400, valid loss-2.2340, acc-0.2888, test loss-2.2350, acc-0.2902
Iter-6400, train loss-2.2360, acc-0.1800, valid loss-2.2339, acc-0.2892, test loss-2.2348, acc-0.2905
Iter-6410, train loss-2.2324, acc-0.2800, valid loss-2.2338, acc-0.2892, test loss-2.2347, acc-0.2904
Iter-6420, train loss-2.2125, acc-0.3200, valid loss-2.2337, acc-0.2902, test loss-2.2346, acc-0.2911
Iter-6430, train loss-2.2210, acc-0.2800, valid loss-2.2336, acc-0.2906, test loss-2.2345, acc-0.2906
Iter-6440, train loss-2.2141, acc-0.3600, valid loss-2.2334, acc-0.2906, test loss-2.2344, acc-0.2913
Iter-6450, train loss-2.2313, acc-0.2600, valid loss-2.2333, acc-0.2914, test loss-2.2342, acc-0.2912
Iter-6460, train loss-2.2065, acc-0.4000, valid loss-2.2332, acc-0.2912, test loss-2.2341, acc-0.2917
Iter-6470, train loss-2.2530, acc-0.2000, valid loss-2.2331, acc-0.2914, test loss-2.2340, acc-0.2921
Iter-6480, train loss-2.2199, acc-0.2600, valid loss-2.2329, acc-0.2920, test loss-2.2339, acc-0.2928
Iter-6490, train loss-2.2140, acc-0.4000, valid loss-2.2328, acc-0.2930, test loss-2.2337, acc-0.2930
Iter-6500, train loss-2.2002, acc-0.3000, valid loss-2.2327, acc-0.2930, test loss-2.2336, acc-0.2934
Iter-6510, train loss-2.2405, acc-0.2400, valid loss-2.2326, acc-0.2928, test loss-2.2335, acc-0.2932
Iter-6520, train loss-2.2507, acc-0.2600, valid loss-2.2324, acc-0.2934, test loss-2.2334, acc-0.2936
Iter-6530, train loss-2.2163, acc-0.4200, valid loss-2.2323, acc-0.2942, test loss-2.2332, acc-0.2941
Iter-6540, train loss-2.2208, acc-0.3000, valid loss-2.2322, acc-0.2942, test loss-2.2331, acc-0.2941
Iter-6550, train loss-2.2425, acc-0.2800, valid loss-2.2321, acc-0.2944, test loss-2.2330, acc-0.2945
Iter-6560, train loss-2.2458, acc-0.2600, valid loss-2.2319, acc-0.2952, test loss-2.2329, acc-0.2948
Iter-6570, train loss-2.2425, acc-0.2200, valid loss-2.2318, acc-0.2954, test loss-2.2327, acc-0.2951
Iter-6580, train loss-2.2550, acc-0.2400, valid loss-2.2317, acc-0.2956, test loss-2.2326, acc-0.2956
Iter-6590, train loss-2.2406, acc-0.2600, valid loss-2.2316, acc-0.2964, test loss-2.2325, acc-0.2956
Iter-6600, train loss-2.2367, acc-0.3000, valid loss-2.2314, acc-0.2968, test loss-2.2324, acc-0.2960
Iter-6610, train loss-2.2493, acc-0.2600, valid loss-2.2313, acc-0.2970, test loss-2.2323, acc-0.2961
Iter-6620, train loss-2.2080, acc-0.4000, valid loss-2.2312, acc-0.2974, test loss-2.2321, acc-0.2963
Iter-6630, train loss-2.2539, acc-0.2800, valid loss-2.2311, acc-0.2978, test loss-2.2320, acc-0.2964
Iter-6640, train loss-2.2174, acc-0.3600, valid loss-2.2310, acc-0.2978, test loss-2.2319, acc-0.2964
Iter-6650, train loss-2.2408, acc-0.2600, valid loss-2.2308, acc-0.2978, test loss-2.2318, acc-0.2968
Iter-6660, train loss-2.2238, acc-0.3000, valid loss-2.2307, acc-0.2980, test loss-2.2316, acc-0.2970
Iter-6670, train loss-2.2507, acc-0.2600, valid loss-2.2306, acc-0.2982, test loss-2.2315, acc-0.2970
Iter-6680, train loss-2.2157, acc-0.3200, valid loss-2.2305, acc-0.2986, test loss-2.2314, acc-0.2974
Iter-6690, train loss-2.2493, acc-0.2400, valid loss-2.2303, acc-0.2986, test loss-2.2313, acc-0.2977
Iter-6700, train loss-2.2293, acc-0.2800, valid loss-2.2302, acc-0.2990, test loss-2.2311, acc-0.2980
Iter-6710, train loss-2.2348, acc-0.3600, valid loss-2.2301, acc-0.2992, test loss-2.2310, acc-0.2982
Iter-6720, train loss-2.2329, acc-0.3000, valid loss-2.2300, acc-0.3004, test loss-2.2309, acc-0.2978
Iter-6730, train loss-2.2435, acc-0.2200, valid loss-2.2298, acc-0.3008, test loss-2.2308, acc-0.2981
Iter-6740, train loss-2.2251, acc-0.3000, valid loss-2.2297, acc-0.3008, test loss-2.2306, acc-0.2987
Iter-6750, train loss-2.2247, acc-0.3600, valid loss-2.2296, acc-0.3014, test loss-2.2305, acc-0.2986
Iter-6760, train loss-2.2159, acc-0.3000, valid loss-2.2294, acc-0.3014, test loss-2.2304, acc-0.2987
Iter-6770, train loss-2.2268, acc-0.2800, valid loss-2.2293, acc-0.3018, test loss-2.2303, acc-0.2988
Iter-6780, train loss-2.2244, acc-0.3800, valid loss-2.2292, acc-0.3018, test loss-2.2301, acc-0.2994
Iter-6790, train loss-2.2330, acc-0.2400, valid loss-2.2291, acc-0.3030, test loss-2.2300, acc-0.2995
Iter-6800, train loss-2.2479, acc-0.2600, valid loss-2.2290, acc-0.3024, test loss-2.2299, acc-0.2998
Iter-6810, train loss-2.2528, acc-0.2400, valid loss-2.2288, acc-0.3032, test loss-2.2298, acc-0.3004
Iter-6820, train loss-2.2288, acc-0.3400, valid loss-2.2287, acc-0.3036, test loss-2.2297, acc-0.3007
Iter-6830, train loss-2.2321, acc-0.1800, valid loss-2.2286, acc-0.3034, test loss-2.2295, acc-0.3007
Iter-6840, train loss-2.2229, acc-0.3600, valid loss-2.2285, acc-0.3040, test loss-2.2294, acc-0.3010
Iter-6850, train loss-2.2204, acc-0.3200, valid loss-2.2283, acc-0.3050, test loss-2.2293, acc-0.3015
Iter-6860, train loss-2.2262, acc-0.2600, valid loss-2.2282, acc-0.3046, test loss-2.2291, acc-0.3018
Iter-6870, train loss-2.2393, acc-0.2400, valid loss-2.2281, acc-0.3046, test loss-2.2290, acc-0.3022
Iter-6880, train loss-2.2451, acc-0.2200, valid loss-2.2279, acc-0.3046, test loss-2.2289, acc-0.3025
Iter-6890, train loss-2.2208, acc-0.3400, valid loss-2.2278, acc-0.3046, test loss-2.2288, acc-0.3028
Iter-6900, train loss-2.2371, acc-0.2800, valid loss-2.2277, acc-0.3048, test loss-2.2287, acc-0.3030
Iter-6910, train loss-2.2287, acc-0.3400, valid loss-2.2276, acc-0.3050, test loss-2.2285, acc-0.3033
Iter-6920, train loss-2.2218, acc-0.3400, valid loss-2.2275, acc-0.3052, test loss-2.2284, acc-0.3032
Iter-6930, train loss-2.2298, acc-0.2600, valid loss-2.2273, acc-0.3056, test loss-2.2283, acc-0.3036
Iter-6940, train loss-2.2126, acc-0.4200, valid loss-2.2272, acc-0.3056, test loss-2.2281, acc-0.3035
Iter-6950, train loss-2.2299, acc-0.2800, valid loss-2.2271, acc-0.3060, test loss-2.2280, acc-0.3039
Iter-6960, train loss-2.2171, acc-0.3800, valid loss-2.2270, acc-0.3062, test loss-2.2279, acc-0.3041
Iter-6970, train loss-2.2295, acc-0.2600, valid loss-2.2268, acc-0.3064, test loss-2.2278, acc-0.3042
Iter-6980, train loss-2.2383, acc-0.3400, valid loss-2.2267, acc-0.3066, test loss-2.2276, acc-0.3045
Iter-6990, train loss-2.2385, acc-0.2600, valid loss-2.2266, acc-0.3068, test loss-2.2275, acc-0.3048
Iter-7000, train loss-2.2316, acc-0.3600, valid loss-2.2265, acc-0.3072, test loss-2.2274, acc-0.3055
Iter-7010, train loss-2.2366, acc-0.2800, valid loss-2.2263, acc-0.3070, test loss-2.2273, acc-0.3059
Iter-7020, train loss-2.2060, acc-0.3400, valid loss-2.2262, acc-0.3078, test loss-2.2271, acc-0.3068
Iter-7030, train loss-2.2189, acc-0.4000, valid loss-2.2261, acc-0.3082, test loss-2.2270, acc-0.3069
Iter-7040, train loss-2.2275, acc-0.3000, valid loss-2.2259, acc-0.3082, test loss-2.2269, acc-0.3073
Iter-7050, train loss-2.2577, acc-0.2600, valid loss-2.2258, acc-0.3080, test loss-2.2268, acc-0.3076
Iter-7060, train loss-2.2329, acc-0.2400, valid loss-2.2257, acc-0.3084, test loss-2.2266, acc-0.3081
Iter-7070, train loss-2.2261, acc-0.2600, valid loss-2.2256, acc-0.3086, test loss-2.2265, acc-0.3081
Iter-7080, train loss-2.2027, acc-0.4000, valid loss-2.2255, acc-0.3090, test loss-2.2264, acc-0.3082
Iter-7090, train loss-2.2384, acc-0.2800, valid loss-2.2253, acc-0.3094, test loss-2.2263, acc-0.3086
Iter-7100, train loss-2.2637, acc-0.1400, valid loss-2.2252, acc-0.3096, test loss-2.2261, acc-0.3088
Iter-7110, train loss-2.2099, acc-0.3200, valid loss-2.2251, acc-0.3096, test loss-2.2260, acc-0.3089
Iter-7120, train loss-2.2321, acc-0.2800, valid loss-2.2250, acc-0.3098, test loss-2.2259, acc-0.3094
Iter-7130, train loss-2.2282, acc-0.3000, valid loss-2.2249, acc-0.3096, test loss-2.2258, acc-0.3092
Iter-7140, train loss-2.2353, acc-0.2800, valid loss-2.2247, acc-0.3104, test loss-2.2257, acc-0.3094
Iter-7150, train loss-2.2284, acc-0.3600, valid loss-2.2246, acc-0.3106, test loss-2.2255, acc-0.3096
Iter-7160, train loss-2.2174, acc-0.4200, valid loss-2.2245, acc-0.3110, test loss-2.2254, acc-0.3098
Iter-7170, train loss-2.2153, acc-0.3800, valid loss-2.2244, acc-0.3112, test loss-2.2253, acc-0.3104
Iter-7180, train loss-2.2430, acc-0.1600, valid loss-2.2242, acc-0.3112, test loss-2.2252, acc-0.3107
Iter-7190, train loss-2.2435, acc-0.2600, valid loss-2.2241, acc-0.3114, test loss-2.2250, acc-0.3111
Iter-7200, train loss-2.2096, acc-0.3600, valid loss-2.2240, acc-0.3116, test loss-2.2249, acc-0.3117
Iter-7210, train loss-2.2488, acc-0.3000, valid loss-2.2239, acc-0.3118, test loss-2.2248, acc-0.3118
Iter-7220, train loss-2.2657, acc-0.2200, valid loss-2.2238, acc-0.3120, test loss-2.2247, acc-0.3120
Iter-7230, train loss-2.2061, acc-0.2400, valid loss-2.2236, acc-0.3122, test loss-2.2246, acc-0.3126
Iter-7240, train loss-2.2566, acc-0.2200, valid loss-2.2235, acc-0.3122, test loss-2.2244, acc-0.3123
Iter-7250, train loss-2.2226, acc-0.2800, valid loss-2.2234, acc-0.3122, test loss-2.2243, acc-0.3123
Iter-7260, train loss-2.2236, acc-0.2800, valid loss-2.2233, acc-0.3126, test loss-2.2242, acc-0.3128
Iter-7270, train loss-2.2155, acc-0.3400, valid loss-2.2231, acc-0.3134, test loss-2.2241, acc-0.3134
Iter-7280, train loss-2.1995, acc-0.3600, valid loss-2.2230, acc-0.3140, test loss-2.2239, acc-0.3137
Iter-7290, train loss-2.2205, acc-0.3000, valid loss-2.2229, acc-0.3140, test loss-2.2238, acc-0.3135
Iter-7300, train loss-2.2002, acc-0.3400, valid loss-2.2228, acc-0.3144, test loss-2.2237, acc-0.3143
Iter-7310, train loss-2.2216, acc-0.4000, valid loss-2.2227, acc-0.3146, test loss-2.2236, acc-0.3144
Iter-7320, train loss-2.2427, acc-0.2600, valid loss-2.2225, acc-0.3154, test loss-2.2235, acc-0.3148
Iter-7330, train loss-2.1985, acc-0.4000, valid loss-2.2224, acc-0.3158, test loss-2.2233, acc-0.3145
Iter-7340, train loss-2.2038, acc-0.3200, valid loss-2.2223, acc-0.3162, test loss-2.2232, acc-0.3146
Iter-7350, train loss-2.2346, acc-0.3200, valid loss-2.2222, acc-0.3166, test loss-2.2231, acc-0.3149
Iter-7360, train loss-2.2160, acc-0.3000, valid loss-2.2220, acc-0.3168, test loss-2.2230, acc-0.3153
Iter-7370, train loss-2.1969, acc-0.4000, valid loss-2.2219, acc-0.3168, test loss-2.2228, acc-0.3153
Iter-7380, train loss-2.1931, acc-0.3200, valid loss-2.2218, acc-0.3176, test loss-2.2227, acc-0.3157
Iter-7390, train loss-2.2455, acc-0.2800, valid loss-2.2217, acc-0.3178, test loss-2.2226, acc-0.3158
Iter-7400, train loss-2.1994, acc-0.5000, valid loss-2.2215, acc-0.3178, test loss-2.2225, acc-0.3158
Iter-7410, train loss-2.2223, acc-0.3000, valid loss-2.2214, acc-0.3178, test loss-2.2224, acc-0.3162
Iter-7420, train loss-2.2238, acc-0.3400, valid loss-2.2213, acc-0.3180, test loss-2.2222, acc-0.3165
Iter-7430, train loss-2.2116, acc-0.3800, valid loss-2.2212, acc-0.3186, test loss-2.2221, acc-0.3164
Iter-7440, train loss-2.2182, acc-0.3200, valid loss-2.2211, acc-0.3188, test loss-2.2220, acc-0.3165
Iter-7450, train loss-2.2252, acc-0.3200, valid loss-2.2209, acc-0.3192, test loss-2.2219, acc-0.3170
Iter-7460, train loss-2.2279, acc-0.3000, valid loss-2.2208, acc-0.3192, test loss-2.2218, acc-0.3168
Iter-7470, train loss-2.2087, acc-0.2800, valid loss-2.2207, acc-0.3198, test loss-2.2216, acc-0.3175
Iter-7480, train loss-2.2528, acc-0.3000, valid loss-2.2206, acc-0.3196, test loss-2.2215, acc-0.3174
Iter-7490, train loss-2.2317, acc-0.2800, valid loss-2.2205, acc-0.3198, test loss-2.2214, acc-0.3172
Iter-7500, train loss-2.2292, acc-0.3000, valid loss-2.2204, acc-0.3196, test loss-2.2213, acc-0.3178
Iter-7510, train loss-2.2435, acc-0.2400, valid loss-2.2202, acc-0.3198, test loss-2.2212, acc-0.3178
Iter-7520, train loss-2.2067, acc-0.4200, valid loss-2.2201, acc-0.3198, test loss-2.2211, acc-0.3179
Iter-7530, train loss-2.1953, acc-0.3600, valid loss-2.2200, acc-0.3200, test loss-2.2209, acc-0.3181
Iter-7540, train loss-2.2253, acc-0.3200, valid loss-2.2199, acc-0.3206, test loss-2.2208, acc-0.3184
Iter-7550, train loss-2.2076, acc-0.4400, valid loss-2.2198, acc-0.3210, test loss-2.2207, acc-0.3187
Iter-7560, train loss-2.2139, acc-0.3200, valid loss-2.2196, acc-0.3210, test loss-2.2206, acc-0.3187
Iter-7570, train loss-2.2264, acc-0.3000, valid loss-2.2195, acc-0.3208, test loss-2.2205, acc-0.3189
Iter-7580, train loss-2.2256, acc-0.3200, valid loss-2.2194, acc-0.3208, test loss-2.2203, acc-0.3193
Iter-7590, train loss-2.2457, acc-0.3000, valid loss-2.2193, acc-0.3208, test loss-2.2202, acc-0.3193
Iter-7600, train loss-2.1889, acc-0.3800, valid loss-2.2191, acc-0.3214, test loss-2.2201, acc-0.3197
Iter-7610, train loss-2.2294, acc-0.2400, valid loss-2.2190, acc-0.3220, test loss-2.2200, acc-0.3199
Iter-7620, train loss-2.2059, acc-0.4200, valid loss-2.2189, acc-0.3230, test loss-2.2198, acc-0.3200
Iter-7630, train loss-2.2254, acc-0.3000, valid loss-2.2188, acc-0.3228, test loss-2.2197, acc-0.3203
Iter-7640, train loss-2.2469, acc-0.2800, valid loss-2.2187, acc-0.3232, test loss-2.2196, acc-0.3205
Iter-7650, train loss-2.2345, acc-0.2200, valid loss-2.2185, acc-0.3234, test loss-2.2195, acc-0.3204
Iter-7660, train loss-2.1801, acc-0.4400, valid loss-2.2184, acc-0.3234, test loss-2.2194, acc-0.3210
Iter-7670, train loss-2.2399, acc-0.3200, valid loss-2.2183, acc-0.3238, test loss-2.2192, acc-0.3208
Iter-7680, train loss-2.2308, acc-0.2600, valid loss-2.2182, acc-0.3232, test loss-2.2191, acc-0.3211
Iter-7690, train loss-2.1783, acc-0.4200, valid loss-2.2181, acc-0.3244, test loss-2.2190, acc-0.3212
Iter-7700, train loss-2.2761, acc-0.1200, valid loss-2.2179, acc-0.3244, test loss-2.2189, acc-0.3213
Iter-7710, train loss-2.2092, acc-0.3600, valid loss-2.2178, acc-0.3244, test loss-2.2188, acc-0.3219
Iter-7720, train loss-2.2211, acc-0.3200, valid loss-2.2177, acc-0.3248, test loss-2.2187, acc-0.3220
Iter-7730, train loss-2.2231, acc-0.3000, valid loss-2.2176, acc-0.3248, test loss-2.2185, acc-0.3224
Iter-7740, train loss-2.2288, acc-0.2400, valid loss-2.2175, acc-0.3250, test loss-2.2184, acc-0.3226
Iter-7750, train loss-2.2224, acc-0.3000, valid loss-2.2173, acc-0.3250, test loss-2.2183, acc-0.3227
Iter-7760, train loss-2.2113, acc-0.2600, valid loss-2.2172, acc-0.3250, test loss-2.2182, acc-0.3230
Iter-7770, train loss-2.2202, acc-0.3600, valid loss-2.2171, acc-0.3250, test loss-2.2180, acc-0.3233
Iter-7780, train loss-2.2290, acc-0.2400, valid loss-2.2170, acc-0.3252, test loss-2.2179, acc-0.3238
Iter-7790, train loss-2.2162, acc-0.3600, valid loss-2.2168, acc-0.3254, test loss-2.2178, acc-0.3243
Iter-7800, train loss-2.2088, acc-0.3000, valid loss-2.2167, acc-0.3260, test loss-2.2177, acc-0.3247
Iter-7810, train loss-2.2335, acc-0.2600, valid loss-2.2166, acc-0.3262, test loss-2.2175, acc-0.3248
Iter-7820, train loss-2.2240, acc-0.2400, valid loss-2.2165, acc-0.3264, test loss-2.2174, acc-0.3245
Iter-7830, train loss-2.2174, acc-0.2800, valid loss-2.2163, acc-0.3266, test loss-2.2173, acc-0.3246
Iter-7840, train loss-2.2079, acc-0.3800, valid loss-2.2162, acc-0.3266, test loss-2.2172, acc-0.3252
Iter-7850, train loss-2.2172, acc-0.3200, valid loss-2.2161, acc-0.3270, test loss-2.2170, acc-0.3253
Iter-7860, train loss-2.2046, acc-0.3600, valid loss-2.2160, acc-0.3268, test loss-2.2169, acc-0.3254
Iter-7870, train loss-2.2363, acc-0.3200, valid loss-2.2159, acc-0.3272, test loss-2.2168, acc-0.3258
Iter-7880, train loss-2.2104, acc-0.4200, valid loss-2.2157, acc-0.3276, test loss-2.2167, acc-0.3261
Iter-7890, train loss-2.2215, acc-0.3200, valid loss-2.2156, acc-0.3280, test loss-2.2166, acc-0.3264
Iter-7900, train loss-2.1800, acc-0.4800, valid loss-2.2155, acc-0.3278, test loss-2.2164, acc-0.3262
Iter-7910, train loss-2.2061, acc-0.3800, valid loss-2.2154, acc-0.3282, test loss-2.2163, acc-0.3266
Iter-7920, train loss-2.2288, acc-0.2400, valid loss-2.2153, acc-0.3282, test loss-2.2162, acc-0.3266
Iter-7930, train loss-2.2240, acc-0.3000, valid loss-2.2151, acc-0.3288, test loss-2.2161, acc-0.3268
Iter-7940, train loss-2.2381, acc-0.3000, valid loss-2.2150, acc-0.3290, test loss-2.2160, acc-0.3270
Iter-7950, train loss-2.2128, acc-0.3800, valid loss-2.2149, acc-0.3296, test loss-2.2158, acc-0.3270
Iter-7960, train loss-2.1901, acc-0.3200, valid loss-2.2148, acc-0.3296, test loss-2.2157, acc-0.3272
Iter-7970, train loss-2.2462, acc-0.2800, valid loss-2.2147, acc-0.3300, test loss-2.2156, acc-0.3273
Iter-7980, train loss-2.1828, acc-0.4200, valid loss-2.2146, acc-0.3304, test loss-2.2155, acc-0.3279
Iter-7990, train loss-2.1808, acc-0.4600, valid loss-2.2144, acc-0.3308, test loss-2.2154, acc-0.3283
Iter-8000, train loss-2.2045, acc-0.3400, valid loss-2.2143, acc-0.3312, test loss-2.2153, acc-0.3286
Iter-8010, train loss-2.1984, acc-0.3400, valid loss-2.2142, acc-0.3320, test loss-2.2151, acc-0.3286
Iter-8020, train loss-2.1933, acc-0.4000, valid loss-2.2141, acc-0.3322, test loss-2.2150, acc-0.3286
Iter-8030, train loss-2.2242, acc-0.2400, valid loss-2.2140, acc-0.3330, test loss-2.2149, acc-0.3286
Iter-8040, train loss-2.2330, acc-0.2600, valid loss-2.2138, acc-0.3330, test loss-2.2148, acc-0.3292
Iter-8050, train loss-2.2057, acc-0.3200, valid loss-2.2137, acc-0.3326, test loss-2.2147, acc-0.3291
Iter-8060, train loss-2.2013, acc-0.3400, valid loss-2.2136, acc-0.3328, test loss-2.2145, acc-0.3295
Iter-8070, train loss-2.2317, acc-0.2600, valid loss-2.2135, acc-0.3326, test loss-2.2144, acc-0.3297
Iter-8080, train loss-2.2303, acc-0.3400, valid loss-2.2134, acc-0.3330, test loss-2.2143, acc-0.3297
Iter-8090, train loss-2.2031, acc-0.3800, valid loss-2.2133, acc-0.3334, test loss-2.2142, acc-0.3303
Iter-8100, train loss-2.2264, acc-0.3400, valid loss-2.2131, acc-0.3338, test loss-2.2141, acc-0.3303
Iter-8110, train loss-2.2024, acc-0.3800, valid loss-2.2130, acc-0.3340, test loss-2.2139, acc-0.3306
Iter-8120, train loss-2.2278, acc-0.2800, valid loss-2.2129, acc-0.3340, test loss-2.2138, acc-0.3308
Iter-8130, train loss-2.2391, acc-0.2600, valid loss-2.2128, acc-0.3342, test loss-2.2137, acc-0.3308
Iter-8140, train loss-2.2135, acc-0.3200, valid loss-2.2126, acc-0.3352, test loss-2.2136, acc-0.3311
Iter-8150, train loss-2.1765, acc-0.5200, valid loss-2.2125, acc-0.3352, test loss-2.2135, acc-0.3310
Iter-8160, train loss-2.2272, acc-0.2000, valid loss-2.2124, acc-0.3354, test loss-2.2134, acc-0.3313
Iter-8170, train loss-2.2061, acc-0.3000, valid loss-2.2123, acc-0.3356, test loss-2.2132, acc-0.3317
Iter-8180, train loss-2.2157, acc-0.3000, valid loss-2.2122, acc-0.3356, test loss-2.2131, acc-0.3320
Iter-8190, train loss-2.2364, acc-0.2800, valid loss-2.2121, acc-0.3358, test loss-2.2130, acc-0.3321
Iter-8200, train loss-2.1748, acc-0.4600, valid loss-2.2119, acc-0.3362, test loss-2.2129, acc-0.3323
Iter-8210, train loss-2.2327, acc-0.2800, valid loss-2.2118, acc-0.3364, test loss-2.2128, acc-0.3324
Iter-8220, train loss-2.2374, acc-0.2600, valid loss-2.2117, acc-0.3364, test loss-2.2126, acc-0.3326
Iter-8230, train loss-2.2092, acc-0.3000, valid loss-2.2116, acc-0.3364, test loss-2.2125, acc-0.3328
Iter-8240, train loss-2.2048, acc-0.3800, valid loss-2.2115, acc-0.3366, test loss-2.2124, acc-0.3331
Iter-8250, train loss-2.2171, acc-0.3000, valid loss-2.2113, acc-0.3372, test loss-2.2123, acc-0.3332
Iter-8260, train loss-2.1962, acc-0.3400, valid loss-2.2112, acc-0.3372, test loss-2.2122, acc-0.3335
Iter-8270, train loss-2.2151, acc-0.3000, valid loss-2.2111, acc-0.3372, test loss-2.2120, acc-0.3336
Iter-8280, train loss-2.2350, acc-0.2400, valid loss-2.2110, acc-0.3376, test loss-2.2119, acc-0.3337
Iter-8290, train loss-2.1861, acc-0.3800, valid loss-2.2108, acc-0.3378, test loss-2.2118, acc-0.3337
Iter-8300, train loss-2.1976, acc-0.3400, valid loss-2.2107, acc-0.3378, test loss-2.2117, acc-0.3339
Iter-8310, train loss-2.2344, acc-0.3000, valid loss-2.2106, acc-0.3386, test loss-2.2115, acc-0.3343
Iter-8320, train loss-2.2409, acc-0.2000, valid loss-2.2105, acc-0.3390, test loss-2.2114, acc-0.3343
Iter-8330, train loss-2.2062, acc-0.3200, valid loss-2.2104, acc-0.3392, test loss-2.2113, acc-0.3344
Iter-8340, train loss-2.2008, acc-0.4200, valid loss-2.2102, acc-0.3390, test loss-2.2112, acc-0.3344
Iter-8350, train loss-2.2259, acc-0.2800, valid loss-2.2101, acc-0.3396, test loss-2.2111, acc-0.3345
Iter-8360, train loss-2.1852, acc-0.3200, valid loss-2.2100, acc-0.3396, test loss-2.2110, acc-0.3344
Iter-8370, train loss-2.2062, acc-0.2600, valid loss-2.2099, acc-0.3400, test loss-2.2108, acc-0.3345
Iter-8380, train loss-2.1934, acc-0.4200, valid loss-2.2098, acc-0.3402, test loss-2.2107, acc-0.3349
Iter-8390, train loss-2.2284, acc-0.2800, valid loss-2.2097, acc-0.3404, test loss-2.2106, acc-0.3349
Iter-8400, train loss-2.2234, acc-0.2800, valid loss-2.2095, acc-0.3408, test loss-2.2105, acc-0.3352
Iter-8410, train loss-2.2433, acc-0.2200, valid loss-2.2094, acc-0.3406, test loss-2.2104, acc-0.3354
Iter-8420, train loss-2.1997, acc-0.3200, valid loss-2.2093, acc-0.3412, test loss-2.2102, acc-0.3357
Iter-8430, train loss-2.2228, acc-0.3000, valid loss-2.2092, acc-0.3412, test loss-2.2101, acc-0.3362
Iter-8440, train loss-2.2333, acc-0.2800, valid loss-2.2091, acc-0.3410, test loss-2.2100, acc-0.3364
Iter-8450, train loss-2.1711, acc-0.5000, valid loss-2.2090, acc-0.3412, test loss-2.2099, acc-0.3366
Iter-8460, train loss-2.2190, acc-0.3600, valid loss-2.2088, acc-0.3412, test loss-2.2098, acc-0.3368
Iter-8470, train loss-2.2151, acc-0.2600, valid loss-2.2087, acc-0.3414, test loss-2.2097, acc-0.3369
Iter-8480, train loss-2.1899, acc-0.3600, valid loss-2.2086, acc-0.3420, test loss-2.2095, acc-0.3373
Iter-8490, train loss-2.1895, acc-0.5600, valid loss-2.2085, acc-0.3422, test loss-2.2094, acc-0.3376
Iter-8500, train loss-2.2237, acc-0.3400, valid loss-2.2084, acc-0.3422, test loss-2.2093, acc-0.3378
Iter-8510, train loss-2.2219, acc-0.3800, valid loss-2.2082, acc-0.3426, test loss-2.2092, acc-0.3382
Iter-8520, train loss-2.2239, acc-0.3000, valid loss-2.2081, acc-0.3422, test loss-2.2091, acc-0.3382
Iter-8530, train loss-2.2001, acc-0.3200, valid loss-2.2080, acc-0.3426, test loss-2.2089, acc-0.3384
Iter-8540, train loss-2.2074, acc-0.3600, valid loss-2.2079, acc-0.3430, test loss-2.2088, acc-0.3384
Iter-8550, train loss-2.2094, acc-0.4000, valid loss-2.2078, acc-0.3430, test loss-2.2087, acc-0.3389
Iter-8560, train loss-2.1669, acc-0.5000, valid loss-2.2077, acc-0.3438, test loss-2.2086, acc-0.3392
Iter-8570, train loss-2.2136, acc-0.2400, valid loss-2.2075, acc-0.3442, test loss-2.2085, acc-0.3393
Iter-8580, train loss-2.2428, acc-0.2800, valid loss-2.2074, acc-0.3442, test loss-2.2083, acc-0.3396
Iter-8590, train loss-2.2117, acc-0.3000, valid loss-2.2073, acc-0.3438, test loss-2.2082, acc-0.3393
Iter-8600, train loss-2.1677, acc-0.4800, valid loss-2.2072, acc-0.3442, test loss-2.2081, acc-0.3393
Iter-8610, train loss-2.2087, acc-0.3600, valid loss-2.2071, acc-0.3442, test loss-2.2080, acc-0.3395
Iter-8620, train loss-2.2003, acc-0.4200, valid loss-2.2069, acc-0.3442, test loss-2.2079, acc-0.3398
Iter-8630, train loss-2.2231, acc-0.3000, valid loss-2.2068, acc-0.3444, test loss-2.2077, acc-0.3399
Iter-8640, train loss-2.2200, acc-0.2600, valid loss-2.2067, acc-0.3450, test loss-2.2076, acc-0.3402
Iter-8650, train loss-2.2087, acc-0.3800, valid loss-2.2066, acc-0.3448, test loss-2.2075, acc-0.3401
Iter-8660, train loss-2.2157, acc-0.3400, valid loss-2.2065, acc-0.3452, test loss-2.2074, acc-0.3403
Iter-8670, train loss-2.2117, acc-0.3000, valid loss-2.2063, acc-0.3454, test loss-2.2073, acc-0.3405
Iter-8680, train loss-2.1975, acc-0.3200, valid loss-2.2062, acc-0.3456, test loss-2.2072, acc-0.3406
Iter-8690, train loss-2.2236, acc-0.3600, valid loss-2.2061, acc-0.3458, test loss-2.2070, acc-0.3406
Iter-8700, train loss-2.1851, acc-0.4200, valid loss-2.2060, acc-0.3460, test loss-2.2069, acc-0.3409
Iter-8710, train loss-2.2354, acc-0.2800, valid loss-2.2059, acc-0.3460, test loss-2.2068, acc-0.3408
Iter-8720, train loss-2.2416, acc-0.2200, valid loss-2.2058, acc-0.3458, test loss-2.2067, acc-0.3413
Iter-8730, train loss-2.2232, acc-0.3400, valid loss-2.2056, acc-0.3460, test loss-2.2066, acc-0.3412
Iter-8740, train loss-2.2268, acc-0.3000, valid loss-2.2055, acc-0.3460, test loss-2.2064, acc-0.3412
Iter-8750, train loss-2.2022, acc-0.3200, valid loss-2.2054, acc-0.3466, test loss-2.2063, acc-0.3413
Iter-8760, train loss-2.2045, acc-0.2800, valid loss-2.2053, acc-0.3464, test loss-2.2062, acc-0.3415
Iter-8770, train loss-2.2189, acc-0.2200, valid loss-2.2052, acc-0.3464, test loss-2.2061, acc-0.3417
Iter-8780, train loss-2.1945, acc-0.4000, valid loss-2.2051, acc-0.3462, test loss-2.2060, acc-0.3418
Iter-8790, train loss-2.1994, acc-0.4200, valid loss-2.2049, acc-0.3464, test loss-2.2059, acc-0.3423
Iter-8800, train loss-2.1918, acc-0.3800, valid loss-2.2048, acc-0.3464, test loss-2.2057, acc-0.3425
Iter-8810, train loss-2.1562, acc-0.5000, valid loss-2.2047, acc-0.3462, test loss-2.2056, acc-0.3422
Iter-8820, train loss-2.2116, acc-0.3600, valid loss-2.2046, acc-0.3460, test loss-2.2055, acc-0.3425
Iter-8830, train loss-2.2167, acc-0.3200, valid loss-2.2045, acc-0.3462, test loss-2.2054, acc-0.3426
Iter-8840, train loss-2.2041, acc-0.2800, valid loss-2.2044, acc-0.3462, test loss-2.2053, acc-0.3429
Iter-8850, train loss-2.2010, acc-0.3600, valid loss-2.2042, acc-0.3462, test loss-2.2051, acc-0.3433
Iter-8860, train loss-2.2174, acc-0.3800, valid loss-2.2041, acc-0.3466, test loss-2.2050, acc-0.3432
Iter-8870, train loss-2.2197, acc-0.3400, valid loss-2.2040, acc-0.3470, test loss-2.2049, acc-0.3433
Iter-8880, train loss-2.2194, acc-0.3000, valid loss-2.2039, acc-0.3472, test loss-2.2048, acc-0.3437
Iter-8890, train loss-2.2157, acc-0.2800, valid loss-2.2038, acc-0.3470, test loss-2.2047, acc-0.3440
Iter-8900, train loss-2.2168, acc-0.3400, valid loss-2.2036, acc-0.3470, test loss-2.2046, acc-0.3441
Iter-8910, train loss-2.1869, acc-0.5200, valid loss-2.2035, acc-0.3474, test loss-2.2044, acc-0.3448
Iter-8920, train loss-2.2143, acc-0.3800, valid loss-2.2034, acc-0.3478, test loss-2.2043, acc-0.3448
Iter-8930, train loss-2.1932, acc-0.3200, valid loss-2.2033, acc-0.3480, test loss-2.2042, acc-0.3446
Iter-8940, train loss-2.2222, acc-0.3000, valid loss-2.2032, acc-0.3478, test loss-2.2041, acc-0.3446
Iter-8950, train loss-2.2084, acc-0.3000, valid loss-2.2030, acc-0.3482, test loss-2.2040, acc-0.3449
Iter-8960, train loss-2.2306, acc-0.3000, valid loss-2.2029, acc-0.3486, test loss-2.2038, acc-0.3448
Iter-8970, train loss-2.1982, acc-0.3200, valid loss-2.2028, acc-0.3494, test loss-2.2037, acc-0.3448
Iter-8980, train loss-2.2020, acc-0.3800, valid loss-2.2027, acc-0.3494, test loss-2.2036, acc-0.3449
Iter-8990, train loss-2.2260, acc-0.3000, valid loss-2.2025, acc-0.3486, test loss-2.2035, acc-0.3452
Iter-9000, train loss-2.2172, acc-0.3600, valid loss-2.2024, acc-0.3492, test loss-2.2033, acc-0.3451
Iter-9010, train loss-2.2239, acc-0.3000, valid loss-2.2023, acc-0.3496, test loss-2.2032, acc-0.3452
Iter-9020, train loss-2.2264, acc-0.3400, valid loss-2.2022, acc-0.3498, test loss-2.2031, acc-0.3454
Iter-9030, train loss-2.1702, acc-0.4200, valid loss-2.2021, acc-0.3498, test loss-2.2030, acc-0.3457
Iter-9040, train loss-2.2282, acc-0.2600, valid loss-2.2019, acc-0.3500, test loss-2.2029, acc-0.3460
Iter-9050, train loss-2.2223, acc-0.2600, valid loss-2.2018, acc-0.3502, test loss-2.2027, acc-0.3461
Iter-9060, train loss-2.1746, acc-0.4400, valid loss-2.2017, acc-0.3508, test loss-2.2026, acc-0.3463
Iter-9070, train loss-2.2016, acc-0.3400, valid loss-2.2016, acc-0.3508, test loss-2.2025, acc-0.3463
Iter-9080, train loss-2.2327, acc-0.2800, valid loss-2.2015, acc-0.3506, test loss-2.2024, acc-0.3466
Iter-9090, train loss-2.1630, acc-0.4200, valid loss-2.2014, acc-0.3506, test loss-2.2023, acc-0.3467
Iter-9100, train loss-2.1907, acc-0.3400, valid loss-2.2012, acc-0.3508, test loss-2.2022, acc-0.3469
Iter-9110, train loss-2.1901, acc-0.3800, valid loss-2.2011, acc-0.3516, test loss-2.2020, acc-0.3477
Iter-9120, train loss-2.1819, acc-0.4200, valid loss-2.2010, acc-0.3512, test loss-2.2019, acc-0.3477
Iter-9130, train loss-2.1900, acc-0.4200, valid loss-2.2009, acc-0.3516, test loss-2.2018, acc-0.3477
Iter-9140, train loss-2.2039, acc-0.2800, valid loss-2.2008, acc-0.3510, test loss-2.2017, acc-0.3476
Iter-9150, train loss-2.1947, acc-0.3400, valid loss-2.2007, acc-0.3518, test loss-2.2016, acc-0.3477
Iter-9160, train loss-2.2087, acc-0.3800, valid loss-2.2005, acc-0.3516, test loss-2.2015, acc-0.3478
Iter-9170, train loss-2.2045, acc-0.3200, valid loss-2.2004, acc-0.3516, test loss-2.2013, acc-0.3477
Iter-9180, train loss-2.1819, acc-0.4800, valid loss-2.2003, acc-0.3518, test loss-2.2012, acc-0.3475
Iter-9190, train loss-2.1937, acc-0.3600, valid loss-2.2002, acc-0.3520, test loss-2.2011, acc-0.3473
Iter-9200, train loss-2.1948, acc-0.3200, valid loss-2.2001, acc-0.3522, test loss-2.2010, acc-0.3478
Iter-9210, train loss-2.2181, acc-0.3800, valid loss-2.1999, acc-0.3522, test loss-2.2009, acc-0.3482
Iter-9220, train loss-2.2300, acc-0.2200, valid loss-2.1998, acc-0.3520, test loss-2.2007, acc-0.3481
Iter-9230, train loss-2.2035, acc-0.3000, valid loss-2.1997, acc-0.3520, test loss-2.2006, acc-0.3487
Iter-9240, train loss-2.1949, acc-0.2800, valid loss-2.1996, acc-0.3522, test loss-2.2005, acc-0.3488
Iter-9250, train loss-2.2167, acc-0.3000, valid loss-2.1995, acc-0.3520, test loss-2.2004, acc-0.3487
Iter-9260, train loss-2.2107, acc-0.3400, valid loss-2.1993, acc-0.3522, test loss-2.2003, acc-0.3488
Iter-9270, train loss-2.1863, acc-0.4200, valid loss-2.1992, acc-0.3524, test loss-2.2001, acc-0.3491
Iter-9280, train loss-2.1862, acc-0.4000, valid loss-2.1991, acc-0.3526, test loss-2.2000, acc-0.3492
Iter-9290, train loss-2.1840, acc-0.3600, valid loss-2.1990, acc-0.3526, test loss-2.1999, acc-0.3492
Iter-9300, train loss-2.2225, acc-0.4000, valid loss-2.1989, acc-0.3528, test loss-2.1998, acc-0.3493
Iter-9310, train loss-2.1886, acc-0.4000, valid loss-2.1987, acc-0.3530, test loss-2.1997, acc-0.3492
Iter-9320, train loss-2.2293, acc-0.2800, valid loss-2.1986, acc-0.3530, test loss-2.1995, acc-0.3494
Iter-9330, train loss-2.1797, acc-0.4600, valid loss-2.1985, acc-0.3536, test loss-2.1994, acc-0.3496
Iter-9340, train loss-2.1683, acc-0.4600, valid loss-2.1984, acc-0.3536, test loss-2.1993, acc-0.3498
Iter-9350, train loss-2.1904, acc-0.4600, valid loss-2.1983, acc-0.3542, test loss-2.1992, acc-0.3500
Iter-9360, train loss-2.1649, acc-0.5000, valid loss-2.1982, acc-0.3540, test loss-2.1991, acc-0.3503
Iter-9370, train loss-2.1968, acc-0.3200, valid loss-2.1980, acc-0.3544, test loss-2.1989, acc-0.3503
Iter-9380, train loss-2.2075, acc-0.3600, valid loss-2.1979, acc-0.3548, test loss-2.1988, acc-0.3505
Iter-9390, train loss-2.1851, acc-0.3400, valid loss-2.1978, acc-0.3548, test loss-2.1987, acc-0.3507
Iter-9400, train loss-2.1955, acc-0.3600, valid loss-2.1977, acc-0.3550, test loss-2.1986, acc-0.3505
Iter-9410, train loss-2.2340, acc-0.2000, valid loss-2.1975, acc-0.3548, test loss-2.1985, acc-0.3506
Iter-9420, train loss-2.1998, acc-0.4000, valid loss-2.1974, acc-0.3548, test loss-2.1983, acc-0.3508
Iter-9430, train loss-2.1639, acc-0.4400, valid loss-2.1973, acc-0.3550, test loss-2.1982, acc-0.3510
Iter-9440, train loss-2.1878, acc-0.4000, valid loss-2.1972, acc-0.3546, test loss-2.1981, acc-0.3510
Iter-9450, train loss-2.2026, acc-0.2400, valid loss-2.1971, acc-0.3546, test loss-2.1980, acc-0.3510
Iter-9460, train loss-2.2106, acc-0.3000, valid loss-2.1970, acc-0.3546, test loss-2.1979, acc-0.3508
Iter-9470, train loss-2.1773, acc-0.3800, valid loss-2.1968, acc-0.3546, test loss-2.1977, acc-0.3510
Iter-9480, train loss-2.2470, acc-0.2000, valid loss-2.1967, acc-0.3548, test loss-2.1976, acc-0.3512
Iter-9490, train loss-2.1971, acc-0.3000, valid loss-2.1966, acc-0.3544, test loss-2.1975, acc-0.3512
Iter-9500, train loss-2.2065, acc-0.3200, valid loss-2.1965, acc-0.3546, test loss-2.1974, acc-0.3515
Iter-9510, train loss-2.1940, acc-0.3800, valid loss-2.1964, acc-0.3552, test loss-2.1973, acc-0.3519
Iter-9520, train loss-2.1643, acc-0.4400, valid loss-2.1962, acc-0.3554, test loss-2.1971, acc-0.3521
Iter-9530, train loss-2.1724, acc-0.3600, valid loss-2.1961, acc-0.3556, test loss-2.1970, acc-0.3520
Iter-9540, train loss-2.1902, acc-0.3000, valid loss-2.1960, acc-0.3552, test loss-2.1969, acc-0.3527
Iter-9550, train loss-2.2096, acc-0.3200, valid loss-2.1959, acc-0.3552, test loss-2.1968, acc-0.3524
Iter-9560, train loss-2.2020, acc-0.2600, valid loss-2.1958, acc-0.3558, test loss-2.1967, acc-0.3530
Iter-9570, train loss-2.1950, acc-0.4000, valid loss-2.1957, acc-0.3560, test loss-2.1965, acc-0.3529
Iter-9580, train loss-2.1993, acc-0.3200, valid loss-2.1955, acc-0.3562, test loss-2.1964, acc-0.3529
Iter-9590, train loss-2.2052, acc-0.3000, valid loss-2.1954, acc-0.3560, test loss-2.1963, acc-0.3533
Iter-9600, train loss-2.2202, acc-0.2800, valid loss-2.1953, acc-0.3560, test loss-2.1962, acc-0.3529
Iter-9610, train loss-2.1949, acc-0.4200, valid loss-2.1952, acc-0.3566, test loss-2.1961, acc-0.3533
Iter-9620, train loss-2.1788, acc-0.4000, valid loss-2.1951, acc-0.3560, test loss-2.1959, acc-0.3535
Iter-9630, train loss-2.2463, acc-0.1800, valid loss-2.1949, acc-0.3564, test loss-2.1958, acc-0.3531
Iter-9640, train loss-2.2127, acc-0.2400, valid loss-2.1948, acc-0.3564, test loss-2.1957, acc-0.3534
Iter-9650, train loss-2.2270, acc-0.2600, valid loss-2.1947, acc-0.3564, test loss-2.1956, acc-0.3537
Iter-9660, train loss-2.1885, acc-0.4000, valid loss-2.1946, acc-0.3562, test loss-2.1955, acc-0.3536
Iter-9670, train loss-2.1956, acc-0.3600, valid loss-2.1945, acc-0.3564, test loss-2.1954, acc-0.3540
Iter-9680, train loss-2.2012, acc-0.3400, valid loss-2.1944, acc-0.3566, test loss-2.1953, acc-0.3541
Iter-9690, train loss-2.2071, acc-0.3600, valid loss-2.1943, acc-0.3566, test loss-2.1951, acc-0.3538
Iter-9700, train loss-2.1825, acc-0.3600, valid loss-2.1941, acc-0.3566, test loss-2.1950, acc-0.3537
Iter-9710, train loss-2.2086, acc-0.2200, valid loss-2.1940, acc-0.3568, test loss-2.1949, acc-0.3540
Iter-9720, train loss-2.1930, acc-0.3400, valid loss-2.1939, acc-0.3572, test loss-2.1948, acc-0.3543
Iter-9730, train loss-2.1869, acc-0.3800, valid loss-2.1938, acc-0.3572, test loss-2.1947, acc-0.3545
Iter-9740, train loss-2.2040, acc-0.3800, valid loss-2.1937, acc-0.3578, test loss-2.1945, acc-0.3544
Iter-9750, train loss-2.2096, acc-0.3000, valid loss-2.1935, acc-0.3576, test loss-2.1944, acc-0.3546
Iter-9760, train loss-2.2235, acc-0.2400, valid loss-2.1934, acc-0.3580, test loss-2.1943, acc-0.3549
Iter-9770, train loss-2.1967, acc-0.3000, valid loss-2.1933, acc-0.3586, test loss-2.1942, acc-0.3551
Iter-9780, train loss-2.1863, acc-0.3800, valid loss-2.1932, acc-0.3582, test loss-2.1941, acc-0.3552
Iter-9790, train loss-2.1417, acc-0.4600, valid loss-2.1931, acc-0.3582, test loss-2.1940, acc-0.3553
Iter-9800, train loss-2.1942, acc-0.3200, valid loss-2.1930, acc-0.3584, test loss-2.1938, acc-0.3554
Iter-9810, train loss-2.2157, acc-0.3000, valid loss-2.1928, acc-0.3582, test loss-2.1937, acc-0.3557
Iter-9820, train loss-2.1677, acc-0.4200, valid loss-2.1927, acc-0.3578, test loss-2.1936, acc-0.3560
Iter-9830, train loss-2.1983, acc-0.3400, valid loss-2.1926, acc-0.3568, test loss-2.1935, acc-0.3558
Iter-9840, train loss-2.1971, acc-0.3400, valid loss-2.1925, acc-0.3580, test loss-2.1934, acc-0.3561
Iter-9850, train loss-2.2237, acc-0.2200, valid loss-2.1923, acc-0.3582, test loss-2.1932, acc-0.3560
Iter-9860, train loss-2.1718, acc-0.3600, valid loss-2.1922, acc-0.3580, test loss-2.1931, acc-0.3562
Iter-9870, train loss-2.2057, acc-0.3000, valid loss-2.1921, acc-0.3582, test loss-2.1930, acc-0.3559
Iter-9880, train loss-2.1950, acc-0.3600, valid loss-2.1920, acc-0.3584, test loss-2.1929, acc-0.3558
Iter-9890, train loss-2.1992, acc-0.4000, valid loss-2.1919, acc-0.3586, test loss-2.1928, acc-0.3560
Iter-9900, train loss-2.1839, acc-0.3600, valid loss-2.1918, acc-0.3586, test loss-2.1927, acc-0.3560
Iter-9910, train loss-2.1946, acc-0.3800, valid loss-2.1916, acc-0.3590, test loss-2.1925, acc-0.3558
Iter-9920, train loss-2.2021, acc-0.3400, valid loss-2.1915, acc-0.3590, test loss-2.1924, acc-0.3563
Iter-9930, train loss-2.2099, acc-0.3200, valid loss-2.1914, acc-0.3590, test loss-2.1923, acc-0.3563
Iter-9940, train loss-2.2125, acc-0.3000, valid loss-2.1913, acc-0.3596, test loss-2.1922, acc-0.3565
Iter-9950, train loss-2.1968, acc-0.3000, valid loss-2.1912, acc-0.3598, test loss-2.1921, acc-0.3565
Iter-9960, train loss-2.1744, acc-0.4000, valid loss-2.1911, acc-0.3598, test loss-2.1920, acc-0.3566
Iter-9970, train loss-2.2108, acc-0.2800, valid loss-2.1910, acc-0.3602, test loss-2.1919, acc-0.3567
Iter-9980, train loss-2.2140, acc-0.2200, valid loss-2.1908, acc-0.3606, test loss-2.1917, acc-0.3569
Iter-9990, train loss-2.2012, acc-0.3200, valid loss-2.1907, acc-0.3608, test loss-2.1916, acc-0.3569
Iter-10000, train loss-2.2054, acc-0.3600, valid loss-2.1906, acc-0.3606, test loss-2.1915, acc-0.3570
Iter-10010, train loss-2.2006, acc-0.2800, valid loss-2.1905, acc-0.3608, test loss-2.1914, acc-0.3569
Iter-10020, train loss-2.2229, acc-0.2400, valid loss-2.1904, acc-0.3606, test loss-2.1913, acc-0.3573
Iter-10030, train loss-2.2034, acc-0.3800, valid loss-2.1902, acc-0.3604, test loss-2.1911, acc-0.3576
Iter-10040, train loss-2.1966, acc-0.3000, valid loss-2.1901, acc-0.3608, test loss-2.1910, acc-0.3576
Iter-10050, train loss-2.1669, acc-0.4800, valid loss-2.1900, acc-0.3602, test loss-2.1909, acc-0.3578
Iter-10060, train loss-2.1594, acc-0.3600, valid loss-2.1899, acc-0.3608, test loss-2.1908, acc-0.3576
Iter-10070, train loss-2.1654, acc-0.4200, valid loss-2.1898, acc-0.3614, test loss-2.1907, acc-0.3577
Iter-10080, train loss-2.2133, acc-0.3400, valid loss-2.1897, acc-0.3606, test loss-2.1905, acc-0.3577
Iter-10090, train loss-2.1765, acc-0.4000, valid loss-2.1895, acc-0.3610, test loss-2.1904, acc-0.3577
Iter-10100, train loss-2.1718, acc-0.4000, valid loss-2.1894, acc-0.3608, test loss-2.1903, acc-0.3584
Iter-10110, train loss-2.1930, acc-0.3200, valid loss-2.1893, acc-0.3610, test loss-2.1902, acc-0.3584
Iter-10120, train loss-2.2053, acc-0.3800, valid loss-2.1892, acc-0.3606, test loss-2.1901, acc-0.3585
Iter-10130, train loss-2.2121, acc-0.3000, valid loss-2.1891, acc-0.3604, test loss-2.1900, acc-0.3588
Iter-10140, train loss-2.2150, acc-0.2600, valid loss-2.1890, acc-0.3608, test loss-2.1898, acc-0.3593
Iter-10150, train loss-2.1754, acc-0.4000, valid loss-2.1888, acc-0.3614, test loss-2.1897, acc-0.3595
Iter-10160, train loss-2.1902, acc-0.3000, valid loss-2.1887, acc-0.3610, test loss-2.1896, acc-0.3595
Iter-10170, train loss-2.2135, acc-0.3200, valid loss-2.1886, acc-0.3610, test loss-2.1895, acc-0.3594
Iter-10180, train loss-2.2077, acc-0.4000, valid loss-2.1885, acc-0.3614, test loss-2.1894, acc-0.3596
Iter-10190, train loss-2.1889, acc-0.3600, valid loss-2.1884, acc-0.3612, test loss-2.1893, acc-0.3594
Iter-10200, train loss-2.1895, acc-0.3600, valid loss-2.1883, acc-0.3614, test loss-2.1892, acc-0.3594
Iter-10210, train loss-2.1717, acc-0.3800, valid loss-2.1881, acc-0.3612, test loss-2.1890, acc-0.3595
Iter-10220, train loss-2.1745, acc-0.3400, valid loss-2.1880, acc-0.3610, test loss-2.1889, acc-0.3592
Iter-10230, train loss-2.2041, acc-0.2400, valid loss-2.1879, acc-0.3614, test loss-2.1888, acc-0.3598
Iter-10240, train loss-2.1759, acc-0.3400, valid loss-2.1878, acc-0.3616, test loss-2.1887, acc-0.3597
Iter-10250, train loss-2.1553, acc-0.4200, valid loss-2.1877, acc-0.3614, test loss-2.1886, acc-0.3603
Iter-10260, train loss-2.1906, acc-0.3600, valid loss-2.1876, acc-0.3616, test loss-2.1885, acc-0.3600
Iter-10270, train loss-2.2084, acc-0.3000, valid loss-2.1875, acc-0.3616, test loss-2.1884, acc-0.3603
Iter-10280, train loss-2.1994, acc-0.4000, valid loss-2.1873, acc-0.3620, test loss-2.1882, acc-0.3601
Iter-10290, train loss-2.1955, acc-0.3400, valid loss-2.1872, acc-0.3618, test loss-2.1881, acc-0.3601
Iter-10300, train loss-2.1774, acc-0.3600, valid loss-2.1871, acc-0.3622, test loss-2.1880, acc-0.3603
Iter-10310, train loss-2.2021, acc-0.3000, valid loss-2.1870, acc-0.3620, test loss-2.1879, acc-0.3601
Iter-10320, train loss-2.1637, acc-0.4400, valid loss-2.1869, acc-0.3616, test loss-2.1878, acc-0.3600
Iter-10330, train loss-2.2186, acc-0.3000, valid loss-2.1868, acc-0.3620, test loss-2.1877, acc-0.3601
Iter-10340, train loss-2.2049, acc-0.3400, valid loss-2.1866, acc-0.3616, test loss-2.1875, acc-0.3602
Iter-10350, train loss-2.1975, acc-0.3000, valid loss-2.1865, acc-0.3620, test loss-2.1874, acc-0.3602
Iter-10360, train loss-2.1775, acc-0.4400, valid loss-2.1864, acc-0.3622, test loss-2.1873, acc-0.3604
Iter-10370, train loss-2.1840, acc-0.3800, valid loss-2.1863, acc-0.3626, test loss-2.1872, acc-0.3605
Iter-10380, train loss-2.2086, acc-0.3000, valid loss-2.1862, acc-0.3630, test loss-2.1871, acc-0.3607
Iter-10390, train loss-2.2247, acc-0.2600, valid loss-2.1860, acc-0.3630, test loss-2.1870, acc-0.3609
Iter-10400, train loss-2.1801, acc-0.3200, valid loss-2.1859, acc-0.3634, test loss-2.1869, acc-0.3609
Iter-10410, train loss-2.1932, acc-0.4200, valid loss-2.1858, acc-0.3634, test loss-2.1867, acc-0.3610
Iter-10420, train loss-2.2039, acc-0.3400, valid loss-2.1857, acc-0.3638, test loss-2.1866, acc-0.3610
Iter-10430, train loss-2.1748, acc-0.4400, valid loss-2.1856, acc-0.3636, test loss-2.1865, acc-0.3610
Iter-10440, train loss-2.1983, acc-0.3600, valid loss-2.1855, acc-0.3638, test loss-2.1864, acc-0.3611
Iter-10450, train loss-2.1957, acc-0.3000, valid loss-2.1854, acc-0.3636, test loss-2.1863, acc-0.3611
Iter-10460, train loss-2.1703, acc-0.4800, valid loss-2.1852, acc-0.3638, test loss-2.1862, acc-0.3613
Iter-10470, train loss-2.1668, acc-0.4400, valid loss-2.1851, acc-0.3646, test loss-2.1860, acc-0.3611
Iter-10480, train loss-2.1777, acc-0.2800, valid loss-2.1850, acc-0.3648, test loss-2.1859, acc-0.3608
Iter-10490, train loss-2.1923, acc-0.2600, valid loss-2.1849, acc-0.3648, test loss-2.1858, acc-0.3614
Iter-10500, train loss-2.1955, acc-0.3400, valid loss-2.1848, acc-0.3650, test loss-2.1857, acc-0.3614
Iter-10510, train loss-2.2097, acc-0.3000, valid loss-2.1847, acc-0.3648, test loss-2.1856, acc-0.3616
Iter-10520, train loss-2.1842, acc-0.3800, valid loss-2.1845, acc-0.3650, test loss-2.1855, acc-0.3614
Iter-10530, train loss-2.1686, acc-0.4200, valid loss-2.1844, acc-0.3650, test loss-2.1853, acc-0.3616
Iter-10540, train loss-2.1539, acc-0.4400, valid loss-2.1843, acc-0.3652, test loss-2.1852, acc-0.3618
Iter-10550, train loss-2.1786, acc-0.3400, valid loss-2.1842, acc-0.3650, test loss-2.1851, acc-0.3617
Iter-10560, train loss-2.2167, acc-0.2600, valid loss-2.1841, acc-0.3646, test loss-2.1850, acc-0.3616
Iter-10570, train loss-2.1847, acc-0.3400, valid loss-2.1839, acc-0.3650, test loss-2.1849, acc-0.3621
Iter-10580, train loss-2.1894, acc-0.3400, valid loss-2.1838, acc-0.3646, test loss-2.1847, acc-0.3620
Iter-10590, train loss-2.2092, acc-0.3000, valid loss-2.1837, acc-0.3650, test loss-2.1846, acc-0.3623
Iter-10600, train loss-2.1453, acc-0.4000, valid loss-2.1836, acc-0.3648, test loss-2.1845, acc-0.3622
Iter-10610, train loss-2.1708, acc-0.4400, valid loss-2.1835, acc-0.3650, test loss-2.1844, acc-0.3625
Iter-10620, train loss-2.1884, acc-0.3400, valid loss-2.1834, acc-0.3656, test loss-2.1843, acc-0.3625
Iter-10630, train loss-2.1809, acc-0.4000, valid loss-2.1832, acc-0.3658, test loss-2.1842, acc-0.3624
Iter-10640, train loss-2.1637, acc-0.4600, valid loss-2.1831, acc-0.3656, test loss-2.1840, acc-0.3622
Iter-10650, train loss-2.1796, acc-0.3800, valid loss-2.1830, acc-0.3660, test loss-2.1839, acc-0.3625
Iter-10660, train loss-2.1937, acc-0.3600, valid loss-2.1829, acc-0.3660, test loss-2.1838, acc-0.3627
Iter-10670, train loss-2.1713, acc-0.3600, valid loss-2.1828, acc-0.3662, test loss-2.1837, acc-0.3628
Iter-10680, train loss-2.1921, acc-0.3000, valid loss-2.1827, acc-0.3662, test loss-2.1836, acc-0.3627
Iter-10690, train loss-2.1886, acc-0.3800, valid loss-2.1825, acc-0.3662, test loss-2.1835, acc-0.3629
Iter-10700, train loss-2.1616, acc-0.5000, valid loss-2.1824, acc-0.3668, test loss-2.1834, acc-0.3632
Iter-10710, train loss-2.2314, acc-0.2400, valid loss-2.1823, acc-0.3668, test loss-2.1832, acc-0.3635
Iter-10720, train loss-2.1406, acc-0.4800, valid loss-2.1822, acc-0.3664, test loss-2.1831, acc-0.3635
Iter-10730, train loss-2.1844, acc-0.3400, valid loss-2.1821, acc-0.3660, test loss-2.1830, acc-0.3635
Iter-10740, train loss-2.1732, acc-0.4200, valid loss-2.1820, acc-0.3658, test loss-2.1829, acc-0.3633
Iter-10750, train loss-2.1606, acc-0.4400, valid loss-2.1819, acc-0.3654, test loss-2.1828, acc-0.3631
Iter-10760, train loss-2.1837, acc-0.3400, valid loss-2.1817, acc-0.3656, test loss-2.1827, acc-0.3629
Iter-10770, train loss-2.1483, acc-0.5000, valid loss-2.1816, acc-0.3660, test loss-2.1825, acc-0.3630
Iter-10780, train loss-2.1886, acc-0.3600, valid loss-2.1815, acc-0.3658, test loss-2.1824, acc-0.3629
Iter-10790, train loss-2.1849, acc-0.4200, valid loss-2.1814, acc-0.3664, test loss-2.1823, acc-0.3633
Iter-10800, train loss-2.1831, acc-0.3400, valid loss-2.1813, acc-0.3670, test loss-2.1822, acc-0.3636
Iter-10810, train loss-2.2025, acc-0.3400, valid loss-2.1812, acc-0.3670, test loss-2.1821, acc-0.3635
Iter-10820, train loss-2.1653, acc-0.4000, valid loss-2.1811, acc-0.3666, test loss-2.1820, acc-0.3636
Iter-10830, train loss-2.2003, acc-0.3000, valid loss-2.1810, acc-0.3662, test loss-2.1819, acc-0.3637
Iter-10840, train loss-2.2038, acc-0.3200, valid loss-2.1809, acc-0.3666, test loss-2.1818, acc-0.3639
Iter-10850, train loss-2.2104, acc-0.3000, valid loss-2.1808, acc-0.3668, test loss-2.1817, acc-0.3641
Iter-10860, train loss-2.1816, acc-0.3400, valid loss-2.1806, acc-0.3668, test loss-2.1815, acc-0.3641
Iter-10870, train loss-2.1637, acc-0.3200, valid loss-2.1805, acc-0.3668, test loss-2.1814, acc-0.3642
Iter-10880, train loss-2.1840, acc-0.3800, valid loss-2.1804, acc-0.3664, test loss-2.1813, acc-0.3640
Iter-10890, train loss-2.2007, acc-0.3400, valid loss-2.1803, acc-0.3670, test loss-2.1812, acc-0.3643
Iter-10900, train loss-2.1855, acc-0.3400, valid loss-2.1802, acc-0.3668, test loss-2.1811, acc-0.3647
Iter-10910, train loss-2.1680, acc-0.3800, valid loss-2.1801, acc-0.3664, test loss-2.1810, acc-0.3647
Iter-10920, train loss-2.2052, acc-0.3600, valid loss-2.1800, acc-0.3668, test loss-2.1809, acc-0.3648
Iter-10930, train loss-2.1612, acc-0.3800, valid loss-2.1798, acc-0.3674, test loss-2.1807, acc-0.3648
Iter-10940, train loss-2.1644, acc-0.4000, valid loss-2.1797, acc-0.3672, test loss-2.1806, acc-0.3651
Iter-10950, train loss-2.1698, acc-0.4600, valid loss-2.1796, acc-0.3674, test loss-2.1805, acc-0.3651
Iter-10960, train loss-2.2246, acc-0.2400, valid loss-2.1795, acc-0.3672, test loss-2.1804, acc-0.3652
Iter-10970, train loss-2.1761, acc-0.4200, valid loss-2.1794, acc-0.3678, test loss-2.1803, acc-0.3652
Iter-10980, train loss-2.1930, acc-0.2600, valid loss-2.1793, acc-0.3676, test loss-2.1802, acc-0.3652
Iter-10990, train loss-2.1953, acc-0.3400, valid loss-2.1792, acc-0.3678, test loss-2.1800, acc-0.3651
Iter-11000, train loss-2.1741, acc-0.3400, valid loss-2.1790, acc-0.3678, test loss-2.1799, acc-0.3654
Iter-11010, train loss-2.2081, acc-0.3400, valid loss-2.1789, acc-0.3680, test loss-2.1798, acc-0.3651
Iter-11020, train loss-2.1774, acc-0.4200, valid loss-2.1788, acc-0.3680, test loss-2.1797, acc-0.3653
Iter-11030, train loss-2.1656, acc-0.3800, valid loss-2.1787, acc-0.3672, test loss-2.1796, acc-0.3650
Iter-11040, train loss-2.1716, acc-0.3600, valid loss-2.1786, acc-0.3674, test loss-2.1795, acc-0.3650
Iter-11050, train loss-2.1596, acc-0.4200, valid loss-2.1785, acc-0.3672, test loss-2.1794, acc-0.3648
Iter-11060, train loss-2.1304, acc-0.5200, valid loss-2.1784, acc-0.3674, test loss-2.1792, acc-0.3649
Iter-11070, train loss-2.1951, acc-0.4200, valid loss-2.1782, acc-0.3674, test loss-2.1791, acc-0.3651
Iter-11080, train loss-2.1793, acc-0.4200, valid loss-2.1781, acc-0.3674, test loss-2.1790, acc-0.3653
Iter-11090, train loss-2.1459, acc-0.3800, valid loss-2.1780, acc-0.3674, test loss-2.1789, acc-0.3653
Iter-11100, train loss-2.1928, acc-0.3200, valid loss-2.1779, acc-0.3674, test loss-2.1788, acc-0.3655
Iter-11110, train loss-2.1759, acc-0.3800, valid loss-2.1778, acc-0.3676, test loss-2.1787, acc-0.3657
Iter-11120, train loss-2.1807, acc-0.3400, valid loss-2.1777, acc-0.3676, test loss-2.1786, acc-0.3657
Iter-11130, train loss-2.1854, acc-0.3200, valid loss-2.1776, acc-0.3678, test loss-2.1784, acc-0.3662
Iter-11140, train loss-2.1822, acc-0.3400, valid loss-2.1774, acc-0.3678, test loss-2.1783, acc-0.3663
Iter-11150, train loss-2.1825, acc-0.4000, valid loss-2.1773, acc-0.3678, test loss-2.1782, acc-0.3665
Iter-11160, train loss-2.1632, acc-0.3800, valid loss-2.1772, acc-0.3676, test loss-2.1781, acc-0.3664
Iter-11170, train loss-2.2076, acc-0.2600, valid loss-2.1771, acc-0.3676, test loss-2.1780, acc-0.3665
Iter-11180, train loss-2.1887, acc-0.3400, valid loss-2.1770, acc-0.3678, test loss-2.1779, acc-0.3665
Iter-11190, train loss-2.1838, acc-0.3600, valid loss-2.1769, acc-0.3676, test loss-2.1778, acc-0.3668
Iter-11200, train loss-2.2106, acc-0.3000, valid loss-2.1768, acc-0.3678, test loss-2.1776, acc-0.3669
Iter-11210, train loss-2.1700, acc-0.3400, valid loss-2.1767, acc-0.3678, test loss-2.1775, acc-0.3670
Iter-11220, train loss-2.1663, acc-0.3600, valid loss-2.1765, acc-0.3678, test loss-2.1774, acc-0.3670
Iter-11230, train loss-2.1896, acc-0.3000, valid loss-2.1764, acc-0.3678, test loss-2.1773, acc-0.3671
Iter-11240, train loss-2.1557, acc-0.5000, valid loss-2.1763, acc-0.3678, test loss-2.1772, acc-0.3670
Iter-11250, train loss-2.1735, acc-0.4000, valid loss-2.1762, acc-0.3678, test loss-2.1771, acc-0.3673
Iter-11260, train loss-2.1790, acc-0.4000, valid loss-2.1761, acc-0.3676, test loss-2.1770, acc-0.3672
Iter-11270, train loss-2.2086, acc-0.2400, valid loss-2.1760, acc-0.3682, test loss-2.1768, acc-0.3669
Iter-11280, train loss-2.1855, acc-0.3800, valid loss-2.1759, acc-0.3680, test loss-2.1767, acc-0.3670
Iter-11290, train loss-2.1792, acc-0.3200, valid loss-2.1757, acc-0.3682, test loss-2.1766, acc-0.3671
Iter-11300, train loss-2.1806, acc-0.3600, valid loss-2.1756, acc-0.3682, test loss-2.1765, acc-0.3673
Iter-11310, train loss-2.1764, acc-0.3400, valid loss-2.1755, acc-0.3688, test loss-2.1764, acc-0.3673
Iter-11320, train loss-2.1864, acc-0.3400, valid loss-2.1754, acc-0.3686, test loss-2.1763, acc-0.3676
Iter-11330, train loss-2.1698, acc-0.4000, valid loss-2.1753, acc-0.3686, test loss-2.1762, acc-0.3679
Iter-11340, train loss-2.2087, acc-0.3000, valid loss-2.1752, acc-0.3688, test loss-2.1761, acc-0.3679
Iter-11350, train loss-2.1872, acc-0.3200, valid loss-2.1751, acc-0.3688, test loss-2.1760, acc-0.3676
Iter-11360, train loss-2.1654, acc-0.3200, valid loss-2.1750, acc-0.3692, test loss-2.1758, acc-0.3678
Iter-11370, train loss-2.1674, acc-0.3600, valid loss-2.1748, acc-0.3686, test loss-2.1757, acc-0.3677
Iter-11380, train loss-2.1914, acc-0.3200, valid loss-2.1747, acc-0.3688, test loss-2.1756, acc-0.3677
Iter-11390, train loss-2.1718, acc-0.4000, valid loss-2.1746, acc-0.3688, test loss-2.1755, acc-0.3673
Iter-11400, train loss-2.2104, acc-0.3400, valid loss-2.1745, acc-0.3690, test loss-2.1754, acc-0.3674
Iter-11410, train loss-2.1724, acc-0.3800, valid loss-2.1744, acc-0.3692, test loss-2.1753, acc-0.3673
Iter-11420, train loss-2.1604, acc-0.4000, valid loss-2.1743, acc-0.3692, test loss-2.1751, acc-0.3672
Iter-11430, train loss-2.1446, acc-0.4000, valid loss-2.1742, acc-0.3694, test loss-2.1750, acc-0.3676
Iter-11440, train loss-2.1784, acc-0.3400, valid loss-2.1741, acc-0.3696, test loss-2.1749, acc-0.3675
Iter-11450, train loss-2.1639, acc-0.4600, valid loss-2.1739, acc-0.3694, test loss-2.1748, acc-0.3673
Iter-11460, train loss-2.1766, acc-0.3800, valid loss-2.1738, acc-0.3692, test loss-2.1747, acc-0.3672
Iter-11470, train loss-2.1434, acc-0.4200, valid loss-2.1737, acc-0.3692, test loss-2.1746, acc-0.3675
Iter-11480, train loss-2.1946, acc-0.2800, valid loss-2.1736, acc-0.3692, test loss-2.1745, acc-0.3672
Iter-11490, train loss-2.2016, acc-0.2600, valid loss-2.1735, acc-0.3696, test loss-2.1744, acc-0.3674
Iter-11500, train loss-2.1340, acc-0.4800, valid loss-2.1734, acc-0.3702, test loss-2.1742, acc-0.3678
Iter-11510, train loss-2.1735, acc-0.3800, valid loss-2.1733, acc-0.3702, test loss-2.1741, acc-0.3679
Iter-11520, train loss-2.1686, acc-0.4200, valid loss-2.1731, acc-0.3702, test loss-2.1740, acc-0.3681
Iter-11530, train loss-2.1918, acc-0.3400, valid loss-2.1730, acc-0.3704, test loss-2.1739, acc-0.3684
Iter-11540, train loss-2.2087, acc-0.2600, valid loss-2.1729, acc-0.3706, test loss-2.1738, acc-0.3682
Iter-11550, train loss-2.1682, acc-0.3400, valid loss-2.1728, acc-0.3708, test loss-2.1737, acc-0.3681
Iter-11560, train loss-2.1919, acc-0.3600, valid loss-2.1727, acc-0.3706, test loss-2.1735, acc-0.3681
Iter-11570, train loss-2.1921, acc-0.3400, valid loss-2.1726, acc-0.3706, test loss-2.1734, acc-0.3682
Iter-11580, train loss-2.1687, acc-0.4200, valid loss-2.1724, acc-0.3708, test loss-2.1733, acc-0.3683
Iter-11590, train loss-2.1866, acc-0.3800, valid loss-2.1723, acc-0.3708, test loss-2.1732, acc-0.3682
Iter-11600, train loss-2.2062, acc-0.3000, valid loss-2.1722, acc-0.3712, test loss-2.1731, acc-0.3685
Iter-11610, train loss-2.1824, acc-0.3800, valid loss-2.1721, acc-0.3710, test loss-2.1730, acc-0.3686
Iter-11620, train loss-2.1901, acc-0.2800, valid loss-2.1720, acc-0.3712, test loss-2.1729, acc-0.3688
Iter-11630, train loss-2.1889, acc-0.3400, valid loss-2.1719, acc-0.3712, test loss-2.1728, acc-0.3691
Iter-11640, train loss-2.1968, acc-0.3000, valid loss-2.1718, acc-0.3718, test loss-2.1726, acc-0.3692
Iter-11650, train loss-2.1666, acc-0.4000, valid loss-2.1717, acc-0.3716, test loss-2.1725, acc-0.3694
Iter-11660, train loss-2.1571, acc-0.4600, valid loss-2.1715, acc-0.3716, test loss-2.1724, acc-0.3695
Iter-11670, train loss-2.1902, acc-0.3400, valid loss-2.1714, acc-0.3716, test loss-2.1723, acc-0.3695
Iter-11680, train loss-2.1793, acc-0.3600, valid loss-2.1713, acc-0.3718, test loss-2.1722, acc-0.3695
Iter-11690, train loss-2.1555, acc-0.3400, valid loss-2.1712, acc-0.3720, test loss-2.1721, acc-0.3695
Iter-11700, train loss-2.1719, acc-0.4200, valid loss-2.1711, acc-0.3724, test loss-2.1720, acc-0.3693
Iter-11710, train loss-2.2185, acc-0.3400, valid loss-2.1710, acc-0.3724, test loss-2.1719, acc-0.3696
Iter-11720, train loss-2.2156, acc-0.2800, valid loss-2.1709, acc-0.3726, test loss-2.1718, acc-0.3697
Iter-11730, train loss-2.2050, acc-0.2800, valid loss-2.1708, acc-0.3722, test loss-2.1717, acc-0.3697
Iter-11740, train loss-2.1833, acc-0.4200, valid loss-2.1707, acc-0.3726, test loss-2.1715, acc-0.3698
Iter-11750, train loss-2.1601, acc-0.3200, valid loss-2.1706, acc-0.3724, test loss-2.1714, acc-0.3699
Iter-11760, train loss-2.1730, acc-0.2800, valid loss-2.1705, acc-0.3724, test loss-2.1713, acc-0.3698
Iter-11770, train loss-2.1738, acc-0.3600, valid loss-2.1703, acc-0.3726, test loss-2.1712, acc-0.3700
Iter-11780, train loss-2.1789, acc-0.3000, valid loss-2.1702, acc-0.3730, test loss-2.1711, acc-0.3698
Iter-11790, train loss-2.1624, acc-0.4200, valid loss-2.1701, acc-0.3728, test loss-2.1710, acc-0.3699
Iter-11800, train loss-2.1750, acc-0.3200, valid loss-2.1700, acc-0.3726, test loss-2.1709, acc-0.3697
Iter-11810, train loss-2.1663, acc-0.4400, valid loss-2.1699, acc-0.3728, test loss-2.1707, acc-0.3698
Iter-11820, train loss-2.2191, acc-0.2800, valid loss-2.1698, acc-0.3732, test loss-2.1706, acc-0.3700
Iter-11830, train loss-2.2051, acc-0.2600, valid loss-2.1697, acc-0.3736, test loss-2.1705, acc-0.3705
Iter-11840, train loss-2.1587, acc-0.3200, valid loss-2.1695, acc-0.3736, test loss-2.1704, acc-0.3703
Iter-11850, train loss-2.2185, acc-0.2800, valid loss-2.1694, acc-0.3744, test loss-2.1703, acc-0.3704
Iter-11860, train loss-2.1840, acc-0.3800, valid loss-2.1693, acc-0.3742, test loss-2.1702, acc-0.3705
Iter-11870, train loss-2.1586, acc-0.3600, valid loss-2.1692, acc-0.3742, test loss-2.1701, acc-0.3708
Iter-11880, train loss-2.1983, acc-0.2800, valid loss-2.1691, acc-0.3742, test loss-2.1699, acc-0.3707
Iter-11890, train loss-2.2243, acc-0.2800, valid loss-2.1690, acc-0.3742, test loss-2.1698, acc-0.3709
Iter-11900, train loss-2.1501, acc-0.3800, valid loss-2.1689, acc-0.3744, test loss-2.1697, acc-0.3708
Iter-11910, train loss-2.1578, acc-0.3600, valid loss-2.1688, acc-0.3750, test loss-2.1696, acc-0.3708
Iter-11920, train loss-2.1860, acc-0.3400, valid loss-2.1687, acc-0.3754, test loss-2.1695, acc-0.3709
Iter-11930, train loss-2.1740, acc-0.3200, valid loss-2.1686, acc-0.3754, test loss-2.1694, acc-0.3710
Iter-11940, train loss-2.1499, acc-0.5200, valid loss-2.1684, acc-0.3752, test loss-2.1693, acc-0.3711
Iter-11950, train loss-2.1425, acc-0.4200, valid loss-2.1683, acc-0.3752, test loss-2.1692, acc-0.3712
Iter-11960, train loss-2.1825, acc-0.4200, valid loss-2.1682, acc-0.3752, test loss-2.1690, acc-0.3711
Iter-11970, train loss-2.2028, acc-0.3800, valid loss-2.1681, acc-0.3752, test loss-2.1689, acc-0.3715
Iter-11980, train loss-2.1761, acc-0.3800, valid loss-2.1680, acc-0.3752, test loss-2.1688, acc-0.3716
Iter-11990, train loss-2.1765, acc-0.3000, valid loss-2.1679, acc-0.3754, test loss-2.1687, acc-0.3716
Iter-12000, train loss-2.1852, acc-0.3200, valid loss-2.1678, acc-0.3754, test loss-2.1686, acc-0.3716
Iter-12010, train loss-2.1869, acc-0.3600, valid loss-2.1677, acc-0.3754, test loss-2.1685, acc-0.3715
Iter-12020, train loss-2.1875, acc-0.2800, valid loss-2.1675, acc-0.3756, test loss-2.1684, acc-0.3718
Iter-12030, train loss-2.1502, acc-0.4400, valid loss-2.1674, acc-0.3756, test loss-2.1683, acc-0.3720
Iter-12040, train loss-2.1607, acc-0.3400, valid loss-2.1673, acc-0.3758, test loss-2.1682, acc-0.3720
Iter-12050, train loss-2.1730, acc-0.3600, valid loss-2.1672, acc-0.3758, test loss-2.1681, acc-0.3721
Iter-12060, train loss-2.1655, acc-0.4000, valid loss-2.1671, acc-0.3760, test loss-2.1679, acc-0.3719
Iter-12070, train loss-2.0989, acc-0.4200, valid loss-2.1670, acc-0.3760, test loss-2.1678, acc-0.3721
Iter-12080, train loss-2.1820, acc-0.3600, valid loss-2.1669, acc-0.3758, test loss-2.1677, acc-0.3721
Iter-12090, train loss-2.1435, acc-0.4000, valid loss-2.1668, acc-0.3756, test loss-2.1676, acc-0.3721
Iter-12100, train loss-2.1962, acc-0.2800, valid loss-2.1667, acc-0.3756, test loss-2.1675, acc-0.3724
Iter-12110, train loss-2.1746, acc-0.3400, valid loss-2.1666, acc-0.3760, test loss-2.1674, acc-0.3725
Iter-12120, train loss-2.2129, acc-0.2600, valid loss-2.1665, acc-0.3760, test loss-2.1673, acc-0.3730
Iter-12130, train loss-2.2010, acc-0.2000, valid loss-2.1664, acc-0.3758, test loss-2.1672, acc-0.3728
Iter-12140, train loss-2.1517, acc-0.4400, valid loss-2.1662, acc-0.3754, test loss-2.1671, acc-0.3729
Iter-12150, train loss-2.1487, acc-0.4000, valid loss-2.1661, acc-0.3758, test loss-2.1670, acc-0.3732
Iter-12160, train loss-2.1589, acc-0.4000, valid loss-2.1660, acc-0.3760, test loss-2.1668, acc-0.3729
Iter-12170, train loss-2.1802, acc-0.3800, valid loss-2.1659, acc-0.3760, test loss-2.1667, acc-0.3728
Iter-12180, train loss-2.2041, acc-0.2800, valid loss-2.1658, acc-0.3760, test loss-2.1666, acc-0.3729
Iter-12190, train loss-2.1759, acc-0.4200, valid loss-2.1657, acc-0.3758, test loss-2.1665, acc-0.3733
Iter-12200, train loss-2.1792, acc-0.4200, valid loss-2.1656, acc-0.3760, test loss-2.1664, acc-0.3732
Iter-12210, train loss-2.1193, acc-0.4400, valid loss-2.1654, acc-0.3760, test loss-2.1663, acc-0.3732
Iter-12220, train loss-2.1619, acc-0.3600, valid loss-2.1653, acc-0.3760, test loss-2.1662, acc-0.3732
Iter-12230, train loss-2.1671, acc-0.3800, valid loss-2.1652, acc-0.3766, test loss-2.1661, acc-0.3732
Iter-12240, train loss-2.2043, acc-0.2800, valid loss-2.1651, acc-0.3764, test loss-2.1660, acc-0.3735
Iter-12250, train loss-2.1841, acc-0.3800, valid loss-2.1650, acc-0.3764, test loss-2.1658, acc-0.3738
Iter-12260, train loss-2.1923, acc-0.3000, valid loss-2.1649, acc-0.3764, test loss-2.1657, acc-0.3738
Iter-12270, train loss-2.1498, acc-0.4400, valid loss-2.1648, acc-0.3762, test loss-2.1656, acc-0.3737
Iter-12280, train loss-2.1482, acc-0.5200, valid loss-2.1647, acc-0.3762, test loss-2.1655, acc-0.3736
Iter-12290, train loss-2.1633, acc-0.4000, valid loss-2.1645, acc-0.3758, test loss-2.1654, acc-0.3738
Iter-12300, train loss-2.1707, acc-0.3400, valid loss-2.1644, acc-0.3758, test loss-2.1653, acc-0.3738
Iter-12310, train loss-2.1612, acc-0.4600, valid loss-2.1643, acc-0.3766, test loss-2.1651, acc-0.3734
Iter-12320, train loss-2.1741, acc-0.3600, valid loss-2.1642, acc-0.3762, test loss-2.1650, acc-0.3735
Iter-12330, train loss-2.1765, acc-0.4600, valid loss-2.1641, acc-0.3764, test loss-2.1649, acc-0.3734
Iter-12340, train loss-2.1709, acc-0.3600, valid loss-2.1640, acc-0.3762, test loss-2.1648, acc-0.3734
Iter-12350, train loss-2.1476, acc-0.3800, valid loss-2.1639, acc-0.3760, test loss-2.1647, acc-0.3736
Iter-12360, train loss-2.1613, acc-0.4400, valid loss-2.1637, acc-0.3758, test loss-2.1646, acc-0.3736
Iter-12370, train loss-2.1577, acc-0.3400, valid loss-2.1636, acc-0.3762, test loss-2.1645, acc-0.3737
Iter-12380, train loss-2.1868, acc-0.3000, valid loss-2.1635, acc-0.3760, test loss-2.1644, acc-0.3738
Iter-12390, train loss-2.2199, acc-0.2200, valid loss-2.1634, acc-0.3760, test loss-2.1643, acc-0.3737
Iter-12400, train loss-2.1547, acc-0.3000, valid loss-2.1633, acc-0.3760, test loss-2.1642, acc-0.3739
Iter-12410, train loss-2.1715, acc-0.4600, valid loss-2.1632, acc-0.3762, test loss-2.1640, acc-0.3742
Iter-12420, train loss-2.1339, acc-0.4000, valid loss-2.1631, acc-0.3764, test loss-2.1639, acc-0.3746
Iter-12430, train loss-2.2222, acc-0.2200, valid loss-2.1630, acc-0.3764, test loss-2.1638, acc-0.3746
Iter-12440, train loss-2.1843, acc-0.3200, valid loss-2.1628, acc-0.3764, test loss-2.1637, acc-0.3749
Iter-12450, train loss-2.1849, acc-0.3400, valid loss-2.1627, acc-0.3766, test loss-2.1636, acc-0.3749
Iter-12460, train loss-2.1724, acc-0.3600, valid loss-2.1626, acc-0.3766, test loss-2.1635, acc-0.3749
Iter-12470, train loss-2.1637, acc-0.4600, valid loss-2.1625, acc-0.3762, test loss-2.1634, acc-0.3747
Iter-12480, train loss-2.1645, acc-0.4000, valid loss-2.1624, acc-0.3764, test loss-2.1632, acc-0.3748
Iter-12490, train loss-2.1871, acc-0.3200, valid loss-2.1623, acc-0.3760, test loss-2.1631, acc-0.3749
Iter-12500, train loss-2.1431, acc-0.4200, valid loss-2.1622, acc-0.3760, test loss-2.1630, acc-0.3747
Iter-12510, train loss-2.1933, acc-0.3000, valid loss-2.1621, acc-0.3762, test loss-2.1629, acc-0.3747
Iter-12520, train loss-2.1696, acc-0.3400, valid loss-2.1619, acc-0.3760, test loss-2.1628, acc-0.3751
Iter-12530, train loss-2.1423, acc-0.4600, valid loss-2.1618, acc-0.3758, test loss-2.1627, acc-0.3749
Iter-12540, train loss-2.1481, acc-0.4400, valid loss-2.1617, acc-0.3760, test loss-2.1626, acc-0.3751
Iter-12550, train loss-2.1763, acc-0.3200, valid loss-2.1616, acc-0.3764, test loss-2.1625, acc-0.3751
Iter-12560, train loss-2.2016, acc-0.2600, valid loss-2.1615, acc-0.3764, test loss-2.1623, acc-0.3752
Iter-12570, train loss-2.1725, acc-0.3400, valid loss-2.1614, acc-0.3766, test loss-2.1622, acc-0.3751
Iter-12580, train loss-2.1895, acc-0.3000, valid loss-2.1613, acc-0.3766, test loss-2.1621, acc-0.3751
Iter-12590, train loss-2.2089, acc-0.3200, valid loss-2.1612, acc-0.3762, test loss-2.1620, acc-0.3755
Iter-12600, train loss-2.1464, acc-0.4000, valid loss-2.1610, acc-0.3760, test loss-2.1619, acc-0.3756
Iter-12610, train loss-2.1455, acc-0.3600, valid loss-2.1609, acc-0.3766, test loss-2.1618, acc-0.3758
Iter-12620, train loss-2.2295, acc-0.3000, valid loss-2.1608, acc-0.3766, test loss-2.1617, acc-0.3758
Iter-12630, train loss-2.1719, acc-0.4400, valid loss-2.1607, acc-0.3766, test loss-2.1616, acc-0.3756
Iter-12640, train loss-2.1781, acc-0.3800, valid loss-2.1606, acc-0.3770, test loss-2.1615, acc-0.3760
Iter-12650, train loss-2.1642, acc-0.3200, valid loss-2.1605, acc-0.3766, test loss-2.1614, acc-0.3760
Iter-12660, train loss-2.1627, acc-0.3800, valid loss-2.1604, acc-0.3766, test loss-2.1613, acc-0.3760
Iter-12670, train loss-2.1767, acc-0.3600, valid loss-2.1603, acc-0.3762, test loss-2.1611, acc-0.3760
Iter-12680, train loss-2.1625, acc-0.3600, valid loss-2.1602, acc-0.3768, test loss-2.1610, acc-0.3762
Iter-12690, train loss-2.1730, acc-0.2600, valid loss-2.1601, acc-0.3768, test loss-2.1609, acc-0.3760
Iter-12700, train loss-2.1666, acc-0.3600, valid loss-2.1600, acc-0.3770, test loss-2.1608, acc-0.3760
Iter-12710, train loss-2.1651, acc-0.3000, valid loss-2.1598, acc-0.3768, test loss-2.1607, acc-0.3762
Iter-12720, train loss-2.1504, acc-0.4000, valid loss-2.1597, acc-0.3772, test loss-2.1606, acc-0.3762
Iter-12730, train loss-2.1781, acc-0.3600, valid loss-2.1596, acc-0.3770, test loss-2.1605, acc-0.3761
Iter-12740, train loss-2.1937, acc-0.2600, valid loss-2.1595, acc-0.3770, test loss-2.1604, acc-0.3759
Iter-12750, train loss-2.1760, acc-0.3600, valid loss-2.1594, acc-0.3774, test loss-2.1603, acc-0.3760
Iter-12760, train loss-2.0980, acc-0.5000, valid loss-2.1593, acc-0.3778, test loss-2.1601, acc-0.3761
Iter-12770, train loss-2.1460, acc-0.4800, valid loss-2.1592, acc-0.3774, test loss-2.1600, acc-0.3761
Iter-12780, train loss-2.1748, acc-0.4000, valid loss-2.1591, acc-0.3776, test loss-2.1599, acc-0.3762
Iter-12790, train loss-2.2234, acc-0.3200, valid loss-2.1590, acc-0.3782, test loss-2.1598, acc-0.3760
Iter-12800, train loss-2.1166, acc-0.5400, valid loss-2.1588, acc-0.3784, test loss-2.1597, acc-0.3761
Iter-12810, train loss-2.1443, acc-0.4000, valid loss-2.1587, acc-0.3782, test loss-2.1596, acc-0.3761
Iter-12820, train loss-2.1486, acc-0.4000, valid loss-2.1586, acc-0.3788, test loss-2.1595, acc-0.3760
Iter-12830, train loss-2.1418, acc-0.4200, valid loss-2.1585, acc-0.3784, test loss-2.1594, acc-0.3762
Iter-12840, train loss-2.1432, acc-0.4200, valid loss-2.1584, acc-0.3790, test loss-2.1592, acc-0.3762
Iter-12850, train loss-2.1870, acc-0.3200, valid loss-2.1583, acc-0.3786, test loss-2.1591, acc-0.3759
Iter-12860, train loss-2.1637, acc-0.3400, valid loss-2.1582, acc-0.3788, test loss-2.1590, acc-0.3762
Iter-12870, train loss-2.1854, acc-0.3200, valid loss-2.1581, acc-0.3792, test loss-2.1589, acc-0.3764
Iter-12880, train loss-2.1396, acc-0.3600, valid loss-2.1579, acc-0.3794, test loss-2.1588, acc-0.3767
Iter-12890, train loss-2.1488, acc-0.3800, valid loss-2.1578, acc-0.3794, test loss-2.1587, acc-0.3768
Iter-12900, train loss-2.1599, acc-0.3400, valid loss-2.1577, acc-0.3798, test loss-2.1586, acc-0.3765
Iter-12910, train loss-2.1819, acc-0.3200, valid loss-2.1576, acc-0.3802, test loss-2.1585, acc-0.3767
Iter-12920, train loss-2.1985, acc-0.2800, valid loss-2.1575, acc-0.3804, test loss-2.1583, acc-0.3772
Iter-12930, train loss-2.1315, acc-0.4800, valid loss-2.1574, acc-0.3808, test loss-2.1582, acc-0.3775
Iter-12940, train loss-2.1657, acc-0.4400, valid loss-2.1573, acc-0.3812, test loss-2.1581, acc-0.3776
Iter-12950, train loss-2.1514, acc-0.3400, valid loss-2.1572, acc-0.3812, test loss-2.1580, acc-0.3778
Iter-12960, train loss-2.1384, acc-0.4000, valid loss-2.1570, acc-0.3806, test loss-2.1579, acc-0.3779
Iter-12970, train loss-2.1668, acc-0.3600, valid loss-2.1569, acc-0.3806, test loss-2.1578, acc-0.3780
Iter-12980, train loss-2.1416, acc-0.3800, valid loss-2.1568, acc-0.3812, test loss-2.1577, acc-0.3778
Iter-12990, train loss-2.1515, acc-0.3400, valid loss-2.1567, acc-0.3808, test loss-2.1576, acc-0.3779
Iter-13000, train loss-2.1502, acc-0.4600, valid loss-2.1566, acc-0.3812, test loss-2.1575, acc-0.3779
Iter-13010, train loss-2.1722, acc-0.2800, valid loss-2.1565, acc-0.3814, test loss-2.1574, acc-0.3778
Iter-13020, train loss-2.1496, acc-0.3600, valid loss-2.1564, acc-0.3814, test loss-2.1572, acc-0.3780
Iter-13030, train loss-2.1560, acc-0.3400, valid loss-2.1563, acc-0.3814, test loss-2.1571, acc-0.3782
Iter-13040, train loss-2.1329, acc-0.3600, valid loss-2.1562, acc-0.3814, test loss-2.1570, acc-0.3784
Iter-13050, train loss-2.1597, acc-0.4000, valid loss-2.1560, acc-0.3816, test loss-2.1569, acc-0.3786
Iter-13060, train loss-2.1516, acc-0.4400, valid loss-2.1559, acc-0.3822, test loss-2.1568, acc-0.3786
Iter-13070, train loss-2.1389, acc-0.4200, valid loss-2.1558, acc-0.3820, test loss-2.1567, acc-0.3786
Iter-13080, train loss-2.1696, acc-0.3400, valid loss-2.1557, acc-0.3822, test loss-2.1566, acc-0.3786
Iter-13090, train loss-2.1696, acc-0.3800, valid loss-2.1556, acc-0.3820, test loss-2.1565, acc-0.3789
Iter-13100, train loss-2.0909, acc-0.4800, valid loss-2.1555, acc-0.3818, test loss-2.1564, acc-0.3793
Iter-13110, train loss-2.1916, acc-0.4800, valid loss-2.1554, acc-0.3818, test loss-2.1563, acc-0.3794
Iter-13120, train loss-2.1347, acc-0.3800, valid loss-2.1553, acc-0.3820, test loss-2.1562, acc-0.3795
Iter-13130, train loss-2.1952, acc-0.2800, valid loss-2.1552, acc-0.3820, test loss-2.1560, acc-0.3795
Iter-13140, train loss-2.1717, acc-0.3600, valid loss-2.1551, acc-0.3822, test loss-2.1559, acc-0.3794
Iter-13150, train loss-2.1421, acc-0.3800, valid loss-2.1550, acc-0.3828, test loss-2.1558, acc-0.3794
Iter-13160, train loss-2.1428, acc-0.3800, valid loss-2.1549, acc-0.3830, test loss-2.1557, acc-0.3793
Iter-13170, train loss-2.1740, acc-0.3600, valid loss-2.1548, acc-0.3832, test loss-2.1556, acc-0.3796
Iter-13180, train loss-2.1307, acc-0.4400, valid loss-2.1547, acc-0.3832, test loss-2.1555, acc-0.3796
Iter-13190, train loss-2.1724, acc-0.4000, valid loss-2.1545, acc-0.3832, test loss-2.1554, acc-0.3795
Iter-13200, train loss-2.1144, acc-0.4800, valid loss-2.1544, acc-0.3832, test loss-2.1553, acc-0.3794
Iter-13210, train loss-2.1853, acc-0.3000, valid loss-2.1543, acc-0.3836, test loss-2.1552, acc-0.3795
Iter-13220, train loss-2.1468, acc-0.3800, valid loss-2.1542, acc-0.3838, test loss-2.1551, acc-0.3800
Iter-13230, train loss-2.1213, acc-0.4600, valid loss-2.1541, acc-0.3836, test loss-2.1549, acc-0.3797
Iter-13240, train loss-2.1616, acc-0.4400, valid loss-2.1540, acc-0.3836, test loss-2.1548, acc-0.3795
Iter-13250, train loss-2.1891, acc-0.2800, valid loss-2.1539, acc-0.3842, test loss-2.1547, acc-0.3801
Iter-13260, train loss-2.1971, acc-0.2200, valid loss-2.1538, acc-0.3838, test loss-2.1546, acc-0.3802
Iter-13270, train loss-2.1410, acc-0.3600, valid loss-2.1537, acc-0.3838, test loss-2.1545, acc-0.3798
Iter-13280, train loss-2.1762, acc-0.3400, valid loss-2.1536, acc-0.3840, test loss-2.1544, acc-0.3797
Iter-13290, train loss-2.1310, acc-0.4600, valid loss-2.1535, acc-0.3840, test loss-2.1543, acc-0.3798
Iter-13300, train loss-2.1820, acc-0.3200, valid loss-2.1533, acc-0.3842, test loss-2.1542, acc-0.3802
Iter-13310, train loss-2.1587, acc-0.2600, valid loss-2.1532, acc-0.3844, test loss-2.1541, acc-0.3802
Iter-13320, train loss-2.1863, acc-0.3400, valid loss-2.1531, acc-0.3844, test loss-2.1540, acc-0.3801
Iter-13330, train loss-2.1216, acc-0.4600, valid loss-2.1530, acc-0.3846, test loss-2.1539, acc-0.3800
Iter-13340, train loss-2.1489, acc-0.3600, valid loss-2.1529, acc-0.3844, test loss-2.1537, acc-0.3798
Iter-13350, train loss-2.1523, acc-0.3400, valid loss-2.1528, acc-0.3842, test loss-2.1536, acc-0.3798
Iter-13360, train loss-2.1406, acc-0.4000, valid loss-2.1527, acc-0.3838, test loss-2.1535, acc-0.3797
Iter-13370, train loss-2.1400, acc-0.3800, valid loss-2.1526, acc-0.3838, test loss-2.1534, acc-0.3795
Iter-13380, train loss-2.1756, acc-0.2600, valid loss-2.1525, acc-0.3838, test loss-2.1533, acc-0.3796
Iter-13390, train loss-2.1845, acc-0.3400, valid loss-2.1524, acc-0.3840, test loss-2.1532, acc-0.3795
Iter-13400, train loss-2.1487, acc-0.3600, valid loss-2.1523, acc-0.3840, test loss-2.1531, acc-0.3796
Iter-13410, train loss-2.1496, acc-0.3600, valid loss-2.1522, acc-0.3840, test loss-2.1530, acc-0.3797
Iter-13420, train loss-2.1145, acc-0.4400, valid loss-2.1521, acc-0.3844, test loss-2.1529, acc-0.3797
Iter-13430, train loss-2.1991, acc-0.2600, valid loss-2.1520, acc-0.3850, test loss-2.1528, acc-0.3799
Iter-13440, train loss-2.1690, acc-0.3400, valid loss-2.1518, acc-0.3846, test loss-2.1527, acc-0.3797
Iter-13450, train loss-2.1406, acc-0.4600, valid loss-2.1517, acc-0.3850, test loss-2.1526, acc-0.3799
Iter-13460, train loss-2.1610, acc-0.3800, valid loss-2.1516, acc-0.3852, test loss-2.1525, acc-0.3800
Iter-13470, train loss-2.1430, acc-0.3600, valid loss-2.1515, acc-0.3848, test loss-2.1524, acc-0.3801
Iter-13480, train loss-2.1535, acc-0.3400, valid loss-2.1514, acc-0.3848, test loss-2.1523, acc-0.3801
Iter-13490, train loss-2.1554, acc-0.3600, valid loss-2.1513, acc-0.3846, test loss-2.1521, acc-0.3802
Iter-13500, train loss-2.2154, acc-0.3000, valid loss-2.1512, acc-0.3852, test loss-2.1520, acc-0.3805
Iter-13510, train loss-2.1366, acc-0.4200, valid loss-2.1511, acc-0.3850, test loss-2.1519, acc-0.3806
Iter-13520, train loss-2.1843, acc-0.3000, valid loss-2.1510, acc-0.3850, test loss-2.1518, acc-0.3806
Iter-13530, train loss-2.1530, acc-0.4000, valid loss-2.1508, acc-0.3852, test loss-2.1517, acc-0.3807
Iter-13540, train loss-2.1514, acc-0.3400, valid loss-2.1507, acc-0.3854, test loss-2.1516, acc-0.3808
Iter-13550, train loss-2.1590, acc-0.3200, valid loss-2.1506, acc-0.3858, test loss-2.1515, acc-0.3806
Iter-13560, train loss-2.1360, acc-0.4000, valid loss-2.1505, acc-0.3858, test loss-2.1514, acc-0.3810
Iter-13570, train loss-2.1847, acc-0.3600, valid loss-2.1504, acc-0.3864, test loss-2.1513, acc-0.3808
Iter-13580, train loss-2.1539, acc-0.3600, valid loss-2.1503, acc-0.3862, test loss-2.1511, acc-0.3809
Iter-13590, train loss-2.1280, acc-0.4400, valid loss-2.1502, acc-0.3862, test loss-2.1510, acc-0.3809
Iter-13600, train loss-2.1456, acc-0.3800, valid loss-2.1501, acc-0.3866, test loss-2.1509, acc-0.3809
Iter-13610, train loss-2.1953, acc-0.2200, valid loss-2.1500, acc-0.3868, test loss-2.1508, acc-0.3812
Iter-13620, train loss-2.1475, acc-0.4200, valid loss-2.1499, acc-0.3870, test loss-2.1507, acc-0.3814
Iter-13630, train loss-2.1626, acc-0.3400, valid loss-2.1498, acc-0.3870, test loss-2.1506, acc-0.3816
Iter-13640, train loss-2.0972, acc-0.5200, valid loss-2.1497, acc-0.3870, test loss-2.1505, acc-0.3814
Iter-13650, train loss-2.1360, acc-0.2800, valid loss-2.1496, acc-0.3872, test loss-2.1504, acc-0.3817
Iter-13660, train loss-2.1808, acc-0.2800, valid loss-2.1495, acc-0.3870, test loss-2.1503, acc-0.3818
Iter-13670, train loss-2.1240, acc-0.4600, valid loss-2.1493, acc-0.3872, test loss-2.1502, acc-0.3818
Iter-13680, train loss-2.1777, acc-0.3400, valid loss-2.1492, acc-0.3874, test loss-2.1501, acc-0.3819
Iter-13690, train loss-2.2009, acc-0.3600, valid loss-2.1491, acc-0.3876, test loss-2.1500, acc-0.3819
Iter-13700, train loss-2.1605, acc-0.3800, valid loss-2.1490, acc-0.3880, test loss-2.1499, acc-0.3817
Iter-13710, train loss-2.1678, acc-0.3400, valid loss-2.1489, acc-0.3880, test loss-2.1498, acc-0.3819
Iter-13720, train loss-2.1871, acc-0.3600, valid loss-2.1488, acc-0.3882, test loss-2.1497, acc-0.3820
Iter-13730, train loss-2.1554, acc-0.3800, valid loss-2.1487, acc-0.3882, test loss-2.1496, acc-0.3821
Iter-13740, train loss-2.1368, acc-0.4600, valid loss-2.1486, acc-0.3884, test loss-2.1494, acc-0.3820
Iter-13750, train loss-2.1518, acc-0.4200, valid loss-2.1485, acc-0.3884, test loss-2.1493, acc-0.3820
Iter-13760, train loss-2.1894, acc-0.2400, valid loss-2.1484, acc-0.3882, test loss-2.1492, acc-0.3822
Iter-13770, train loss-2.1396, acc-0.3600, valid loss-2.1483, acc-0.3882, test loss-2.1491, acc-0.3823
Iter-13780, train loss-2.1454, acc-0.3800, valid loss-2.1482, acc-0.3880, test loss-2.1490, acc-0.3825
Iter-13790, train loss-2.1180, acc-0.4600, valid loss-2.1481, acc-0.3880, test loss-2.1489, acc-0.3823
Iter-13800, train loss-2.1487, acc-0.4000, valid loss-2.1480, acc-0.3876, test loss-2.1488, acc-0.3822
Iter-13810, train loss-2.1477, acc-0.4600, valid loss-2.1479, acc-0.3876, test loss-2.1487, acc-0.3823
Iter-13820, train loss-2.2242, acc-0.2600, valid loss-2.1478, acc-0.3876, test loss-2.1486, acc-0.3823
Iter-13830, train loss-2.1875, acc-0.2600, valid loss-2.1476, acc-0.3878, test loss-2.1485, acc-0.3825
Iter-13840, train loss-2.1236, acc-0.4600, valid loss-2.1475, acc-0.3878, test loss-2.1484, acc-0.3826
Iter-13850, train loss-2.1366, acc-0.5200, valid loss-2.1474, acc-0.3880, test loss-2.1483, acc-0.3826
Iter-13860, train loss-2.1534, acc-0.3200, valid loss-2.1473, acc-0.3882, test loss-2.1482, acc-0.3826
Iter-13870, train loss-2.1229, acc-0.5000, valid loss-2.1472, acc-0.3886, test loss-2.1481, acc-0.3830
Iter-13880, train loss-2.1332, acc-0.5000, valid loss-2.1471, acc-0.3888, test loss-2.1479, acc-0.3830
Iter-13890, train loss-2.0910, acc-0.5400, valid loss-2.1470, acc-0.3888, test loss-2.1478, acc-0.3830
Iter-13900, train loss-2.1502, acc-0.3400, valid loss-2.1469, acc-0.3890, test loss-2.1477, acc-0.3832
Iter-13910, train loss-2.1467, acc-0.4200, valid loss-2.1468, acc-0.3890, test loss-2.1476, acc-0.3831
Iter-13920, train loss-2.0881, acc-0.4800, valid loss-2.1467, acc-0.3888, test loss-2.1475, acc-0.3830
Iter-13930, train loss-2.1704, acc-0.4000, valid loss-2.1466, acc-0.3886, test loss-2.1474, acc-0.3832
Iter-13940, train loss-2.0887, acc-0.5200, valid loss-2.1465, acc-0.3888, test loss-2.1473, acc-0.3832
Iter-13950, train loss-2.1552, acc-0.4400, valid loss-2.1463, acc-0.3890, test loss-2.1472, acc-0.3830
Iter-13960, train loss-2.1595, acc-0.3800, valid loss-2.1462, acc-0.3890, test loss-2.1471, acc-0.3831
Iter-13970, train loss-2.1526, acc-0.3400, valid loss-2.1461, acc-0.3890, test loss-2.1470, acc-0.3833
Iter-13980, train loss-2.1473, acc-0.3800, valid loss-2.1460, acc-0.3890, test loss-2.1469, acc-0.3831
Iter-13990, train loss-2.1233, acc-0.4600, valid loss-2.1459, acc-0.3890, test loss-2.1467, acc-0.3831
Iter-14000, train loss-2.1604, acc-0.3000, valid loss-2.1458, acc-0.3890, test loss-2.1466, acc-0.3832
Iter-14010, train loss-2.1298, acc-0.4400, valid loss-2.1457, acc-0.3890, test loss-2.1465, acc-0.3832
Iter-14020, train loss-2.1667, acc-0.3200, valid loss-2.1456, acc-0.3890, test loss-2.1464, acc-0.3835
Iter-14030, train loss-2.1237, acc-0.3400, valid loss-2.1455, acc-0.3892, test loss-2.1463, acc-0.3835
Iter-14040, train loss-2.1518, acc-0.3400, valid loss-2.1454, acc-0.3896, test loss-2.1462, acc-0.3834
Iter-14050, train loss-2.1544, acc-0.3800, valid loss-2.1453, acc-0.3898, test loss-2.1461, acc-0.3836
Iter-14060, train loss-2.1376, acc-0.4000, valid loss-2.1452, acc-0.3898, test loss-2.1460, acc-0.3835
Iter-14070, train loss-2.1501, acc-0.3800, valid loss-2.1450, acc-0.3898, test loss-2.1459, acc-0.3833
Iter-14080, train loss-2.1473, acc-0.4200, valid loss-2.1449, acc-0.3900, test loss-2.1458, acc-0.3837
Iter-14090, train loss-2.1409, acc-0.3800, valid loss-2.1448, acc-0.3900, test loss-2.1457, acc-0.3837
Iter-14100, train loss-2.1666, acc-0.4000, valid loss-2.1447, acc-0.3896, test loss-2.1455, acc-0.3837
Iter-14110, train loss-2.1566, acc-0.3800, valid loss-2.1446, acc-0.3896, test loss-2.1454, acc-0.3839
Iter-14120, train loss-2.1186, acc-0.3600, valid loss-2.1445, acc-0.3898, test loss-2.1453, acc-0.3838
Iter-14130, train loss-2.1148, acc-0.4400, valid loss-2.1444, acc-0.3900, test loss-2.1452, acc-0.3837
Iter-14140, train loss-2.1829, acc-0.2000, valid loss-2.1443, acc-0.3900, test loss-2.1451, acc-0.3837
Iter-14150, train loss-2.1431, acc-0.4400, valid loss-2.1442, acc-0.3898, test loss-2.1450, acc-0.3837
Iter-14160, train loss-2.1251, acc-0.4400, valid loss-2.1441, acc-0.3904, test loss-2.1449, acc-0.3837
Iter-14170, train loss-2.1196, acc-0.4800, valid loss-2.1440, acc-0.3902, test loss-2.1448, acc-0.3836
Iter-14180, train loss-2.1656, acc-0.3400, valid loss-2.1439, acc-0.3904, test loss-2.1447, acc-0.3837
Iter-14190, train loss-2.1732, acc-0.3200, valid loss-2.1438, acc-0.3902, test loss-2.1446, acc-0.3838
Iter-14200, train loss-2.1079, acc-0.4400, valid loss-2.1436, acc-0.3904, test loss-2.1445, acc-0.3837
Iter-14210, train loss-2.1753, acc-0.2800, valid loss-2.1435, acc-0.3906, test loss-2.1444, acc-0.3837
Iter-14220, train loss-2.1826, acc-0.3000, valid loss-2.1434, acc-0.3906, test loss-2.1443, acc-0.3838
Iter-14230, train loss-2.1344, acc-0.4000, valid loss-2.1433, acc-0.3910, test loss-2.1442, acc-0.3840
Iter-14240, train loss-2.1103, acc-0.4400, valid loss-2.1432, acc-0.3906, test loss-2.1440, acc-0.3848
Iter-14250, train loss-2.1211, acc-0.4000, valid loss-2.1431, acc-0.3908, test loss-2.1439, acc-0.3847
Iter-14260, train loss-2.1466, acc-0.3800, valid loss-2.1430, acc-0.3908, test loss-2.1438, acc-0.3848
Iter-14270, train loss-2.1337, acc-0.3400, valid loss-2.1429, acc-0.3906, test loss-2.1437, acc-0.3847
Iter-14280, train loss-2.1584, acc-0.3800, valid loss-2.1428, acc-0.3910, test loss-2.1436, acc-0.3850
Iter-14290, train loss-2.1628, acc-0.4000, valid loss-2.1427, acc-0.3908, test loss-2.1435, acc-0.3849
Iter-14300, train loss-2.1480, acc-0.4400, valid loss-2.1426, acc-0.3908, test loss-2.1434, acc-0.3848
Iter-14310, train loss-2.1186, acc-0.4000, valid loss-2.1425, acc-0.3908, test loss-2.1433, acc-0.3848
Iter-14320, train loss-2.1575, acc-0.3000, valid loss-2.1424, acc-0.3906, test loss-2.1432, acc-0.3848
Iter-14330, train loss-2.0983, acc-0.4800, valid loss-2.1423, acc-0.3906, test loss-2.1431, acc-0.3850
Iter-14340, train loss-2.1560, acc-0.3800, valid loss-2.1422, acc-0.3906, test loss-2.1430, acc-0.3848
Iter-14350, train loss-2.1478, acc-0.4400, valid loss-2.1421, acc-0.3906, test loss-2.1429, acc-0.3846
Iter-14360, train loss-2.1210, acc-0.4000, valid loss-2.1420, acc-0.3910, test loss-2.1427, acc-0.3848
Iter-14370, train loss-2.1703, acc-0.3200, valid loss-2.1419, acc-0.3912, test loss-2.1426, acc-0.3851
Iter-14380, train loss-2.1241, acc-0.4400, valid loss-2.1417, acc-0.3910, test loss-2.1425, acc-0.3852
Iter-14390, train loss-2.1582, acc-0.3600, valid loss-2.1416, acc-0.3912, test loss-2.1424, acc-0.3851
Iter-14400, train loss-2.1745, acc-0.2800, valid loss-2.1415, acc-0.3910, test loss-2.1423, acc-0.3854
Iter-14410, train loss-2.1395, acc-0.3200, valid loss-2.1414, acc-0.3914, test loss-2.1422, acc-0.3856
Iter-14420, train loss-2.1347, acc-0.4400, valid loss-2.1413, acc-0.3914, test loss-2.1421, acc-0.3856
Iter-14430, train loss-2.1682, acc-0.2800, valid loss-2.1412, acc-0.3918, test loss-2.1420, acc-0.3853
Iter-14440, train loss-2.1503, acc-0.3600, valid loss-2.1411, acc-0.3918, test loss-2.1419, acc-0.3855
Iter-14450, train loss-2.1471, acc-0.4000, valid loss-2.1410, acc-0.3920, test loss-2.1418, acc-0.3854
Iter-14460, train loss-2.1459, acc-0.3200, valid loss-2.1409, acc-0.3922, test loss-2.1417, acc-0.3856
Iter-14470, train loss-2.2094, acc-0.2000, valid loss-2.1408, acc-0.3924, test loss-2.1416, acc-0.3859
Iter-14480, train loss-2.1478, acc-0.4200, valid loss-2.1407, acc-0.3924, test loss-2.1415, acc-0.3858
Iter-14490, train loss-2.1853, acc-0.2200, valid loss-2.1405, acc-0.3920, test loss-2.1414, acc-0.3858
Iter-14500, train loss-2.1335, acc-0.4600, valid loss-2.1404, acc-0.3922, test loss-2.1412, acc-0.3863
Iter-14510, train loss-2.1485, acc-0.3600, valid loss-2.1403, acc-0.3922, test loss-2.1411, acc-0.3861
Iter-14520, train loss-2.1672, acc-0.3400, valid loss-2.1402, acc-0.3922, test loss-2.1410, acc-0.3862
Iter-14530, train loss-2.1101, acc-0.4800, valid loss-2.1401, acc-0.3922, test loss-2.1409, acc-0.3865
Iter-14540, train loss-2.1480, acc-0.3600, valid loss-2.1400, acc-0.3926, test loss-2.1408, acc-0.3864
Iter-14550, train loss-2.1524, acc-0.3000, valid loss-2.1399, acc-0.3926, test loss-2.1407, acc-0.3866
Iter-14560, train loss-2.1601, acc-0.3400, valid loss-2.1398, acc-0.3924, test loss-2.1406, acc-0.3868
Iter-14570, train loss-2.1542, acc-0.3800, valid loss-2.1397, acc-0.3922, test loss-2.1405, acc-0.3869
Iter-14580, train loss-2.1284, acc-0.4400, valid loss-2.1396, acc-0.3926, test loss-2.1404, acc-0.3868
Iter-14590, train loss-2.1505, acc-0.4200, valid loss-2.1395, acc-0.3924, test loss-2.1403, acc-0.3870
Iter-14600, train loss-2.1381, acc-0.3000, valid loss-2.1394, acc-0.3924, test loss-2.1402, acc-0.3869
Iter-14610, train loss-2.1621, acc-0.4000, valid loss-2.1393, acc-0.3924, test loss-2.1401, acc-0.3873
Iter-14620, train loss-2.1633, acc-0.4000, valid loss-2.1392, acc-0.3926, test loss-2.1400, acc-0.3873
Iter-14630, train loss-2.1718, acc-0.3200, valid loss-2.1391, acc-0.3926, test loss-2.1399, acc-0.3872
Iter-14640, train loss-2.1481, acc-0.3800, valid loss-2.1390, acc-0.3928, test loss-2.1398, acc-0.3872
Iter-14650, train loss-2.1506, acc-0.3200, valid loss-2.1389, acc-0.3926, test loss-2.1397, acc-0.3871
Iter-14660, train loss-2.1317, acc-0.3400, valid loss-2.1388, acc-0.3926, test loss-2.1396, acc-0.3876
Iter-14670, train loss-2.1018, acc-0.4800, valid loss-2.1387, acc-0.3924, test loss-2.1395, acc-0.3876
Iter-14680, train loss-2.1159, acc-0.4600, valid loss-2.1385, acc-0.3926, test loss-2.1393, acc-0.3877
Iter-14690, train loss-2.1481, acc-0.3200, valid loss-2.1384, acc-0.3928, test loss-2.1392, acc-0.3878
Iter-14700, train loss-2.1741, acc-0.3600, valid loss-2.1383, acc-0.3932, test loss-2.1391, acc-0.3880
Iter-14710, train loss-2.1287, acc-0.4600, valid loss-2.1382, acc-0.3932, test loss-2.1390, acc-0.3880
Iter-14720, train loss-2.1136, acc-0.4800, valid loss-2.1381, acc-0.3936, test loss-2.1389, acc-0.3882
Iter-14730, train loss-2.1680, acc-0.3800, valid loss-2.1380, acc-0.3930, test loss-2.1388, acc-0.3883
Iter-14740, train loss-2.1497, acc-0.3000, valid loss-2.1379, acc-0.3932, test loss-2.1387, acc-0.3883
Iter-14750, train loss-2.1198, acc-0.4400, valid loss-2.1378, acc-0.3932, test loss-2.1386, acc-0.3884
Iter-14760, train loss-2.1686, acc-0.2600, valid loss-2.1377, acc-0.3934, test loss-2.1385, acc-0.3887
Iter-14770, train loss-2.1440, acc-0.4200, valid loss-2.1376, acc-0.3932, test loss-2.1384, acc-0.3887
Iter-14780, train loss-2.1070, acc-0.4200, valid loss-2.1375, acc-0.3934, test loss-2.1383, acc-0.3886
Iter-14790, train loss-2.1414, acc-0.4400, valid loss-2.1374, acc-0.3934, test loss-2.1382, acc-0.3888
Iter-14800, train loss-2.1488, acc-0.3600, valid loss-2.1373, acc-0.3930, test loss-2.1381, acc-0.3885
Iter-14810, train loss-2.1027, acc-0.4000, valid loss-2.1372, acc-0.3932, test loss-2.1380, acc-0.3885
Iter-14820, train loss-2.1543, acc-0.3800, valid loss-2.1371, acc-0.3932, test loss-2.1379, acc-0.3890
Iter-14830, train loss-2.1971, acc-0.2000, valid loss-2.1369, acc-0.3934, test loss-2.1377, acc-0.3887
Iter-14840, train loss-2.1229, acc-0.3400, valid loss-2.1368, acc-0.3936, test loss-2.1376, acc-0.3887
Iter-14850, train loss-2.1320, acc-0.4600, valid loss-2.1367, acc-0.3932, test loss-2.1375, acc-0.3887
Iter-14860, train loss-2.1081, acc-0.4400, valid loss-2.1366, acc-0.3932, test loss-2.1374, acc-0.3890
Iter-14870, train loss-2.1688, acc-0.3400, valid loss-2.1365, acc-0.3932, test loss-2.1373, acc-0.3891
Iter-14880, train loss-2.1520, acc-0.3600, valid loss-2.1364, acc-0.3934, test loss-2.1372, acc-0.3890
Iter-14890, train loss-2.1376, acc-0.4400, valid loss-2.1363, acc-0.3934, test loss-2.1371, acc-0.3890
Iter-14900, train loss-2.1738, acc-0.2400, valid loss-2.1362, acc-0.3932, test loss-2.1370, acc-0.3891
Iter-14910, train loss-2.1670, acc-0.3200, valid loss-2.1361, acc-0.3934, test loss-2.1369, acc-0.3893
Iter-14920, train loss-2.1378, acc-0.4000, valid loss-2.1360, acc-0.3934, test loss-2.1368, acc-0.3892
Iter-14930, train loss-2.1139, acc-0.3800, valid loss-2.1359, acc-0.3936, test loss-2.1367, acc-0.3895
Iter-14940, train loss-2.1427, acc-0.4000, valid loss-2.1358, acc-0.3936, test loss-2.1366, acc-0.3899
Iter-14950, train loss-2.2034, acc-0.2400, valid loss-2.1357, acc-0.3936, test loss-2.1365, acc-0.3900
Iter-14960, train loss-2.1606, acc-0.3600, valid loss-2.1356, acc-0.3938, test loss-2.1364, acc-0.3901
Iter-14970, train loss-2.1908, acc-0.3200, valid loss-2.1355, acc-0.3938, test loss-2.1363, acc-0.3902
Iter-14980, train loss-2.0908, acc-0.4400, valid loss-2.1354, acc-0.3938, test loss-2.1362, acc-0.3902
Iter-14990, train loss-2.1166, acc-0.4000, valid loss-2.1353, acc-0.3940, test loss-2.1361, acc-0.3903
Iter-15000, train loss-2.1524, acc-0.3200, valid loss-2.1352, acc-0.3938, test loss-2.1360, acc-0.3901
Iter-15010, train loss-2.1601, acc-0.3200, valid loss-2.1351, acc-0.3940, test loss-2.1359, acc-0.3901
Iter-15020, train loss-2.1220, acc-0.4000, valid loss-2.1350, acc-0.3940, test loss-2.1358, acc-0.3901
Iter-15030, train loss-2.1211, acc-0.4200, valid loss-2.1349, acc-0.3942, test loss-2.1357, acc-0.3901
Iter-15040, train loss-2.1563, acc-0.2800, valid loss-2.1348, acc-0.3942, test loss-2.1356, acc-0.3900
Iter-15050, train loss-2.1233, acc-0.4200, valid loss-2.1347, acc-0.3942, test loss-2.1355, acc-0.3903
Iter-15060, train loss-2.1362, acc-0.3800, valid loss-2.1346, acc-0.3944, test loss-2.1353, acc-0.3904
Iter-15070, train loss-2.1438, acc-0.3000, valid loss-2.1345, acc-0.3948, test loss-2.1352, acc-0.3902
Iter-15080, train loss-2.1105, acc-0.4400, valid loss-2.1343, acc-0.3944, test loss-2.1351, acc-0.3903
Iter-15090, train loss-2.1185, acc-0.4800, valid loss-2.1342, acc-0.3948, test loss-2.1350, acc-0.3905
Iter-15100, train loss-2.1748, acc-0.3200, valid loss-2.1341, acc-0.3950, test loss-2.1349, acc-0.3907
Iter-15110, train loss-2.1462, acc-0.3400, valid loss-2.1340, acc-0.3946, test loss-2.1348, acc-0.3908
Iter-15120, train loss-2.1300, acc-0.4600, valid loss-2.1339, acc-0.3948, test loss-2.1347, acc-0.3906
Iter-15130, train loss-2.1463, acc-0.4000, valid loss-2.1338, acc-0.3946, test loss-2.1346, acc-0.3907
Iter-15140, train loss-2.1193, acc-0.4800, valid loss-2.1337, acc-0.3946, test loss-2.1345, acc-0.3908
Iter-15150, train loss-2.1660, acc-0.3800, valid loss-2.1336, acc-0.3946, test loss-2.1344, acc-0.3908
Iter-15160, train loss-2.1255, acc-0.4600, valid loss-2.1335, acc-0.3948, test loss-2.1343, acc-0.3910
Iter-15170, train loss-2.1730, acc-0.3200, valid loss-2.1334, acc-0.3950, test loss-2.1342, acc-0.3912
Iter-15180, train loss-2.1551, acc-0.3400, valid loss-2.1333, acc-0.3950, test loss-2.1341, acc-0.3909
Iter-15190, train loss-2.1758, acc-0.3000, valid loss-2.1332, acc-0.3948, test loss-2.1340, acc-0.3905
Iter-15200, train loss-2.1528, acc-0.3800, valid loss-2.1331, acc-0.3948, test loss-2.1339, acc-0.3910
Iter-15210, train loss-2.1384, acc-0.4200, valid loss-2.1330, acc-0.3950, test loss-2.1338, acc-0.3908
Iter-15220, train loss-2.1504, acc-0.4000, valid loss-2.1329, acc-0.3948, test loss-2.1337, acc-0.3906
Iter-15230, train loss-2.1180, acc-0.4400, valid loss-2.1328, acc-0.3952, test loss-2.1336, acc-0.3908
Iter-15240, train loss-2.1029, acc-0.4600, valid loss-2.1327, acc-0.3952, test loss-2.1335, acc-0.3907
Iter-15250, train loss-2.1707, acc-0.4200, valid loss-2.1326, acc-0.3954, test loss-2.1334, acc-0.3909
Iter-15260, train loss-2.1150, acc-0.3800, valid loss-2.1324, acc-0.3952, test loss-2.1332, acc-0.3909
Iter-15270, train loss-2.1242, acc-0.4400, valid loss-2.1323, acc-0.3952, test loss-2.1331, acc-0.3910
Iter-15280, train loss-2.1477, acc-0.3800, valid loss-2.1322, acc-0.3952, test loss-2.1330, acc-0.3909
Iter-15290, train loss-2.1459, acc-0.4200, valid loss-2.1321, acc-0.3954, test loss-2.1329, acc-0.3910
Iter-15300, train loss-2.1473, acc-0.3400, valid loss-2.1320, acc-0.3958, test loss-2.1328, acc-0.3911
Iter-15310, train loss-2.1530, acc-0.4000, valid loss-2.1319, acc-0.3958, test loss-2.1327, acc-0.3913
Iter-15320, train loss-2.1522, acc-0.3200, valid loss-2.1318, acc-0.3956, test loss-2.1326, acc-0.3911
Iter-15330, train loss-2.1422, acc-0.3200, valid loss-2.1317, acc-0.3956, test loss-2.1325, acc-0.3912
Iter-15340, train loss-2.1397, acc-0.3000, valid loss-2.1316, acc-0.3958, test loss-2.1324, acc-0.3914
Iter-15350, train loss-2.1478, acc-0.3200, valid loss-2.1315, acc-0.3956, test loss-2.1323, acc-0.3914
Iter-15360, train loss-2.1191, acc-0.4600, valid loss-2.1314, acc-0.3954, test loss-2.1322, acc-0.3911
Iter-15370, train loss-2.1665, acc-0.3600, valid loss-2.1313, acc-0.3956, test loss-2.1321, acc-0.3913
Iter-15380, train loss-2.1823, acc-0.2800, valid loss-2.1312, acc-0.3958, test loss-2.1320, acc-0.3913
Iter-15390, train loss-2.1667, acc-0.4000, valid loss-2.1311, acc-0.3960, test loss-2.1319, acc-0.3914
Iter-15400, train loss-2.1781, acc-0.3000, valid loss-2.1310, acc-0.3962, test loss-2.1318, acc-0.3916
Iter-15410, train loss-2.1849, acc-0.2600, valid loss-2.1309, acc-0.3962, test loss-2.1317, acc-0.3917
Iter-15420, train loss-2.1466, acc-0.3400, valid loss-2.1308, acc-0.3964, test loss-2.1316, acc-0.3914
Iter-15430, train loss-2.1335, acc-0.4200, valid loss-2.1307, acc-0.3966, test loss-2.1315, acc-0.3914
Iter-15440, train loss-2.1153, acc-0.4000, valid loss-2.1306, acc-0.3964, test loss-2.1314, acc-0.3913
Iter-15450, train loss-2.1500, acc-0.3600, valid loss-2.1305, acc-0.3968, test loss-2.1313, acc-0.3914
Iter-15460, train loss-2.0887, acc-0.4800, valid loss-2.1304, acc-0.3972, test loss-2.1312, acc-0.3914
Iter-15470, train loss-2.1074, acc-0.4800, valid loss-2.1303, acc-0.3964, test loss-2.1311, acc-0.3913
Iter-15480, train loss-2.0856, acc-0.5800, valid loss-2.1302, acc-0.3966, test loss-2.1310, acc-0.3915
Iter-15490, train loss-2.1264, acc-0.3600, valid loss-2.1301, acc-0.3968, test loss-2.1309, acc-0.3913
Iter-15500, train loss-2.1272, acc-0.3600, valid loss-2.1299, acc-0.3972, test loss-2.1308, acc-0.3911
Iter-15510, train loss-2.1480, acc-0.4400, valid loss-2.1298, acc-0.3974, test loss-2.1307, acc-0.3914
Iter-15520, train loss-2.1202, acc-0.4600, valid loss-2.1297, acc-0.3972, test loss-2.1305, acc-0.3914
Iter-15530, train loss-2.1112, acc-0.5200, valid loss-2.1296, acc-0.3972, test loss-2.1304, acc-0.3917
Iter-15540, train loss-2.1619, acc-0.4000, valid loss-2.1295, acc-0.3972, test loss-2.1303, acc-0.3919
Iter-15550, train loss-2.1301, acc-0.3600, valid loss-2.1294, acc-0.3976, test loss-2.1302, acc-0.3924
Iter-15560, train loss-2.1237, acc-0.4200, valid loss-2.1293, acc-0.3972, test loss-2.1301, acc-0.3923
Iter-15570, train loss-2.0760, acc-0.4600, valid loss-2.1292, acc-0.3972, test loss-2.1300, acc-0.3924
Iter-15580, train loss-2.1568, acc-0.3200, valid loss-2.1291, acc-0.3972, test loss-2.1299, acc-0.3925
Iter-15590, train loss-2.1235, acc-0.4000, valid loss-2.1290, acc-0.3970, test loss-2.1298, acc-0.3921
Iter-15600, train loss-2.1261, acc-0.4200, valid loss-2.1289, acc-0.3970, test loss-2.1297, acc-0.3920
Iter-15610, train loss-2.1166, acc-0.4400, valid loss-2.1288, acc-0.3970, test loss-2.1296, acc-0.3925
Iter-15620, train loss-2.1089, acc-0.4200, valid loss-2.1287, acc-0.3966, test loss-2.1295, acc-0.3925
Iter-15630, train loss-2.1705, acc-0.2800, valid loss-2.1286, acc-0.3968, test loss-2.1294, acc-0.3924
Iter-15640, train loss-2.1400, acc-0.2800, valid loss-2.1285, acc-0.3970, test loss-2.1293, acc-0.3925
Iter-15650, train loss-2.1603, acc-0.2800, valid loss-2.1284, acc-0.3966, test loss-2.1292, acc-0.3926
Iter-15660, train loss-2.1000, acc-0.4400, valid loss-2.1283, acc-0.3968, test loss-2.1291, acc-0.3925
Iter-15670, train loss-2.1753, acc-0.3800, valid loss-2.1282, acc-0.3972, test loss-2.1290, acc-0.3925
Iter-15680, train loss-2.1495, acc-0.3800, valid loss-2.1281, acc-0.3972, test loss-2.1289, acc-0.3926
Iter-15690, train loss-2.1468, acc-0.3800, valid loss-2.1280, acc-0.3974, test loss-2.1288, acc-0.3928
Iter-15700, train loss-2.1551, acc-0.3000, valid loss-2.1279, acc-0.3976, test loss-2.1287, acc-0.3929
Iter-15710, train loss-2.1229, acc-0.4000, valid loss-2.1277, acc-0.3976, test loss-2.1286, acc-0.3929
Iter-15720, train loss-2.1199, acc-0.4200, valid loss-2.1276, acc-0.3976, test loss-2.1285, acc-0.3929
Iter-15730, train loss-2.1295, acc-0.4400, valid loss-2.1275, acc-0.3978, test loss-2.1283, acc-0.3926
Iter-15740, train loss-2.1408, acc-0.3600, valid loss-2.1274, acc-0.3976, test loss-2.1282, acc-0.3925
Iter-15750, train loss-2.1334, acc-0.3600, valid loss-2.1273, acc-0.3978, test loss-2.1281, acc-0.3926
Iter-15760, train loss-2.1318, acc-0.3600, valid loss-2.1272, acc-0.3986, test loss-2.1280, acc-0.3928
Iter-15770, train loss-2.1257, acc-0.4400, valid loss-2.1271, acc-0.3986, test loss-2.1279, acc-0.3929
Iter-15780, train loss-2.1000, acc-0.4600, valid loss-2.1270, acc-0.3986, test loss-2.1278, acc-0.3929
Iter-15790, train loss-2.1373, acc-0.3600, valid loss-2.1269, acc-0.3986, test loss-2.1277, acc-0.3929
Iter-15800, train loss-2.1155, acc-0.4000, valid loss-2.1268, acc-0.3984, test loss-2.1276, acc-0.3931
Iter-15810, train loss-2.1648, acc-0.3600, valid loss-2.1267, acc-0.3986, test loss-2.1275, acc-0.3929
Iter-15820, train loss-2.1214, acc-0.3600, valid loss-2.1266, acc-0.3990, test loss-2.1274, acc-0.3927
Iter-15830, train loss-2.1284, acc-0.4600, valid loss-2.1265, acc-0.3990, test loss-2.1273, acc-0.3929
Iter-15840, train loss-2.1700, acc-0.3000, valid loss-2.1264, acc-0.3992, test loss-2.1272, acc-0.3930
Iter-15850, train loss-2.1642, acc-0.3200, valid loss-2.1263, acc-0.3988, test loss-2.1271, acc-0.3930
Iter-15860, train loss-2.1356, acc-0.4000, valid loss-2.1262, acc-0.3990, test loss-2.1270, acc-0.3932
Iter-15870, train loss-2.1561, acc-0.3200, valid loss-2.1261, acc-0.3992, test loss-2.1269, acc-0.3933
Iter-15880, train loss-2.1483, acc-0.3600, valid loss-2.1260, acc-0.3994, test loss-2.1268, acc-0.3933
Iter-15890, train loss-2.1233, acc-0.4000, valid loss-2.1259, acc-0.3992, test loss-2.1267, acc-0.3933
Iter-15900, train loss-2.1295, acc-0.4600, valid loss-2.1258, acc-0.3994, test loss-2.1266, acc-0.3931
Iter-15910, train loss-2.1481, acc-0.4200, valid loss-2.1257, acc-0.3994, test loss-2.1265, acc-0.3930
Iter-15920, train loss-2.0958, acc-0.4600, valid loss-2.1256, acc-0.3994, test loss-2.1264, acc-0.3933
Iter-15930, train loss-2.1462, acc-0.3400, valid loss-2.1255, acc-0.3996, test loss-2.1263, acc-0.3935
Iter-15940, train loss-2.1055, acc-0.3800, valid loss-2.1253, acc-0.3996, test loss-2.1262, acc-0.3936
Iter-15950, train loss-2.0695, acc-0.5000, valid loss-2.1252, acc-0.3994, test loss-2.1261, acc-0.3934
Iter-15960, train loss-2.1225, acc-0.3600, valid loss-2.1251, acc-0.3992, test loss-2.1259, acc-0.3935
Iter-15970, train loss-2.1557, acc-0.2800, valid loss-2.1250, acc-0.3992, test loss-2.1258, acc-0.3936
Iter-15980, train loss-2.1549, acc-0.3200, valid loss-2.1249, acc-0.3994, test loss-2.1257, acc-0.3934
Iter-15990, train loss-2.1415, acc-0.4200, valid loss-2.1248, acc-0.3994, test loss-2.1256, acc-0.3935
Iter-16000, train loss-2.1205, acc-0.4200, valid loss-2.1247, acc-0.3998, test loss-2.1255, acc-0.3930
Iter-16010, train loss-2.1280, acc-0.4200, valid loss-2.1246, acc-0.3998, test loss-2.1254, acc-0.3930
Iter-16020, train loss-2.1012, acc-0.5000, valid loss-2.1245, acc-0.3998, test loss-2.1253, acc-0.3932
Iter-16030, train loss-2.1360, acc-0.4000, valid loss-2.1244, acc-0.4000, test loss-2.1252, acc-0.3935
Iter-16040, train loss-2.1430, acc-0.3600, valid loss-2.1243, acc-0.4000, test loss-2.1251, acc-0.3936
Iter-16050, train loss-2.0868, acc-0.4200, valid loss-2.1242, acc-0.4000, test loss-2.1250, acc-0.3936
Iter-16060, train loss-2.1527, acc-0.3600, valid loss-2.1241, acc-0.4000, test loss-2.1249, acc-0.3937
Iter-16070, train loss-2.1557, acc-0.3600, valid loss-2.1240, acc-0.3996, test loss-2.1248, acc-0.3939
Iter-16080, train loss-2.1364, acc-0.4200, valid loss-2.1239, acc-0.3996, test loss-2.1247, acc-0.3937
Iter-16090, train loss-2.1549, acc-0.3200, valid loss-2.1238, acc-0.3998, test loss-2.1246, acc-0.3939
Iter-16100, train loss-2.1649, acc-0.3200, valid loss-2.1237, acc-0.3996, test loss-2.1245, acc-0.3940
Iter-16110, train loss-2.1255, acc-0.3200, valid loss-2.1236, acc-0.3996, test loss-2.1244, acc-0.3939
Iter-16120, train loss-2.1075, acc-0.4200, valid loss-2.1235, acc-0.4000, test loss-2.1243, acc-0.3940
Iter-16130, train loss-2.0824, acc-0.5000, valid loss-2.1234, acc-0.4000, test loss-2.1242, acc-0.3944
Iter-16140, train loss-2.1096, acc-0.3600, valid loss-2.1233, acc-0.4002, test loss-2.1241, acc-0.3943
Iter-16150, train loss-2.1351, acc-0.3800, valid loss-2.1232, acc-0.3996, test loss-2.1240, acc-0.3942
Iter-16160, train loss-2.1184, acc-0.4200, valid loss-2.1231, acc-0.3998, test loss-2.1239, acc-0.3945
Iter-16170, train loss-2.1576, acc-0.3000, valid loss-2.1230, acc-0.4002, test loss-2.1238, acc-0.3944
Iter-16180, train loss-2.1221, acc-0.3600, valid loss-2.1229, acc-0.4004, test loss-2.1237, acc-0.3942
Iter-16190, train loss-2.1076, acc-0.4200, valid loss-2.1228, acc-0.4006, test loss-2.1236, acc-0.3945
Iter-16200, train loss-2.1162, acc-0.3400, valid loss-2.1227, acc-0.4006, test loss-2.1235, acc-0.3945
Iter-16210, train loss-2.1466, acc-0.4000, valid loss-2.1226, acc-0.4006, test loss-2.1234, acc-0.3944
Iter-16220, train loss-2.1653, acc-0.3000, valid loss-2.1225, acc-0.4006, test loss-2.1233, acc-0.3943
Iter-16230, train loss-2.1454, acc-0.3600, valid loss-2.1224, acc-0.4010, test loss-2.1232, acc-0.3944
Iter-16240, train loss-2.1293, acc-0.3600, valid loss-2.1223, acc-0.4010, test loss-2.1231, acc-0.3945
Iter-16250, train loss-2.1428, acc-0.4200, valid loss-2.1221, acc-0.4010, test loss-2.1229, acc-0.3944
Iter-16260, train loss-2.1023, acc-0.4400, valid loss-2.1220, acc-0.4014, test loss-2.1228, acc-0.3946
Iter-16270, train loss-2.1058, acc-0.4400, valid loss-2.1220, acc-0.4014, test loss-2.1227, acc-0.3947
Iter-16280, train loss-2.1003, acc-0.4600, valid loss-2.1219, acc-0.4018, test loss-2.1227, acc-0.3946
Iter-16290, train loss-2.1218, acc-0.4000, valid loss-2.1218, acc-0.4016, test loss-2.1226, acc-0.3948
Iter-16300, train loss-2.0947, acc-0.3800, valid loss-2.1217, acc-0.4014, test loss-2.1225, acc-0.3947
Iter-16310, train loss-2.0925, acc-0.4000, valid loss-2.1215, acc-0.4014, test loss-2.1223, acc-0.3947
Iter-16320, train loss-2.1332, acc-0.3200, valid loss-2.1214, acc-0.4016, test loss-2.1222, acc-0.3948
Iter-16330, train loss-2.1084, acc-0.4600, valid loss-2.1213, acc-0.4018, test loss-2.1221, acc-0.3947
Iter-16340, train loss-2.1443, acc-0.3000, valid loss-2.1212, acc-0.4014, test loss-2.1220, acc-0.3951
Iter-16350, train loss-2.1154, acc-0.4000, valid loss-2.1211, acc-0.4016, test loss-2.1219, acc-0.3951
Iter-16360, train loss-2.0747, acc-0.5600, valid loss-2.1210, acc-0.4016, test loss-2.1218, acc-0.3951
Iter-16370, train loss-2.1343, acc-0.4200, valid loss-2.1209, acc-0.4018, test loss-2.1217, acc-0.3953
Iter-16380, train loss-2.1630, acc-0.3800, valid loss-2.1208, acc-0.4018, test loss-2.1216, acc-0.3954
Iter-16390, train loss-2.1120, acc-0.4000, valid loss-2.1207, acc-0.4016, test loss-2.1215, acc-0.3954
Iter-16400, train loss-2.0838, acc-0.5000, valid loss-2.1206, acc-0.4016, test loss-2.1214, acc-0.3953
Iter-16410, train loss-2.1008, acc-0.4600, valid loss-2.1205, acc-0.4016, test loss-2.1213, acc-0.3953
Iter-16420, train loss-2.0785, acc-0.5000, valid loss-2.1204, acc-0.4016, test loss-2.1212, acc-0.3954
Iter-16430, train loss-2.1157, acc-0.4600, valid loss-2.1203, acc-0.4016, test loss-2.1211, acc-0.3954
Iter-16440, train loss-2.0883, acc-0.4800, valid loss-2.1202, acc-0.4020, test loss-2.1210, acc-0.3954
Iter-16450, train loss-2.0988, acc-0.4000, valid loss-2.1201, acc-0.4022, test loss-2.1209, acc-0.3957
Iter-16460, train loss-2.0901, acc-0.4200, valid loss-2.1200, acc-0.4022, test loss-2.1208, acc-0.3957
Iter-16470, train loss-2.1701, acc-0.3600, valid loss-2.1199, acc-0.4026, test loss-2.1207, acc-0.3959
Iter-16480, train loss-2.1151, acc-0.4200, valid loss-2.1198, acc-0.4024, test loss-2.1206, acc-0.3958
Iter-16490, train loss-2.1312, acc-0.3400, valid loss-2.1197, acc-0.4026, test loss-2.1205, acc-0.3959
Iter-16500, train loss-2.1408, acc-0.3800, valid loss-2.1196, acc-0.4022, test loss-2.1204, acc-0.3961
Iter-16510, train loss-2.1573, acc-0.3000, valid loss-2.1195, acc-0.4028, test loss-2.1203, acc-0.3959
Iter-16520, train loss-2.1102, acc-0.4200, valid loss-2.1194, acc-0.4026, test loss-2.1202, acc-0.3959
Iter-16530, train loss-2.1262, acc-0.4400, valid loss-2.1193, acc-0.4026, test loss-2.1201, acc-0.3962
Iter-16540, train loss-2.1186, acc-0.4000, valid loss-2.1192, acc-0.4026, test loss-2.1200, acc-0.3962
Iter-16550, train loss-2.1126, acc-0.4000, valid loss-2.1191, acc-0.4026, test loss-2.1199, acc-0.3965
Iter-16560, train loss-2.1072, acc-0.3800, valid loss-2.1190, acc-0.4026, test loss-2.1198, acc-0.3966
Iter-16570, train loss-2.0993, acc-0.4400, valid loss-2.1189, acc-0.4026, test loss-2.1197, acc-0.3965
Iter-16580, train loss-2.0948, acc-0.3800, valid loss-2.1188, acc-0.4026, test loss-2.1196, acc-0.3964
Iter-16590, train loss-2.1281, acc-0.3800, valid loss-2.1187, acc-0.4028, test loss-2.1195, acc-0.3970
Iter-16600, train loss-2.0973, acc-0.4200, valid loss-2.1186, acc-0.4028, test loss-2.1194, acc-0.3968
Iter-16610, train loss-2.1118, acc-0.4800, valid loss-2.1185, acc-0.4028, test loss-2.1193, acc-0.3968
Iter-16620, train loss-2.1889, acc-0.2600, valid loss-2.1184, acc-0.4028, test loss-2.1192, acc-0.3969
Iter-16630, train loss-2.1280, acc-0.3200, valid loss-2.1183, acc-0.4026, test loss-2.1191, acc-0.3972
Iter-16640, train loss-2.1443, acc-0.3400, valid loss-2.1182, acc-0.4026, test loss-2.1190, acc-0.3974
Iter-16650, train loss-2.1274, acc-0.3800, valid loss-2.1181, acc-0.4026, test loss-2.1189, acc-0.3974
Iter-16660, train loss-2.1415, acc-0.3600, valid loss-2.1180, acc-0.4028, test loss-2.1188, acc-0.3974
Iter-16670, train loss-2.0907, acc-0.4400, valid loss-2.1179, acc-0.4028, test loss-2.1187, acc-0.3970
Iter-16680, train loss-2.1138, acc-0.4400, valid loss-2.1178, acc-0.4032, test loss-2.1186, acc-0.3970
Iter-16690, train loss-2.1043, acc-0.4600, valid loss-2.1177, acc-0.4032, test loss-2.1185, acc-0.3971
Iter-16700, train loss-2.1290, acc-0.4600, valid loss-2.1176, acc-0.4032, test loss-2.1184, acc-0.3972
Iter-16710, train loss-2.1152, acc-0.4000, valid loss-2.1175, acc-0.4032, test loss-2.1183, acc-0.3974
Iter-16720, train loss-2.0958, acc-0.4200, valid loss-2.1174, acc-0.4030, test loss-2.1182, acc-0.3976
Iter-16730, train loss-2.1780, acc-0.2600, valid loss-2.1173, acc-0.4030, test loss-2.1181, acc-0.3975
Iter-16740, train loss-2.0924, acc-0.4600, valid loss-2.1172, acc-0.4032, test loss-2.1180, acc-0.3977
Iter-16750, train loss-2.1341, acc-0.3600, valid loss-2.1171, acc-0.4034, test loss-2.1179, acc-0.3978
Iter-16760, train loss-2.0956, acc-0.5000, valid loss-2.1170, acc-0.4036, test loss-2.1178, acc-0.3978
Iter-16770, train loss-2.1163, acc-0.4200, valid loss-2.1169, acc-0.4038, test loss-2.1177, acc-0.3978
Iter-16780, train loss-2.1276, acc-0.3800, valid loss-2.1168, acc-0.4036, test loss-2.1176, acc-0.3981
Iter-16790, train loss-2.1205, acc-0.3400, valid loss-2.1167, acc-0.4042, test loss-2.1175, acc-0.3978
Iter-16800, train loss-2.1141, acc-0.4400, valid loss-2.1166, acc-0.4040, test loss-2.1174, acc-0.3980
Iter-16810, train loss-2.1001, acc-0.4800, valid loss-2.1165, acc-0.4040, test loss-2.1173, acc-0.3980
Iter-16820, train loss-2.1236, acc-0.2600, valid loss-2.1164, acc-0.4042, test loss-2.1172, acc-0.3979
Iter-16830, train loss-2.1074, acc-0.3800, valid loss-2.1163, acc-0.4042, test loss-2.1171, acc-0.3979
Iter-16840, train loss-2.1486, acc-0.4000, valid loss-2.1162, acc-0.4042, test loss-2.1170, acc-0.3979
Iter-16850, train loss-2.1206, acc-0.3800, valid loss-2.1161, acc-0.4044, test loss-2.1169, acc-0.3980
Iter-16860, train loss-2.1170, acc-0.4200, valid loss-2.1160, acc-0.4046, test loss-2.1168, acc-0.3982
Iter-16870, train loss-2.0788, acc-0.5600, valid loss-2.1159, acc-0.4046, test loss-2.1167, acc-0.3984
Iter-16880, train loss-2.0829, acc-0.4400, valid loss-2.1158, acc-0.4046, test loss-2.1166, acc-0.3982
Iter-16890, train loss-2.1424, acc-0.2600, valid loss-2.1157, acc-0.4046, test loss-2.1165, acc-0.3983
Iter-16900, train loss-2.1064, acc-0.4200, valid loss-2.1156, acc-0.4048, test loss-2.1164, acc-0.3984
Iter-16910, train loss-2.1554, acc-0.2200, valid loss-2.1155, acc-0.4050, test loss-2.1163, acc-0.3982
Iter-16920, train loss-2.1330, acc-0.4000, valid loss-2.1154, acc-0.4048, test loss-2.1162, acc-0.3985
Iter-16930, train loss-2.0725, acc-0.4400, valid loss-2.1153, acc-0.4050, test loss-2.1161, acc-0.3984
Iter-16940, train loss-2.1480, acc-0.3200, valid loss-2.1152, acc-0.4048, test loss-2.1160, acc-0.3985
Iter-16950, train loss-2.1337, acc-0.3200, valid loss-2.1151, acc-0.4046, test loss-2.1159, acc-0.3985
Iter-16960, train loss-2.1242, acc-0.3600, valid loss-2.1149, acc-0.4048, test loss-2.1158, acc-0.3986
Iter-16970, train loss-2.1557, acc-0.3200, valid loss-2.1148, acc-0.4052, test loss-2.1157, acc-0.3984
Iter-16980, train loss-2.0972, acc-0.4200, valid loss-2.1147, acc-0.4054, test loss-2.1156, acc-0.3987
Iter-16990, train loss-2.1461, acc-0.4000, valid loss-2.1146, acc-0.4054, test loss-2.1155, acc-0.3988
Iter-17000, train loss-2.0747, acc-0.4400, valid loss-2.1145, acc-0.4052, test loss-2.1154, acc-0.3988
Iter-17010, train loss-2.1851, acc-0.3400, valid loss-2.1144, acc-0.4052, test loss-2.1152, acc-0.3989
Iter-17020, train loss-2.1423, acc-0.3800, valid loss-2.1143, acc-0.4052, test loss-2.1151, acc-0.3988
Iter-17030, train loss-2.0883, acc-0.4600, valid loss-2.1142, acc-0.4052, test loss-2.1150, acc-0.3988
Iter-17040, train loss-2.1919, acc-0.3000, valid loss-2.1141, acc-0.4054, test loss-2.1149, acc-0.3992
Iter-17050, train loss-2.0968, acc-0.4800, valid loss-2.1140, acc-0.4054, test loss-2.1148, acc-0.3991
Iter-17060, train loss-2.1033, acc-0.4600, valid loss-2.1139, acc-0.4052, test loss-2.1147, acc-0.3990
Iter-17070, train loss-2.1143, acc-0.3600, valid loss-2.1138, acc-0.4052, test loss-2.1146, acc-0.3994
Iter-17080, train loss-2.1121, acc-0.4200, valid loss-2.1137, acc-0.4052, test loss-2.1145, acc-0.3992
Iter-17090, train loss-2.1569, acc-0.3400, valid loss-2.1136, acc-0.4054, test loss-2.1144, acc-0.3997
Iter-17100, train loss-2.1276, acc-0.3200, valid loss-2.1135, acc-0.4054, test loss-2.1143, acc-0.3998
Iter-17110, train loss-2.1039, acc-0.5000, valid loss-2.1134, acc-0.4054, test loss-2.1142, acc-0.4001
Iter-17120, train loss-2.1362, acc-0.4000, valid loss-2.1133, acc-0.4054, test loss-2.1141, acc-0.3999
Iter-17130, train loss-2.1161, acc-0.4200, valid loss-2.1132, acc-0.4054, test loss-2.1140, acc-0.4005
Iter-17140, train loss-2.1323, acc-0.3600, valid loss-2.1131, acc-0.4054, test loss-2.1139, acc-0.4002
Iter-17150, train loss-2.1264, acc-0.3800, valid loss-2.1130, acc-0.4056, test loss-2.1138, acc-0.4000
Iter-17160, train loss-2.1127, acc-0.5000, valid loss-2.1129, acc-0.4060, test loss-2.1137, acc-0.3999
Iter-17170, train loss-2.0883, acc-0.4600, valid loss-2.1128, acc-0.4056, test loss-2.1136, acc-0.3999
Iter-17180, train loss-2.1228, acc-0.3800, valid loss-2.1127, acc-0.4058, test loss-2.1135, acc-0.4000
Iter-17190, train loss-2.0957, acc-0.4400, valid loss-2.1126, acc-0.4056, test loss-2.1134, acc-0.3998
Iter-17200, train loss-2.0945, acc-0.4000, valid loss-2.1125, acc-0.4058, test loss-2.1133, acc-0.4000
Iter-17210, train loss-2.0790, acc-0.4600, valid loss-2.1124, acc-0.4058, test loss-2.1132, acc-0.3997
Iter-17220, train loss-2.1557, acc-0.3600, valid loss-2.1123, acc-0.4058, test loss-2.1131, acc-0.4000
Iter-17230, train loss-2.1361, acc-0.3800, valid loss-2.1122, acc-0.4058, test loss-2.1130, acc-0.4002
Iter-17240, train loss-2.1468, acc-0.3200, valid loss-2.1121, acc-0.4064, test loss-2.1129, acc-0.3999
Iter-17250, train loss-2.1213, acc-0.3400, valid loss-2.1120, acc-0.4062, test loss-2.1128, acc-0.4001
Iter-17260, train loss-2.0486, acc-0.5000, valid loss-2.1119, acc-0.4062, test loss-2.1127, acc-0.4003
Iter-17270, train loss-2.1114, acc-0.3800, valid loss-2.1118, acc-0.4062, test loss-2.1127, acc-0.4003
Iter-17280, train loss-2.0871, acc-0.4400, valid loss-2.1117, acc-0.4060, test loss-2.1125, acc-0.4004
Iter-17290, train loss-2.1114, acc-0.4200, valid loss-2.1116, acc-0.4064, test loss-2.1124, acc-0.4004
Iter-17300, train loss-2.0977, acc-0.4000, valid loss-2.1115, acc-0.4066, test loss-2.1123, acc-0.4006
Iter-17310, train loss-2.1353, acc-0.3200, valid loss-2.1114, acc-0.4068, test loss-2.1122, acc-0.4006
Iter-17320, train loss-2.1616, acc-0.3400, valid loss-2.1113, acc-0.4068, test loss-2.1121, acc-0.4007
Iter-17330, train loss-2.1286, acc-0.3800, valid loss-2.1112, acc-0.4068, test loss-2.1120, acc-0.4007
Iter-17340, train loss-2.1369, acc-0.3800, valid loss-2.1111, acc-0.4064, test loss-2.1119, acc-0.4008
Iter-17350, train loss-2.0743, acc-0.4600, valid loss-2.1110, acc-0.4062, test loss-2.1118, acc-0.4006
Iter-17360, train loss-2.1303, acc-0.3600, valid loss-2.1109, acc-0.4060, test loss-2.1117, acc-0.4003
Iter-17370, train loss-2.1085, acc-0.4000, valid loss-2.1108, acc-0.4064, test loss-2.1116, acc-0.4005
Iter-17380, train loss-2.1207, acc-0.4400, valid loss-2.1107, acc-0.4062, test loss-2.1115, acc-0.4007
Iter-17390, train loss-2.1417, acc-0.3600, valid loss-2.1106, acc-0.4068, test loss-2.1114, acc-0.4010
Iter-17400, train loss-2.0875, acc-0.4400, valid loss-2.1105, acc-0.4070, test loss-2.1113, acc-0.4012
Iter-17410, train loss-2.1094, acc-0.4000, valid loss-2.1104, acc-0.4068, test loss-2.1112, acc-0.4013
Iter-17420, train loss-2.1160, acc-0.3600, valid loss-2.1103, acc-0.4068, test loss-2.1111, acc-0.4012
Iter-17430, train loss-2.0810, acc-0.5000, valid loss-2.1102, acc-0.4068, test loss-2.1110, acc-0.4012
Iter-17440, train loss-2.1633, acc-0.2800, valid loss-2.1101, acc-0.4066, test loss-2.1109, acc-0.4012
Iter-17450, train loss-2.1667, acc-0.3400, valid loss-2.1100, acc-0.4070, test loss-2.1108, acc-0.4014
Iter-17460, train loss-2.1255, acc-0.3400, valid loss-2.1099, acc-0.4066, test loss-2.1107, acc-0.4014
Iter-17470, train loss-2.1462, acc-0.2600, valid loss-2.1098, acc-0.4064, test loss-2.1106, acc-0.4015
Iter-17480, train loss-2.1714, acc-0.2400, valid loss-2.1097, acc-0.4064, test loss-2.1105, acc-0.4016
Iter-17490, train loss-2.0916, acc-0.5000, valid loss-2.1096, acc-0.4068, test loss-2.1104, acc-0.4018
Iter-17500, train loss-2.1687, acc-0.3000, valid loss-2.1095, acc-0.4066, test loss-2.1103, acc-0.4019
Iter-17510, train loss-2.1058, acc-0.4200, valid loss-2.1094, acc-0.4066, test loss-2.1102, acc-0.4015
Iter-17520, train loss-2.1284, acc-0.3800, valid loss-2.1093, acc-0.4064, test loss-2.1102, acc-0.4017
Iter-17530, train loss-2.1195, acc-0.4000, valid loss-2.1092, acc-0.4068, test loss-2.1101, acc-0.4016
Iter-17540, train loss-2.0794, acc-0.4400, valid loss-2.1091, acc-0.4068, test loss-2.1099, acc-0.4018
Iter-17550, train loss-2.1342, acc-0.3000, valid loss-2.1090, acc-0.4068, test loss-2.1099, acc-0.4018
Iter-17560, train loss-2.1582, acc-0.3400, valid loss-2.1089, acc-0.4070, test loss-2.1098, acc-0.4019
Iter-17570, train loss-2.0912, acc-0.4400, valid loss-2.1088, acc-0.4072, test loss-2.1097, acc-0.4020
Iter-17580, train loss-2.1409, acc-0.4200, valid loss-2.1088, acc-0.4074, test loss-2.1096, acc-0.4021
Iter-17590, train loss-2.1057, acc-0.4000, valid loss-2.1087, acc-0.4078, test loss-2.1095, acc-0.4020
Iter-17600, train loss-2.1102, acc-0.3400, valid loss-2.1086, acc-0.4074, test loss-2.1094, acc-0.4021
Iter-17610, train loss-2.1018, acc-0.5200, valid loss-2.1085, acc-0.4080, test loss-2.1093, acc-0.4022
Iter-17620, train loss-2.1318, acc-0.4000, valid loss-2.1084, acc-0.4080, test loss-2.1092, acc-0.4023
Iter-17630, train loss-2.0822, acc-0.5000, valid loss-2.1083, acc-0.4080, test loss-2.1091, acc-0.4023
Iter-17640, train loss-2.1597, acc-0.4000, valid loss-2.1082, acc-0.4080, test loss-2.1090, acc-0.4023
Iter-17650, train loss-2.0816, acc-0.5000, valid loss-2.1081, acc-0.4078, test loss-2.1089, acc-0.4025
Iter-17660, train loss-2.1201, acc-0.4000, valid loss-2.1079, acc-0.4078, test loss-2.1088, acc-0.4026
Iter-17670, train loss-2.1040, acc-0.4200, valid loss-2.1079, acc-0.4084, test loss-2.1087, acc-0.4024
Iter-17680, train loss-2.1326, acc-0.3600, valid loss-2.1078, acc-0.4084, test loss-2.1086, acc-0.4024
Iter-17690, train loss-2.1125, acc-0.4000, valid loss-2.1077, acc-0.4082, test loss-2.1085, acc-0.4025
Iter-17700, train loss-2.0931, acc-0.5400, valid loss-2.1076, acc-0.4078, test loss-2.1084, acc-0.4028
Iter-17710, train loss-2.1261, acc-0.4200, valid loss-2.1074, acc-0.4082, test loss-2.1083, acc-0.4029
Iter-17720, train loss-2.0357, acc-0.5600, valid loss-2.1073, acc-0.4080, test loss-2.1082, acc-0.4031
Iter-17730, train loss-2.1058, acc-0.4200, valid loss-2.1072, acc-0.4078, test loss-2.1081, acc-0.4027
Iter-17740, train loss-2.1376, acc-0.3200, valid loss-2.1071, acc-0.4084, test loss-2.1080, acc-0.4031
Iter-17750, train loss-2.1330, acc-0.4000, valid loss-2.1070, acc-0.4082, test loss-2.1079, acc-0.4030
Iter-17760, train loss-2.0772, acc-0.4000, valid loss-2.1069, acc-0.4082, test loss-2.1078, acc-0.4030
Iter-17770, train loss-2.0343, acc-0.5000, valid loss-2.1068, acc-0.4082, test loss-2.1077, acc-0.4031
Iter-17780, train loss-2.0879, acc-0.4200, valid loss-2.1067, acc-0.4082, test loss-2.1076, acc-0.4033
Iter-17790, train loss-2.1442, acc-0.3600, valid loss-2.1066, acc-0.4080, test loss-2.1075, acc-0.4034
Iter-17800, train loss-2.1258, acc-0.3400, valid loss-2.1065, acc-0.4082, test loss-2.1074, acc-0.4033
Iter-17810, train loss-2.1283, acc-0.3800, valid loss-2.1064, acc-0.4082, test loss-2.1073, acc-0.4034
Iter-17820, train loss-2.1710, acc-0.3600, valid loss-2.1063, acc-0.4082, test loss-2.1072, acc-0.4033
Iter-17830, train loss-2.1199, acc-0.3800, valid loss-2.1062, acc-0.4082, test loss-2.1070, acc-0.4034
Iter-17840, train loss-2.1336, acc-0.3400, valid loss-2.1061, acc-0.4080, test loss-2.1069, acc-0.4034
Iter-17850, train loss-2.0874, acc-0.5000, valid loss-2.1060, acc-0.4082, test loss-2.1069, acc-0.4035
Iter-17860, train loss-2.1276, acc-0.3200, valid loss-2.1059, acc-0.4082, test loss-2.1067, acc-0.4035
Iter-17870, train loss-2.1025, acc-0.3800, valid loss-2.1058, acc-0.4084, test loss-2.1067, acc-0.4036
Iter-17880, train loss-2.1281, acc-0.3200, valid loss-2.1057, acc-0.4084, test loss-2.1066, acc-0.4036
Iter-17890, train loss-2.1040, acc-0.4400, valid loss-2.1056, acc-0.4084, test loss-2.1065, acc-0.4034
Iter-17900, train loss-2.0944, acc-0.4400, valid loss-2.1055, acc-0.4084, test loss-2.1064, acc-0.4034
Iter-17910, train loss-2.1276, acc-0.3600, valid loss-2.1054, acc-0.4084, test loss-2.1063, acc-0.4034
Iter-17920, train loss-2.0951, acc-0.4200, valid loss-2.1053, acc-0.4082, test loss-2.1062, acc-0.4039
Iter-17930, train loss-2.0464, acc-0.4600, valid loss-2.1052, acc-0.4082, test loss-2.1061, acc-0.4036
Iter-17940, train loss-2.1148, acc-0.3400, valid loss-2.1051, acc-0.4084, test loss-2.1060, acc-0.4039
Iter-17950, train loss-2.1629, acc-0.2800, valid loss-2.1050, acc-0.4084, test loss-2.1059, acc-0.4039
Iter-17960, train loss-2.0750, acc-0.4600, valid loss-2.1049, acc-0.4082, test loss-2.1057, acc-0.4039
Iter-17970, train loss-2.1164, acc-0.3800, valid loss-2.1048, acc-0.4080, test loss-2.1057, acc-0.4039
Iter-17980, train loss-2.1406, acc-0.3600, valid loss-2.1047, acc-0.4082, test loss-2.1055, acc-0.4039
Iter-17990, train loss-2.0857, acc-0.4400, valid loss-2.1046, acc-0.4084, test loss-2.1054, acc-0.4039
Iter-18000, train loss-2.0901, acc-0.4400, valid loss-2.1045, acc-0.4082, test loss-2.1054, acc-0.4037
Iter-18010, train loss-2.1549, acc-0.3400, valid loss-2.1044, acc-0.4084, test loss-2.1052, acc-0.4038
Iter-18020, train loss-2.0848, acc-0.4600, valid loss-2.1043, acc-0.4082, test loss-2.1051, acc-0.4038
Iter-18030, train loss-2.1221, acc-0.3600, valid loss-2.1042, acc-0.4082, test loss-2.1051, acc-0.4042
Iter-18040, train loss-2.1212, acc-0.3600, valid loss-2.1041, acc-0.4082, test loss-2.1049, acc-0.4042
Iter-18050, train loss-2.1074, acc-0.3200, valid loss-2.1040, acc-0.4084, test loss-2.1048, acc-0.4041
Iter-18060, train loss-2.0480, acc-0.4000, valid loss-2.1039, acc-0.4084, test loss-2.1047, acc-0.4038
Iter-18070, train loss-2.1368, acc-0.3000, valid loss-2.1038, acc-0.4082, test loss-2.1046, acc-0.4040
Iter-18080, train loss-2.0986, acc-0.4000, valid loss-2.1037, acc-0.4084, test loss-2.1045, acc-0.4038
Iter-18090, train loss-2.1100, acc-0.4600, valid loss-2.1036, acc-0.4084, test loss-2.1044, acc-0.4038
Iter-18100, train loss-2.1173, acc-0.3800, valid loss-2.1035, acc-0.4084, test loss-2.1043, acc-0.4037
Iter-18110, train loss-2.1316, acc-0.3800, valid loss-2.1034, acc-0.4084, test loss-2.1042, acc-0.4037
Iter-18120, train loss-2.1339, acc-0.3200, valid loss-2.1033, acc-0.4084, test loss-2.1041, acc-0.4039
Iter-18130, train loss-2.0790, acc-0.4400, valid loss-2.1032, acc-0.4084, test loss-2.1040, acc-0.4041
Iter-18140, train loss-2.0620, acc-0.5000, valid loss-2.1031, acc-0.4084, test loss-2.1039, acc-0.4041
Iter-18150, train loss-2.1158, acc-0.4800, valid loss-2.1030, acc-0.4084, test loss-2.1038, acc-0.4039
Iter-18160, train loss-2.0897, acc-0.4600, valid loss-2.1029, acc-0.4084, test loss-2.1037, acc-0.4041
Iter-18170, train loss-2.1057, acc-0.4600, valid loss-2.1028, acc-0.4082, test loss-2.1036, acc-0.4041
Iter-18180, train loss-2.1380, acc-0.3800, valid loss-2.1027, acc-0.4082, test loss-2.1035, acc-0.4041
Iter-18190, train loss-2.1769, acc-0.3600, valid loss-2.1026, acc-0.4084, test loss-2.1034, acc-0.4042
Iter-18200, train loss-2.0486, acc-0.4800, valid loss-2.1025, acc-0.4082, test loss-2.1033, acc-0.4041
Iter-18210, train loss-2.1401, acc-0.3800, valid loss-2.1024, acc-0.4084, test loss-2.1032, acc-0.4041
Iter-18220, train loss-2.0817, acc-0.3800, valid loss-2.1023, acc-0.4086, test loss-2.1031, acc-0.4042
Iter-18230, train loss-2.0635, acc-0.4800, valid loss-2.1022, acc-0.4084, test loss-2.1030, acc-0.4043
Iter-18240, train loss-2.0728, acc-0.4000, valid loss-2.1021, acc-0.4086, test loss-2.1029, acc-0.4044
Iter-18250, train loss-2.1570, acc-0.2800, valid loss-2.1020, acc-0.4086, test loss-2.1028, acc-0.4042
Iter-18260, train loss-2.1176, acc-0.3800, valid loss-2.1019, acc-0.4086, test loss-2.1027, acc-0.4046
Iter-18270, train loss-2.0957, acc-0.4400, valid loss-2.1018, acc-0.4086, test loss-2.1026, acc-0.4043
Iter-18280, train loss-2.1083, acc-0.3400, valid loss-2.1017, acc-0.4086, test loss-2.1025, acc-0.4046
Iter-18290, train loss-2.1400, acc-0.3600, valid loss-2.1016, acc-0.4086, test loss-2.1024, acc-0.4045
Iter-18300, train loss-2.0726, acc-0.3800, valid loss-2.1015, acc-0.4086, test loss-2.1023, acc-0.4043
Iter-18310, train loss-2.1469, acc-0.3000, valid loss-2.1014, acc-0.4086, test loss-2.1022, acc-0.4049
Iter-18320, train loss-2.1043, acc-0.4000, valid loss-2.1013, acc-0.4086, test loss-2.1021, acc-0.4047
Iter-18330, train loss-2.0698, acc-0.4800, valid loss-2.1012, acc-0.4086, test loss-2.1020, acc-0.4050
Iter-18340, train loss-2.0782, acc-0.5000, valid loss-2.1011, acc-0.4086, test loss-2.1019, acc-0.4055
Iter-18350, train loss-2.1290, acc-0.3800, valid loss-2.1010, acc-0.4086, test loss-2.1018, acc-0.4056
Iter-18360, train loss-2.1305, acc-0.4200, valid loss-2.1009, acc-0.4090, test loss-2.1017, acc-0.4057
Iter-18370, train loss-2.0825, acc-0.5400, valid loss-2.1008, acc-0.4090, test loss-2.1016, acc-0.4057
Iter-18380, train loss-2.1109, acc-0.3800, valid loss-2.1007, acc-0.4090, test loss-2.1015, acc-0.4059
Iter-18390, train loss-2.0881, acc-0.3600, valid loss-2.1006, acc-0.4088, test loss-2.1014, acc-0.4061
Iter-18400, train loss-2.1119, acc-0.3800, valid loss-2.1005, acc-0.4086, test loss-2.1013, acc-0.4058
Iter-18410, train loss-2.0618, acc-0.4200, valid loss-2.1004, acc-0.4086, test loss-2.1012, acc-0.4058
Iter-18420, train loss-2.1261, acc-0.3800, valid loss-2.1003, acc-0.4088, test loss-2.1011, acc-0.4058
Iter-18430, train loss-2.0758, acc-0.4000, valid loss-2.1002, acc-0.4088, test loss-2.1010, acc-0.4056
Iter-18440, train loss-2.1553, acc-0.2800, valid loss-2.1001, acc-0.4090, test loss-2.1009, acc-0.4060
Iter-18450, train loss-2.1010, acc-0.4000, valid loss-2.1000, acc-0.4094, test loss-2.1008, acc-0.4059
Iter-18460, train loss-2.0823, acc-0.4600, valid loss-2.0999, acc-0.4092, test loss-2.1007, acc-0.4061
Iter-18470, train loss-2.1189, acc-0.4400, valid loss-2.0998, acc-0.4090, test loss-2.1006, acc-0.4060
Iter-18480, train loss-2.0809, acc-0.4400, valid loss-2.0997, acc-0.4092, test loss-2.1005, acc-0.4059
Iter-18490, train loss-2.0737, acc-0.3800, valid loss-2.0996, acc-0.4096, test loss-2.1004, acc-0.4060
Iter-18500, train loss-2.0698, acc-0.5400, valid loss-2.0995, acc-0.4094, test loss-2.1003, acc-0.4060
Iter-18510, train loss-2.1071, acc-0.4400, valid loss-2.0994, acc-0.4094, test loss-2.1002, acc-0.4058
Iter-18520, train loss-2.1109, acc-0.4200, valid loss-2.0993, acc-0.4094, test loss-2.1001, acc-0.4060
Iter-18530, train loss-2.1029, acc-0.4000, valid loss-2.0992, acc-0.4096, test loss-2.1000, acc-0.4060
Iter-18540, train loss-2.1044, acc-0.3200, valid loss-2.0991, acc-0.4096, test loss-2.0999, acc-0.4062
Iter-18550, train loss-2.1265, acc-0.4000, valid loss-2.0990, acc-0.4098, test loss-2.0998, acc-0.4060
Iter-18560, train loss-2.0866, acc-0.3200, valid loss-2.0989, acc-0.4098, test loss-2.0998, acc-0.4060
Iter-18570, train loss-2.0487, acc-0.4000, valid loss-2.0988, acc-0.4096, test loss-2.0997, acc-0.4060
Iter-18580, train loss-2.0664, acc-0.4400, valid loss-2.0987, acc-0.4102, test loss-2.0996, acc-0.4061
Iter-18590, train loss-2.0620, acc-0.4400, valid loss-2.0986, acc-0.4098, test loss-2.0995, acc-0.4058
Iter-18600, train loss-2.0557, acc-0.5400, valid loss-2.0985, acc-0.4100, test loss-2.0993, acc-0.4060
Iter-18610, train loss-2.1166, acc-0.3400, valid loss-2.0984, acc-0.4102, test loss-2.0992, acc-0.4059
Iter-18620, train loss-2.1150, acc-0.3400, valid loss-2.0983, acc-0.4102, test loss-2.0991, acc-0.4060
Iter-18630, train loss-2.0713, acc-0.4400, valid loss-2.0982, acc-0.4100, test loss-2.0990, acc-0.4060
Iter-18640, train loss-2.1226, acc-0.3000, valid loss-2.0981, acc-0.4102, test loss-2.0989, acc-0.4062
Iter-18650, train loss-2.1064, acc-0.4200, valid loss-2.0980, acc-0.4100, test loss-2.0989, acc-0.4061
Iter-18660, train loss-2.0907, acc-0.4200, valid loss-2.0979, acc-0.4100, test loss-2.0987, acc-0.4063
Iter-18670, train loss-2.0586, acc-0.4600, valid loss-2.0978, acc-0.4098, test loss-2.0987, acc-0.4063
Iter-18680, train loss-2.1298, acc-0.4200, valid loss-2.0977, acc-0.4098, test loss-2.0986, acc-0.4064
Iter-18690, train loss-2.0775, acc-0.4400, valid loss-2.0976, acc-0.4100, test loss-2.0985, acc-0.4063
Iter-18700, train loss-2.0953, acc-0.4400, valid loss-2.0975, acc-0.4102, test loss-2.0984, acc-0.4063
Iter-18710, train loss-2.1139, acc-0.4000, valid loss-2.0974, acc-0.4102, test loss-2.0983, acc-0.4062
Iter-18720, train loss-2.1596, acc-0.3000, valid loss-2.0973, acc-0.4106, test loss-2.0982, acc-0.4064
Iter-18730, train loss-2.1489, acc-0.3200, valid loss-2.0972, acc-0.4108, test loss-2.0981, acc-0.4062
Iter-18740, train loss-2.0951, acc-0.4600, valid loss-2.0971, acc-0.4110, test loss-2.0980, acc-0.4064
Iter-18750, train loss-2.1412, acc-0.3200, valid loss-2.0970, acc-0.4112, test loss-2.0979, acc-0.4066
Iter-18760, train loss-2.1067, acc-0.4200, valid loss-2.0969, acc-0.4112, test loss-2.0978, acc-0.4067
Iter-18770, train loss-2.0921, acc-0.3400, valid loss-2.0968, acc-0.4112, test loss-2.0977, acc-0.4070
Iter-18780, train loss-2.0889, acc-0.4800, valid loss-2.0967, acc-0.4114, test loss-2.0976, acc-0.4071
Iter-18790, train loss-2.0842, acc-0.4000, valid loss-2.0966, acc-0.4114, test loss-2.0975, acc-0.4071
Iter-18800, train loss-2.1379, acc-0.2800, valid loss-2.0965, acc-0.4114, test loss-2.0974, acc-0.4071
Iter-18810, train loss-2.0923, acc-0.3600, valid loss-2.0964, acc-0.4112, test loss-2.0973, acc-0.4074
Iter-18820, train loss-2.0839, acc-0.4400, valid loss-2.0963, acc-0.4114, test loss-2.0972, acc-0.4076
Iter-18830, train loss-2.1337, acc-0.3600, valid loss-2.0962, acc-0.4112, test loss-2.0971, acc-0.4076
Iter-18840, train loss-2.0908, acc-0.4600, valid loss-2.0961, acc-0.4112, test loss-2.0970, acc-0.4076
Iter-18850, train loss-2.0543, acc-0.5400, valid loss-2.0960, acc-0.4112, test loss-2.0969, acc-0.4076
Iter-18860, train loss-2.1125, acc-0.4000, valid loss-2.0959, acc-0.4118, test loss-2.0968, acc-0.4077
Iter-18870, train loss-2.1874, acc-0.2400, valid loss-2.0958, acc-0.4126, test loss-2.0967, acc-0.4075
Iter-18880, train loss-2.1052, acc-0.4600, valid loss-2.0957, acc-0.4122, test loss-2.0966, acc-0.4077
Iter-18890, train loss-2.0972, acc-0.4600, valid loss-2.0956, acc-0.4122, test loss-2.0965, acc-0.4077
Iter-18900, train loss-2.1365, acc-0.3200, valid loss-2.0955, acc-0.4126, test loss-2.0964, acc-0.4076
Iter-18910, train loss-2.1752, acc-0.2400, valid loss-2.0954, acc-0.4128, test loss-2.0963, acc-0.4077
Iter-18920, train loss-2.0955, acc-0.3400, valid loss-2.0953, acc-0.4128, test loss-2.0962, acc-0.4079
Iter-18930, train loss-2.1331, acc-0.4200, valid loss-2.0953, acc-0.4126, test loss-2.0961, acc-0.4080
Iter-18940, train loss-2.0892, acc-0.4200, valid loss-2.0952, acc-0.4128, test loss-2.0960, acc-0.4079
Iter-18950, train loss-2.0996, acc-0.4000, valid loss-2.0951, acc-0.4130, test loss-2.0959, acc-0.4079
Iter-18960, train loss-2.0988, acc-0.4400, valid loss-2.0950, acc-0.4130, test loss-2.0958, acc-0.4079
Iter-18970, train loss-2.1152, acc-0.4400, valid loss-2.0949, acc-0.4128, test loss-2.0957, acc-0.4081
Iter-18980, train loss-2.0944, acc-0.4400, valid loss-2.0948, acc-0.4132, test loss-2.0956, acc-0.4080
Iter-18990, train loss-2.0649, acc-0.4800, valid loss-2.0946, acc-0.4134, test loss-2.0955, acc-0.4081
Iter-19000, train loss-2.1228, acc-0.4200, valid loss-2.0946, acc-0.4138, test loss-2.0954, acc-0.4082
Iter-19010, train loss-2.1326, acc-0.3400, valid loss-2.0945, acc-0.4138, test loss-2.0953, acc-0.4081
Iter-19020, train loss-2.1144, acc-0.4600, valid loss-2.0944, acc-0.4134, test loss-2.0952, acc-0.4081
Iter-19030, train loss-2.0836, acc-0.3800, valid loss-2.0943, acc-0.4136, test loss-2.0951, acc-0.4081
Iter-19040, train loss-2.1003, acc-0.3600, valid loss-2.0942, acc-0.4134, test loss-2.0950, acc-0.4084
Iter-19050, train loss-2.0868, acc-0.4200, valid loss-2.0941, acc-0.4134, test loss-2.0949, acc-0.4083
Iter-19060, train loss-2.0973, acc-0.4200, valid loss-2.0940, acc-0.4138, test loss-2.0948, acc-0.4083
Iter-19070, train loss-2.1746, acc-0.3000, valid loss-2.0939, acc-0.4138, test loss-2.0947, acc-0.4083
Iter-19080, train loss-2.0948, acc-0.3800, valid loss-2.0938, acc-0.4136, test loss-2.0946, acc-0.4084
Iter-19090, train loss-2.0756, acc-0.3800, valid loss-2.0937, acc-0.4136, test loss-2.0945, acc-0.4084
Iter-19100, train loss-2.1345, acc-0.3000, valid loss-2.0936, acc-0.4136, test loss-2.0944, acc-0.4083
Iter-19110, train loss-2.0831, acc-0.3600, valid loss-2.0935, acc-0.4136, test loss-2.0943, acc-0.4086
Iter-19120, train loss-2.1257, acc-0.4200, valid loss-2.0934, acc-0.4142, test loss-2.0942, acc-0.4082
Iter-19130, train loss-2.0699, acc-0.5200, valid loss-2.0933, acc-0.4140, test loss-2.0941, acc-0.4085
Iter-19140, train loss-2.0770, acc-0.4200, valid loss-2.0932, acc-0.4138, test loss-2.0941, acc-0.4086
Iter-19150, train loss-2.1212, acc-0.3400, valid loss-2.0931, acc-0.4142, test loss-2.0940, acc-0.4084
Iter-19160, train loss-2.1143, acc-0.4600, valid loss-2.0930, acc-0.4144, test loss-2.0939, acc-0.4087
Iter-19170, train loss-2.0966, acc-0.3600, valid loss-2.0929, acc-0.4144, test loss-2.0938, acc-0.4088
Iter-19180, train loss-2.1036, acc-0.3400, valid loss-2.0928, acc-0.4144, test loss-2.0937, acc-0.4087
Iter-19190, train loss-2.1261, acc-0.3600, valid loss-2.0927, acc-0.4144, test loss-2.0936, acc-0.4089
Iter-19200, train loss-2.0935, acc-0.4400, valid loss-2.0926, acc-0.4144, test loss-2.0935, acc-0.4093
Iter-19210, train loss-2.1048, acc-0.3800, valid loss-2.0925, acc-0.4144, test loss-2.0934, acc-0.4092
Iter-19220, train loss-2.1586, acc-0.2800, valid loss-2.0924, acc-0.4144, test loss-2.0933, acc-0.4095
Iter-19230, train loss-2.0911, acc-0.3000, valid loss-2.0923, acc-0.4150, test loss-2.0932, acc-0.4095
Iter-19240, train loss-2.1245, acc-0.4000, valid loss-2.0922, acc-0.4150, test loss-2.0931, acc-0.4094
Iter-19250, train loss-2.0750, acc-0.5000, valid loss-2.0921, acc-0.4150, test loss-2.0930, acc-0.4096
Iter-19260, train loss-2.0992, acc-0.3600, valid loss-2.0920, acc-0.4148, test loss-2.0929, acc-0.4096
Iter-19270, train loss-2.1006, acc-0.3600, valid loss-2.0919, acc-0.4148, test loss-2.0928, acc-0.4098
Iter-19280, train loss-2.0062, acc-0.6400, valid loss-2.0918, acc-0.4150, test loss-2.0927, acc-0.4097
Iter-19290, train loss-2.0709, acc-0.5200, valid loss-2.0917, acc-0.4148, test loss-2.0926, acc-0.4097
Iter-19300, train loss-2.0992, acc-0.4400, valid loss-2.0917, acc-0.4154, test loss-2.0925, acc-0.4096
Iter-19310, train loss-2.0902, acc-0.5200, valid loss-2.0915, acc-0.4154, test loss-2.0924, acc-0.4096
Iter-19320, train loss-2.0968, acc-0.4000, valid loss-2.0914, acc-0.4156, test loss-2.0923, acc-0.4094
Iter-19330, train loss-2.0510, acc-0.4400, valid loss-2.0913, acc-0.4156, test loss-2.0922, acc-0.4094
Iter-19340, train loss-2.0811, acc-0.4600, valid loss-2.0913, acc-0.4154, test loss-2.0921, acc-0.4094
Iter-19350, train loss-2.0783, acc-0.4200, valid loss-2.0911, acc-0.4154, test loss-2.0920, acc-0.4097
Iter-19360, train loss-2.0622, acc-0.4600, valid loss-2.0911, acc-0.4154, test loss-2.0919, acc-0.4096
Iter-19370, train loss-2.0956, acc-0.5000, valid loss-2.0909, acc-0.4154, test loss-2.0918, acc-0.4094
Iter-19380, train loss-2.0703, acc-0.4200, valid loss-2.0908, acc-0.4152, test loss-2.0917, acc-0.4094
Iter-19390, train loss-2.1022, acc-0.3600, valid loss-2.0907, acc-0.4152, test loss-2.0916, acc-0.4096
Iter-19400, train loss-2.0826, acc-0.4000, valid loss-2.0906, acc-0.4154, test loss-2.0915, acc-0.4097
Iter-19410, train loss-2.0834, acc-0.4800, valid loss-2.0905, acc-0.4152, test loss-2.0914, acc-0.4097
Iter-19420, train loss-2.0496, acc-0.4800, valid loss-2.0905, acc-0.4154, test loss-2.0913, acc-0.4093
Iter-19430, train loss-2.1035, acc-0.4400, valid loss-2.0904, acc-0.4152, test loss-2.0912, acc-0.4095
Iter-19440, train loss-2.1412, acc-0.3800, valid loss-2.0903, acc-0.4154, test loss-2.0911, acc-0.4095
Iter-19450, train loss-2.0815, acc-0.4400, valid loss-2.0902, acc-0.4154, test loss-2.0910, acc-0.4096
Iter-19460, train loss-2.0741, acc-0.3600, valid loss-2.0900, acc-0.4152, test loss-2.0909, acc-0.4095
Iter-19470, train loss-2.1343, acc-0.3200, valid loss-2.0899, acc-0.4150, test loss-2.0908, acc-0.4096
Iter-19480, train loss-2.1259, acc-0.3400, valid loss-2.0898, acc-0.4148, test loss-2.0907, acc-0.4094
Iter-19490, train loss-2.0856, acc-0.3600, valid loss-2.0898, acc-0.4150, test loss-2.0906, acc-0.4094
Iter-19500, train loss-2.1102, acc-0.4200, valid loss-2.0896, acc-0.4152, test loss-2.0905, acc-0.4096
Iter-19510, train loss-2.1433, acc-0.2800, valid loss-2.0895, acc-0.4150, test loss-2.0904, acc-0.4097
Iter-19520, train loss-2.1205, acc-0.4600, valid loss-2.0894, acc-0.4148, test loss-2.0903, acc-0.4096
Iter-19530, train loss-2.0744, acc-0.4200, valid loss-2.0893, acc-0.4152, test loss-2.0902, acc-0.4099
Iter-19540, train loss-2.0493, acc-0.4600, valid loss-2.0892, acc-0.4152, test loss-2.0901, acc-0.4099
Iter-19550, train loss-2.1099, acc-0.4000, valid loss-2.0892, acc-0.4150, test loss-2.0900, acc-0.4099
Iter-19560, train loss-2.0633, acc-0.4400, valid loss-2.0891, acc-0.4148, test loss-2.0899, acc-0.4097
Iter-19570, train loss-2.1045, acc-0.3200, valid loss-2.0890, acc-0.4152, test loss-2.0898, acc-0.4099
Iter-19580, train loss-2.0657, acc-0.4200, valid loss-2.0889, acc-0.4150, test loss-2.0897, acc-0.4099
Iter-19590, train loss-2.0880, acc-0.4400, valid loss-2.0888, acc-0.4152, test loss-2.0896, acc-0.4099
Iter-19600, train loss-2.1003, acc-0.3600, valid loss-2.0887, acc-0.4152, test loss-2.0895, acc-0.4101
Iter-19610, train loss-2.0909, acc-0.3800, valid loss-2.0886, acc-0.4154, test loss-2.0894, acc-0.4102
Iter-19620, train loss-2.1093, acc-0.4200, valid loss-2.0885, acc-0.4152, test loss-2.0893, acc-0.4101
Iter-19630, train loss-2.0588, acc-0.4600, valid loss-2.0884, acc-0.4152, test loss-2.0892, acc-0.4101
Iter-19640, train loss-2.1074, acc-0.2800, valid loss-2.0883, acc-0.4156, test loss-2.0891, acc-0.4104
Iter-19650, train loss-2.1258, acc-0.3800, valid loss-2.0882, acc-0.4158, test loss-2.0890, acc-0.4103
Iter-19660, train loss-2.0998, acc-0.4000, valid loss-2.0881, acc-0.4156, test loss-2.0890, acc-0.4101
Iter-19670, train loss-2.0871, acc-0.3800, valid loss-2.0880, acc-0.4156, test loss-2.0889, acc-0.4104
Iter-19680, train loss-2.0492, acc-0.4400, valid loss-2.0879, acc-0.4154, test loss-2.0888, acc-0.4105
Iter-19690, train loss-2.1233, acc-0.3000, valid loss-2.0878, acc-0.4154, test loss-2.0887, acc-0.4105
Iter-19700, train loss-2.0353, acc-0.4800, valid loss-2.0877, acc-0.4154, test loss-2.0886, acc-0.4103
Iter-19710, train loss-2.0881, acc-0.4400, valid loss-2.0876, acc-0.4154, test loss-2.0884, acc-0.4104
Iter-19720, train loss-2.0975, acc-0.3400, valid loss-2.0875, acc-0.4156, test loss-2.0883, acc-0.4105
Iter-19730, train loss-2.0410, acc-0.5000, valid loss-2.0874, acc-0.4154, test loss-2.0883, acc-0.4104
Iter-19740, train loss-2.0909, acc-0.4200, valid loss-2.0873, acc-0.4156, test loss-2.0882, acc-0.4109
Iter-19750, train loss-2.1200, acc-0.3200, valid loss-2.0872, acc-0.4156, test loss-2.0881, acc-0.4107
Iter-19760, train loss-2.1170, acc-0.3400, valid loss-2.0871, acc-0.4158, test loss-2.0880, acc-0.4107
Iter-19770, train loss-2.0910, acc-0.3000, valid loss-2.0870, acc-0.4158, test loss-2.0879, acc-0.4108
Iter-19780, train loss-2.1048, acc-0.3600, valid loss-2.0869, acc-0.4158, test loss-2.0878, acc-0.4109
Iter-19790, train loss-2.0932, acc-0.4000, valid loss-2.0869, acc-0.4158, test loss-2.0877, acc-0.4110
Iter-19800, train loss-2.1064, acc-0.3200, valid loss-2.0868, acc-0.4160, test loss-2.0876, acc-0.4111
Iter-19810, train loss-2.1127, acc-0.4200, valid loss-2.0867, acc-0.4158, test loss-2.0875, acc-0.4112
Iter-19820, train loss-2.0879, acc-0.3600, valid loss-2.0866, acc-0.4158, test loss-2.0874, acc-0.4113
Iter-19830, train loss-2.1156, acc-0.3600, valid loss-2.0865, acc-0.4158, test loss-2.0873, acc-0.4112
Iter-19840, train loss-2.0650, acc-0.3800, valid loss-2.0864, acc-0.4158, test loss-2.0872, acc-0.4112
Iter-19850, train loss-2.1017, acc-0.4800, valid loss-2.0863, acc-0.4160, test loss-2.0871, acc-0.4114
Iter-19860, train loss-2.1103, acc-0.3800, valid loss-2.0862, acc-0.4162, test loss-2.0870, acc-0.4118
Iter-19870, train loss-2.0052, acc-0.5800, valid loss-2.0861, acc-0.4160, test loss-2.0869, acc-0.4118
Iter-19880, train loss-2.0693, acc-0.4600, valid loss-2.0860, acc-0.4160, test loss-2.0868, acc-0.4120
Iter-19890, train loss-2.0817, acc-0.4000, valid loss-2.0859, acc-0.4160, test loss-2.0868, acc-0.4119
Iter-19900, train loss-2.0407, acc-0.5600, valid loss-2.0858, acc-0.4160, test loss-2.0867, acc-0.4117
Iter-19910, train loss-2.0830, acc-0.4400, valid loss-2.0857, acc-0.4162, test loss-2.0865, acc-0.4117
Iter-19920, train loss-2.0964, acc-0.4400, valid loss-2.0856, acc-0.4162, test loss-2.0864, acc-0.4118
Iter-19930, train loss-2.0619, acc-0.5400, valid loss-2.0855, acc-0.4164, test loss-2.0863, acc-0.4120
Iter-19940, train loss-2.1157, acc-0.3800, valid loss-2.0854, acc-0.4164, test loss-2.0863, acc-0.4119
Iter-19950, train loss-2.0852, acc-0.4000, valid loss-2.0853, acc-0.4162, test loss-2.0862, acc-0.4120
Iter-19960, train loss-2.0649, acc-0.4400, valid loss-2.0852, acc-0.4168, test loss-2.0861, acc-0.4120
Iter-19970, train loss-2.1159, acc-0.4400, valid loss-2.0851, acc-0.4172, test loss-2.0860, acc-0.4125
Iter-19980, train loss-2.1195, acc-0.3800, valid loss-2.0850, acc-0.4174, test loss-2.0859, acc-0.4126
Iter-19990, train loss-2.1146, acc-0.3600, valid loss-2.0849, acc-0.4176, test loss-2.0858, acc-0.4120
Iter-20000, train loss-2.0831, acc-0.3800, valid loss-2.0848, acc-0.4174, test loss-2.0857, acc-0.4122
Iter-20010, train loss-2.1121, acc-0.4000, valid loss-2.0847, acc-0.4178, test loss-2.0856, acc-0.4124
Iter-20020, train loss-2.0778, acc-0.4400, valid loss-2.0847, acc-0.4176, test loss-2.0855, acc-0.4126
Iter-20030, train loss-2.1173, acc-0.3200, valid loss-2.0846, acc-0.4176, test loss-2.0854, acc-0.4125
Iter-20040, train loss-2.1408, acc-0.2800, valid loss-2.0845, acc-0.4178, test loss-2.0853, acc-0.4123
Iter-20050, train loss-2.1200, acc-0.3200, valid loss-2.0844, acc-0.4178, test loss-2.0852, acc-0.4121
Iter-20060, train loss-2.0186, acc-0.4800, valid loss-2.0843, acc-0.4180, test loss-2.0851, acc-0.4125
Iter-20070, train loss-2.1140, acc-0.3000, valid loss-2.0842, acc-0.4182, test loss-2.0850, acc-0.4126
Iter-20080, train loss-2.0624, acc-0.4400, valid loss-2.0841, acc-0.4184, test loss-2.0849, acc-0.4126
Iter-20090, train loss-2.0414, acc-0.4400, valid loss-2.0840, acc-0.4184, test loss-2.0848, acc-0.4125
Iter-20100, train loss-2.1127, acc-0.3800, valid loss-2.0839, acc-0.4186, test loss-2.0847, acc-0.4126
Iter-20110, train loss-2.1630, acc-0.3000, valid loss-2.0838, acc-0.4186, test loss-2.0847, acc-0.4127
Iter-20120, train loss-2.0959, acc-0.3800, valid loss-2.0837, acc-0.4186, test loss-2.0846, acc-0.4130
Iter-20130, train loss-2.1206, acc-0.3400, valid loss-2.0836, acc-0.4186, test loss-2.0845, acc-0.4129
Iter-20140, train loss-2.1081, acc-0.4000, valid loss-2.0835, acc-0.4184, test loss-2.0844, acc-0.4128
Iter-20150, train loss-2.1241, acc-0.3600, valid loss-2.0834, acc-0.4186, test loss-2.0843, acc-0.4129
Iter-20160, train loss-2.0845, acc-0.4400, valid loss-2.0833, acc-0.4188, test loss-2.0842, acc-0.4129
Iter-20170, train loss-2.1101, acc-0.3000, valid loss-2.0832, acc-0.4188, test loss-2.0841, acc-0.4129
Iter-20180, train loss-2.0572, acc-0.4000, valid loss-2.0831, acc-0.4190, test loss-2.0840, acc-0.4128
Iter-20190, train loss-2.0634, acc-0.4600, valid loss-2.0831, acc-0.4190, test loss-2.0839, acc-0.4128
Iter-20200, train loss-2.1059, acc-0.4000, valid loss-2.0830, acc-0.4190, test loss-2.0838, acc-0.4127
Iter-20210, train loss-2.0742, acc-0.4200, valid loss-2.0829, acc-0.4184, test loss-2.0837, acc-0.4127
Iter-20220, train loss-2.0041, acc-0.6600, valid loss-2.0828, acc-0.4184, test loss-2.0836, acc-0.4126
Iter-20230, train loss-2.0774, acc-0.4000, valid loss-2.0827, acc-0.4186, test loss-2.0835, acc-0.4126
Iter-20240, train loss-2.0923, acc-0.3800, valid loss-2.0826, acc-0.4184, test loss-2.0834, acc-0.4126
Iter-20250, train loss-2.0389, acc-0.3800, valid loss-2.0825, acc-0.4184, test loss-2.0833, acc-0.4127
Iter-20260, train loss-2.1058, acc-0.4400, valid loss-2.0824, acc-0.4184, test loss-2.0832, acc-0.4128
Iter-20270, train loss-2.0735, acc-0.4800, valid loss-2.0823, acc-0.4184, test loss-2.0831, acc-0.4129
Iter-20280, train loss-2.0860, acc-0.3200, valid loss-2.0822, acc-0.4186, test loss-2.0830, acc-0.4128
Iter-20290, train loss-2.1004, acc-0.3800, valid loss-2.0821, acc-0.4184, test loss-2.0829, acc-0.4128
Iter-20300, train loss-2.1396, acc-0.3600, valid loss-2.0820, acc-0.4186, test loss-2.0828, acc-0.4129
Iter-20310, train loss-2.0692, acc-0.4400, valid loss-2.0819, acc-0.4184, test loss-2.0828, acc-0.4130
Iter-20320, train loss-2.1080, acc-0.3600, valid loss-2.0818, acc-0.4184, test loss-2.0827, acc-0.4130
Iter-20330, train loss-2.0909, acc-0.5400, valid loss-2.0817, acc-0.4188, test loss-2.0826, acc-0.4129
Iter-20340, train loss-2.0451, acc-0.5000, valid loss-2.0816, acc-0.4184, test loss-2.0825, acc-0.4131
Iter-20350, train loss-2.0745, acc-0.4400, valid loss-2.0815, acc-0.4188, test loss-2.0824, acc-0.4129
Iter-20360, train loss-2.0818, acc-0.3800, valid loss-2.0814, acc-0.4188, test loss-2.0823, acc-0.4132
Iter-20370, train loss-2.1209, acc-0.2800, valid loss-2.0813, acc-0.4194, test loss-2.0822, acc-0.4133
Iter-20380, train loss-2.0607, acc-0.5400, valid loss-2.0812, acc-0.4194, test loss-2.0821, acc-0.4137
Iter-20390, train loss-2.0648, acc-0.4200, valid loss-2.0811, acc-0.4194, test loss-2.0820, acc-0.4135
Iter-20400, train loss-2.1186, acc-0.3600, valid loss-2.0811, acc-0.4192, test loss-2.0819, acc-0.4136
Iter-20410, train loss-2.0656, acc-0.3400, valid loss-2.0810, acc-0.4192, test loss-2.0818, acc-0.4135
Iter-20420, train loss-2.0993, acc-0.4200, valid loss-2.0809, acc-0.4192, test loss-2.0817, acc-0.4135
Iter-20430, train loss-2.0772, acc-0.3600, valid loss-2.0808, acc-0.4194, test loss-2.0816, acc-0.4137
Iter-20440, train loss-2.0584, acc-0.5200, valid loss-2.0807, acc-0.4196, test loss-2.0815, acc-0.4139
Iter-20450, train loss-2.0725, acc-0.4200, valid loss-2.0806, acc-0.4194, test loss-2.0814, acc-0.4140
Iter-20460, train loss-2.0648, acc-0.3800, valid loss-2.0805, acc-0.4198, test loss-2.0813, acc-0.4140
Iter-20470, train loss-2.1173, acc-0.3600, valid loss-2.0804, acc-0.4198, test loss-2.0812, acc-0.4141
Iter-20480, train loss-2.0661, acc-0.3800, valid loss-2.0803, acc-0.4198, test loss-2.0811, acc-0.4144
Iter-20490, train loss-2.0806, acc-0.4200, valid loss-2.0802, acc-0.4198, test loss-2.0810, acc-0.4144
Iter-20500, train loss-2.0873, acc-0.4400, valid loss-2.0801, acc-0.4198, test loss-2.0809, acc-0.4142
Iter-20510, train loss-2.0529, acc-0.5400, valid loss-2.0800, acc-0.4198, test loss-2.0809, acc-0.4142
Iter-20520, train loss-2.1606, acc-0.2800, valid loss-2.0799, acc-0.4198, test loss-2.0808, acc-0.4144
Iter-20530, train loss-2.1038, acc-0.3600, valid loss-2.0798, acc-0.4200, test loss-2.0807, acc-0.4144
Iter-20540, train loss-2.0901, acc-0.4600, valid loss-2.0797, acc-0.4198, test loss-2.0806, acc-0.4147
Iter-20550, train loss-2.0870, acc-0.3600, valid loss-2.0796, acc-0.4198, test loss-2.0805, acc-0.4149
Iter-20560, train loss-2.0277, acc-0.4800, valid loss-2.0795, acc-0.4198, test loss-2.0804, acc-0.4147
Iter-20570, train loss-2.1213, acc-0.3400, valid loss-2.0794, acc-0.4198, test loss-2.0803, acc-0.4149
Iter-20580, train loss-2.1158, acc-0.3400, valid loss-2.0793, acc-0.4200, test loss-2.0802, acc-0.4148
Iter-20590, train loss-2.0479, acc-0.4600, valid loss-2.0792, acc-0.4198, test loss-2.0801, acc-0.4144
Iter-20600, train loss-2.0540, acc-0.4200, valid loss-2.0791, acc-0.4198, test loss-2.0800, acc-0.4147
Iter-20610, train loss-2.0492, acc-0.5000, valid loss-2.0790, acc-0.4198, test loss-2.0799, acc-0.4145
Iter-20620, train loss-2.0926, acc-0.3600, valid loss-2.0789, acc-0.4200, test loss-2.0798, acc-0.4146
Iter-20630, train loss-2.1335, acc-0.4000, valid loss-2.0788, acc-0.4198, test loss-2.0797, acc-0.4147
Iter-20640, train loss-2.1371, acc-0.2800, valid loss-2.0787, acc-0.4198, test loss-2.0796, acc-0.4147
Iter-20650, train loss-2.0458, acc-0.5000, valid loss-2.0786, acc-0.4198, test loss-2.0795, acc-0.4147
Iter-20660, train loss-2.1422, acc-0.3200, valid loss-2.0785, acc-0.4198, test loss-2.0794, acc-0.4146
Iter-20670, train loss-2.1058, acc-0.4600, valid loss-2.0784, acc-0.4198, test loss-2.0793, acc-0.4149
Iter-20680, train loss-2.0129, acc-0.5800, valid loss-2.0784, acc-0.4198, test loss-2.0792, acc-0.4148
Iter-20690, train loss-2.1014, acc-0.4000, valid loss-2.0783, acc-0.4198, test loss-2.0791, acc-0.4151
Iter-20700, train loss-2.1014, acc-0.4000, valid loss-2.0782, acc-0.4198, test loss-2.0790, acc-0.4149
Iter-20710, train loss-2.1233, acc-0.3200, valid loss-2.0781, acc-0.4198, test loss-2.0789, acc-0.4146
Iter-20720, train loss-2.0794, acc-0.5400, valid loss-2.0780, acc-0.4200, test loss-2.0788, acc-0.4148
Iter-20730, train loss-2.0918, acc-0.4400, valid loss-2.0779, acc-0.4200, test loss-2.0787, acc-0.4145
Iter-20740, train loss-2.0989, acc-0.3800, valid loss-2.0778, acc-0.4200, test loss-2.0786, acc-0.4142
Iter-20750, train loss-2.0610, acc-0.4600, valid loss-2.0777, acc-0.4196, test loss-2.0785, acc-0.4144
Iter-20760, train loss-2.0800, acc-0.3400, valid loss-2.0776, acc-0.4200, test loss-2.0784, acc-0.4143
Iter-20770, train loss-2.0424, acc-0.4800, valid loss-2.0775, acc-0.4200, test loss-2.0783, acc-0.4146
Iter-20780, train loss-2.0617, acc-0.4400, valid loss-2.0774, acc-0.4198, test loss-2.0783, acc-0.4145
Iter-20790, train loss-2.0814, acc-0.4200, valid loss-2.0773, acc-0.4198, test loss-2.0782, acc-0.4145
Iter-20800, train loss-1.9866, acc-0.5000, valid loss-2.0772, acc-0.4198, test loss-2.0781, acc-0.4149
Iter-20810, train loss-2.1030, acc-0.3800, valid loss-2.0771, acc-0.4198, test loss-2.0780, acc-0.4151
Iter-20820, train loss-2.0680, acc-0.4400, valid loss-2.0771, acc-0.4196, test loss-2.0779, acc-0.4151
Iter-20830, train loss-2.0290, acc-0.4800, valid loss-2.0770, acc-0.4198, test loss-2.0778, acc-0.4151
Iter-20840, train loss-2.0312, acc-0.4600, valid loss-2.0769, acc-0.4200, test loss-2.0777, acc-0.4152
Iter-20850, train loss-2.0397, acc-0.4400, valid loss-2.0768, acc-0.4200, test loss-2.0776, acc-0.4151
Iter-20860, train loss-2.0656, acc-0.3800, valid loss-2.0767, acc-0.4200, test loss-2.0775, acc-0.4152
Iter-20870, train loss-2.1313, acc-0.2800, valid loss-2.0766, acc-0.4200, test loss-2.0774, acc-0.4153
Iter-20880, train loss-2.1280, acc-0.3600, valid loss-2.0765, acc-0.4200, test loss-2.0773, acc-0.4154
Iter-20890, train loss-2.1090, acc-0.3600, valid loss-2.0764, acc-0.4200, test loss-2.0772, acc-0.4154
Iter-20900, train loss-2.0977, acc-0.3400, valid loss-2.0763, acc-0.4200, test loss-2.0771, acc-0.4154
Iter-20910, train loss-2.0751, acc-0.4400, valid loss-2.0762, acc-0.4200, test loss-2.0770, acc-0.4153
Iter-20920, train loss-2.0685, acc-0.4200, valid loss-2.0761, acc-0.4202, test loss-2.0769, acc-0.4154
Iter-20930, train loss-2.0553, acc-0.4600, valid loss-2.0760, acc-0.4202, test loss-2.0768, acc-0.4154
Iter-20940, train loss-2.0849, acc-0.4800, valid loss-2.0759, acc-0.4204, test loss-2.0767, acc-0.4154
Iter-20950, train loss-2.0775, acc-0.3800, valid loss-2.0758, acc-0.4204, test loss-2.0766, acc-0.4154
Iter-20960, train loss-2.1003, acc-0.3800, valid loss-2.0757, acc-0.4202, test loss-2.0765, acc-0.4154
Iter-20970, train loss-2.1061, acc-0.3600, valid loss-2.0756, acc-0.4204, test loss-2.0764, acc-0.4155
Iter-20980, train loss-2.0372, acc-0.4600, valid loss-2.0755, acc-0.4202, test loss-2.0764, acc-0.4155
Iter-20990, train loss-2.1054, acc-0.3600, valid loss-2.0754, acc-0.4200, test loss-2.0763, acc-0.4155
Iter-21000, train loss-2.0576, acc-0.3400, valid loss-2.0753, acc-0.4200, test loss-2.0762, acc-0.4154
Iter-21010, train loss-2.0770, acc-0.4000, valid loss-2.0752, acc-0.4200, test loss-2.0761, acc-0.4153
Iter-21020, train loss-2.0597, acc-0.4800, valid loss-2.0751, acc-0.4200, test loss-2.0760, acc-0.4154
Iter-21030, train loss-2.0877, acc-0.4000, valid loss-2.0750, acc-0.4200, test loss-2.0759, acc-0.4154
Iter-21040, train loss-2.0560, acc-0.4600, valid loss-2.0750, acc-0.4200, test loss-2.0758, acc-0.4154
Iter-21050, train loss-2.0929, acc-0.3200, valid loss-2.0749, acc-0.4200, test loss-2.0757, acc-0.4156
Iter-21060, train loss-2.0124, acc-0.6000, valid loss-2.0748, acc-0.4202, test loss-2.0756, acc-0.4156
Iter-21070, train loss-2.1167, acc-0.2800, valid loss-2.0747, acc-0.4200, test loss-2.0755, acc-0.4156
Iter-21080, train loss-2.1104, acc-0.3400, valid loss-2.0746, acc-0.4198, test loss-2.0754, acc-0.4156
Iter-21090, train loss-2.0646, acc-0.4200, valid loss-2.0745, acc-0.4198, test loss-2.0753, acc-0.4156
Iter-21100, train loss-2.1168, acc-0.3600, valid loss-2.0744, acc-0.4200, test loss-2.0752, acc-0.4156
Iter-21110, train loss-2.0163, acc-0.6000, valid loss-2.0743, acc-0.4200, test loss-2.0751, acc-0.4157
Iter-21120, train loss-2.0837, acc-0.3600, valid loss-2.0742, acc-0.4204, test loss-2.0750, acc-0.4158
Iter-21130, train loss-2.0932, acc-0.4600, valid loss-2.0741, acc-0.4204, test loss-2.0749, acc-0.4158
Iter-21140, train loss-2.0801, acc-0.4200, valid loss-2.0740, acc-0.4202, test loss-2.0748, acc-0.4159
Iter-21150, train loss-2.0645, acc-0.4000, valid loss-2.0739, acc-0.4202, test loss-2.0747, acc-0.4162
Iter-21160, train loss-2.0389, acc-0.4600, valid loss-2.0738, acc-0.4202, test loss-2.0746, acc-0.4161
Iter-21170, train loss-2.0533, acc-0.4400, valid loss-2.0737, acc-0.4204, test loss-2.0746, acc-0.4161
Iter-21180, train loss-2.0826, acc-0.3800, valid loss-2.0736, acc-0.4206, test loss-2.0745, acc-0.4160
Iter-21190, train loss-2.0413, acc-0.4400, valid loss-2.0735, acc-0.4206, test loss-2.0744, acc-0.4158
Iter-21200, train loss-2.0877, acc-0.3800, valid loss-2.0735, acc-0.4202, test loss-2.0743, acc-0.4158
Iter-21210, train loss-2.1040, acc-0.4400, valid loss-2.0734, acc-0.4202, test loss-2.0742, acc-0.4159
Iter-21220, train loss-2.0973, acc-0.3800, valid loss-2.0733, acc-0.4202, test loss-2.0741, acc-0.4160
Iter-21230, train loss-2.1154, acc-0.3000, valid loss-2.0732, acc-0.4206, test loss-2.0740, acc-0.4160
Iter-21240, train loss-2.0872, acc-0.3000, valid loss-2.0731, acc-0.4206, test loss-2.0739, acc-0.4164
Iter-21250, train loss-2.0464, acc-0.4600, valid loss-2.0730, acc-0.4208, test loss-2.0738, acc-0.4165
Iter-21260, train loss-2.0747, acc-0.3800, valid loss-2.0729, acc-0.4206, test loss-2.0737, acc-0.4164
Iter-21270, train loss-2.0708, acc-0.4400, valid loss-2.0728, acc-0.4204, test loss-2.0736, acc-0.4163
Iter-21280, train loss-2.0911, acc-0.3600, valid loss-2.0727, acc-0.4204, test loss-2.0735, acc-0.4164
Iter-21290, train loss-2.0483, acc-0.4000, valid loss-2.0726, acc-0.4204, test loss-2.0734, acc-0.4163
Iter-21300, train loss-2.0298, acc-0.4200, valid loss-2.0725, acc-0.4202, test loss-2.0733, acc-0.4163
Iter-21310, train loss-2.0485, acc-0.5400, valid loss-2.0724, acc-0.4202, test loss-2.0732, acc-0.4165
Iter-21320, train loss-2.0844, acc-0.4600, valid loss-2.0723, acc-0.4204, test loss-2.0731, acc-0.4167
Iter-21330, train loss-2.0629, acc-0.4800, valid loss-2.0723, acc-0.4200, test loss-2.0730, acc-0.4164
Iter-21340, train loss-2.0779, acc-0.3600, valid loss-2.0722, acc-0.4198, test loss-2.0730, acc-0.4163
Iter-21350, train loss-2.1029, acc-0.3000, valid loss-2.0721, acc-0.4196, test loss-2.0729, acc-0.4163
Iter-21360, train loss-2.0698, acc-0.5000, valid loss-2.0720, acc-0.4196, test loss-2.0728, acc-0.4162
Iter-21370, train loss-2.0936, acc-0.3600, valid loss-2.0719, acc-0.4198, test loss-2.0727, acc-0.4163
Iter-21380, train loss-2.1093, acc-0.3600, valid loss-2.0718, acc-0.4196, test loss-2.0726, acc-0.4164
Iter-21390, train loss-2.1187, acc-0.3200, valid loss-2.0717, acc-0.4196, test loss-2.0725, acc-0.4163
Iter-21400, train loss-2.1020, acc-0.4400, valid loss-2.0716, acc-0.4204, test loss-2.0724, acc-0.4164
Iter-21410, train loss-2.0380, acc-0.4200, valid loss-2.0715, acc-0.4200, test loss-2.0723, acc-0.4164
Iter-21420, train loss-2.0401, acc-0.4800, valid loss-2.0714, acc-0.4202, test loss-2.0722, acc-0.4162
Iter-21430, train loss-2.1092, acc-0.3000, valid loss-2.0713, acc-0.4204, test loss-2.0721, acc-0.4162
Iter-21440, train loss-2.0572, acc-0.3800, valid loss-2.0712, acc-0.4202, test loss-2.0720, acc-0.4165
Iter-21450, train loss-2.0672, acc-0.3600, valid loss-2.0711, acc-0.4204, test loss-2.0719, acc-0.4165
Iter-21460, train loss-2.0690, acc-0.4000, valid loss-2.0710, acc-0.4202, test loss-2.0718, acc-0.4163
Iter-21470, train loss-2.0996, acc-0.4000, valid loss-2.0709, acc-0.4204, test loss-2.0717, acc-0.4165
Iter-21480, train loss-2.1105, acc-0.3000, valid loss-2.0708, acc-0.4206, test loss-2.0716, acc-0.4167
Iter-21490, train loss-2.1059, acc-0.3800, valid loss-2.0707, acc-0.4210, test loss-2.0715, acc-0.4168
Iter-21500, train loss-2.0842, acc-0.3800, valid loss-2.0707, acc-0.4210, test loss-2.0715, acc-0.4169
Iter-21510, train loss-2.0600, acc-0.3800, valid loss-2.0706, acc-0.4214, test loss-2.0714, acc-0.4168
Iter-21520, train loss-2.0410, acc-0.5200, valid loss-2.0705, acc-0.4214, test loss-2.0713, acc-0.4169
Iter-21530, train loss-2.0760, acc-0.5000, valid loss-2.0704, acc-0.4214, test loss-2.0712, acc-0.4170
Iter-21540, train loss-2.0657, acc-0.4600, valid loss-2.0703, acc-0.4212, test loss-2.0711, acc-0.4170
Iter-21550, train loss-2.0639, acc-0.4400, valid loss-2.0702, acc-0.4212, test loss-2.0710, acc-0.4169
Iter-21560, train loss-2.0777, acc-0.3800, valid loss-2.0701, acc-0.4212, test loss-2.0709, acc-0.4170
Iter-21570, train loss-2.0992, acc-0.3600, valid loss-2.0700, acc-0.4212, test loss-2.0708, acc-0.4169
Iter-21580, train loss-2.0319, acc-0.5400, valid loss-2.0699, acc-0.4212, test loss-2.0707, acc-0.4171
Iter-21590, train loss-2.0883, acc-0.3600, valid loss-2.0698, acc-0.4218, test loss-2.0706, acc-0.4170
Iter-21600, train loss-2.0667, acc-0.4400, valid loss-2.0697, acc-0.4216, test loss-2.0705, acc-0.4172
Iter-21610, train loss-2.1033, acc-0.3400, valid loss-2.0696, acc-0.4218, test loss-2.0704, acc-0.4174
Iter-21620, train loss-2.0861, acc-0.4200, valid loss-2.0696, acc-0.4220, test loss-2.0703, acc-0.4173
Iter-21630, train loss-2.0860, acc-0.3400, valid loss-2.0695, acc-0.4222, test loss-2.0702, acc-0.4173
Iter-21640, train loss-2.1492, acc-0.2600, valid loss-2.0694, acc-0.4216, test loss-2.0702, acc-0.4172
Iter-21650, train loss-2.0574, acc-0.3600, valid loss-2.0693, acc-0.4212, test loss-2.0701, acc-0.4170
Iter-21660, train loss-2.0759, acc-0.4800, valid loss-2.0692, acc-0.4216, test loss-2.0700, acc-0.4171
Iter-21670, train loss-2.0216, acc-0.5200, valid loss-2.0691, acc-0.4216, test loss-2.0699, acc-0.4170
Iter-21680, train loss-2.1055, acc-0.3200, valid loss-2.0690, acc-0.4220, test loss-2.0698, acc-0.4170
Iter-21690, train loss-2.0557, acc-0.4400, valid loss-2.0689, acc-0.4222, test loss-2.0697, acc-0.4171
Iter-21700, train loss-2.0889, acc-0.3800, valid loss-2.0688, acc-0.4220, test loss-2.0696, acc-0.4170
Iter-21710, train loss-2.0142, acc-0.4600, valid loss-2.0687, acc-0.4220, test loss-2.0695, acc-0.4170
Iter-21720, train loss-2.0951, acc-0.4400, valid loss-2.0686, acc-0.4220, test loss-2.0694, acc-0.4171
Iter-21730, train loss-2.0206, acc-0.4600, valid loss-2.0685, acc-0.4222, test loss-2.0693, acc-0.4170
Iter-21740, train loss-2.0668, acc-0.4400, valid loss-2.0684, acc-0.4218, test loss-2.0692, acc-0.4171
Iter-21750, train loss-2.0963, acc-0.3600, valid loss-2.0683, acc-0.4222, test loss-2.0691, acc-0.4171
Iter-21760, train loss-2.0320, acc-0.4800, valid loss-2.0682, acc-0.4222, test loss-2.0690, acc-0.4167
Iter-21770, train loss-2.0904, acc-0.3400, valid loss-2.0682, acc-0.4224, test loss-2.0689, acc-0.4169
Iter-21780, train loss-2.1066, acc-0.3400, valid loss-2.0681, acc-0.4226, test loss-2.0688, acc-0.4171
Iter-21790, train loss-2.0563, acc-0.4400, valid loss-2.0680, acc-0.4224, test loss-2.0687, acc-0.4176
Iter-21800, train loss-2.0804, acc-0.4000, valid loss-2.0679, acc-0.4228, test loss-2.0687, acc-0.4177
Iter-21810, train loss-2.0218, acc-0.4600, valid loss-2.0678, acc-0.4228, test loss-2.0686, acc-0.4174
Iter-21820, train loss-2.0854, acc-0.4400, valid loss-2.0677, acc-0.4228, test loss-2.0685, acc-0.4173
Iter-21830, train loss-2.0613, acc-0.3800, valid loss-2.0676, acc-0.4232, test loss-2.0684, acc-0.4172
Iter-21840, train loss-2.0998, acc-0.3400, valid loss-2.0675, acc-0.4230, test loss-2.0683, acc-0.4172
Iter-21850, train loss-2.0160, acc-0.4600, valid loss-2.0674, acc-0.4230, test loss-2.0682, acc-0.4172
Iter-21860, train loss-2.0668, acc-0.4400, valid loss-2.0673, acc-0.4230, test loss-2.0681, acc-0.4172
Iter-21870, train loss-2.0386, acc-0.3800, valid loss-2.0672, acc-0.4230, test loss-2.0680, acc-0.4173
Iter-21880, train loss-2.0602, acc-0.4400, valid loss-2.0671, acc-0.4232, test loss-2.0679, acc-0.4178
Iter-21890, train loss-2.0678, acc-0.4600, valid loss-2.0670, acc-0.4230, test loss-2.0678, acc-0.4176
Iter-21900, train loss-2.0580, acc-0.4200, valid loss-2.0670, acc-0.4232, test loss-2.0677, acc-0.4178
Iter-21910, train loss-2.0502, acc-0.5000, valid loss-2.0669, acc-0.4230, test loss-2.0676, acc-0.4176
Iter-21920, train loss-2.0508, acc-0.4000, valid loss-2.0668, acc-0.4230, test loss-2.0675, acc-0.4174
Iter-21930, train loss-2.0818, acc-0.3600, valid loss-2.0667, acc-0.4234, test loss-2.0674, acc-0.4175
Iter-21940, train loss-2.0548, acc-0.4200, valid loss-2.0666, acc-0.4232, test loss-2.0673, acc-0.4175
Iter-21950, train loss-2.1513, acc-0.2800, valid loss-2.0665, acc-0.4234, test loss-2.0673, acc-0.4172
Iter-21960, train loss-2.0586, acc-0.4800, valid loss-2.0664, acc-0.4232, test loss-2.0672, acc-0.4174
Iter-21970, train loss-2.0554, acc-0.4600, valid loss-2.0663, acc-0.4236, test loss-2.0671, acc-0.4174
Iter-21980, train loss-2.0666, acc-0.3200, valid loss-2.0662, acc-0.4240, test loss-2.0670, acc-0.4176
Iter-21990, train loss-2.1145, acc-0.3000, valid loss-2.0661, acc-0.4240, test loss-2.0669, acc-0.4176
Iter-22000, train loss-2.1199, acc-0.4200, valid loss-2.0660, acc-0.4240, test loss-2.0668, acc-0.4177
Iter-22010, train loss-2.0805, acc-0.4200, valid loss-2.0659, acc-0.4242, test loss-2.0667, acc-0.4177
Iter-22020, train loss-2.1062, acc-0.3000, valid loss-2.0658, acc-0.4242, test loss-2.0666, acc-0.4177
Iter-22030, train loss-2.0946, acc-0.3600, valid loss-2.0657, acc-0.4242, test loss-2.0665, acc-0.4178
Iter-22040, train loss-2.1041, acc-0.4200, valid loss-2.0656, acc-0.4240, test loss-2.0664, acc-0.4180
Iter-22050, train loss-2.1077, acc-0.3200, valid loss-2.0655, acc-0.4244, test loss-2.0663, acc-0.4180
Iter-22060, train loss-2.0870, acc-0.4000, valid loss-2.0654, acc-0.4238, test loss-2.0662, acc-0.4176
Iter-22070, train loss-2.0457, acc-0.4400, valid loss-2.0653, acc-0.4238, test loss-2.0661, acc-0.4175
Iter-22080, train loss-2.0408, acc-0.4200, valid loss-2.0652, acc-0.4236, test loss-2.0660, acc-0.4172
Iter-22090, train loss-2.1008, acc-0.3400, valid loss-2.0651, acc-0.4240, test loss-2.0659, acc-0.4174
Iter-22100, train loss-2.0751, acc-0.4200, valid loss-2.0651, acc-0.4240, test loss-2.0658, acc-0.4174
Iter-22110, train loss-2.0586, acc-0.3800, valid loss-2.0650, acc-0.4236, test loss-2.0658, acc-0.4173
Iter-22120, train loss-2.0939, acc-0.4400, valid loss-2.0649, acc-0.4238, test loss-2.0657, acc-0.4174
Iter-22130, train loss-2.0695, acc-0.4200, valid loss-2.0648, acc-0.4238, test loss-2.0656, acc-0.4171
Iter-22140, train loss-2.0370, acc-0.4200, valid loss-2.0647, acc-0.4234, test loss-2.0655, acc-0.4170
Iter-22150, train loss-2.0410, acc-0.4600, valid loss-2.0646, acc-0.4238, test loss-2.0654, acc-0.4172
Iter-22160, train loss-2.0505, acc-0.5200, valid loss-2.0645, acc-0.4236, test loss-2.0653, acc-0.4172
Iter-22170, train loss-2.0353, acc-0.4400, valid loss-2.0644, acc-0.4236, test loss-2.0652, acc-0.4173
Iter-22180, train loss-2.0927, acc-0.4000, valid loss-2.0643, acc-0.4238, test loss-2.0651, acc-0.4175
Iter-22190, train loss-2.0921, acc-0.4200, valid loss-2.0642, acc-0.4238, test loss-2.0650, acc-0.4172
Iter-22200, train loss-2.0572, acc-0.4400, valid loss-2.0641, acc-0.4238, test loss-2.0649, acc-0.4171
Iter-22210, train loss-2.0795, acc-0.4200, valid loss-2.0640, acc-0.4240, test loss-2.0648, acc-0.4171
Iter-22220, train loss-2.0551, acc-0.4200, valid loss-2.0639, acc-0.4242, test loss-2.0647, acc-0.4173
Iter-22230, train loss-2.0827, acc-0.4000, valid loss-2.0638, acc-0.4242, test loss-2.0646, acc-0.4173
Iter-22240, train loss-2.1132, acc-0.4200, valid loss-2.0637, acc-0.4244, test loss-2.0645, acc-0.4173
Iter-22250, train loss-2.0586, acc-0.4600, valid loss-2.0636, acc-0.4246, test loss-2.0644, acc-0.4173
Iter-22260, train loss-2.0718, acc-0.4600, valid loss-2.0636, acc-0.4244, test loss-2.0644, acc-0.4174
Iter-22270, train loss-2.0367, acc-0.4400, valid loss-2.0635, acc-0.4246, test loss-2.0643, acc-0.4174
Iter-22280, train loss-2.1129, acc-0.3400, valid loss-2.0634, acc-0.4246, test loss-2.0642, acc-0.4172
Iter-22290, train loss-2.0771, acc-0.3600, valid loss-2.0633, acc-0.4244, test loss-2.0641, acc-0.4172
Iter-22300, train loss-2.0595, acc-0.4600, valid loss-2.0632, acc-0.4244, test loss-2.0640, acc-0.4171
Iter-22310, train loss-2.0803, acc-0.4600, valid loss-2.0631, acc-0.4246, test loss-2.0639, acc-0.4171
Iter-22320, train loss-1.9551, acc-0.6400, valid loss-2.0630, acc-0.4246, test loss-2.0638, acc-0.4172
Iter-22330, train loss-2.1066, acc-0.3000, valid loss-2.0629, acc-0.4244, test loss-2.0637, acc-0.4173
Iter-22340, train loss-2.0763, acc-0.4200, valid loss-2.0628, acc-0.4244, test loss-2.0636, acc-0.4174
Iter-22350, train loss-2.0344, acc-0.5200, valid loss-2.0627, acc-0.4244, test loss-2.0635, acc-0.4175
Iter-22360, train loss-2.0703, acc-0.4000, valid loss-2.0626, acc-0.4246, test loss-2.0634, acc-0.4177
Iter-22370, train loss-2.1109, acc-0.3600, valid loss-2.0625, acc-0.4244, test loss-2.0633, acc-0.4174
Iter-22380, train loss-2.0512, acc-0.4800, valid loss-2.0624, acc-0.4244, test loss-2.0632, acc-0.4175
Iter-22390, train loss-2.0903, acc-0.3400, valid loss-2.0623, acc-0.4250, test loss-2.0632, acc-0.4178
Iter-22400, train loss-2.0663, acc-0.4600, valid loss-2.0623, acc-0.4250, test loss-2.0631, acc-0.4179
Iter-22410, train loss-2.1072, acc-0.3200, valid loss-2.0622, acc-0.4250, test loss-2.0630, acc-0.4177
Iter-22420, train loss-2.0656, acc-0.4600, valid loss-2.0621, acc-0.4246, test loss-2.0629, acc-0.4176
Iter-22430, train loss-2.0555, acc-0.3600, valid loss-2.0620, acc-0.4248, test loss-2.0628, acc-0.4176
Iter-22440, train loss-2.0299, acc-0.5000, valid loss-2.0619, acc-0.4250, test loss-2.0627, acc-0.4175
Iter-22450, train loss-2.0482, acc-0.5400, valid loss-2.0618, acc-0.4248, test loss-2.0626, acc-0.4177
Iter-22460, train loss-2.0328, acc-0.4400, valid loss-2.0617, acc-0.4252, test loss-2.0625, acc-0.4175
Iter-22470, train loss-2.0476, acc-0.4000, valid loss-2.0616, acc-0.4254, test loss-2.0624, acc-0.4177
Iter-22480, train loss-2.0274, acc-0.3800, valid loss-2.0615, acc-0.4254, test loss-2.0623, acc-0.4179
Iter-22490, train loss-2.0412, acc-0.4000, valid loss-2.0614, acc-0.4252, test loss-2.0622, acc-0.4178
Iter-22500, train loss-2.0965, acc-0.4400, valid loss-2.0613, acc-0.4252, test loss-2.0621, acc-0.4177
Iter-22510, train loss-2.0749, acc-0.3800, valid loss-2.0612, acc-0.4252, test loss-2.0620, acc-0.4177
Iter-22520, train loss-2.0869, acc-0.3600, valid loss-2.0611, acc-0.4252, test loss-2.0619, acc-0.4175
Iter-22530, train loss-2.0677, acc-0.4000, valid loss-2.0610, acc-0.4254, test loss-2.0618, acc-0.4179
Iter-22540, train loss-2.0752, acc-0.4200, valid loss-2.0609, acc-0.4256, test loss-2.0618, acc-0.4177
Iter-22550, train loss-2.0437, acc-0.3400, valid loss-2.0608, acc-0.4250, test loss-2.0617, acc-0.4179
Iter-22560, train loss-2.1083, acc-0.2800, valid loss-2.0607, acc-0.4248, test loss-2.0616, acc-0.4175
Iter-22570, train loss-2.0240, acc-0.3400, valid loss-2.0607, acc-0.4246, test loss-2.0615, acc-0.4176
Iter-22580, train loss-2.1060, acc-0.3600, valid loss-2.0606, acc-0.4244, test loss-2.0614, acc-0.4179
Iter-22590, train loss-2.0257, acc-0.3800, valid loss-2.0605, acc-0.4246, test loss-2.0613, acc-0.4178
Iter-22600, train loss-2.0671, acc-0.3400, valid loss-2.0604, acc-0.4252, test loss-2.0612, acc-0.4177
Iter-22610, train loss-2.0553, acc-0.4000, valid loss-2.0603, acc-0.4254, test loss-2.0611, acc-0.4179
Iter-22620, train loss-2.0633, acc-0.3400, valid loss-2.0602, acc-0.4252, test loss-2.0610, acc-0.4175
Iter-22630, train loss-2.0686, acc-0.4400, valid loss-2.0601, acc-0.4254, test loss-2.0609, acc-0.4177
Iter-22640, train loss-2.1094, acc-0.3600, valid loss-2.0600, acc-0.4262, test loss-2.0609, acc-0.4180
Iter-22650, train loss-2.0928, acc-0.3800, valid loss-2.0599, acc-0.4262, test loss-2.0608, acc-0.4175
Iter-22660, train loss-2.1235, acc-0.3400, valid loss-2.0599, acc-0.4258, test loss-2.0607, acc-0.4176
Iter-22670, train loss-2.0621, acc-0.5000, valid loss-2.0598, acc-0.4256, test loss-2.0606, acc-0.4176
Iter-22680, train loss-2.0898, acc-0.4600, valid loss-2.0597, acc-0.4258, test loss-2.0605, acc-0.4178
Iter-22690, train loss-2.0504, acc-0.4000, valid loss-2.0596, acc-0.4260, test loss-2.0604, acc-0.4178
Iter-22700, train loss-2.0897, acc-0.4400, valid loss-2.0595, acc-0.4258, test loss-2.0603, acc-0.4180
Iter-22710, train loss-2.0009, acc-0.5800, valid loss-2.0594, acc-0.4256, test loss-2.0602, acc-0.4181
Iter-22720, train loss-2.0789, acc-0.4000, valid loss-2.0593, acc-0.4256, test loss-2.0601, acc-0.4181
Iter-22730, train loss-2.0548, acc-0.4400, valid loss-2.0592, acc-0.4256, test loss-2.0600, acc-0.4180
Iter-22740, train loss-2.1278, acc-0.2600, valid loss-2.0591, acc-0.4256, test loss-2.0599, acc-0.4183
Iter-22750, train loss-2.1434, acc-0.2200, valid loss-2.0590, acc-0.4258, test loss-2.0598, acc-0.4181
Iter-22760, train loss-2.0564, acc-0.4000, valid loss-2.0589, acc-0.4258, test loss-2.0597, acc-0.4180
Iter-22770, train loss-2.0849, acc-0.3400, valid loss-2.0588, acc-0.4260, test loss-2.0597, acc-0.4181
Iter-22780, train loss-1.9757, acc-0.5600, valid loss-2.0587, acc-0.4262, test loss-2.0596, acc-0.4183
Iter-22790, train loss-2.0614, acc-0.4200, valid loss-2.0586, acc-0.4262, test loss-2.0595, acc-0.4183
Iter-22800, train loss-2.0686, acc-0.4000, valid loss-2.0586, acc-0.4262, test loss-2.0594, acc-0.4184
Iter-22810, train loss-2.0775, acc-0.4400, valid loss-2.0585, acc-0.4264, test loss-2.0593, acc-0.4184
Iter-22820, train loss-2.0995, acc-0.4000, valid loss-2.0584, acc-0.4264, test loss-2.0592, acc-0.4184
Iter-22830, train loss-2.0785, acc-0.3600, valid loss-2.0583, acc-0.4264, test loss-2.0591, acc-0.4185
Iter-22840, train loss-2.1352, acc-0.2600, valid loss-2.0582, acc-0.4262, test loss-2.0590, acc-0.4185
Iter-22850, train loss-2.0184, acc-0.5200, valid loss-2.0581, acc-0.4268, test loss-2.0589, acc-0.4186
Iter-22860, train loss-2.0498, acc-0.4800, valid loss-2.0580, acc-0.4268, test loss-2.0588, acc-0.4185
Iter-22870, train loss-2.0668, acc-0.3800, valid loss-2.0579, acc-0.4270, test loss-2.0587, acc-0.4189
Iter-22880, train loss-2.0828, acc-0.4600, valid loss-2.0578, acc-0.4270, test loss-2.0586, acc-0.4191
Iter-22890, train loss-2.1076, acc-0.3600, valid loss-2.0577, acc-0.4268, test loss-2.0586, acc-0.4189
Iter-22900, train loss-2.0343, acc-0.4400, valid loss-2.0577, acc-0.4270, test loss-2.0585, acc-0.4190
Iter-22910, train loss-2.0480, acc-0.5400, valid loss-2.0576, acc-0.4278, test loss-2.0584, acc-0.4191
Iter-22920, train loss-2.0525, acc-0.4000, valid loss-2.0575, acc-0.4274, test loss-2.0583, acc-0.4189
Iter-22930, train loss-2.1248, acc-0.4600, valid loss-2.0574, acc-0.4276, test loss-2.0582, acc-0.4189
Iter-22940, train loss-2.0722, acc-0.4200, valid loss-2.0573, acc-0.4278, test loss-2.0581, acc-0.4191
Iter-22950, train loss-2.0335, acc-0.4800, valid loss-2.0572, acc-0.4276, test loss-2.0580, acc-0.4191
Iter-22960, train loss-2.0775, acc-0.3600, valid loss-2.0571, acc-0.4272, test loss-2.0579, acc-0.4190
Iter-22970, train loss-2.0427, acc-0.4200, valid loss-2.0570, acc-0.4274, test loss-2.0578, acc-0.4191
Iter-22980, train loss-2.0929, acc-0.3800, valid loss-2.0569, acc-0.4272, test loss-2.0577, acc-0.4191
Iter-22990, train loss-2.0502, acc-0.4000, valid loss-2.0568, acc-0.4272, test loss-2.0576, acc-0.4190
Iter-23000, train loss-2.0572, acc-0.4800, valid loss-2.0567, acc-0.4268, test loss-2.0576, acc-0.4191
Iter-23010, train loss-2.0155, acc-0.4800, valid loss-2.0567, acc-0.4272, test loss-2.0575, acc-0.4192
Iter-23020, train loss-2.0255, acc-0.4600, valid loss-2.0566, acc-0.4272, test loss-2.0574, acc-0.4192
Iter-23030, train loss-2.0611, acc-0.4200, valid loss-2.0565, acc-0.4276, test loss-2.0573, acc-0.4189
Iter-23040, train loss-2.0504, acc-0.5200, valid loss-2.0564, acc-0.4274, test loss-2.0572, acc-0.4189
Iter-23050, train loss-2.0617, acc-0.4600, valid loss-2.0563, acc-0.4274, test loss-2.0571, acc-0.4188
Iter-23060, train loss-2.0520, acc-0.3200, valid loss-2.0562, acc-0.4276, test loss-2.0570, acc-0.4188
Iter-23070, train loss-2.0941, acc-0.3000, valid loss-2.0561, acc-0.4276, test loss-2.0569, acc-0.4189
Iter-23080, train loss-2.0488, acc-0.4000, valid loss-2.0560, acc-0.4274, test loss-2.0568, acc-0.4192
Iter-23090, train loss-2.1288, acc-0.3400, valid loss-2.0559, acc-0.4276, test loss-2.0567, acc-0.4191
Iter-23100, train loss-2.0988, acc-0.3600, valid loss-2.0558, acc-0.4278, test loss-2.0566, acc-0.4191
Iter-23110, train loss-2.0400, acc-0.4400, valid loss-2.0557, acc-0.4276, test loss-2.0565, acc-0.4195
Iter-23120, train loss-2.0424, acc-0.4800, valid loss-2.0557, acc-0.4274, test loss-2.0564, acc-0.4194
Iter-23130, train loss-2.0164, acc-0.4600, valid loss-2.0556, acc-0.4278, test loss-2.0564, acc-0.4195
Iter-23140, train loss-2.0373, acc-0.5000, valid loss-2.0555, acc-0.4280, test loss-2.0563, acc-0.4196
Iter-23150, train loss-2.0803, acc-0.3200, valid loss-2.0554, acc-0.4276, test loss-2.0562, acc-0.4193
Iter-23160, train loss-2.0996, acc-0.3800, valid loss-2.0553, acc-0.4276, test loss-2.0561, acc-0.4193
Iter-23170, train loss-2.0912, acc-0.3600, valid loss-2.0552, acc-0.4278, test loss-2.0560, acc-0.4192
Iter-23180, train loss-2.0706, acc-0.5200, valid loss-2.0551, acc-0.4274, test loss-2.0559, acc-0.4195
Iter-23190, train loss-2.0401, acc-0.4800, valid loss-2.0550, acc-0.4274, test loss-2.0558, acc-0.4194
Iter-23200, train loss-2.0045, acc-0.6000, valid loss-2.0549, acc-0.4280, test loss-2.0557, acc-0.4194
Iter-23210, train loss-2.0830, acc-0.4000, valid loss-2.0548, acc-0.4278, test loss-2.0556, acc-0.4197
Iter-23220, train loss-2.0541, acc-0.4600, valid loss-2.0547, acc-0.4280, test loss-2.0555, acc-0.4197
Iter-23230, train loss-2.0265, acc-0.4000, valid loss-2.0547, acc-0.4276, test loss-2.0554, acc-0.4196
Iter-23240, train loss-2.1331, acc-0.2800, valid loss-2.0546, acc-0.4274, test loss-2.0554, acc-0.4199
Iter-23250, train loss-2.0691, acc-0.4200, valid loss-2.0545, acc-0.4280, test loss-2.0553, acc-0.4200
Iter-23260, train loss-2.0401, acc-0.4400, valid loss-2.0544, acc-0.4278, test loss-2.0552, acc-0.4199
Iter-23270, train loss-2.0682, acc-0.3400, valid loss-2.0543, acc-0.4276, test loss-2.0551, acc-0.4200
Iter-23280, train loss-2.0632, acc-0.3400, valid loss-2.0542, acc-0.4276, test loss-2.0550, acc-0.4199
Iter-23290, train loss-2.0195, acc-0.4400, valid loss-2.0541, acc-0.4276, test loss-2.0549, acc-0.4199
Iter-23300, train loss-2.0539, acc-0.4600, valid loss-2.0540, acc-0.4276, test loss-2.0548, acc-0.4202
Iter-23310, train loss-2.0548, acc-0.4400, valid loss-2.0540, acc-0.4278, test loss-2.0547, acc-0.4198
Iter-23320, train loss-2.0318, acc-0.5000, valid loss-2.0539, acc-0.4278, test loss-2.0546, acc-0.4199
Iter-23330, train loss-2.0272, acc-0.3800, valid loss-2.0538, acc-0.4278, test loss-2.0546, acc-0.4202
Iter-23340, train loss-1.9598, acc-0.5600, valid loss-2.0537, acc-0.4280, test loss-2.0545, acc-0.4203
Iter-23350, train loss-2.0660, acc-0.3800, valid loss-2.0536, acc-0.4280, test loss-2.0544, acc-0.4202
Iter-23360, train loss-2.1164, acc-0.4200, valid loss-2.0535, acc-0.4278, test loss-2.0543, acc-0.4203
Iter-23370, train loss-2.0448, acc-0.3800, valid loss-2.0534, acc-0.4280, test loss-2.0542, acc-0.4202
Iter-23380, train loss-1.9900, acc-0.6000, valid loss-2.0533, acc-0.4278, test loss-2.0541, acc-0.4203
Iter-23390, train loss-2.1008, acc-0.4000, valid loss-2.0532, acc-0.4280, test loss-2.0540, acc-0.4203
Iter-23400, train loss-2.0810, acc-0.3400, valid loss-2.0531, acc-0.4282, test loss-2.0539, acc-0.4205
Iter-23410, train loss-2.0632, acc-0.4600, valid loss-2.0530, acc-0.4284, test loss-2.0538, acc-0.4206
Iter-23420, train loss-2.0579, acc-0.4200, valid loss-2.0529, acc-0.4288, test loss-2.0537, acc-0.4205
Iter-23430, train loss-2.1253, acc-0.3200, valid loss-2.0529, acc-0.4284, test loss-2.0536, acc-0.4208
Iter-23440, train loss-2.0527, acc-0.4200, valid loss-2.0528, acc-0.4284, test loss-2.0536, acc-0.4205
Iter-23450, train loss-2.0838, acc-0.3800, valid loss-2.0527, acc-0.4282, test loss-2.0535, acc-0.4207
Iter-23460, train loss-2.0662, acc-0.4200, valid loss-2.0526, acc-0.4282, test loss-2.0534, acc-0.4206
Iter-23470, train loss-2.1125, acc-0.3400, valid loss-2.0525, acc-0.4280, test loss-2.0533, acc-0.4206
Iter-23480, train loss-2.0383, acc-0.3600, valid loss-2.0524, acc-0.4278, test loss-2.0532, acc-0.4208
Iter-23490, train loss-2.0275, acc-0.4600, valid loss-2.0523, acc-0.4276, test loss-2.0531, acc-0.4211
Iter-23500, train loss-2.0666, acc-0.4200, valid loss-2.0522, acc-0.4278, test loss-2.0530, acc-0.4210
Iter-23510, train loss-2.0678, acc-0.3600, valid loss-2.0521, acc-0.4274, test loss-2.0529, acc-0.4211
Iter-23520, train loss-2.0781, acc-0.3800, valid loss-2.0520, acc-0.4274, test loss-2.0528, acc-0.4211
Iter-23530, train loss-2.0942, acc-0.3800, valid loss-2.0519, acc-0.4276, test loss-2.0527, acc-0.4211
Iter-23540, train loss-2.0536, acc-0.4600, valid loss-2.0519, acc-0.4276, test loss-2.0526, acc-0.4212
Iter-23550, train loss-2.0526, acc-0.4000, valid loss-2.0518, acc-0.4278, test loss-2.0526, acc-0.4213
Iter-23560, train loss-2.1000, acc-0.3400, valid loss-2.0517, acc-0.4276, test loss-2.0525, acc-0.4215
Iter-23570, train loss-2.0220, acc-0.4200, valid loss-2.0516, acc-0.4278, test loss-2.0524, acc-0.4215
Iter-23580, train loss-2.0443, acc-0.4000, valid loss-2.0515, acc-0.4280, test loss-2.0523, acc-0.4218
Iter-23590, train loss-2.0527, acc-0.4800, valid loss-2.0514, acc-0.4280, test loss-2.0522, acc-0.4220
Iter-23600, train loss-2.0756, acc-0.4200, valid loss-2.0513, acc-0.4284, test loss-2.0521, acc-0.4218
Iter-23610, train loss-2.0039, acc-0.4800, valid loss-2.0512, acc-0.4282, test loss-2.0520, acc-0.4218
Iter-23620, train loss-2.0230, acc-0.4200, valid loss-2.0511, acc-0.4282, test loss-2.0519, acc-0.4220
Iter-23630, train loss-2.0925, acc-0.3200, valid loss-2.0510, acc-0.4286, test loss-2.0518, acc-0.4218
Iter-23640, train loss-2.0332, acc-0.3600, valid loss-2.0510, acc-0.4286, test loss-2.0517, acc-0.4221
Iter-23650, train loss-2.0575, acc-0.4000, valid loss-2.0509, acc-0.4284, test loss-2.0517, acc-0.4224
Iter-23660, train loss-2.0337, acc-0.5800, valid loss-2.0508, acc-0.4286, test loss-2.0516, acc-0.4224
Iter-23670, train loss-2.0451, acc-0.4000, valid loss-2.0507, acc-0.4284, test loss-2.0515, acc-0.4225
Iter-23680, train loss-2.0184, acc-0.5000, valid loss-2.0506, acc-0.4286, test loss-2.0514, acc-0.4226
Iter-23690, train loss-2.1148, acc-0.3200, valid loss-2.0505, acc-0.4290, test loss-2.0513, acc-0.4225
Iter-23700, train loss-2.0942, acc-0.3600, valid loss-2.0504, acc-0.4288, test loss-2.0512, acc-0.4226
Iter-23710, train loss-2.0213, acc-0.4600, valid loss-2.0503, acc-0.4296, test loss-2.0511, acc-0.4226
Iter-23720, train loss-2.0712, acc-0.3800, valid loss-2.0502, acc-0.4294, test loss-2.0510, acc-0.4226
Iter-23730, train loss-2.0240, acc-0.4200, valid loss-2.0501, acc-0.4294, test loss-2.0510, acc-0.4226
Iter-23740, train loss-2.1016, acc-0.3800, valid loss-2.0501, acc-0.4294, test loss-2.0509, acc-0.4228
Iter-23750, train loss-2.0926, acc-0.4200, valid loss-2.0500, acc-0.4292, test loss-2.0508, acc-0.4229
Iter-23760, train loss-2.0803, acc-0.4800, valid loss-2.0499, acc-0.4292, test loss-2.0507, acc-0.4230
Iter-23770, train loss-2.0422, acc-0.4800, valid loss-2.0498, acc-0.4290, test loss-2.0506, acc-0.4230
Iter-23780, train loss-2.0472, acc-0.5200, valid loss-2.0497, acc-0.4290, test loss-2.0505, acc-0.4231
Iter-23790, train loss-2.0214, acc-0.5000, valid loss-2.0496, acc-0.4290, test loss-2.0504, acc-0.4229
Iter-23800, train loss-2.1076, acc-0.3800, valid loss-2.0495, acc-0.4286, test loss-2.0503, acc-0.4227
Iter-23810, train loss-2.0435, acc-0.3800, valid loss-2.0494, acc-0.4286, test loss-2.0502, acc-0.4227
Iter-23820, train loss-2.0460, acc-0.4600, valid loss-2.0493, acc-0.4284, test loss-2.0501, acc-0.4230
Iter-23830, train loss-2.0087, acc-0.5600, valid loss-2.0493, acc-0.4286, test loss-2.0501, acc-0.4230
Iter-23840, train loss-2.0223, acc-0.5000, valid loss-2.0492, acc-0.4292, test loss-2.0500, acc-0.4231
Iter-23850, train loss-2.0715, acc-0.4000, valid loss-2.0491, acc-0.4290, test loss-2.0499, acc-0.4230
Iter-23860, train loss-2.0309, acc-0.3600, valid loss-2.0490, acc-0.4294, test loss-2.0498, acc-0.4230
Iter-23870, train loss-2.0584, acc-0.3800, valid loss-2.0489, acc-0.4292, test loss-2.0497, acc-0.4231
Iter-23880, train loss-2.0295, acc-0.5200, valid loss-2.0488, acc-0.4292, test loss-2.0496, acc-0.4230
Iter-23890, train loss-2.0674, acc-0.4200, valid loss-2.0487, acc-0.4294, test loss-2.0495, acc-0.4231
Iter-23900, train loss-2.0303, acc-0.4800, valid loss-2.0486, acc-0.4294, test loss-2.0494, acc-0.4232
Iter-23910, train loss-2.1036, acc-0.4400, valid loss-2.0485, acc-0.4292, test loss-2.0493, acc-0.4233
Iter-23920, train loss-2.0488, acc-0.4400, valid loss-2.0484, acc-0.4294, test loss-2.0492, acc-0.4235
Iter-23930, train loss-2.0393, acc-0.5000, valid loss-2.0484, acc-0.4290, test loss-2.0491, acc-0.4235
Iter-23940, train loss-2.0825, acc-0.3200, valid loss-2.0483, acc-0.4288, test loss-2.0491, acc-0.4234
Iter-23950, train loss-2.0793, acc-0.4400, valid loss-2.0482, acc-0.4290, test loss-2.0490, acc-0.4234
Iter-23960, train loss-2.0139, acc-0.4600, valid loss-2.0481, acc-0.4288, test loss-2.0489, acc-0.4234
Iter-23970, train loss-2.0567, acc-0.4000, valid loss-2.0480, acc-0.4288, test loss-2.0488, acc-0.4234
Iter-23980, train loss-2.0528, acc-0.4600, valid loss-2.0479, acc-0.4292, test loss-2.0487, acc-0.4236
Iter-23990, train loss-2.0576, acc-0.4200, valid loss-2.0478, acc-0.4290, test loss-2.0486, acc-0.4240
Iter-24000, train loss-2.0611, acc-0.4400, valid loss-2.0478, acc-0.4290, test loss-2.0485, acc-0.4237
Iter-24010, train loss-2.0062, acc-0.4200, valid loss-2.0477, acc-0.4290, test loss-2.0484, acc-0.4237
Iter-24020, train loss-2.0667, acc-0.4600, valid loss-2.0476, acc-0.4290, test loss-2.0484, acc-0.4236
Iter-24030, train loss-2.0621, acc-0.3800, valid loss-2.0475, acc-0.4290, test loss-2.0483, acc-0.4238
Iter-24040, train loss-2.0430, acc-0.4800, valid loss-2.0474, acc-0.4288, test loss-2.0482, acc-0.4241
Iter-24050, train loss-2.0008, acc-0.5000, valid loss-2.0473, acc-0.4288, test loss-2.0481, acc-0.4243
Iter-24060, train loss-2.1219, acc-0.2600, valid loss-2.0472, acc-0.4288, test loss-2.0480, acc-0.4239
Iter-24070, train loss-1.9445, acc-0.6800, valid loss-2.0471, acc-0.4288, test loss-2.0479, acc-0.4241
Iter-24080, train loss-2.1135, acc-0.3400, valid loss-2.0470, acc-0.4290, test loss-2.0478, acc-0.4244
Iter-24090, train loss-2.0345, acc-0.4200, valid loss-2.0470, acc-0.4294, test loss-2.0477, acc-0.4243
Iter-24100, train loss-2.0437, acc-0.5000, valid loss-2.0469, acc-0.4294, test loss-2.0477, acc-0.4244
Iter-24110, train loss-2.0114, acc-0.4400, valid loss-2.0468, acc-0.4290, test loss-2.0476, acc-0.4244
Iter-24120, train loss-2.0654, acc-0.3200, valid loss-2.0467, acc-0.4294, test loss-2.0475, acc-0.4245
Iter-24130, train loss-2.0450, acc-0.4400, valid loss-2.0466, acc-0.4290, test loss-2.0474, acc-0.4243
Iter-24140, train loss-2.0197, acc-0.4200, valid loss-2.0465, acc-0.4292, test loss-2.0473, acc-0.4241
Iter-24150, train loss-2.0121, acc-0.5000, valid loss-2.0464, acc-0.4292, test loss-2.0472, acc-0.4242
Iter-24160, train loss-2.0458, acc-0.4200, valid loss-2.0463, acc-0.4296, test loss-2.0471, acc-0.4241
Iter-24170, train loss-2.1157, acc-0.3000, valid loss-2.0462, acc-0.4296, test loss-2.0470, acc-0.4242
Iter-24180, train loss-2.0820, acc-0.3600, valid loss-2.0461, acc-0.4300, test loss-2.0469, acc-0.4245
Iter-24190, train loss-2.1332, acc-0.3400, valid loss-2.0461, acc-0.4300, test loss-2.0469, acc-0.4244
Iter-24200, train loss-2.1033, acc-0.4200, valid loss-2.0460, acc-0.4300, test loss-2.0468, acc-0.4244
Iter-24210, train loss-2.0741, acc-0.3800, valid loss-2.0459, acc-0.4304, test loss-2.0467, acc-0.4244
Iter-24220, train loss-2.0398, acc-0.4600, valid loss-2.0458, acc-0.4302, test loss-2.0466, acc-0.4242
Iter-24230, train loss-2.0669, acc-0.4400, valid loss-2.0457, acc-0.4302, test loss-2.0465, acc-0.4242
Iter-24240, train loss-2.0238, acc-0.5200, valid loss-2.0456, acc-0.4300, test loss-2.0464, acc-0.4241
Iter-24250, train loss-2.1082, acc-0.3400, valid loss-2.0455, acc-0.4304, test loss-2.0463, acc-0.4244
Iter-24260, train loss-2.0633, acc-0.4000, valid loss-2.0454, acc-0.4306, test loss-2.0462, acc-0.4243
Iter-24270, train loss-2.0853, acc-0.3200, valid loss-2.0454, acc-0.4304, test loss-2.0461, acc-0.4245
Iter-24280, train loss-2.0770, acc-0.4000, valid loss-2.0453, acc-0.4308, test loss-2.0460, acc-0.4244
Iter-24290, train loss-2.0276, acc-0.4600, valid loss-2.0452, acc-0.4312, test loss-2.0459, acc-0.4245
Iter-24300, train loss-2.0336, acc-0.4600, valid loss-2.0451, acc-0.4310, test loss-2.0459, acc-0.4246
Iter-24310, train loss-2.0028, acc-0.5600, valid loss-2.0450, acc-0.4310, test loss-2.0458, acc-0.4245
Iter-24320, train loss-2.0536, acc-0.4000, valid loss-2.0449, acc-0.4312, test loss-2.0457, acc-0.4244
Iter-24330, train loss-2.0331, acc-0.4800, valid loss-2.0448, acc-0.4312, test loss-2.0456, acc-0.4246
Iter-24340, train loss-2.0825, acc-0.3800, valid loss-2.0447, acc-0.4312, test loss-2.0455, acc-0.4248
Iter-24350, train loss-2.1283, acc-0.2800, valid loss-2.0446, acc-0.4312, test loss-2.0454, acc-0.4247
Iter-24360, train loss-2.0544, acc-0.3800, valid loss-2.0446, acc-0.4312, test loss-2.0453, acc-0.4246
Iter-24370, train loss-2.1180, acc-0.3200, valid loss-2.0445, acc-0.4310, test loss-2.0452, acc-0.4245
Iter-24380, train loss-2.0573, acc-0.3400, valid loss-2.0444, acc-0.4314, test loss-2.0452, acc-0.4247
Iter-24390, train loss-2.0662, acc-0.3800, valid loss-2.0443, acc-0.4314, test loss-2.0451, acc-0.4248
Iter-24400, train loss-2.1166, acc-0.3000, valid loss-2.0442, acc-0.4314, test loss-2.0450, acc-0.4249
Iter-24410, train loss-2.0624, acc-0.4600, valid loss-2.0441, acc-0.4314, test loss-2.0449, acc-0.4249
Iter-24420, train loss-2.0514, acc-0.3600, valid loss-2.0441, acc-0.4314, test loss-2.0448, acc-0.4252
Iter-24430, train loss-2.0506, acc-0.4400, valid loss-2.0440, acc-0.4312, test loss-2.0447, acc-0.4251
Iter-24440, train loss-2.0368, acc-0.4200, valid loss-2.0439, acc-0.4316, test loss-2.0446, acc-0.4252
Iter-24450, train loss-2.0639, acc-0.3800, valid loss-2.0438, acc-0.4316, test loss-2.0446, acc-0.4256
Iter-24460, train loss-2.1021, acc-0.2800, valid loss-2.0437, acc-0.4320, test loss-2.0445, acc-0.4254
Iter-24470, train loss-2.0157, acc-0.5000, valid loss-2.0436, acc-0.4324, test loss-2.0444, acc-0.4252
Iter-24480, train loss-2.0802, acc-0.4200, valid loss-2.0435, acc-0.4320, test loss-2.0443, acc-0.4252
Iter-24490, train loss-2.0095, acc-0.4600, valid loss-2.0434, acc-0.4318, test loss-2.0442, acc-0.4251
Iter-24500, train loss-2.0169, acc-0.5600, valid loss-2.0434, acc-0.4316, test loss-2.0441, acc-0.4251
Iter-24510, train loss-2.0517, acc-0.4200, valid loss-2.0433, acc-0.4324, test loss-2.0440, acc-0.4252
Iter-24520, train loss-2.0502, acc-0.2800, valid loss-2.0432, acc-0.4322, test loss-2.0439, acc-0.4252
Iter-24530, train loss-2.0838, acc-0.3800, valid loss-2.0431, acc-0.4318, test loss-2.0439, acc-0.4252
Iter-24540, train loss-2.0377, acc-0.5000, valid loss-2.0430, acc-0.4322, test loss-2.0438, acc-0.4253
Iter-24550, train loss-2.0863, acc-0.3600, valid loss-2.0429, acc-0.4322, test loss-2.0437, acc-0.4255
Iter-24560, train loss-2.0559, acc-0.4200, valid loss-2.0428, acc-0.4318, test loss-2.0436, acc-0.4252
Iter-24570, train loss-2.0096, acc-0.4400, valid loss-2.0427, acc-0.4318, test loss-2.0435, acc-0.4254
Iter-24580, train loss-2.0232, acc-0.3800, valid loss-2.0426, acc-0.4320, test loss-2.0434, acc-0.4254
Iter-24590, train loss-2.1640, acc-0.2200, valid loss-2.0425, acc-0.4318, test loss-2.0433, acc-0.4254
Iter-24600, train loss-2.0373, acc-0.4000, valid loss-2.0424, acc-0.4322, test loss-2.0432, acc-0.4256
Iter-24610, train loss-2.0756, acc-0.4200, valid loss-2.0424, acc-0.4320, test loss-2.0431, acc-0.4256
Iter-24620, train loss-2.0833, acc-0.4000, valid loss-2.0423, acc-0.4320, test loss-2.0431, acc-0.4256
Iter-24630, train loss-1.9944, acc-0.4200, valid loss-2.0422, acc-0.4324, test loss-2.0430, acc-0.4258
Iter-24640, train loss-2.0418, acc-0.4000, valid loss-2.0421, acc-0.4320, test loss-2.0429, acc-0.4259
Iter-24650, train loss-2.0330, acc-0.4400, valid loss-2.0420, acc-0.4324, test loss-2.0428, acc-0.4258
Iter-24660, train loss-1.9991, acc-0.4800, valid loss-2.0419, acc-0.4324, test loss-2.0427, acc-0.4257
Iter-24670, train loss-2.0504, acc-0.4200, valid loss-2.0418, acc-0.4324, test loss-2.0426, acc-0.4259
Iter-24680, train loss-2.1240, acc-0.3000, valid loss-2.0417, acc-0.4324, test loss-2.0425, acc-0.4256
Iter-24690, train loss-2.1139, acc-0.2800, valid loss-2.0417, acc-0.4322, test loss-2.0424, acc-0.4258
Iter-24700, train loss-2.0289, acc-0.4200, valid loss-2.0416, acc-0.4320, test loss-2.0423, acc-0.4258
Iter-24710, train loss-2.0899, acc-0.2600, valid loss-2.0415, acc-0.4328, test loss-2.0423, acc-0.4258
Iter-24720, train loss-2.0244, acc-0.4200, valid loss-2.0414, acc-0.4326, test loss-2.0422, acc-0.4257
Iter-24730, train loss-2.0515, acc-0.4200, valid loss-2.0413, acc-0.4322, test loss-2.0421, acc-0.4256
Iter-24740, train loss-2.0168, acc-0.4400, valid loss-2.0412, acc-0.4324, test loss-2.0420, acc-0.4258
Iter-24750, train loss-1.9762, acc-0.5400, valid loss-2.0411, acc-0.4318, test loss-2.0419, acc-0.4259
Iter-24760, train loss-2.0468, acc-0.4000, valid loss-2.0410, acc-0.4318, test loss-2.0418, acc-0.4260
Iter-24770, train loss-2.1173, acc-0.3000, valid loss-2.0409, acc-0.4322, test loss-2.0417, acc-0.4259
Iter-24780, train loss-2.0685, acc-0.3800, valid loss-2.0409, acc-0.4322, test loss-2.0416, acc-0.4258
Iter-24790, train loss-2.0374, acc-0.4600, valid loss-2.0408, acc-0.4324, test loss-2.0416, acc-0.4260
Iter-24800, train loss-2.0350, acc-0.4600, valid loss-2.0407, acc-0.4320, test loss-2.0415, acc-0.4260
Iter-24810, train loss-2.0545, acc-0.3800, valid loss-2.0406, acc-0.4324, test loss-2.0414, acc-0.4262
Iter-24820, train loss-2.0738, acc-0.3600, valid loss-2.0405, acc-0.4326, test loss-2.0413, acc-0.4262
Iter-24830, train loss-2.0886, acc-0.3400, valid loss-2.0404, acc-0.4326, test loss-2.0412, acc-0.4265
Iter-24840, train loss-2.0552, acc-0.4200, valid loss-2.0403, acc-0.4328, test loss-2.0411, acc-0.4265
Iter-24850, train loss-2.0472, acc-0.5200, valid loss-2.0402, acc-0.4332, test loss-2.0410, acc-0.4266
Iter-24860, train loss-2.0081, acc-0.5200, valid loss-2.0402, acc-0.4326, test loss-2.0409, acc-0.4265
Iter-24870, train loss-2.0616, acc-0.3600, valid loss-2.0401, acc-0.4330, test loss-2.0409, acc-0.4264
Iter-24880, train loss-2.0718, acc-0.3800, valid loss-2.0400, acc-0.4330, test loss-2.0408, acc-0.4265
Iter-24890, train loss-2.0358, acc-0.4400, valid loss-2.0399, acc-0.4326, test loss-2.0407, acc-0.4263
Iter-24900, train loss-1.9991, acc-0.5200, valid loss-2.0398, acc-0.4330, test loss-2.0406, acc-0.4265
Iter-24910, train loss-2.0540, acc-0.4400, valid loss-2.0397, acc-0.4328, test loss-2.0405, acc-0.4266
Iter-24920, train loss-2.0754, acc-0.4200, valid loss-2.0396, acc-0.4328, test loss-2.0404, acc-0.4266
Iter-24930, train loss-2.0383, acc-0.4600, valid loss-2.0395, acc-0.4332, test loss-2.0403, acc-0.4266
Iter-24940, train loss-2.0276, acc-0.4800, valid loss-2.0395, acc-0.4330, test loss-2.0402, acc-0.4266
Iter-24950, train loss-2.0531, acc-0.4200, valid loss-2.0394, acc-0.4334, test loss-2.0402, acc-0.4268
Iter-24960, train loss-2.0894, acc-0.3400, valid loss-2.0393, acc-0.4334, test loss-2.0401, acc-0.4267
Iter-24970, train loss-2.0744, acc-0.3000, valid loss-2.0392, acc-0.4334, test loss-2.0400, acc-0.4266
Iter-24980, train loss-2.0560, acc-0.3800, valid loss-2.0391, acc-0.4338, test loss-2.0399, acc-0.4267
Iter-24990, train loss-2.0356, acc-0.4200, valid loss-2.0390, acc-0.4342, test loss-2.0398, acc-0.4268
Iter-25000, train loss-2.0505, acc-0.4600, valid loss-2.0389, acc-0.4344, test loss-2.0397, acc-0.4267
Iter-25010, train loss-2.0456, acc-0.3800, valid loss-2.0388, acc-0.4344, test loss-2.0396, acc-0.4270
Iter-25020, train loss-2.0457, acc-0.4000, valid loss-2.0388, acc-0.4342, test loss-2.0396, acc-0.4269
Iter-25030, train loss-2.0119, acc-0.4600, valid loss-2.0387, acc-0.4344, test loss-2.0395, acc-0.4269
Iter-25040, train loss-2.0400, acc-0.4600, valid loss-2.0386, acc-0.4348, test loss-2.0394, acc-0.4269
Iter-25050, train loss-2.1035, acc-0.4400, valid loss-2.0385, acc-0.4342, test loss-2.0393, acc-0.4269
Iter-25060, train loss-2.0214, acc-0.4800, valid loss-2.0384, acc-0.4340, test loss-2.0392, acc-0.4269
Iter-25070, train loss-2.0144, acc-0.4400, valid loss-2.0383, acc-0.4342, test loss-2.0391, acc-0.4268
Iter-25080, train loss-2.0243, acc-0.4600, valid loss-2.0382, acc-0.4338, test loss-2.0390, acc-0.4268
Iter-25090, train loss-1.9955, acc-0.4800, valid loss-2.0381, acc-0.4338, test loss-2.0389, acc-0.4268
Iter-25100, train loss-1.9965, acc-0.5400, valid loss-2.0380, acc-0.4342, test loss-2.0388, acc-0.4271
Iter-25110, train loss-2.0308, acc-0.5200, valid loss-2.0380, acc-0.4340, test loss-2.0388, acc-0.4269
Iter-25120, train loss-2.0036, acc-0.4800, valid loss-2.0379, acc-0.4344, test loss-2.0387, acc-0.4269
Iter-25130, train loss-2.0302, acc-0.3600, valid loss-2.0378, acc-0.4342, test loss-2.0386, acc-0.4270
Iter-25140, train loss-2.0421, acc-0.4400, valid loss-2.0377, acc-0.4342, test loss-2.0385, acc-0.4270
Iter-25150, train loss-2.0768, acc-0.3600, valid loss-2.0376, acc-0.4346, test loss-2.0384, acc-0.4270
Iter-25160, train loss-2.0757, acc-0.3000, valid loss-2.0375, acc-0.4352, test loss-2.0383, acc-0.4273
Iter-25170, train loss-2.0126, acc-0.5400, valid loss-2.0374, acc-0.4348, test loss-2.0382, acc-0.4273
Iter-25180, train loss-2.0924, acc-0.3200, valid loss-2.0374, acc-0.4350, test loss-2.0381, acc-0.4275
Iter-25190, train loss-2.0585, acc-0.4000, valid loss-2.0373, acc-0.4350, test loss-2.0381, acc-0.4277
Iter-25200, train loss-2.0266, acc-0.5000, valid loss-2.0372, acc-0.4352, test loss-2.0380, acc-0.4279
Iter-25210, train loss-2.0450, acc-0.4000, valid loss-2.0371, acc-0.4354, test loss-2.0379, acc-0.4281
Iter-25220, train loss-2.0426, acc-0.4600, valid loss-2.0370, acc-0.4352, test loss-2.0378, acc-0.4281
Iter-25230, train loss-2.0625, acc-0.3800, valid loss-2.0369, acc-0.4356, test loss-2.0377, acc-0.4281
Iter-25240, train loss-2.0626, acc-0.4200, valid loss-2.0368, acc-0.4352, test loss-2.0376, acc-0.4283
Iter-25250, train loss-2.0651, acc-0.4000, valid loss-2.0367, acc-0.4358, test loss-2.0375, acc-0.4282
Iter-25260, train loss-2.0455, acc-0.4600, valid loss-2.0367, acc-0.4360, test loss-2.0375, acc-0.4285
Iter-25270, train loss-2.0519, acc-0.4600, valid loss-2.0366, acc-0.4362, test loss-2.0374, acc-0.4284
Iter-25280, train loss-2.0654, acc-0.3600, valid loss-2.0365, acc-0.4362, test loss-2.0373, acc-0.4286
Iter-25290, train loss-2.0225, acc-0.4400, valid loss-2.0364, acc-0.4362, test loss-2.0372, acc-0.4285
Iter-25300, train loss-2.0357, acc-0.4800, valid loss-2.0363, acc-0.4360, test loss-2.0371, acc-0.4286
Iter-25310, train loss-2.0620, acc-0.4000, valid loss-2.0362, acc-0.4360, test loss-2.0370, acc-0.4286
Iter-25320, train loss-2.0672, acc-0.3800, valid loss-2.0361, acc-0.4358, test loss-2.0369, acc-0.4286
Iter-25330, train loss-2.0419, acc-0.4800, valid loss-2.0361, acc-0.4356, test loss-2.0369, acc-0.4287
Iter-25340, train loss-2.0333, acc-0.3400, valid loss-2.0360, acc-0.4360, test loss-2.0368, acc-0.4286
Iter-25350, train loss-1.9948, acc-0.4800, valid loss-2.0359, acc-0.4360, test loss-2.0367, acc-0.4288
Iter-25360, train loss-2.0790, acc-0.3400, valid loss-2.0358, acc-0.4358, test loss-2.0366, acc-0.4290
Iter-25370, train loss-2.0626, acc-0.4000, valid loss-2.0357, acc-0.4360, test loss-2.0365, acc-0.4291
Iter-25380, train loss-1.9758, acc-0.6000, valid loss-2.0356, acc-0.4360, test loss-2.0364, acc-0.4291
Iter-25390, train loss-2.1006, acc-0.3800, valid loss-2.0356, acc-0.4362, test loss-2.0363, acc-0.4293
Iter-25400, train loss-1.9883, acc-0.5000, valid loss-2.0355, acc-0.4366, test loss-2.0362, acc-0.4294
Iter-25410, train loss-2.0599, acc-0.4200, valid loss-2.0354, acc-0.4370, test loss-2.0361, acc-0.4294
Iter-25420, train loss-2.0478, acc-0.3600, valid loss-2.0353, acc-0.4366, test loss-2.0361, acc-0.4295
Iter-25430, train loss-2.0665, acc-0.4400, valid loss-2.0352, acc-0.4366, test loss-2.0360, acc-0.4295
Iter-25440, train loss-2.0292, acc-0.5400, valid loss-2.0351, acc-0.4372, test loss-2.0359, acc-0.4296
Iter-25450, train loss-2.0364, acc-0.4000, valid loss-2.0350, acc-0.4374, test loss-2.0358, acc-0.4294
Iter-25460, train loss-2.0402, acc-0.4600, valid loss-2.0349, acc-0.4370, test loss-2.0357, acc-0.4291
Iter-25470, train loss-2.0079, acc-0.4600, valid loss-2.0348, acc-0.4370, test loss-2.0356, acc-0.4292
Iter-25480, train loss-2.0514, acc-0.4600, valid loss-2.0348, acc-0.4376, test loss-2.0355, acc-0.4296
Iter-25490, train loss-2.0437, acc-0.4600, valid loss-2.0347, acc-0.4372, test loss-2.0355, acc-0.4295
Iter-25500, train loss-2.0133, acc-0.5200, valid loss-2.0346, acc-0.4372, test loss-2.0354, acc-0.4298
Iter-25510, train loss-2.0476, acc-0.4400, valid loss-2.0345, acc-0.4376, test loss-2.0353, acc-0.4297
Iter-25520, train loss-2.0178, acc-0.5200, valid loss-2.0344, acc-0.4376, test loss-2.0352, acc-0.4299
Iter-25530, train loss-2.0743, acc-0.4000, valid loss-2.0343, acc-0.4374, test loss-2.0351, acc-0.4299
Iter-25540, train loss-1.9971, acc-0.4800, valid loss-2.0342, acc-0.4376, test loss-2.0350, acc-0.4297
Iter-25550, train loss-2.0693, acc-0.4000, valid loss-2.0341, acc-0.4376, test loss-2.0349, acc-0.4299
Iter-25560, train loss-2.0294, acc-0.4200, valid loss-2.0341, acc-0.4376, test loss-2.0348, acc-0.4300
Iter-25570, train loss-2.0054, acc-0.4400, valid loss-2.0340, acc-0.4376, test loss-2.0347, acc-0.4298
Iter-25580, train loss-2.0948, acc-0.3200, valid loss-2.0339, acc-0.4376, test loss-2.0347, acc-0.4299
Iter-25590, train loss-2.0738, acc-0.2600, valid loss-2.0338, acc-0.4376, test loss-2.0346, acc-0.4298
Iter-25600, train loss-1.9909, acc-0.5400, valid loss-2.0337, acc-0.4376, test loss-2.0345, acc-0.4297
Iter-25610, train loss-1.9502, acc-0.6400, valid loss-2.0336, acc-0.4378, test loss-2.0344, acc-0.4296
Iter-25620, train loss-2.1137, acc-0.3000, valid loss-2.0335, acc-0.4376, test loss-2.0343, acc-0.4297
Iter-25630, train loss-2.0753, acc-0.4000, valid loss-2.0334, acc-0.4380, test loss-2.0342, acc-0.4298
Iter-25640, train loss-2.0257, acc-0.5200, valid loss-2.0333, acc-0.4378, test loss-2.0341, acc-0.4300
Iter-25650, train loss-2.0722, acc-0.4400, valid loss-2.0333, acc-0.4380, test loss-2.0340, acc-0.4300
Iter-25660, train loss-2.0641, acc-0.4200, valid loss-2.0332, acc-0.4380, test loss-2.0340, acc-0.4304
Iter-25670, train loss-2.0230, acc-0.4600, valid loss-2.0331, acc-0.4376, test loss-2.0339, acc-0.4302
Iter-25680, train loss-2.0036, acc-0.4800, valid loss-2.0330, acc-0.4376, test loss-2.0338, acc-0.4303
Iter-25690, train loss-2.0798, acc-0.3600, valid loss-2.0329, acc-0.4380, test loss-2.0337, acc-0.4304
Iter-25700, train loss-2.0657, acc-0.3800, valid loss-2.0328, acc-0.4380, test loss-2.0336, acc-0.4303
Iter-25710, train loss-2.0301, acc-0.4200, valid loss-2.0327, acc-0.4384, test loss-2.0335, acc-0.4304
Iter-25720, train loss-2.0431, acc-0.4600, valid loss-2.0326, acc-0.4382, test loss-2.0334, acc-0.4305
Iter-25730, train loss-2.0089, acc-0.4400, valid loss-2.0326, acc-0.4386, test loss-2.0333, acc-0.4306
Iter-25740, train loss-2.0718, acc-0.3400, valid loss-2.0325, acc-0.4384, test loss-2.0332, acc-0.4306
Iter-25750, train loss-2.0400, acc-0.4600, valid loss-2.0324, acc-0.4388, test loss-2.0332, acc-0.4306
Iter-25760, train loss-2.0861, acc-0.3000, valid loss-2.0323, acc-0.4388, test loss-2.0331, acc-0.4307
Iter-25770, train loss-2.0201, acc-0.3800, valid loss-2.0322, acc-0.4388, test loss-2.0330, acc-0.4308
Iter-25780, train loss-2.0589, acc-0.4000, valid loss-2.0321, acc-0.4386, test loss-2.0329, acc-0.4304
Iter-25790, train loss-2.0546, acc-0.3000, valid loss-2.0320, acc-0.4386, test loss-2.0328, acc-0.4307
Iter-25800, train loss-2.0394, acc-0.3600, valid loss-2.0319, acc-0.4386, test loss-2.0327, acc-0.4307
Iter-25810, train loss-2.0279, acc-0.4600, valid loss-2.0319, acc-0.4388, test loss-2.0326, acc-0.4307
Iter-25820, train loss-2.0017, acc-0.4600, valid loss-2.0318, acc-0.4384, test loss-2.0326, acc-0.4308
Iter-25830, train loss-2.0179, acc-0.5000, valid loss-2.0317, acc-0.4390, test loss-2.0325, acc-0.4308
Iter-25840, train loss-2.0698, acc-0.3800, valid loss-2.0316, acc-0.4388, test loss-2.0324, acc-0.4308
Iter-25850, train loss-2.0460, acc-0.4600, valid loss-2.0315, acc-0.4386, test loss-2.0323, acc-0.4309
Iter-25860, train loss-1.9555, acc-0.5600, valid loss-2.0314, acc-0.4388, test loss-2.0322, acc-0.4310
Iter-25870, train loss-1.9922, acc-0.5200, valid loss-2.0313, acc-0.4390, test loss-2.0321, acc-0.4309
Iter-25880, train loss-2.0298, acc-0.4000, valid loss-2.0313, acc-0.4390, test loss-2.0320, acc-0.4307
Iter-25890, train loss-2.0385, acc-0.4400, valid loss-2.0312, acc-0.4394, test loss-2.0320, acc-0.4309
Iter-25900, train loss-2.0187, acc-0.4600, valid loss-2.0311, acc-0.4394, test loss-2.0319, acc-0.4309
Iter-25910, train loss-2.0733, acc-0.4400, valid loss-2.0310, acc-0.4390, test loss-2.0318, acc-0.4311
Iter-25920, train loss-2.0240, acc-0.4400, valid loss-2.0309, acc-0.4384, test loss-2.0317, acc-0.4311
Iter-25930, train loss-1.9928, acc-0.4800, valid loss-2.0308, acc-0.4390, test loss-2.0316, acc-0.4312
Iter-25940, train loss-1.9762, acc-0.4200, valid loss-2.0307, acc-0.4390, test loss-2.0315, acc-0.4312
Iter-25950, train loss-2.0433, acc-0.4600, valid loss-2.0306, acc-0.4394, test loss-2.0314, acc-0.4312
Iter-25960, train loss-1.9897, acc-0.5200, valid loss-2.0306, acc-0.4392, test loss-2.0313, acc-0.4311
Iter-25970, train loss-2.0604, acc-0.4800, valid loss-2.0305, acc-0.4396, test loss-2.0313, acc-0.4313
Iter-25980, train loss-2.0544, acc-0.4400, valid loss-2.0304, acc-0.4392, test loss-2.0312, acc-0.4313
Iter-25990, train loss-2.0669, acc-0.3800, valid loss-2.0303, acc-0.4396, test loss-2.0311, acc-0.4313
Iter-26000, train loss-2.0222, acc-0.3600, valid loss-2.0302, acc-0.4398, test loss-2.0310, acc-0.4310
Iter-26010, train loss-2.0308, acc-0.4000, valid loss-2.0301, acc-0.4392, test loss-2.0309, acc-0.4313
Iter-26020, train loss-2.0564, acc-0.4000, valid loss-2.0300, acc-0.4396, test loss-2.0308, acc-0.4314
Iter-26030, train loss-2.0071, acc-0.5200, valid loss-2.0300, acc-0.4390, test loss-2.0308, acc-0.4315
Iter-26040, train loss-2.0034, acc-0.4400, valid loss-2.0299, acc-0.4398, test loss-2.0307, acc-0.4313
Iter-26050, train loss-2.0040, acc-0.4400, valid loss-2.0298, acc-0.4398, test loss-2.0306, acc-0.4314
Iter-26060, train loss-2.0673, acc-0.3800, valid loss-2.0297, acc-0.4402, test loss-2.0305, acc-0.4314
Iter-26070, train loss-2.0746, acc-0.3600, valid loss-2.0296, acc-0.4398, test loss-2.0304, acc-0.4314
Iter-26080, train loss-2.0743, acc-0.4000, valid loss-2.0296, acc-0.4402, test loss-2.0303, acc-0.4317
Iter-26090, train loss-2.0196, acc-0.4000, valid loss-2.0295, acc-0.4404, test loss-2.0303, acc-0.4314
Iter-26100, train loss-2.0699, acc-0.3600, valid loss-2.0294, acc-0.4402, test loss-2.0302, acc-0.4316
Iter-26110, train loss-2.0473, acc-0.4200, valid loss-2.0293, acc-0.4404, test loss-2.0301, acc-0.4318
Iter-26120, train loss-2.1264, acc-0.3200, valid loss-2.0292, acc-0.4404, test loss-2.0300, acc-0.4323
Iter-26130, train loss-2.1009, acc-0.2600, valid loss-2.0291, acc-0.4404, test loss-2.0299, acc-0.4323
Iter-26140, train loss-2.0081, acc-0.5400, valid loss-2.0290, acc-0.4404, test loss-2.0298, acc-0.4321
Iter-26150, train loss-2.0821, acc-0.3600, valid loss-2.0289, acc-0.4406, test loss-2.0297, acc-0.4320
Iter-26160, train loss-2.0463, acc-0.3400, valid loss-2.0289, acc-0.4402, test loss-2.0297, acc-0.4322
Iter-26170, train loss-2.0387, acc-0.3400, valid loss-2.0288, acc-0.4400, test loss-2.0296, acc-0.4323
Iter-26180, train loss-2.0535, acc-0.3800, valid loss-2.0287, acc-0.4404, test loss-2.0295, acc-0.4325
Iter-26190, train loss-2.0400, acc-0.4400, valid loss-2.0286, acc-0.4406, test loss-2.0294, acc-0.4326
Iter-26200, train loss-2.0996, acc-0.3600, valid loss-2.0285, acc-0.4408, test loss-2.0293, acc-0.4327
Iter-26210, train loss-2.0169, acc-0.5400, valid loss-2.0284, acc-0.4408, test loss-2.0292, acc-0.4325
Iter-26220, train loss-2.0621, acc-0.4200, valid loss-2.0283, acc-0.4404, test loss-2.0291, acc-0.4327
Iter-26230, train loss-2.0916, acc-0.2800, valid loss-2.0283, acc-0.4404, test loss-2.0290, acc-0.4328
Iter-26240, train loss-1.9866, acc-0.5000, valid loss-2.0282, acc-0.4402, test loss-2.0290, acc-0.4324
Iter-26250, train loss-2.0540, acc-0.3800, valid loss-2.0281, acc-0.4408, test loss-2.0289, acc-0.4322
Iter-26260, train loss-2.0612, acc-0.3200, valid loss-2.0280, acc-0.4410, test loss-2.0288, acc-0.4325
Iter-26270, train loss-2.0364, acc-0.4000, valid loss-2.0279, acc-0.4410, test loss-2.0287, acc-0.4327
Iter-26280, train loss-2.0222, acc-0.4400, valid loss-2.0278, acc-0.4412, test loss-2.0286, acc-0.4327
Iter-26290, train loss-2.0165, acc-0.5000, valid loss-2.0277, acc-0.4404, test loss-2.0285, acc-0.4325
Iter-26300, train loss-2.0897, acc-0.2800, valid loss-2.0276, acc-0.4406, test loss-2.0284, acc-0.4325
Iter-26310, train loss-2.0845, acc-0.3200, valid loss-2.0276, acc-0.4410, test loss-2.0283, acc-0.4324
Iter-26320, train loss-1.9958, acc-0.5000, valid loss-2.0275, acc-0.4404, test loss-2.0283, acc-0.4325
Iter-26330, train loss-2.0280, acc-0.4800, valid loss-2.0274, acc-0.4404, test loss-2.0282, acc-0.4324
Iter-26340, train loss-2.0061, acc-0.4800, valid loss-2.0273, acc-0.4404, test loss-2.0281, acc-0.4325
Iter-26350, train loss-2.0149, acc-0.5600, valid loss-2.0272, acc-0.4402, test loss-2.0280, acc-0.4323
Iter-26360, train loss-2.0285, acc-0.5000, valid loss-2.0271, acc-0.4404, test loss-2.0279, acc-0.4326
Iter-26370, train loss-2.0863, acc-0.2400, valid loss-2.0270, acc-0.4404, test loss-2.0278, acc-0.4328
Iter-26380, train loss-2.0468, acc-0.4000, valid loss-2.0270, acc-0.4406, test loss-2.0277, acc-0.4328
Iter-26390, train loss-2.0985, acc-0.3200, valid loss-2.0269, acc-0.4406, test loss-2.0277, acc-0.4328
Iter-26400, train loss-2.0094, acc-0.3600, valid loss-2.0268, acc-0.4412, test loss-2.0276, acc-0.4323
Iter-26410, train loss-2.0502, acc-0.4800, valid loss-2.0267, acc-0.4412, test loss-2.0275, acc-0.4327
Iter-26420, train loss-2.0012, acc-0.5600, valid loss-2.0266, acc-0.4412, test loss-2.0274, acc-0.4327
Iter-26430, train loss-2.0035, acc-0.5600, valid loss-2.0265, acc-0.4410, test loss-2.0273, acc-0.4328
Iter-26440, train loss-1.9588, acc-0.4800, valid loss-2.0264, acc-0.4408, test loss-2.0272, acc-0.4327
Iter-26450, train loss-2.0675, acc-0.3600, valid loss-2.0264, acc-0.4408, test loss-2.0272, acc-0.4328
Iter-26460, train loss-2.0657, acc-0.3600, valid loss-2.0263, acc-0.4410, test loss-2.0271, acc-0.4328
Iter-26470, train loss-2.0245, acc-0.5800, valid loss-2.0262, acc-0.4410, test loss-2.0270, acc-0.4327
Iter-26480, train loss-2.0337, acc-0.4000, valid loss-2.0261, acc-0.4412, test loss-2.0269, acc-0.4328
Iter-26490, train loss-2.0241, acc-0.4000, valid loss-2.0260, acc-0.4412, test loss-2.0268, acc-0.4327
Iter-26500, train loss-1.9896, acc-0.5000, valid loss-2.0259, acc-0.4412, test loss-2.0267, acc-0.4328
Iter-26510, train loss-2.0984, acc-0.3200, valid loss-2.0259, acc-0.4414, test loss-2.0267, acc-0.4330
Iter-26520, train loss-2.0337, acc-0.4000, valid loss-2.0258, acc-0.4410, test loss-2.0266, acc-0.4328
Iter-26530, train loss-2.0337, acc-0.3200, valid loss-2.0257, acc-0.4408, test loss-2.0265, acc-0.4329
Iter-26540, train loss-2.0667, acc-0.3400, valid loss-2.0256, acc-0.4410, test loss-2.0264, acc-0.4329
Iter-26550, train loss-1.9991, acc-0.5000, valid loss-2.0255, acc-0.4408, test loss-2.0263, acc-0.4330
Iter-26560, train loss-2.0598, acc-0.4400, valid loss-2.0254, acc-0.4412, test loss-2.0262, acc-0.4330
Iter-26570, train loss-1.9789, acc-0.4800, valid loss-2.0254, acc-0.4410, test loss-2.0261, acc-0.4331
Iter-26580, train loss-2.0840, acc-0.4000, valid loss-2.0253, acc-0.4412, test loss-2.0261, acc-0.4331
Iter-26590, train loss-2.0754, acc-0.3000, valid loss-2.0252, acc-0.4418, test loss-2.0260, acc-0.4330
Iter-26600, train loss-2.0063, acc-0.4000, valid loss-2.0251, acc-0.4410, test loss-2.0259, acc-0.4331
Iter-26610, train loss-2.0960, acc-0.3200, valid loss-2.0250, acc-0.4410, test loss-2.0258, acc-0.4328
Iter-26620, train loss-2.0493, acc-0.3200, valid loss-2.0249, acc-0.4410, test loss-2.0257, acc-0.4329
Iter-26630, train loss-1.9911, acc-0.4800, valid loss-2.0248, acc-0.4410, test loss-2.0256, acc-0.4330
Iter-26640, train loss-1.9654, acc-0.5400, valid loss-2.0247, acc-0.4408, test loss-2.0255, acc-0.4332
Iter-26650, train loss-2.0297, acc-0.4200, valid loss-2.0247, acc-0.4406, test loss-2.0255, acc-0.4331
Iter-26660, train loss-2.0199, acc-0.4600, valid loss-2.0246, acc-0.4406, test loss-2.0254, acc-0.4333
Iter-26670, train loss-2.0227, acc-0.4800, valid loss-2.0245, acc-0.4406, test loss-2.0253, acc-0.4333
Iter-26680, train loss-2.0014, acc-0.4400, valid loss-2.0244, acc-0.4406, test loss-2.0252, acc-0.4331
Iter-26690, train loss-2.0056, acc-0.4200, valid loss-2.0243, acc-0.4406, test loss-2.0251, acc-0.4332
Iter-26700, train loss-2.0561, acc-0.4000, valid loss-2.0242, acc-0.4404, test loss-2.0250, acc-0.4332
Iter-26710, train loss-1.9924, acc-0.4800, valid loss-2.0242, acc-0.4410, test loss-2.0250, acc-0.4333
Iter-26720, train loss-2.0601, acc-0.4400, valid loss-2.0241, acc-0.4412, test loss-2.0249, acc-0.4331
Iter-26730, train loss-2.0112, acc-0.4400, valid loss-2.0240, acc-0.4412, test loss-2.0248, acc-0.4332
Iter-26740, train loss-2.0860, acc-0.3400, valid loss-2.0239, acc-0.4414, test loss-2.0247, acc-0.4333
Iter-26750, train loss-2.0162, acc-0.5200, valid loss-2.0238, acc-0.4422, test loss-2.0246, acc-0.4334
Iter-26760, train loss-1.9865, acc-0.4200, valid loss-2.0237, acc-0.4420, test loss-2.0245, acc-0.4335
Iter-26770, train loss-2.0720, acc-0.3800, valid loss-2.0237, acc-0.4416, test loss-2.0245, acc-0.4334
Iter-26780, train loss-2.0298, acc-0.3800, valid loss-2.0236, acc-0.4422, test loss-2.0244, acc-0.4336
Iter-26790, train loss-2.0852, acc-0.2800, valid loss-2.0235, acc-0.4422, test loss-2.0243, acc-0.4336
Iter-26800, train loss-2.0194, acc-0.5200, valid loss-2.0234, acc-0.4428, test loss-2.0242, acc-0.4334
Iter-26810, train loss-1.9934, acc-0.4000, valid loss-2.0233, acc-0.4422, test loss-2.0241, acc-0.4334
Iter-26820, train loss-1.9741, acc-0.5200, valid loss-2.0232, acc-0.4422, test loss-2.0240, acc-0.4332
Iter-26830, train loss-2.1024, acc-0.3000, valid loss-2.0231, acc-0.4424, test loss-2.0239, acc-0.4335
Iter-26840, train loss-2.0649, acc-0.3600, valid loss-2.0231, acc-0.4424, test loss-2.0239, acc-0.4337
Iter-26850, train loss-2.0315, acc-0.3800, valid loss-2.0230, acc-0.4424, test loss-2.0238, acc-0.4336
Iter-26860, train loss-1.9369, acc-0.5200, valid loss-2.0229, acc-0.4426, test loss-2.0237, acc-0.4336
Iter-26870, train loss-2.0896, acc-0.3000, valid loss-2.0228, acc-0.4428, test loss-2.0236, acc-0.4336
Iter-26880, train loss-2.0432, acc-0.4000, valid loss-2.0227, acc-0.4424, test loss-2.0235, acc-0.4337
Iter-26890, train loss-1.9837, acc-0.5000, valid loss-2.0226, acc-0.4428, test loss-2.0234, acc-0.4338
Iter-26900, train loss-2.0584, acc-0.4000, valid loss-2.0225, acc-0.4428, test loss-2.0233, acc-0.4338
Iter-26910, train loss-2.0540, acc-0.4200, valid loss-2.0225, acc-0.4428, test loss-2.0233, acc-0.4341
Iter-26920, train loss-2.0849, acc-0.3400, valid loss-2.0224, acc-0.4422, test loss-2.0232, acc-0.4338
Iter-26930, train loss-2.0458, acc-0.4000, valid loss-2.0223, acc-0.4428, test loss-2.0231, acc-0.4339
Iter-26940, train loss-2.0258, acc-0.3800, valid loss-2.0222, acc-0.4424, test loss-2.0230, acc-0.4338
Iter-26950, train loss-2.0335, acc-0.3800, valid loss-2.0221, acc-0.4426, test loss-2.0229, acc-0.4336
Iter-26960, train loss-2.0457, acc-0.4200, valid loss-2.0220, acc-0.4428, test loss-2.0228, acc-0.4338
Iter-26970, train loss-2.0400, acc-0.4000, valid loss-2.0220, acc-0.4430, test loss-2.0227, acc-0.4340
Iter-26980, train loss-2.0182, acc-0.5200, valid loss-2.0219, acc-0.4428, test loss-2.0227, acc-0.4338
Iter-26990, train loss-2.0139, acc-0.5200, valid loss-2.0218, acc-0.4428, test loss-2.0226, acc-0.4339
Iter-27000, train loss-2.0144, acc-0.5000, valid loss-2.0217, acc-0.4428, test loss-2.0225, acc-0.4338
Iter-27010, train loss-2.0305, acc-0.4200, valid loss-2.0216, acc-0.4426, test loss-2.0224, acc-0.4340
Iter-27020, train loss-2.0658, acc-0.3600, valid loss-2.0215, acc-0.4426, test loss-2.0223, acc-0.4340
Iter-27030, train loss-1.9907, acc-0.4600, valid loss-2.0214, acc-0.4426, test loss-2.0222, acc-0.4340
Iter-27040, train loss-2.0138, acc-0.4000, valid loss-2.0213, acc-0.4426, test loss-2.0221, acc-0.4340
Iter-27050, train loss-2.0561, acc-0.4000, valid loss-2.0213, acc-0.4426, test loss-2.0221, acc-0.4339
Iter-27060, train loss-2.0372, acc-0.4000, valid loss-2.0212, acc-0.4428, test loss-2.0220, acc-0.4341
Iter-27070, train loss-2.0230, acc-0.4800, valid loss-2.0211, acc-0.4430, test loss-2.0219, acc-0.4341
Iter-27080, train loss-2.0874, acc-0.3400, valid loss-2.0210, acc-0.4430, test loss-2.0218, acc-0.4341
Iter-27090, train loss-1.9796, acc-0.4800, valid loss-2.0209, acc-0.4432, test loss-2.0217, acc-0.4341
Iter-27100, train loss-2.0127, acc-0.5000, valid loss-2.0208, acc-0.4432, test loss-2.0216, acc-0.4341
Iter-27110, train loss-2.0259, acc-0.3600, valid loss-2.0207, acc-0.4428, test loss-2.0215, acc-0.4343
Iter-27120, train loss-2.0561, acc-0.4400, valid loss-2.0207, acc-0.4432, test loss-2.0215, acc-0.4344
Iter-27130, train loss-1.9637, acc-0.5600, valid loss-2.0206, acc-0.4434, test loss-2.0214, acc-0.4343
Iter-27140, train loss-2.0282, acc-0.4000, valid loss-2.0205, acc-0.4430, test loss-2.0213, acc-0.4342
Iter-27150, train loss-2.0878, acc-0.2800, valid loss-2.0204, acc-0.4434, test loss-2.0212, acc-0.4344
Iter-27160, train loss-2.1302, acc-0.2800, valid loss-2.0203, acc-0.4436, test loss-2.0211, acc-0.4344
Iter-27170, train loss-2.0320, acc-0.4400, valid loss-2.0202, acc-0.4438, test loss-2.0210, acc-0.4346
Iter-27180, train loss-2.0076, acc-0.4200, valid loss-2.0202, acc-0.4440, test loss-2.0210, acc-0.4345
Iter-27190, train loss-2.0753, acc-0.4000, valid loss-2.0201, acc-0.4438, test loss-2.0209, acc-0.4345
Iter-27200, train loss-2.0089, acc-0.4800, valid loss-2.0200, acc-0.4438, test loss-2.0208, acc-0.4346
Iter-27210, train loss-1.9636, acc-0.5400, valid loss-2.0199, acc-0.4436, test loss-2.0207, acc-0.4343
Iter-27220, train loss-2.0818, acc-0.4400, valid loss-2.0198, acc-0.4438, test loss-2.0206, acc-0.4345
Iter-27230, train loss-2.1017, acc-0.3400, valid loss-2.0198, acc-0.4436, test loss-2.0206, acc-0.4344
Iter-27240, train loss-1.9791, acc-0.4200, valid loss-2.0197, acc-0.4436, test loss-2.0205, acc-0.4345
Iter-27250, train loss-1.9649, acc-0.5000, valid loss-2.0196, acc-0.4434, test loss-2.0204, acc-0.4346
Iter-27260, train loss-2.0345, acc-0.4400, valid loss-2.0195, acc-0.4434, test loss-2.0203, acc-0.4344
Iter-27270, train loss-2.0759, acc-0.2600, valid loss-2.0194, acc-0.4430, test loss-2.0202, acc-0.4344
Iter-27280, train loss-2.0637, acc-0.3600, valid loss-2.0193, acc-0.4430, test loss-2.0201, acc-0.4345
Iter-27290, train loss-2.0009, acc-0.4800, valid loss-2.0192, acc-0.4434, test loss-2.0201, acc-0.4345
Iter-27300, train loss-2.0458, acc-0.4000, valid loss-2.0192, acc-0.4432, test loss-2.0200, acc-0.4346
Iter-27310, train loss-2.0075, acc-0.4600, valid loss-2.0191, acc-0.4432, test loss-2.0199, acc-0.4345
Iter-27320, train loss-2.0560, acc-0.4400, valid loss-2.0190, acc-0.4432, test loss-2.0198, acc-0.4347
Iter-27330, train loss-2.0121, acc-0.4600, valid loss-2.0189, acc-0.4436, test loss-2.0197, acc-0.4346
Iter-27340, train loss-2.0412, acc-0.4000, valid loss-2.0188, acc-0.4434, test loss-2.0196, acc-0.4349
Iter-27350, train loss-2.0019, acc-0.5000, valid loss-2.0187, acc-0.4434, test loss-2.0196, acc-0.4352
Iter-27360, train loss-2.0205, acc-0.4200, valid loss-2.0187, acc-0.4436, test loss-2.0195, acc-0.4352
Iter-27370, train loss-1.9833, acc-0.4800, valid loss-2.0186, acc-0.4436, test loss-2.0194, acc-0.4352
Iter-27380, train loss-2.1363, acc-0.2600, valid loss-2.0185, acc-0.4438, test loss-2.0193, acc-0.4351
Iter-27390, train loss-1.9889, acc-0.5200, valid loss-2.0184, acc-0.4438, test loss-2.0192, acc-0.4350
Iter-27400, train loss-2.1035, acc-0.3200, valid loss-2.0183, acc-0.4440, test loss-2.0191, acc-0.4350
Iter-27410, train loss-2.0441, acc-0.3800, valid loss-2.0182, acc-0.4442, test loss-2.0191, acc-0.4350
Iter-27420, train loss-2.0448, acc-0.4000, valid loss-2.0181, acc-0.4442, test loss-2.0190, acc-0.4350
Iter-27430, train loss-2.0952, acc-0.3600, valid loss-2.0181, acc-0.4436, test loss-2.0189, acc-0.4352
Iter-27440, train loss-1.9728, acc-0.4200, valid loss-2.0180, acc-0.4436, test loss-2.0188, acc-0.4352
Iter-27450, train loss-2.0669, acc-0.3000, valid loss-2.0179, acc-0.4436, test loss-2.0187, acc-0.4352
Iter-27460, train loss-2.0267, acc-0.4000, valid loss-2.0178, acc-0.4434, test loss-2.0186, acc-0.4352
Iter-27470, train loss-1.9911, acc-0.4800, valid loss-2.0177, acc-0.4436, test loss-2.0186, acc-0.4351
Iter-27480, train loss-2.0104, acc-0.5200, valid loss-2.0176, acc-0.4434, test loss-2.0185, acc-0.4355
Iter-27490, train loss-1.9908, acc-0.4800, valid loss-2.0176, acc-0.4442, test loss-2.0184, acc-0.4353
Iter-27500, train loss-2.0076, acc-0.4400, valid loss-2.0175, acc-0.4442, test loss-2.0183, acc-0.4353
Iter-27510, train loss-2.0563, acc-0.4000, valid loss-2.0174, acc-0.4440, test loss-2.0182, acc-0.4353
Iter-27520, train loss-2.0189, acc-0.5000, valid loss-2.0173, acc-0.4440, test loss-2.0181, acc-0.4350
Iter-27530, train loss-1.9829, acc-0.4200, valid loss-2.0172, acc-0.4438, test loss-2.0180, acc-0.4350
Iter-27540, train loss-2.0515, acc-0.3400, valid loss-2.0171, acc-0.4438, test loss-2.0180, acc-0.4351
Iter-27550, train loss-2.0410, acc-0.3200, valid loss-2.0170, acc-0.4436, test loss-2.0179, acc-0.4349
Iter-27560, train loss-1.9781, acc-0.5800, valid loss-2.0170, acc-0.4440, test loss-2.0178, acc-0.4351
Iter-27570, train loss-2.0287, acc-0.4400, valid loss-2.0169, acc-0.4436, test loss-2.0177, acc-0.4353
Iter-27580, train loss-2.0722, acc-0.3000, valid loss-2.0168, acc-0.4436, test loss-2.0176, acc-0.4353
Iter-27590, train loss-2.0471, acc-0.3600, valid loss-2.0167, acc-0.4436, test loss-2.0175, acc-0.4354
Iter-27600, train loss-2.0773, acc-0.3400, valid loss-2.0166, acc-0.4436, test loss-2.0175, acc-0.4356
Iter-27610, train loss-2.0818, acc-0.3600, valid loss-2.0166, acc-0.4440, test loss-2.0174, acc-0.4355
Iter-27620, train loss-2.0454, acc-0.4200, valid loss-2.0165, acc-0.4440, test loss-2.0173, acc-0.4354
Iter-27630, train loss-2.0390, acc-0.4600, valid loss-2.0164, acc-0.4440, test loss-2.0172, acc-0.4353
Iter-27640, train loss-1.9896, acc-0.4400, valid loss-2.0163, acc-0.4438, test loss-2.0171, acc-0.4353
Iter-27650, train loss-2.0392, acc-0.4400, valid loss-2.0162, acc-0.4442, test loss-2.0170, acc-0.4356
Iter-27660, train loss-2.0191, acc-0.5000, valid loss-2.0161, acc-0.4440, test loss-2.0170, acc-0.4357
Iter-27670, train loss-1.9801, acc-0.4000, valid loss-2.0161, acc-0.4442, test loss-2.0169, acc-0.4357
Iter-27680, train loss-1.9890, acc-0.5000, valid loss-2.0160, acc-0.4444, test loss-2.0168, acc-0.4359
Iter-27690, train loss-2.0932, acc-0.3800, valid loss-2.0159, acc-0.4444, test loss-2.0167, acc-0.4358
Iter-27700, train loss-2.0384, acc-0.3600, valid loss-2.0158, acc-0.4450, test loss-2.0166, acc-0.4358
Iter-27710, train loss-2.0031, acc-0.4400, valid loss-2.0157, acc-0.4446, test loss-2.0165, acc-0.4359
Iter-27720, train loss-2.0571, acc-0.4200, valid loss-2.0156, acc-0.4444, test loss-2.0165, acc-0.4358
Iter-27730, train loss-2.0607, acc-0.4600, valid loss-2.0156, acc-0.4444, test loss-2.0164, acc-0.4358
Iter-27740, train loss-1.9938, acc-0.4000, valid loss-2.0155, acc-0.4444, test loss-2.0163, acc-0.4359
Iter-27750, train loss-2.0254, acc-0.4000, valid loss-2.0154, acc-0.4444, test loss-2.0162, acc-0.4357
Iter-27760, train loss-2.0158, acc-0.5000, valid loss-2.0153, acc-0.4444, test loss-2.0161, acc-0.4357
Iter-27770, train loss-2.0126, acc-0.4000, valid loss-2.0152, acc-0.4444, test loss-2.0161, acc-0.4354
Iter-27780, train loss-2.0053, acc-0.4400, valid loss-2.0151, acc-0.4446, test loss-2.0160, acc-0.4355
Iter-27790, train loss-1.9265, acc-0.6000, valid loss-2.0151, acc-0.4446, test loss-2.0159, acc-0.4355
Iter-27800, train loss-1.9849, acc-0.4400, valid loss-2.0150, acc-0.4444, test loss-2.0158, acc-0.4355
Iter-27810, train loss-2.0198, acc-0.4400, valid loss-2.0149, acc-0.4446, test loss-2.0157, acc-0.4355
Iter-27820, train loss-2.0524, acc-0.4400, valid loss-2.0148, acc-0.4446, test loss-2.0156, acc-0.4354
Iter-27830, train loss-2.0161, acc-0.4400, valid loss-2.0147, acc-0.4446, test loss-2.0155, acc-0.4356
Iter-27840, train loss-2.0307, acc-0.4000, valid loss-2.0146, acc-0.4446, test loss-2.0155, acc-0.4354
Iter-27850, train loss-1.9859, acc-0.4600, valid loss-2.0146, acc-0.4444, test loss-2.0154, acc-0.4354
Iter-27860, train loss-1.9933, acc-0.4800, valid loss-2.0145, acc-0.4444, test loss-2.0153, acc-0.4353
Iter-27870, train loss-1.9925, acc-0.5400, valid loss-2.0144, acc-0.4446, test loss-2.0152, acc-0.4354
Iter-27880, train loss-1.9696, acc-0.5200, valid loss-2.0143, acc-0.4446, test loss-2.0151, acc-0.4353
Iter-27890, train loss-1.9646, acc-0.5000, valid loss-2.0142, acc-0.4446, test loss-2.0151, acc-0.4354
Iter-27900, train loss-1.9751, acc-0.5000, valid loss-2.0141, acc-0.4446, test loss-2.0150, acc-0.4352
Iter-27910, train loss-2.0362, acc-0.3800, valid loss-2.0141, acc-0.4446, test loss-2.0149, acc-0.4353
Iter-27920, train loss-2.0427, acc-0.3400, valid loss-2.0140, acc-0.4446, test loss-2.0148, acc-0.4354
Iter-27930, train loss-2.0085, acc-0.4600, valid loss-2.0139, acc-0.4442, test loss-2.0147, acc-0.4356
Iter-27940, train loss-2.0035, acc-0.5600, valid loss-2.0138, acc-0.4444, test loss-2.0146, acc-0.4356
Iter-27950, train loss-1.9805, acc-0.4000, valid loss-2.0137, acc-0.4444, test loss-2.0146, acc-0.4357
Iter-27960, train loss-2.0076, acc-0.4600, valid loss-2.0137, acc-0.4446, test loss-2.0145, acc-0.4355
Iter-27970, train loss-1.9593, acc-0.5200, valid loss-2.0136, acc-0.4444, test loss-2.0144, acc-0.4356
Iter-27980, train loss-1.9018, acc-0.5600, valid loss-2.0135, acc-0.4444, test loss-2.0143, acc-0.4357
Iter-27990, train loss-2.0095, acc-0.4000, valid loss-2.0134, acc-0.4448, test loss-2.0142, acc-0.4358
Iter-28000, train loss-2.0320, acc-0.4200, valid loss-2.0133, acc-0.4448, test loss-2.0141, acc-0.4358
Iter-28010, train loss-2.0500, acc-0.4000, valid loss-2.0132, acc-0.4448, test loss-2.0141, acc-0.4359
Iter-28020, train loss-2.0324, acc-0.4400, valid loss-2.0132, acc-0.4446, test loss-2.0140, acc-0.4361
Iter-28030, train loss-1.9826, acc-0.5200, valid loss-2.0131, acc-0.4446, test loss-2.0139, acc-0.4359
Iter-28040, train loss-1.9977, acc-0.4400, valid loss-2.0130, acc-0.4446, test loss-2.0138, acc-0.4362
Iter-28050, train loss-2.0453, acc-0.3800, valid loss-2.0129, acc-0.4448, test loss-2.0137, acc-0.4359
Iter-28060, train loss-2.0763, acc-0.3600, valid loss-2.0128, acc-0.4448, test loss-2.0136, acc-0.4360
Iter-28070, train loss-1.9929, acc-0.5000, valid loss-2.0127, acc-0.4446, test loss-2.0136, acc-0.4365
Iter-28080, train loss-2.0021, acc-0.4800, valid loss-2.0127, acc-0.4448, test loss-2.0135, acc-0.4362
Iter-28090, train loss-2.0375, acc-0.4200, valid loss-2.0126, acc-0.4446, test loss-2.0134, acc-0.4363
Iter-28100, train loss-2.0568, acc-0.3800, valid loss-2.0125, acc-0.4446, test loss-2.0133, acc-0.4367
Iter-28110, train loss-2.0043, acc-0.5000, valid loss-2.0124, acc-0.4444, test loss-2.0132, acc-0.4366
Iter-28120, train loss-2.0416, acc-0.3200, valid loss-2.0123, acc-0.4444, test loss-2.0132, acc-0.4365
Iter-28130, train loss-2.0540, acc-0.4200, valid loss-2.0123, acc-0.4444, test loss-2.0131, acc-0.4366
Iter-28140, train loss-1.9990, acc-0.4800, valid loss-2.0122, acc-0.4444, test loss-2.0130, acc-0.4365
Iter-28150, train loss-2.0610, acc-0.4000, valid loss-2.0121, acc-0.4442, test loss-2.0129, acc-0.4365
Iter-28160, train loss-2.0001, acc-0.4800, valid loss-2.0120, acc-0.4446, test loss-2.0128, acc-0.4363
Iter-28170, train loss-2.0419, acc-0.4400, valid loss-2.0119, acc-0.4448, test loss-2.0127, acc-0.4362
Iter-28180, train loss-2.0590, acc-0.3000, valid loss-2.0118, acc-0.4446, test loss-2.0127, acc-0.4364
Iter-28190, train loss-1.9973, acc-0.3800, valid loss-2.0118, acc-0.4444, test loss-2.0126, acc-0.4364
Iter-28200, train loss-2.0321, acc-0.4200, valid loss-2.0117, acc-0.4444, test loss-2.0125, acc-0.4365
Iter-28210, train loss-2.0138, acc-0.5000, valid loss-2.0116, acc-0.4444, test loss-2.0124, acc-0.4361
Iter-28220, train loss-2.0094, acc-0.4000, valid loss-2.0115, acc-0.4444, test loss-2.0123, acc-0.4360
Iter-28230, train loss-2.0642, acc-0.4000, valid loss-2.0114, acc-0.4444, test loss-2.0122, acc-0.4358
Iter-28240, train loss-2.0361, acc-0.3200, valid loss-2.0113, acc-0.4446, test loss-2.0122, acc-0.4358
Iter-28250, train loss-2.0374, acc-0.4800, valid loss-2.0113, acc-0.4448, test loss-2.0121, acc-0.4356
Iter-28260, train loss-2.0606, acc-0.3800, valid loss-2.0112, acc-0.4448, test loss-2.0120, acc-0.4356
Iter-28270, train loss-2.0464, acc-0.4200, valid loss-2.0111, acc-0.4450, test loss-2.0119, acc-0.4356
Iter-28280, train loss-1.9891, acc-0.3800, valid loss-2.0110, acc-0.4448, test loss-2.0118, acc-0.4356
Iter-28290, train loss-2.0399, acc-0.4000, valid loss-2.0109, acc-0.4448, test loss-2.0118, acc-0.4357
Iter-28300, train loss-2.0175, acc-0.4200, valid loss-2.0109, acc-0.4448, test loss-2.0117, acc-0.4354
Iter-28310, train loss-2.0200, acc-0.3400, valid loss-2.0108, acc-0.4450, test loss-2.0116, acc-0.4356
Iter-28320, train loss-1.9846, acc-0.5800, valid loss-2.0107, acc-0.4448, test loss-2.0115, acc-0.4357
Iter-28330, train loss-1.9677, acc-0.5000, valid loss-2.0106, acc-0.4446, test loss-2.0114, acc-0.4361
Iter-28340, train loss-1.9897, acc-0.4200, valid loss-2.0105, acc-0.4448, test loss-2.0113, acc-0.4362
Iter-28350, train loss-2.0242, acc-0.3400, valid loss-2.0104, acc-0.4446, test loss-2.0113, acc-0.4362
Iter-28360, train loss-1.9631, acc-0.5000, valid loss-2.0103, acc-0.4450, test loss-2.0112, acc-0.4361
Iter-28370, train loss-2.0215, acc-0.4400, valid loss-2.0103, acc-0.4450, test loss-2.0111, acc-0.4361
Iter-28380, train loss-2.0378, acc-0.3800, valid loss-2.0102, acc-0.4450, test loss-2.0110, acc-0.4363
Iter-28390, train loss-2.0669, acc-0.3400, valid loss-2.0101, acc-0.4456, test loss-2.0109, acc-0.4364
Iter-28400, train loss-2.0933, acc-0.3400, valid loss-2.0100, acc-0.4454, test loss-2.0108, acc-0.4365
Iter-28410, train loss-1.9956, acc-0.5400, valid loss-2.0099, acc-0.4450, test loss-2.0108, acc-0.4364
Iter-28420, train loss-2.0375, acc-0.4200, valid loss-2.0099, acc-0.4452, test loss-2.0107, acc-0.4363
Iter-28430, train loss-1.9805, acc-0.6200, valid loss-2.0098, acc-0.4450, test loss-2.0106, acc-0.4362
Iter-28440, train loss-1.9897, acc-0.4000, valid loss-2.0097, acc-0.4450, test loss-2.0105, acc-0.4363
Iter-28450, train loss-1.9979, acc-0.4400, valid loss-2.0096, acc-0.4452, test loss-2.0104, acc-0.4365
Iter-28460, train loss-1.9911, acc-0.4800, valid loss-2.0095, acc-0.4454, test loss-2.0104, acc-0.4364
Iter-28470, train loss-2.0030, acc-0.4000, valid loss-2.0095, acc-0.4454, test loss-2.0103, acc-0.4365
Iter-28480, train loss-2.0279, acc-0.4000, valid loss-2.0094, acc-0.4456, test loss-2.0102, acc-0.4366
Iter-28490, train loss-2.0550, acc-0.3600, valid loss-2.0093, acc-0.4456, test loss-2.0101, acc-0.4366
Iter-28500, train loss-2.0025, acc-0.5400, valid loss-2.0092, acc-0.4454, test loss-2.0100, acc-0.4364
Iter-28510, train loss-2.0355, acc-0.3000, valid loss-2.0091, acc-0.4454, test loss-2.0099, acc-0.4366
Iter-28520, train loss-2.0668, acc-0.4000, valid loss-2.0090, acc-0.4454, test loss-2.0099, acc-0.4365
Iter-28530, train loss-1.9087, acc-0.5200, valid loss-2.0090, acc-0.4454, test loss-2.0098, acc-0.4366
Iter-28540, train loss-1.9987, acc-0.4800, valid loss-2.0089, acc-0.4452, test loss-2.0097, acc-0.4367
Iter-28550, train loss-2.0123, acc-0.4400, valid loss-2.0088, acc-0.4452, test loss-2.0096, acc-0.4367
Iter-28560, train loss-2.0445, acc-0.4400, valid loss-2.0087, acc-0.4454, test loss-2.0095, acc-0.4368
Iter-28570, train loss-1.9338, acc-0.5600, valid loss-2.0086, acc-0.4454, test loss-2.0095, acc-0.4365
Iter-28580, train loss-2.0076, acc-0.3600, valid loss-2.0086, acc-0.4458, test loss-2.0094, acc-0.4368
Iter-28590, train loss-2.0039, acc-0.4600, valid loss-2.0085, acc-0.4456, test loss-2.0093, acc-0.4365
Iter-28600, train loss-1.9912, acc-0.4600, valid loss-2.0084, acc-0.4456, test loss-2.0092, acc-0.4364
Iter-28610, train loss-2.0240, acc-0.4400, valid loss-2.0083, acc-0.4460, test loss-2.0091, acc-0.4366
Iter-28620, train loss-2.0311, acc-0.5000, valid loss-2.0082, acc-0.4462, test loss-2.0091, acc-0.4365
Iter-28630, train loss-2.0392, acc-0.3400, valid loss-2.0082, acc-0.4466, test loss-2.0090, acc-0.4366
Iter-28640, train loss-1.9749, acc-0.5200, valid loss-2.0081, acc-0.4464, test loss-2.0089, acc-0.4367
Iter-28650, train loss-2.0165, acc-0.3600, valid loss-2.0080, acc-0.4460, test loss-2.0088, acc-0.4368
Iter-28660, train loss-2.0492, acc-0.3400, valid loss-2.0079, acc-0.4460, test loss-2.0087, acc-0.4369
Iter-28670, train loss-1.9878, acc-0.4200, valid loss-2.0078, acc-0.4460, test loss-2.0087, acc-0.4368
Iter-28680, train loss-2.0445, acc-0.4000, valid loss-2.0078, acc-0.4462, test loss-2.0086, acc-0.4370
Iter-28690, train loss-2.0401, acc-0.4200, valid loss-2.0077, acc-0.4460, test loss-2.0085, acc-0.4370
Iter-28700, train loss-2.0091, acc-0.3400, valid loss-2.0076, acc-0.4460, test loss-2.0084, acc-0.4370
Iter-28710, train loss-2.0130, acc-0.4600, valid loss-2.0075, acc-0.4462, test loss-2.0083, acc-0.4370
Iter-28720, train loss-2.0538, acc-0.3200, valid loss-2.0074, acc-0.4462, test loss-2.0082, acc-0.4371
Iter-28730, train loss-2.0180, acc-0.4800, valid loss-2.0074, acc-0.4464, test loss-2.0082, acc-0.4372
Iter-28740, train loss-2.0044, acc-0.4200, valid loss-2.0073, acc-0.4464, test loss-2.0081, acc-0.4371
Iter-28750, train loss-2.0036, acc-0.4400, valid loss-2.0072, acc-0.4466, test loss-2.0080, acc-0.4371
Iter-28760, train loss-1.9613, acc-0.5200, valid loss-2.0071, acc-0.4466, test loss-2.0079, acc-0.4371
Iter-28770, train loss-2.0881, acc-0.3000, valid loss-2.0070, acc-0.4464, test loss-2.0078, acc-0.4372
Iter-28780, train loss-2.0682, acc-0.5000, valid loss-2.0070, acc-0.4466, test loss-2.0078, acc-0.4371
Iter-28790, train loss-1.9565, acc-0.4400, valid loss-2.0069, acc-0.4466, test loss-2.0077, acc-0.4375
Iter-28800, train loss-2.0886, acc-0.3400, valid loss-2.0068, acc-0.4466, test loss-2.0076, acc-0.4375
Iter-28810, train loss-2.0481, acc-0.3600, valid loss-2.0067, acc-0.4466, test loss-2.0075, acc-0.4374
Iter-28820, train loss-2.0417, acc-0.4000, valid loss-2.0066, acc-0.4468, test loss-2.0074, acc-0.4376
Iter-28830, train loss-2.0784, acc-0.3800, valid loss-2.0066, acc-0.4468, test loss-2.0074, acc-0.4375
Iter-28840, train loss-2.0395, acc-0.3800, valid loss-2.0065, acc-0.4470, test loss-2.0073, acc-0.4378
Iter-28850, train loss-2.0558, acc-0.3400, valid loss-2.0064, acc-0.4468, test loss-2.0072, acc-0.4376
Iter-28860, train loss-2.0510, acc-0.3600, valid loss-2.0063, acc-0.4472, test loss-2.0071, acc-0.4376
Iter-28870, train loss-2.0092, acc-0.5400, valid loss-2.0062, acc-0.4470, test loss-2.0070, acc-0.4379
Iter-28880, train loss-2.0544, acc-0.4800, valid loss-2.0062, acc-0.4474, test loss-2.0070, acc-0.4376
Iter-28890, train loss-2.0275, acc-0.4600, valid loss-2.0061, acc-0.4472, test loss-2.0069, acc-0.4375
Iter-28900, train loss-2.0348, acc-0.4400, valid loss-2.0060, acc-0.4470, test loss-2.0068, acc-0.4376
Iter-28910, train loss-2.0876, acc-0.3200, valid loss-2.0059, acc-0.4470, test loss-2.0067, acc-0.4376
Iter-28920, train loss-1.9729, acc-0.5200, valid loss-2.0058, acc-0.4470, test loss-2.0066, acc-0.4377
Iter-28930, train loss-2.1257, acc-0.2200, valid loss-2.0058, acc-0.4470, test loss-2.0066, acc-0.4376
Iter-28940, train loss-2.0117, acc-0.5000, valid loss-2.0057, acc-0.4470, test loss-2.0065, acc-0.4377
Iter-28950, train loss-2.1181, acc-0.2800, valid loss-2.0056, acc-0.4472, test loss-2.0064, acc-0.4376
Iter-28960, train loss-2.0071, acc-0.3800, valid loss-2.0055, acc-0.4472, test loss-2.0063, acc-0.4376
Iter-28970, train loss-2.0231, acc-0.4800, valid loss-2.0054, acc-0.4472, test loss-2.0062, acc-0.4376
Iter-28980, train loss-1.9952, acc-0.4800, valid loss-2.0054, acc-0.4472, test loss-2.0062, acc-0.4377
Iter-28990, train loss-2.0063, acc-0.4600, valid loss-2.0053, acc-0.4472, test loss-2.0061, acc-0.4378
Iter-29000, train loss-2.0277, acc-0.3800, valid loss-2.0052, acc-0.4472, test loss-2.0060, acc-0.4376
Iter-29010, train loss-1.9856, acc-0.5000, valid loss-2.0051, acc-0.4472, test loss-2.0059, acc-0.4376
Iter-29020, train loss-2.0213, acc-0.4200, valid loss-2.0050, acc-0.4474, test loss-2.0058, acc-0.4374
Iter-29030, train loss-2.0003, acc-0.4200, valid loss-2.0050, acc-0.4474, test loss-2.0058, acc-0.4371
Iter-29040, train loss-2.0066, acc-0.4400, valid loss-2.0049, acc-0.4474, test loss-2.0057, acc-0.4375
Iter-29050, train loss-2.0583, acc-0.4600, valid loss-2.0048, acc-0.4476, test loss-2.0056, acc-0.4376
Iter-29060, train loss-2.0024, acc-0.4200, valid loss-2.0047, acc-0.4474, test loss-2.0055, acc-0.4375
Iter-29070, train loss-2.0144, acc-0.3800, valid loss-2.0046, acc-0.4474, test loss-2.0054, acc-0.4374
Iter-29080, train loss-2.0445, acc-0.2800, valid loss-2.0046, acc-0.4474, test loss-2.0054, acc-0.4374
Iter-29090, train loss-1.9545, acc-0.5200, valid loss-2.0045, acc-0.4474, test loss-2.0053, acc-0.4374
Iter-29100, train loss-2.0871, acc-0.3400, valid loss-2.0044, acc-0.4478, test loss-2.0052, acc-0.4375
Iter-29110, train loss-1.9362, acc-0.4600, valid loss-2.0043, acc-0.4476, test loss-2.0051, acc-0.4377
Iter-29120, train loss-2.0192, acc-0.3600, valid loss-2.0042, acc-0.4476, test loss-2.0050, acc-0.4376
Iter-29130, train loss-2.0474, acc-0.4200, valid loss-2.0041, acc-0.4476, test loss-2.0050, acc-0.4377
Iter-29140, train loss-2.0144, acc-0.4600, valid loss-2.0041, acc-0.4476, test loss-2.0049, acc-0.4378
Iter-29150, train loss-2.0031, acc-0.4400, valid loss-2.0040, acc-0.4478, test loss-2.0048, acc-0.4379
Iter-29160, train loss-1.9618, acc-0.5200, valid loss-2.0039, acc-0.4476, test loss-2.0047, acc-0.4378
Iter-29170, train loss-2.0471, acc-0.3600, valid loss-2.0038, acc-0.4478, test loss-2.0046, acc-0.4379
Iter-29180, train loss-2.0176, acc-0.5000, valid loss-2.0037, acc-0.4474, test loss-2.0045, acc-0.4382
Iter-29190, train loss-1.9707, acc-0.4400, valid loss-2.0037, acc-0.4474, test loss-2.0045, acc-0.4380
Iter-29200, train loss-2.0344, acc-0.4000, valid loss-2.0036, acc-0.4472, test loss-2.0044, acc-0.4378
Iter-29210, train loss-2.0234, acc-0.4800, valid loss-2.0035, acc-0.4472, test loss-2.0043, acc-0.4381
Iter-29220, train loss-2.0561, acc-0.4200, valid loss-2.0034, acc-0.4472, test loss-2.0042, acc-0.4378
Iter-29230, train loss-1.9761, acc-0.4400, valid loss-2.0033, acc-0.4474, test loss-2.0041, acc-0.4378
Iter-29240, train loss-2.0244, acc-0.4000, valid loss-2.0032, acc-0.4476, test loss-2.0041, acc-0.4377
Iter-29250, train loss-1.9911, acc-0.4000, valid loss-2.0032, acc-0.4478, test loss-2.0040, acc-0.4378
Iter-29260, train loss-1.9668, acc-0.5600, valid loss-2.0031, acc-0.4480, test loss-2.0039, acc-0.4381
Iter-29270, train loss-1.9681, acc-0.5200, valid loss-2.0030, acc-0.4480, test loss-2.0038, acc-0.4380
Iter-29280, train loss-2.0342, acc-0.4200, valid loss-2.0029, acc-0.4478, test loss-2.0037, acc-0.4379
Iter-29290, train loss-2.0029, acc-0.4400, valid loss-2.0028, acc-0.4480, test loss-2.0036, acc-0.4382
Iter-29300, train loss-2.0086, acc-0.3200, valid loss-2.0028, acc-0.4484, test loss-2.0036, acc-0.4385
Iter-29310, train loss-2.0075, acc-0.4600, valid loss-2.0027, acc-0.4486, test loss-2.0035, acc-0.4386
Iter-29320, train loss-2.0605, acc-0.3800, valid loss-2.0026, acc-0.4490, test loss-2.0034, acc-0.4386
Iter-29330, train loss-2.0694, acc-0.4000, valid loss-2.0025, acc-0.4484, test loss-2.0033, acc-0.4388
Iter-29340, train loss-2.0314, acc-0.4800, valid loss-2.0024, acc-0.4490, test loss-2.0032, acc-0.4389
Iter-29350, train loss-2.0402, acc-0.4400, valid loss-2.0024, acc-0.4486, test loss-2.0032, acc-0.4389
Iter-29360, train loss-2.0303, acc-0.3800, valid loss-2.0023, acc-0.4488, test loss-2.0031, acc-0.4389
Iter-29370, train loss-2.0017, acc-0.4400, valid loss-2.0022, acc-0.4490, test loss-2.0030, acc-0.4388
Iter-29380, train loss-2.0261, acc-0.4400, valid loss-2.0021, acc-0.4492, test loss-2.0029, acc-0.4386
Iter-29390, train loss-2.0682, acc-0.3400, valid loss-2.0020, acc-0.4492, test loss-2.0028, acc-0.4385
Iter-29400, train loss-1.9666, acc-0.5800, valid loss-2.0020, acc-0.4492, test loss-2.0028, acc-0.4385
Iter-29410, train loss-1.9862, acc-0.4600, valid loss-2.0019, acc-0.4490, test loss-2.0027, acc-0.4388
Iter-29420, train loss-1.9739, acc-0.5200, valid loss-2.0018, acc-0.4494, test loss-2.0026, acc-0.4391
Iter-29430, train loss-2.0454, acc-0.4000, valid loss-2.0017, acc-0.4490, test loss-2.0025, acc-0.4393
Iter-29440, train loss-1.9515, acc-0.5400, valid loss-2.0016, acc-0.4494, test loss-2.0024, acc-0.4393
Iter-29450, train loss-2.0924, acc-0.3200, valid loss-2.0016, acc-0.4496, test loss-2.0024, acc-0.4395
Iter-29460, train loss-1.9825, acc-0.4800, valid loss-2.0015, acc-0.4496, test loss-2.0023, acc-0.4393
Iter-29470, train loss-2.0248, acc-0.3800, valid loss-2.0014, acc-0.4496, test loss-2.0022, acc-0.4396
Iter-29480, train loss-1.9661, acc-0.5000, valid loss-2.0013, acc-0.4490, test loss-2.0021, acc-0.4395
Iter-29490, train loss-1.9519, acc-0.5400, valid loss-2.0012, acc-0.4492, test loss-2.0020, acc-0.4393
Iter-29500, train loss-1.9556, acc-0.4800, valid loss-2.0012, acc-0.4494, test loss-2.0020, acc-0.4393
Iter-29510, train loss-2.0497, acc-0.3800, valid loss-2.0011, acc-0.4498, test loss-2.0019, acc-0.4392
Iter-29520, train loss-2.0229, acc-0.4200, valid loss-2.0010, acc-0.4496, test loss-2.0018, acc-0.4392
Iter-29530, train loss-2.0213, acc-0.4200, valid loss-2.0009, acc-0.4498, test loss-2.0017, acc-0.4395
Iter-29540, train loss-1.9666, acc-0.4400, valid loss-2.0008, acc-0.4498, test loss-2.0017, acc-0.4394
Iter-29550, train loss-2.0303, acc-0.4200, valid loss-2.0007, acc-0.4496, test loss-2.0016, acc-0.4393
Iter-29560, train loss-2.0702, acc-0.3400, valid loss-2.0007, acc-0.4500, test loss-2.0015, acc-0.4390
Iter-29570, train loss-1.9827, acc-0.5200, valid loss-2.0006, acc-0.4502, test loss-2.0014, acc-0.4389
Iter-29580, train loss-2.0052, acc-0.4400, valid loss-2.0005, acc-0.4504, test loss-2.0013, acc-0.4388
Iter-29590, train loss-1.9890, acc-0.4600, valid loss-2.0004, acc-0.4500, test loss-2.0012, acc-0.4389
Iter-29600, train loss-2.0735, acc-0.3800, valid loss-2.0003, acc-0.4504, test loss-2.0012, acc-0.4386
Iter-29610, train loss-2.0332, acc-0.4400, valid loss-2.0002, acc-0.4498, test loss-2.0011, acc-0.4390
Iter-29620, train loss-1.9921, acc-0.4600, valid loss-2.0002, acc-0.4502, test loss-2.0010, acc-0.4391
Iter-29630, train loss-2.0274, acc-0.4400, valid loss-2.0001, acc-0.4502, test loss-2.0009, acc-0.4390
Iter-29640, train loss-1.9881, acc-0.5000, valid loss-2.0000, acc-0.4500, test loss-2.0008, acc-0.4392
Iter-29650, train loss-2.0782, acc-0.3000, valid loss-1.9999, acc-0.4502, test loss-2.0008, acc-0.4393
Iter-29660, train loss-2.0031, acc-0.4600, valid loss-1.9998, acc-0.4504, test loss-2.0007, acc-0.4392
Iter-29670, train loss-1.9057, acc-0.6800, valid loss-1.9998, acc-0.4506, test loss-2.0006, acc-0.4393
Iter-29680, train loss-1.9567, acc-0.5400, valid loss-1.9997, acc-0.4506, test loss-2.0005, acc-0.4393
Iter-29690, train loss-2.0292, acc-0.4800, valid loss-1.9996, acc-0.4506, test loss-2.0004, acc-0.4393
Iter-29700, train loss-1.9431, acc-0.6000, valid loss-1.9995, acc-0.4502, test loss-2.0004, acc-0.4395
Iter-29710, train loss-2.0958, acc-0.4000, valid loss-1.9995, acc-0.4506, test loss-2.0003, acc-0.4397
Iter-29720, train loss-2.0289, acc-0.4600, valid loss-1.9994, acc-0.4508, test loss-2.0002, acc-0.4397
Iter-29730, train loss-1.9858, acc-0.4600, valid loss-1.9993, acc-0.4506, test loss-2.0001, acc-0.4399
Iter-29740, train loss-2.0405, acc-0.4000, valid loss-1.9992, acc-0.4506, test loss-2.0000, acc-0.4400
Iter-29750, train loss-1.9966, acc-0.4600, valid loss-1.9991, acc-0.4506, test loss-1.9999, acc-0.4400
Iter-29760, train loss-1.9712, acc-0.5400, valid loss-1.9990, acc-0.4506, test loss-1.9999, acc-0.4400
Iter-29770, train loss-1.9961, acc-0.5000, valid loss-1.9990, acc-0.4506, test loss-1.9998, acc-0.4398
Iter-29780, train loss-2.0175, acc-0.4400, valid loss-1.9989, acc-0.4504, test loss-1.9997, acc-0.4401
Iter-29790, train loss-2.0182, acc-0.4200, valid loss-1.9988, acc-0.4510, test loss-1.9996, acc-0.4402
Iter-29800, train loss-2.0769, acc-0.3200, valid loss-1.9987, acc-0.4506, test loss-1.9995, acc-0.4401
Iter-29810, train loss-2.0621, acc-0.3600, valid loss-1.9986, acc-0.4504, test loss-1.9995, acc-0.4402
Iter-29820, train loss-1.9924, acc-0.4000, valid loss-1.9985, acc-0.4504, test loss-1.9994, acc-0.4401
Iter-29830, train loss-1.9713, acc-0.4400, valid loss-1.9985, acc-0.4504, test loss-1.9993, acc-0.4402
Iter-29840, train loss-1.9729, acc-0.4400, valid loss-1.9984, acc-0.4506, test loss-1.9992, acc-0.4403
Iter-29850, train loss-1.9447, acc-0.5000, valid loss-1.9983, acc-0.4508, test loss-1.9991, acc-0.4402
Iter-29860, train loss-2.0336, acc-0.3800, valid loss-1.9982, acc-0.4508, test loss-1.9991, acc-0.4402
Iter-29870, train loss-2.0173, acc-0.4000, valid loss-1.9982, acc-0.4510, test loss-1.9990, acc-0.4404
Iter-29880, train loss-1.9806, acc-0.4800, valid loss-1.9981, acc-0.4508, test loss-1.9989, acc-0.4402
Iter-29890, train loss-1.8939, acc-0.5600, valid loss-1.9980, acc-0.4504, test loss-1.9988, acc-0.4402
Iter-29900, train loss-2.0433, acc-0.3800, valid loss-1.9979, acc-0.4504, test loss-1.9987, acc-0.4401
Iter-29910, train loss-2.0032, acc-0.5000, valid loss-1.9978, acc-0.4504, test loss-1.9987, acc-0.4399
Iter-29920, train loss-2.0061, acc-0.4400, valid loss-1.9977, acc-0.4504, test loss-1.9986, acc-0.4397
Iter-29930, train loss-2.0445, acc-0.4000, valid loss-1.9977, acc-0.4508, test loss-1.9985, acc-0.4397
Iter-29940, train loss-2.0521, acc-0.3200, valid loss-1.9976, acc-0.4508, test loss-1.9984, acc-0.4399
Iter-29950, train loss-2.0445, acc-0.4200, valid loss-1.9975, acc-0.4510, test loss-1.9983, acc-0.4396
Iter-29960, train loss-1.9867, acc-0.5200, valid loss-1.9974, acc-0.4512, test loss-1.9983, acc-0.4395
Iter-29970, train loss-2.0030, acc-0.4600, valid loss-1.9973, acc-0.4512, test loss-1.9982, acc-0.4395
Iter-29980, train loss-1.9921, acc-0.3800, valid loss-1.9973, acc-0.4512, test loss-1.9981, acc-0.4399
Iter-29990, train loss-2.0468, acc-0.4200, valid loss-1.9972, acc-0.4512, test loss-1.9980, acc-0.4399
Iter-30000, train loss-1.9679, acc-0.4800, valid loss-1.9971, acc-0.4512, test loss-1.9979, acc-0.4398
Iter-30010, train loss-1.9994, acc-0.4200, valid loss-1.9970, acc-0.4512, test loss-1.9979, acc-0.4399
Iter-30020, train loss-2.0568, acc-0.4200, valid loss-1.9969, acc-0.4512, test loss-1.9978, acc-0.4401
Iter-30030, train loss-1.9854, acc-0.5000, valid loss-1.9969, acc-0.4514, test loss-1.9977, acc-0.4401
Iter-30040, train loss-1.9650, acc-0.5000, valid loss-1.9968, acc-0.4514, test loss-1.9976, acc-0.4400
Iter-30050, train loss-2.0803, acc-0.3800, valid loss-1.9967, acc-0.4514, test loss-1.9975, acc-0.4397
Iter-30060, train loss-1.9922, acc-0.4600, valid loss-1.9966, acc-0.4514, test loss-1.9975, acc-0.4396
Iter-30070, train loss-2.0206, acc-0.5200, valid loss-1.9965, acc-0.4516, test loss-1.9974, acc-0.4396
Iter-30080, train loss-1.9989, acc-0.4800, valid loss-1.9965, acc-0.4516, test loss-1.9973, acc-0.4395
Iter-30090, train loss-1.9787, acc-0.4800, valid loss-1.9964, acc-0.4512, test loss-1.9972, acc-0.4394
Iter-30100, train loss-2.0800, acc-0.2400, valid loss-1.9963, acc-0.4514, test loss-1.9971, acc-0.4395
Iter-30110, train loss-1.9782, acc-0.5000, valid loss-1.9962, acc-0.4514, test loss-1.9971, acc-0.4395
Iter-30120, train loss-2.0044, acc-0.5200, valid loss-1.9961, acc-0.4514, test loss-1.9970, acc-0.4397
Iter-30130, train loss-1.9604, acc-0.5000, valid loss-1.9961, acc-0.4512, test loss-1.9969, acc-0.4397
Iter-30140, train loss-1.9592, acc-0.4600, valid loss-1.9960, acc-0.4514, test loss-1.9968, acc-0.4397
Iter-30150, train loss-2.0096, acc-0.4000, valid loss-1.9959, acc-0.4514, test loss-1.9967, acc-0.4397
Iter-30160, train loss-2.0406, acc-0.4400, valid loss-1.9958, acc-0.4512, test loss-1.9967, acc-0.4396
Iter-30170, train loss-2.0171, acc-0.3400, valid loss-1.9957, acc-0.4508, test loss-1.9966, acc-0.4397
Iter-30180, train loss-2.0569, acc-0.4000, valid loss-1.9957, acc-0.4512, test loss-1.9965, acc-0.4398
Iter-30190, train loss-2.0587, acc-0.4800, valid loss-1.9956, acc-0.4506, test loss-1.9964, acc-0.4394
Iter-30200, train loss-1.9936, acc-0.4400, valid loss-1.9955, acc-0.4508, test loss-1.9963, acc-0.4395
Iter-30210, train loss-1.9837, acc-0.4600, valid loss-1.9954, acc-0.4510, test loss-1.9963, acc-0.4398
Iter-30220, train loss-2.0676, acc-0.3000, valid loss-1.9953, acc-0.4516, test loss-1.9962, acc-0.4400
Iter-30230, train loss-2.0152, acc-0.4600, valid loss-1.9953, acc-0.4510, test loss-1.9961, acc-0.4401
Iter-30240, train loss-2.0283, acc-0.4000, valid loss-1.9952, acc-0.4512, test loss-1.9960, acc-0.4399
Iter-30250, train loss-2.0101, acc-0.4400, valid loss-1.9951, acc-0.4516, test loss-1.9959, acc-0.4402
Iter-30260, train loss-1.9376, acc-0.5400, valid loss-1.9950, acc-0.4516, test loss-1.9959, acc-0.4403
Iter-30270, train loss-2.0501, acc-0.3600, valid loss-1.9949, acc-0.4516, test loss-1.9958, acc-0.4402
Iter-30280, train loss-1.9440, acc-0.5400, valid loss-1.9949, acc-0.4514, test loss-1.9957, acc-0.4403
Iter-30290, train loss-2.0069, acc-0.5000, valid loss-1.9948, acc-0.4510, test loss-1.9956, acc-0.4400
Iter-30300, train loss-1.9464, acc-0.4200, valid loss-1.9947, acc-0.4510, test loss-1.9955, acc-0.4399
Iter-30310, train loss-1.9660, acc-0.4000, valid loss-1.9946, acc-0.4512, test loss-1.9954, acc-0.4400
Iter-30320, train loss-2.0700, acc-0.3600, valid loss-1.9945, acc-0.4510, test loss-1.9954, acc-0.4402
Iter-30330, train loss-2.0587, acc-0.3400, valid loss-1.9945, acc-0.4514, test loss-1.9953, acc-0.4400
Iter-30340, train loss-1.9980, acc-0.5800, valid loss-1.9944, acc-0.4514, test loss-1.9952, acc-0.4400
Iter-30350, train loss-2.0555, acc-0.3800, valid loss-1.9943, acc-0.4514, test loss-1.9951, acc-0.4399
Iter-30360, train loss-1.9906, acc-0.4200, valid loss-1.9942, acc-0.4512, test loss-1.9950, acc-0.4403
Iter-30370, train loss-1.9270, acc-0.5800, valid loss-1.9941, acc-0.4512, test loss-1.9950, acc-0.4403
Iter-30380, train loss-2.0230, acc-0.4200, valid loss-1.9940, acc-0.4512, test loss-1.9949, acc-0.4403
Iter-30390, train loss-1.9767, acc-0.4600, valid loss-1.9940, acc-0.4512, test loss-1.9948, acc-0.4403
Iter-30400, train loss-2.0251, acc-0.5000, valid loss-1.9939, acc-0.4514, test loss-1.9947, acc-0.4404
Iter-30410, train loss-2.0792, acc-0.3200, valid loss-1.9938, acc-0.4512, test loss-1.9947, acc-0.4404
Iter-30420, train loss-2.0127, acc-0.4800, valid loss-1.9937, acc-0.4512, test loss-1.9946, acc-0.4403
Iter-30430, train loss-1.9513, acc-0.5200, valid loss-1.9937, acc-0.4512, test loss-1.9945, acc-0.4403
Iter-30440, train loss-2.0570, acc-0.3600, valid loss-1.9936, acc-0.4512, test loss-1.9944, acc-0.4405
Iter-30450, train loss-2.0061, acc-0.4600, valid loss-1.9935, acc-0.4516, test loss-1.9943, acc-0.4406
Iter-30460, train loss-2.0072, acc-0.4800, valid loss-1.9934, acc-0.4512, test loss-1.9943, acc-0.4404
Iter-30470, train loss-2.0045, acc-0.4800, valid loss-1.9933, acc-0.4514, test loss-1.9942, acc-0.4405
Iter-30480, train loss-1.9988, acc-0.5800, valid loss-1.9933, acc-0.4514, test loss-1.9941, acc-0.4407
Iter-30490, train loss-1.9800, acc-0.5000, valid loss-1.9932, acc-0.4518, test loss-1.9940, acc-0.4407
Iter-30500, train loss-1.9496, acc-0.5000, valid loss-1.9931, acc-0.4518, test loss-1.9939, acc-0.4405
Iter-30510, train loss-2.0476, acc-0.3400, valid loss-1.9930, acc-0.4516, test loss-1.9939, acc-0.4404
Iter-30520, train loss-2.0105, acc-0.5400, valid loss-1.9929, acc-0.4518, test loss-1.9938, acc-0.4404
Iter-30530, train loss-2.0106, acc-0.4200, valid loss-1.9929, acc-0.4516, test loss-1.9937, acc-0.4406
Iter-30540, train loss-2.0017, acc-0.4400, valid loss-1.9928, acc-0.4516, test loss-1.9936, acc-0.4405
Iter-30550, train loss-2.0598, acc-0.3600, valid loss-1.9927, acc-0.4518, test loss-1.9936, acc-0.4405
Iter-30560, train loss-1.9205, acc-0.4600, valid loss-1.9926, acc-0.4524, test loss-1.9935, acc-0.4405
Iter-30570, train loss-2.0068, acc-0.3800, valid loss-1.9926, acc-0.4528, test loss-1.9934, acc-0.4407
Iter-30580, train loss-1.9896, acc-0.4400, valid loss-1.9925, acc-0.4528, test loss-1.9933, acc-0.4406
Iter-30590, train loss-2.0142, acc-0.4800, valid loss-1.9924, acc-0.4528, test loss-1.9932, acc-0.4408
Iter-30600, train loss-2.0716, acc-0.3800, valid loss-1.9923, acc-0.4526, test loss-1.9932, acc-0.4406
Iter-30610, train loss-2.0232, acc-0.4800, valid loss-1.9922, acc-0.4526, test loss-1.9931, acc-0.4405
Iter-30620, train loss-1.9852, acc-0.4200, valid loss-1.9922, acc-0.4524, test loss-1.9930, acc-0.4409
Iter-30630, train loss-2.0826, acc-0.3000, valid loss-1.9921, acc-0.4524, test loss-1.9929, acc-0.4411
Iter-30640, train loss-2.0395, acc-0.4000, valid loss-1.9920, acc-0.4524, test loss-1.9928, acc-0.4409
Iter-30650, train loss-1.9716, acc-0.4600, valid loss-1.9919, acc-0.4524, test loss-1.9928, acc-0.4409
Iter-30660, train loss-2.0214, acc-0.3600, valid loss-1.9919, acc-0.4526, test loss-1.9927, acc-0.4410
Iter-30670, train loss-2.0111, acc-0.3600, valid loss-1.9918, acc-0.4526, test loss-1.9926, acc-0.4411
Iter-30680, train loss-1.9897, acc-0.4600, valid loss-1.9917, acc-0.4524, test loss-1.9925, acc-0.4411
Iter-30690, train loss-2.0322, acc-0.4000, valid loss-1.9916, acc-0.4522, test loss-1.9924, acc-0.4410
Iter-30700, train loss-1.9027, acc-0.5600, valid loss-1.9915, acc-0.4522, test loss-1.9924, acc-0.4412
Iter-30710, train loss-1.9907, acc-0.4200, valid loss-1.9915, acc-0.4522, test loss-1.9923, acc-0.4415
Iter-30720, train loss-2.0140, acc-0.4200, valid loss-1.9914, acc-0.4522, test loss-1.9922, acc-0.4413
Iter-30730, train loss-1.9993, acc-0.3800, valid loss-1.9913, acc-0.4524, test loss-1.9921, acc-0.4413
Iter-30740, train loss-2.0825, acc-0.3400, valid loss-1.9912, acc-0.4522, test loss-1.9920, acc-0.4413
Iter-30750, train loss-2.0217, acc-0.3400, valid loss-1.9911, acc-0.4522, test loss-1.9919, acc-0.4411
Iter-30760, train loss-1.9417, acc-0.5000, valid loss-1.9910, acc-0.4522, test loss-1.9919, acc-0.4410
Iter-30770, train loss-2.0073, acc-0.4400, valid loss-1.9910, acc-0.4520, test loss-1.9918, acc-0.4413
Iter-30780, train loss-1.9786, acc-0.5200, valid loss-1.9909, acc-0.4522, test loss-1.9917, acc-0.4415
Iter-30790, train loss-2.0634, acc-0.3600, valid loss-1.9908, acc-0.4522, test loss-1.9916, acc-0.4412
Iter-30800, train loss-2.0910, acc-0.3200, valid loss-1.9907, acc-0.4520, test loss-1.9916, acc-0.4415
Iter-30810, train loss-1.9889, acc-0.5400, valid loss-1.9906, acc-0.4522, test loss-1.9915, acc-0.4415
Iter-30820, train loss-1.9771, acc-0.4600, valid loss-1.9906, acc-0.4522, test loss-1.9914, acc-0.4415
Iter-30830, train loss-1.9979, acc-0.4400, valid loss-1.9905, acc-0.4520, test loss-1.9913, acc-0.4416
Iter-30840, train loss-1.9460, acc-0.5000, valid loss-1.9904, acc-0.4524, test loss-1.9912, acc-0.4418
Iter-30850, train loss-1.9820, acc-0.4400, valid loss-1.9903, acc-0.4524, test loss-1.9912, acc-0.4416
Iter-30860, train loss-1.9707, acc-0.5000, valid loss-1.9902, acc-0.4526, test loss-1.9911, acc-0.4416
Iter-30870, train loss-2.0285, acc-0.4000, valid loss-1.9902, acc-0.4526, test loss-1.9910, acc-0.4416
Iter-30880, train loss-2.0095, acc-0.3600, valid loss-1.9901, acc-0.4526, test loss-1.9909, acc-0.4416
Iter-30890, train loss-1.9784, acc-0.4600, valid loss-1.9900, acc-0.4528, test loss-1.9908, acc-0.4418
Iter-30900, train loss-2.0322, acc-0.4400, valid loss-1.9899, acc-0.4528, test loss-1.9908, acc-0.4419
Iter-30910, train loss-1.9710, acc-0.4600, valid loss-1.9898, acc-0.4528, test loss-1.9907, acc-0.4419
Iter-30920, train loss-2.0261, acc-0.5200, valid loss-1.9898, acc-0.4528, test loss-1.9906, acc-0.4420
Iter-30930, train loss-2.0125, acc-0.4200, valid loss-1.9897, acc-0.4530, test loss-1.9905, acc-0.4420
Iter-30940, train loss-1.9661, acc-0.5400, valid loss-1.9896, acc-0.4528, test loss-1.9905, acc-0.4419
Iter-30950, train loss-1.9309, acc-0.4800, valid loss-1.9895, acc-0.4530, test loss-1.9904, acc-0.4421
Iter-30960, train loss-1.9564, acc-0.4600, valid loss-1.9894, acc-0.4530, test loss-1.9903, acc-0.4422
Iter-30970, train loss-2.0145, acc-0.4400, valid loss-1.9894, acc-0.4530, test loss-1.9902, acc-0.4421
Iter-30980, train loss-1.9897, acc-0.4000, valid loss-1.9893, acc-0.4532, test loss-1.9901, acc-0.4421
Iter-30990, train loss-2.0190, acc-0.4200, valid loss-1.9892, acc-0.4530, test loss-1.9901, acc-0.4420
Iter-31000, train loss-2.0333, acc-0.4600, valid loss-1.9891, acc-0.4528, test loss-1.9900, acc-0.4422
Iter-31010, train loss-1.9524, acc-0.5000, valid loss-1.9891, acc-0.4528, test loss-1.9899, acc-0.4423
Iter-31020, train loss-1.9892, acc-0.4400, valid loss-1.9890, acc-0.4530, test loss-1.9898, acc-0.4423
Iter-31030, train loss-1.9863, acc-0.4600, valid loss-1.9889, acc-0.4532, test loss-1.9897, acc-0.4423
Iter-31040, train loss-1.9205, acc-0.5600, valid loss-1.9888, acc-0.4532, test loss-1.9897, acc-0.4422
Iter-31050, train loss-1.9812, acc-0.4000, valid loss-1.9887, acc-0.4534, test loss-1.9896, acc-0.4424
Iter-31060, train loss-2.0156, acc-0.4200, valid loss-1.9886, acc-0.4536, test loss-1.9895, acc-0.4425
Iter-31070, train loss-1.9472, acc-0.4200, valid loss-1.9886, acc-0.4538, test loss-1.9894, acc-0.4424
Iter-31080, train loss-1.9521, acc-0.5000, valid loss-1.9885, acc-0.4538, test loss-1.9893, acc-0.4425
Iter-31090, train loss-1.9760, acc-0.5200, valid loss-1.9884, acc-0.4536, test loss-1.9893, acc-0.4425
Iter-31100, train loss-2.0248, acc-0.4200, valid loss-1.9883, acc-0.4536, test loss-1.9892, acc-0.4424
Iter-31110, train loss-1.9863, acc-0.4400, valid loss-1.9883, acc-0.4538, test loss-1.9891, acc-0.4425
Iter-31120, train loss-1.9083, acc-0.6000, valid loss-1.9882, acc-0.4536, test loss-1.9890, acc-0.4423
Iter-31130, train loss-2.0330, acc-0.4200, valid loss-1.9881, acc-0.4532, test loss-1.9890, acc-0.4425
Iter-31140, train loss-1.9780, acc-0.5000, valid loss-1.9880, acc-0.4530, test loss-1.9889, acc-0.4422
Iter-31150, train loss-2.0057, acc-0.4600, valid loss-1.9879, acc-0.4536, test loss-1.9888, acc-0.4426
Iter-31160, train loss-1.9949, acc-0.4600, valid loss-1.9879, acc-0.4536, test loss-1.9887, acc-0.4427
Iter-31170, train loss-2.0484, acc-0.4400, valid loss-1.9878, acc-0.4530, test loss-1.9886, acc-0.4424
Iter-31180, train loss-1.9645, acc-0.5200, valid loss-1.9877, acc-0.4530, test loss-1.9886, acc-0.4427
Iter-31190, train loss-2.0055, acc-0.4200, valid loss-1.9876, acc-0.4528, test loss-1.9885, acc-0.4428
Iter-31200, train loss-1.9396, acc-0.5000, valid loss-1.9875, acc-0.4528, test loss-1.9884, acc-0.4427
Iter-31210, train loss-2.0805, acc-0.2600, valid loss-1.9875, acc-0.4534, test loss-1.9883, acc-0.4427
Iter-31220, train loss-1.9834, acc-0.5000, valid loss-1.9874, acc-0.4534, test loss-1.9882, acc-0.4426
Iter-31230, train loss-1.9971, acc-0.5000, valid loss-1.9873, acc-0.4534, test loss-1.9882, acc-0.4424
Iter-31240, train loss-2.0061, acc-0.4200, valid loss-1.9872, acc-0.4534, test loss-1.9881, acc-0.4424
Iter-31250, train loss-2.0067, acc-0.4800, valid loss-1.9872, acc-0.4536, test loss-1.9880, acc-0.4426
Iter-31260, train loss-2.0610, acc-0.2800, valid loss-1.9871, acc-0.4534, test loss-1.9879, acc-0.4426
Iter-31270, train loss-2.0127, acc-0.4200, valid loss-1.9870, acc-0.4534, test loss-1.9879, acc-0.4430
Iter-31280, train loss-2.0337, acc-0.3400, valid loss-1.9869, acc-0.4540, test loss-1.9878, acc-0.4430
Iter-31290, train loss-2.0029, acc-0.3600, valid loss-1.9868, acc-0.4538, test loss-1.9877, acc-0.4431
Iter-31300, train loss-1.9519, acc-0.5200, valid loss-1.9868, acc-0.4540, test loss-1.9876, acc-0.4430
Iter-31310, train loss-1.9690, acc-0.5200, valid loss-1.9867, acc-0.4538, test loss-1.9875, acc-0.4429
Iter-31320, train loss-2.0691, acc-0.3600, valid loss-1.9866, acc-0.4540, test loss-1.9875, acc-0.4432
Iter-31330, train loss-2.0170, acc-0.4000, valid loss-1.9865, acc-0.4540, test loss-1.9874, acc-0.4432
Iter-31340, train loss-2.0213, acc-0.3400, valid loss-1.9864, acc-0.4540, test loss-1.9873, acc-0.4434
Iter-31350, train loss-2.0000, acc-0.4200, valid loss-1.9864, acc-0.4540, test loss-1.9872, acc-0.4431
Iter-31360, train loss-2.0441, acc-0.4800, valid loss-1.9863, acc-0.4542, test loss-1.9872, acc-0.4434
Iter-31370, train loss-1.9665, acc-0.5600, valid loss-1.9862, acc-0.4542, test loss-1.9871, acc-0.4433
Iter-31380, train loss-1.9987, acc-0.3400, valid loss-1.9861, acc-0.4544, test loss-1.9870, acc-0.4433
Iter-31390, train loss-2.0167, acc-0.3200, valid loss-1.9861, acc-0.4544, test loss-1.9869, acc-0.4434
Iter-31400, train loss-1.9928, acc-0.4200, valid loss-1.9860, acc-0.4548, test loss-1.9868, acc-0.4435
Iter-31410, train loss-1.9883, acc-0.4200, valid loss-1.9859, acc-0.4548, test loss-1.9868, acc-0.4436
Iter-31420, train loss-2.0357, acc-0.4400, valid loss-1.9858, acc-0.4544, test loss-1.9867, acc-0.4436
Iter-31430, train loss-2.0256, acc-0.3800, valid loss-1.9857, acc-0.4544, test loss-1.9866, acc-0.4436
Iter-31440, train loss-1.9548, acc-0.5400, valid loss-1.9857, acc-0.4542, test loss-1.9865, acc-0.4434
Iter-31450, train loss-1.9805, acc-0.4400, valid loss-1.9856, acc-0.4544, test loss-1.9865, acc-0.4434
Iter-31460, train loss-1.9226, acc-0.5000, valid loss-1.9855, acc-0.4544, test loss-1.9864, acc-0.4433
Iter-31470, train loss-1.9764, acc-0.4600, valid loss-1.9854, acc-0.4542, test loss-1.9863, acc-0.4435
Iter-31480, train loss-2.0025, acc-0.4600, valid loss-1.9853, acc-0.4546, test loss-1.9862, acc-0.4435
Iter-31490, train loss-1.9334, acc-0.5800, valid loss-1.9853, acc-0.4542, test loss-1.9861, acc-0.4439
Iter-31500, train loss-2.0005, acc-0.4000, valid loss-1.9852, acc-0.4542, test loss-1.9861, acc-0.4434
Iter-31510, train loss-1.9835, acc-0.4600, valid loss-1.9851, acc-0.4538, test loss-1.9860, acc-0.4436
Iter-31520, train loss-1.9650, acc-0.4800, valid loss-1.9850, acc-0.4538, test loss-1.9859, acc-0.4439
Iter-31530, train loss-2.0520, acc-0.3200, valid loss-1.9849, acc-0.4536, test loss-1.9858, acc-0.4438
Iter-31540, train loss-2.0072, acc-0.4400, valid loss-1.9849, acc-0.4536, test loss-1.9857, acc-0.4438
Iter-31550, train loss-1.9571, acc-0.4200, valid loss-1.9848, acc-0.4536, test loss-1.9857, acc-0.4437
Iter-31560, train loss-2.0199, acc-0.4600, valid loss-1.9847, acc-0.4534, test loss-1.9856, acc-0.4435
Iter-31570, train loss-2.0123, acc-0.4200, valid loss-1.9846, acc-0.4538, test loss-1.9855, acc-0.4438
Iter-31580, train loss-2.0557, acc-0.3000, valid loss-1.9846, acc-0.4538, test loss-1.9854, acc-0.4439
Iter-31590, train loss-2.0107, acc-0.4200, valid loss-1.9845, acc-0.4540, test loss-1.9854, acc-0.4441
Iter-31600, train loss-1.8820, acc-0.6200, valid loss-1.9844, acc-0.4538, test loss-1.9853, acc-0.4440
Iter-31610, train loss-1.9471, acc-0.4600, valid loss-1.9843, acc-0.4540, test loss-1.9852, acc-0.4445
Iter-31620, train loss-1.9400, acc-0.4400, valid loss-1.9842, acc-0.4538, test loss-1.9851, acc-0.4441
Iter-31630, train loss-2.0295, acc-0.4800, valid loss-1.9842, acc-0.4538, test loss-1.9850, acc-0.4442
Iter-31640, train loss-2.0523, acc-0.3800, valid loss-1.9841, acc-0.4540, test loss-1.9850, acc-0.4442
Iter-31650, train loss-2.0454, acc-0.4800, valid loss-1.9840, acc-0.4540, test loss-1.9849, acc-0.4443
Iter-31660, train loss-1.9328, acc-0.4600, valid loss-1.9839, acc-0.4542, test loss-1.9848, acc-0.4446
Iter-31670, train loss-1.9460, acc-0.4000, valid loss-1.9839, acc-0.4542, test loss-1.9847, acc-0.4446
Iter-31680, train loss-2.0031, acc-0.4400, valid loss-1.9838, acc-0.4542, test loss-1.9847, acc-0.4446
Iter-31690, train loss-1.9552, acc-0.4600, valid loss-1.9837, acc-0.4546, test loss-1.9846, acc-0.4446
Iter-31700, train loss-2.0075, acc-0.4800, valid loss-1.9836, acc-0.4544, test loss-1.9845, acc-0.4447
Iter-31710, train loss-1.9476, acc-0.4800, valid loss-1.9835, acc-0.4546, test loss-1.9844, acc-0.4448
Iter-31720, train loss-1.9943, acc-0.4200, valid loss-1.9835, acc-0.4544, test loss-1.9843, acc-0.4450
Iter-31730, train loss-1.8922, acc-0.5200, valid loss-1.9834, acc-0.4542, test loss-1.9843, acc-0.4445
Iter-31740, train loss-1.9687, acc-0.4600, valid loss-1.9833, acc-0.4542, test loss-1.9842, acc-0.4448
Iter-31750, train loss-2.0176, acc-0.4400, valid loss-1.9832, acc-0.4544, test loss-1.9841, acc-0.4448
Iter-31760, train loss-2.0192, acc-0.4000, valid loss-1.9831, acc-0.4538, test loss-1.9840, acc-0.4448
Iter-31770, train loss-2.0285, acc-0.4000, valid loss-1.9831, acc-0.4540, test loss-1.9840, acc-0.4447
Iter-31780, train loss-1.9264, acc-0.5000, valid loss-1.9830, acc-0.4540, test loss-1.9839, acc-0.4446
Iter-31790, train loss-1.9924, acc-0.4400, valid loss-1.9829, acc-0.4540, test loss-1.9838, acc-0.4447
Iter-31800, train loss-2.0693, acc-0.3000, valid loss-1.9828, acc-0.4542, test loss-1.9837, acc-0.4447
Iter-31810, train loss-1.9967, acc-0.4400, valid loss-1.9828, acc-0.4542, test loss-1.9836, acc-0.4446
Iter-31820, train loss-1.9838, acc-0.4000, valid loss-1.9827, acc-0.4540, test loss-1.9836, acc-0.4447
Iter-31830, train loss-1.9420, acc-0.4400, valid loss-1.9826, acc-0.4542, test loss-1.9835, acc-0.4445
Iter-31840, train loss-1.9974, acc-0.5200, valid loss-1.9825, acc-0.4542, test loss-1.9834, acc-0.4447
Iter-31850, train loss-1.9510, acc-0.5200, valid loss-1.9824, acc-0.4540, test loss-1.9833, acc-0.4448
Iter-31860, train loss-1.8978, acc-0.5600, valid loss-1.9824, acc-0.4540, test loss-1.9833, acc-0.4448
Iter-31870, train loss-2.0182, acc-0.4200, valid loss-1.9823, acc-0.4542, test loss-1.9832, acc-0.4447
Iter-31880, train loss-1.9178, acc-0.4000, valid loss-1.9822, acc-0.4542, test loss-1.9831, acc-0.4447
Iter-31890, train loss-1.9907, acc-0.5400, valid loss-1.9821, acc-0.4538, test loss-1.9830, acc-0.4449
Iter-31900, train loss-1.8957, acc-0.5800, valid loss-1.9820, acc-0.4540, test loss-1.9829, acc-0.4448
Iter-31910, train loss-1.9970, acc-0.4200, valid loss-1.9820, acc-0.4544, test loss-1.9829, acc-0.4448
Iter-31920, train loss-2.0006, acc-0.4400, valid loss-1.9819, acc-0.4544, test loss-1.9828, acc-0.4450
Iter-31930, train loss-2.0646, acc-0.3200, valid loss-1.9818, acc-0.4544, test loss-1.9827, acc-0.4450
Iter-31940, train loss-2.0140, acc-0.3600, valid loss-1.9817, acc-0.4544, test loss-1.9826, acc-0.4449
Iter-31950, train loss-1.9660, acc-0.4200, valid loss-1.9817, acc-0.4542, test loss-1.9826, acc-0.4448
Iter-31960, train loss-1.9419, acc-0.5600, valid loss-1.9816, acc-0.4542, test loss-1.9825, acc-0.4448
Iter-31970, train loss-1.9790, acc-0.5000, valid loss-1.9815, acc-0.4542, test loss-1.9824, acc-0.4448
Iter-31980, train loss-2.0351, acc-0.3800, valid loss-1.9814, acc-0.4540, test loss-1.9823, acc-0.4448
Iter-31990, train loss-2.0003, acc-0.4200, valid loss-1.9813, acc-0.4538, test loss-1.9822, acc-0.4446
Iter-32000, train loss-1.9287, acc-0.5400, valid loss-1.9813, acc-0.4538, test loss-1.9822, acc-0.4448
Iter-32010, train loss-1.9383, acc-0.5200, valid loss-1.9812, acc-0.4538, test loss-1.9821, acc-0.4448
Iter-32020, train loss-1.9590, acc-0.4400, valid loss-1.9811, acc-0.4538, test loss-1.9820, acc-0.4448
Iter-32030, train loss-2.0215, acc-0.4000, valid loss-1.9810, acc-0.4540, test loss-1.9819, acc-0.4445
Iter-32040, train loss-1.9943, acc-0.5200, valid loss-1.9809, acc-0.4538, test loss-1.9819, acc-0.4445
Iter-32050, train loss-2.0681, acc-0.3400, valid loss-1.9809, acc-0.4538, test loss-1.9818, acc-0.4448
Iter-32060, train loss-1.9616, acc-0.4800, valid loss-1.9808, acc-0.4540, test loss-1.9817, acc-0.4446
Iter-32070, train loss-1.9679, acc-0.4600, valid loss-1.9807, acc-0.4540, test loss-1.9816, acc-0.4446
Iter-32080, train loss-2.0153, acc-0.3800, valid loss-1.9806, acc-0.4540, test loss-1.9815, acc-0.4446
Iter-32090, train loss-1.9508, acc-0.5200, valid loss-1.9806, acc-0.4542, test loss-1.9815, acc-0.4446
Iter-32100, train loss-2.0407, acc-0.3600, valid loss-1.9805, acc-0.4540, test loss-1.9814, acc-0.4446
Iter-32110, train loss-2.0221, acc-0.3600, valid loss-1.9804, acc-0.4542, test loss-1.9813, acc-0.4448
Iter-32120, train loss-1.9284, acc-0.5400, valid loss-1.9803, acc-0.4536, test loss-1.9812, acc-0.4444
Iter-32130, train loss-1.9212, acc-0.5400, valid loss-1.9803, acc-0.4536, test loss-1.9812, acc-0.4445
Iter-32140, train loss-1.9730, acc-0.4800, valid loss-1.9802, acc-0.4536, test loss-1.9811, acc-0.4446
Iter-32150, train loss-1.9794, acc-0.5000, valid loss-1.9801, acc-0.4536, test loss-1.9810, acc-0.4446
Iter-32160, train loss-1.9894, acc-0.4200, valid loss-1.9800, acc-0.4538, test loss-1.9809, acc-0.4448
Iter-32170, train loss-1.9442, acc-0.5600, valid loss-1.9799, acc-0.4536, test loss-1.9808, acc-0.4449
Iter-32180, train loss-1.9931, acc-0.4200, valid loss-1.9799, acc-0.4536, test loss-1.9808, acc-0.4448
Iter-32190, train loss-2.0211, acc-0.3600, valid loss-1.9798, acc-0.4538, test loss-1.9807, acc-0.4452
Iter-32200, train loss-1.9360, acc-0.5400, valid loss-1.9797, acc-0.4542, test loss-1.9806, acc-0.4450
Iter-32210, train loss-1.9515, acc-0.4200, valid loss-1.9796, acc-0.4540, test loss-1.9805, acc-0.4450
Iter-32220, train loss-1.9897, acc-0.3800, valid loss-1.9795, acc-0.4544, test loss-1.9804, acc-0.4452
Iter-32230, train loss-1.9899, acc-0.5000, valid loss-1.9795, acc-0.4544, test loss-1.9804, acc-0.4452
Iter-32240, train loss-2.0490, acc-0.3600, valid loss-1.9794, acc-0.4544, test loss-1.9803, acc-0.4452
Iter-32250, train loss-1.9375, acc-0.5000, valid loss-1.9793, acc-0.4544, test loss-1.9802, acc-0.4453
Iter-32260, train loss-1.9764, acc-0.4200, valid loss-1.9792, acc-0.4542, test loss-1.9801, acc-0.4453
Iter-32270, train loss-2.0477, acc-0.3800, valid loss-1.9792, acc-0.4546, test loss-1.9801, acc-0.4455
Iter-32280, train loss-2.0211, acc-0.3800, valid loss-1.9791, acc-0.4548, test loss-1.9800, acc-0.4456
Iter-32290, train loss-1.9893, acc-0.4400, valid loss-1.9790, acc-0.4548, test loss-1.9799, acc-0.4454
Iter-32300, train loss-2.0443, acc-0.3600, valid loss-1.9789, acc-0.4554, test loss-1.9798, acc-0.4455
Iter-32310, train loss-2.0177, acc-0.4400, valid loss-1.9789, acc-0.4556, test loss-1.9798, acc-0.4454
Iter-32320, train loss-1.9907, acc-0.4400, valid loss-1.9788, acc-0.4554, test loss-1.9797, acc-0.4457
Iter-32330, train loss-1.9678, acc-0.5000, valid loss-1.9787, acc-0.4554, test loss-1.9796, acc-0.4455
Iter-32340, train loss-1.9741, acc-0.3400, valid loss-1.9786, acc-0.4552, test loss-1.9795, acc-0.4456
Iter-32350, train loss-2.0160, acc-0.4000, valid loss-1.9785, acc-0.4554, test loss-1.9794, acc-0.4456
Iter-32360, train loss-1.9762, acc-0.4200, valid loss-1.9785, acc-0.4552, test loss-1.9794, acc-0.4454
Iter-32370, train loss-1.9488, acc-0.4400, valid loss-1.9784, acc-0.4554, test loss-1.9793, acc-0.4456
Iter-32380, train loss-1.9871, acc-0.4600, valid loss-1.9783, acc-0.4548, test loss-1.9792, acc-0.4457
Iter-32390, train loss-2.0271, acc-0.3400, valid loss-1.9782, acc-0.4550, test loss-1.9791, acc-0.4455
Iter-32400, train loss-1.9129, acc-0.4800, valid loss-1.9782, acc-0.4550, test loss-1.9791, acc-0.4455
Iter-32410, train loss-1.9713, acc-0.5000, valid loss-1.9781, acc-0.4554, test loss-1.9790, acc-0.4457
Iter-32420, train loss-1.9775, acc-0.4600, valid loss-1.9780, acc-0.4556, test loss-1.9789, acc-0.4456
Iter-32430, train loss-1.9856, acc-0.4600, valid loss-1.9779, acc-0.4556, test loss-1.9788, acc-0.4458
Iter-32440, train loss-2.0017, acc-0.4400, valid loss-1.9778, acc-0.4556, test loss-1.9787, acc-0.4461
Iter-32450, train loss-1.9077, acc-0.5200, valid loss-1.9778, acc-0.4558, test loss-1.9787, acc-0.4461
Iter-32460, train loss-2.0262, acc-0.4600, valid loss-1.9777, acc-0.4562, test loss-1.9786, acc-0.4462
Iter-32470, train loss-2.0351, acc-0.3400, valid loss-1.9776, acc-0.4564, test loss-1.9785, acc-0.4461
Iter-32480, train loss-1.9805, acc-0.5200, valid loss-1.9775, acc-0.4564, test loss-1.9784, acc-0.4460
Iter-32490, train loss-1.9983, acc-0.4600, valid loss-1.9775, acc-0.4560, test loss-1.9784, acc-0.4462
Iter-32500, train loss-2.1083, acc-0.3200, valid loss-1.9774, acc-0.4558, test loss-1.9783, acc-0.4462
Iter-32510, train loss-2.0389, acc-0.4600, valid loss-1.9773, acc-0.4562, test loss-1.9782, acc-0.4460
Iter-32520, train loss-2.0268, acc-0.2400, valid loss-1.9772, acc-0.4562, test loss-1.9781, acc-0.4460
Iter-32530, train loss-1.9210, acc-0.4600, valid loss-1.9772, acc-0.4562, test loss-1.9781, acc-0.4459
Iter-32540, train loss-2.0703, acc-0.3600, valid loss-1.9771, acc-0.4564, test loss-1.9780, acc-0.4460
Iter-32550, train loss-1.9614, acc-0.4200, valid loss-1.9770, acc-0.4562, test loss-1.9779, acc-0.4461
Iter-32560, train loss-1.9661, acc-0.4600, valid loss-1.9769, acc-0.4560, test loss-1.9778, acc-0.4464
Iter-32570, train loss-2.0110, acc-0.4000, valid loss-1.9768, acc-0.4562, test loss-1.9777, acc-0.4461
Iter-32580, train loss-1.9839, acc-0.4200, valid loss-1.9768, acc-0.4562, test loss-1.9777, acc-0.4462
Iter-32590, train loss-2.0147, acc-0.4400, valid loss-1.9767, acc-0.4566, test loss-1.9776, acc-0.4462
Iter-32600, train loss-1.9709, acc-0.5200, valid loss-1.9766, acc-0.4566, test loss-1.9775, acc-0.4462
Iter-32610, train loss-1.9639, acc-0.5200, valid loss-1.9765, acc-0.4566, test loss-1.9774, acc-0.4463
Iter-32620, train loss-2.0517, acc-0.3200, valid loss-1.9765, acc-0.4566, test loss-1.9774, acc-0.4464
Iter-32630, train loss-2.0353, acc-0.3800, valid loss-1.9764, acc-0.4566, test loss-1.9773, acc-0.4464
Iter-32640, train loss-2.0601, acc-0.3200, valid loss-1.9763, acc-0.4566, test loss-1.9772, acc-0.4462
Iter-32650, train loss-1.9727, acc-0.4200, valid loss-1.9762, acc-0.4564, test loss-1.9771, acc-0.4463
Iter-32660, train loss-1.9618, acc-0.4400, valid loss-1.9762, acc-0.4570, test loss-1.9771, acc-0.4463
Iter-32670, train loss-2.0466, acc-0.3000, valid loss-1.9761, acc-0.4572, test loss-1.9770, acc-0.4464
Iter-32680, train loss-1.9930, acc-0.4000, valid loss-1.9760, acc-0.4570, test loss-1.9769, acc-0.4465
Iter-32690, train loss-2.0156, acc-0.3400, valid loss-1.9759, acc-0.4570, test loss-1.9768, acc-0.4464
Iter-32700, train loss-2.0004, acc-0.3600, valid loss-1.9758, acc-0.4572, test loss-1.9767, acc-0.4465
Iter-32710, train loss-2.0764, acc-0.3200, valid loss-1.9758, acc-0.4572, test loss-1.9767, acc-0.4464
Iter-32720, train loss-2.0448, acc-0.3600, valid loss-1.9757, acc-0.4572, test loss-1.9766, acc-0.4465
Iter-32730, train loss-1.9536, acc-0.5800, valid loss-1.9756, acc-0.4570, test loss-1.9765, acc-0.4464
Iter-32740, train loss-1.9629, acc-0.3600, valid loss-1.9755, acc-0.4570, test loss-1.9765, acc-0.4466
Iter-32750, train loss-1.9599, acc-0.5000, valid loss-1.9755, acc-0.4570, test loss-1.9764, acc-0.4466
Iter-32760, train loss-1.9600, acc-0.5400, valid loss-1.9754, acc-0.4570, test loss-1.9763, acc-0.4466
Iter-32770, train loss-2.0049, acc-0.3400, valid loss-1.9753, acc-0.4574, test loss-1.9762, acc-0.4470
Iter-32780, train loss-1.8499, acc-0.6200, valid loss-1.9752, acc-0.4572, test loss-1.9762, acc-0.4467
Iter-32790, train loss-2.0080, acc-0.4000, valid loss-1.9752, acc-0.4574, test loss-1.9761, acc-0.4466
Iter-32800, train loss-2.0810, acc-0.3200, valid loss-1.9751, acc-0.4574, test loss-1.9760, acc-0.4467
Iter-32810, train loss-1.9721, acc-0.4800, valid loss-1.9750, acc-0.4574, test loss-1.9759, acc-0.4467
Iter-32820, train loss-2.0369, acc-0.4000, valid loss-1.9749, acc-0.4574, test loss-1.9758, acc-0.4467
Iter-32830, train loss-2.0029, acc-0.3200, valid loss-1.9749, acc-0.4574, test loss-1.9758, acc-0.4467
Iter-32840, train loss-1.9208, acc-0.5000, valid loss-1.9748, acc-0.4568, test loss-1.9757, acc-0.4468
Iter-32850, train loss-1.9950, acc-0.4000, valid loss-1.9747, acc-0.4568, test loss-1.9756, acc-0.4467
Iter-32860, train loss-2.0007, acc-0.4400, valid loss-1.9746, acc-0.4568, test loss-1.9755, acc-0.4467
Iter-32870, train loss-2.0143, acc-0.4800, valid loss-1.9746, acc-0.4568, test loss-1.9755, acc-0.4468
Iter-32880, train loss-2.0205, acc-0.4200, valid loss-1.9745, acc-0.4574, test loss-1.9754, acc-0.4470
Iter-32890, train loss-1.9239, acc-0.5600, valid loss-1.9744, acc-0.4574, test loss-1.9753, acc-0.4470
Iter-32900, train loss-1.8826, acc-0.5600, valid loss-1.9743, acc-0.4574, test loss-1.9753, acc-0.4471
Iter-32910, train loss-2.0218, acc-0.4000, valid loss-1.9743, acc-0.4574, test loss-1.9752, acc-0.4469
Iter-32920, train loss-1.9866, acc-0.4800, valid loss-1.9742, acc-0.4574, test loss-1.9751, acc-0.4469
Iter-32930, train loss-2.0120, acc-0.4400, valid loss-1.9741, acc-0.4574, test loss-1.9750, acc-0.4469
Iter-32940, train loss-2.0230, acc-0.4400, valid loss-1.9740, acc-0.4572, test loss-1.9749, acc-0.4468
Iter-32950, train loss-1.9740, acc-0.4600, valid loss-1.9739, acc-0.4572, test loss-1.9749, acc-0.4469
Iter-32960, train loss-2.0191, acc-0.4600, valid loss-1.9739, acc-0.4572, test loss-1.9748, acc-0.4468
Iter-32970, train loss-1.8528, acc-0.6200, valid loss-1.9738, acc-0.4574, test loss-1.9747, acc-0.4468
Iter-32980, train loss-2.0536, acc-0.3400, valid loss-1.9737, acc-0.4574, test loss-1.9746, acc-0.4469
Iter-32990, train loss-2.0183, acc-0.3400, valid loss-1.9736, acc-0.4578, test loss-1.9746, acc-0.4468
Iter-33000, train loss-2.0199, acc-0.3800, valid loss-1.9736, acc-0.4576, test loss-1.9745, acc-0.4468
Iter-33010, train loss-1.9729, acc-0.5200, valid loss-1.9735, acc-0.4580, test loss-1.9744, acc-0.4468
Iter-33020, train loss-1.9442, acc-0.5200, valid loss-1.9734, acc-0.4578, test loss-1.9743, acc-0.4470
Iter-33030, train loss-1.9530, acc-0.5200, valid loss-1.9733, acc-0.4578, test loss-1.9743, acc-0.4470
Iter-33040, train loss-1.9938, acc-0.4600, valid loss-1.9733, acc-0.4578, test loss-1.9742, acc-0.4470
Iter-33050, train loss-1.9890, acc-0.4200, valid loss-1.9732, acc-0.4578, test loss-1.9741, acc-0.4469
Iter-33060, train loss-2.0536, acc-0.3200, valid loss-1.9731, acc-0.4574, test loss-1.9740, acc-0.4470
Iter-33070, train loss-2.0217, acc-0.3800, valid loss-1.9730, acc-0.4574, test loss-1.9740, acc-0.4469
Iter-33080, train loss-1.9329, acc-0.4800, valid loss-1.9730, acc-0.4578, test loss-1.9739, acc-0.4469
Iter-33090, train loss-1.9909, acc-0.4200, valid loss-1.9729, acc-0.4574, test loss-1.9738, acc-0.4469
Iter-33100, train loss-1.9810, acc-0.4000, valid loss-1.9728, acc-0.4578, test loss-1.9737, acc-0.4471
Iter-33110, train loss-1.9141, acc-0.5200, valid loss-1.9727, acc-0.4574, test loss-1.9736, acc-0.4470
Iter-33120, train loss-2.0018, acc-0.3800, valid loss-1.9727, acc-0.4576, test loss-1.9736, acc-0.4471
Iter-33130, train loss-1.9353, acc-0.4800, valid loss-1.9726, acc-0.4578, test loss-1.9735, acc-0.4472
Iter-33140, train loss-2.0101, acc-0.3200, valid loss-1.9725, acc-0.4576, test loss-1.9734, acc-0.4470
Iter-33150, train loss-1.9510, acc-0.4800, valid loss-1.9724, acc-0.4574, test loss-1.9733, acc-0.4471
Iter-33160, train loss-2.0359, acc-0.4400, valid loss-1.9724, acc-0.4574, test loss-1.9733, acc-0.4472
Iter-33170, train loss-2.0115, acc-0.4400, valid loss-1.9723, acc-0.4572, test loss-1.9732, acc-0.4472
Iter-33180, train loss-1.9599, acc-0.5000, valid loss-1.9722, acc-0.4576, test loss-1.9731, acc-0.4473
Iter-33190, train loss-1.9932, acc-0.4000, valid loss-1.9721, acc-0.4578, test loss-1.9730, acc-0.4474
Iter-33200, train loss-2.0094, acc-0.4200, valid loss-1.9720, acc-0.4574, test loss-1.9730, acc-0.4472
Iter-33210, train loss-1.9647, acc-0.5000, valid loss-1.9720, acc-0.4574, test loss-1.9729, acc-0.4471
Iter-33220, train loss-2.0360, acc-0.4400, valid loss-1.9719, acc-0.4576, test loss-1.9728, acc-0.4472
Iter-33230, train loss-2.0868, acc-0.3200, valid loss-1.9718, acc-0.4576, test loss-1.9727, acc-0.4471
Iter-33240, train loss-2.0029, acc-0.4200, valid loss-1.9717, acc-0.4578, test loss-1.9727, acc-0.4472
Iter-33250, train loss-2.0953, acc-0.2600, valid loss-1.9717, acc-0.4576, test loss-1.9726, acc-0.4473
Iter-33260, train loss-2.0090, acc-0.4000, valid loss-1.9716, acc-0.4576, test loss-1.9725, acc-0.4473
Iter-33270, train loss-1.9568, acc-0.4800, valid loss-1.9715, acc-0.4576, test loss-1.9724, acc-0.4473
Iter-33280, train loss-2.0655, acc-0.3400, valid loss-1.9714, acc-0.4576, test loss-1.9724, acc-0.4471
Iter-33290, train loss-2.0112, acc-0.3400, valid loss-1.9714, acc-0.4576, test loss-1.9723, acc-0.4472
Iter-33300, train loss-1.9884, acc-0.4400, valid loss-1.9713, acc-0.4574, test loss-1.9722, acc-0.4473
Iter-33310, train loss-1.9462, acc-0.5200, valid loss-1.9712, acc-0.4578, test loss-1.9721, acc-0.4474
Iter-33320, train loss-1.8967, acc-0.4800, valid loss-1.9711, acc-0.4572, test loss-1.9720, acc-0.4473
Iter-33330, train loss-2.0447, acc-0.3200, valid loss-1.9711, acc-0.4574, test loss-1.9720, acc-0.4472
Iter-33340, train loss-2.0204, acc-0.3600, valid loss-1.9710, acc-0.4576, test loss-1.9719, acc-0.4472
Iter-33350, train loss-1.9886, acc-0.4800, valid loss-1.9709, acc-0.4576, test loss-1.9718, acc-0.4470
Iter-33360, train loss-2.0045, acc-0.4600, valid loss-1.9708, acc-0.4576, test loss-1.9717, acc-0.4471
Iter-33370, train loss-1.9555, acc-0.5200, valid loss-1.9708, acc-0.4576, test loss-1.9717, acc-0.4471
Iter-33380, train loss-1.9191, acc-0.5800, valid loss-1.9707, acc-0.4580, test loss-1.9716, acc-0.4472
Iter-33390, train loss-1.9940, acc-0.4400, valid loss-1.9706, acc-0.4580, test loss-1.9715, acc-0.4472
Iter-33400, train loss-2.0064, acc-0.3600, valid loss-1.9705, acc-0.4578, test loss-1.9714, acc-0.4472
Iter-33410, train loss-1.9553, acc-0.5200, valid loss-1.9704, acc-0.4576, test loss-1.9714, acc-0.4471
Iter-33420, train loss-1.9952, acc-0.3800, valid loss-1.9704, acc-0.4578, test loss-1.9713, acc-0.4473
Iter-33430, train loss-2.0291, acc-0.4000, valid loss-1.9703, acc-0.4576, test loss-1.9712, acc-0.4473
Iter-33440, train loss-1.9250, acc-0.4000, valid loss-1.9702, acc-0.4578, test loss-1.9711, acc-0.4472
Iter-33450, train loss-1.9408, acc-0.4800, valid loss-1.9701, acc-0.4576, test loss-1.9711, acc-0.4474
Iter-33460, train loss-2.0568, acc-0.4000, valid loss-1.9701, acc-0.4578, test loss-1.9710, acc-0.4473
Iter-33470, train loss-1.9488, acc-0.5200, valid loss-1.9700, acc-0.4578, test loss-1.9709, acc-0.4473
Iter-33480, train loss-1.9514, acc-0.4600, valid loss-1.9699, acc-0.4576, test loss-1.9708, acc-0.4471
Iter-33490, train loss-2.0509, acc-0.2200, valid loss-1.9699, acc-0.4576, test loss-1.9708, acc-0.4471
Iter-33500, train loss-2.0415, acc-0.3800, valid loss-1.9698, acc-0.4576, test loss-1.9707, acc-0.4473
Iter-33510, train loss-1.9986, acc-0.4400, valid loss-1.9697, acc-0.4578, test loss-1.9706, acc-0.4473
Iter-33520, train loss-2.0361, acc-0.2800, valid loss-1.9696, acc-0.4576, test loss-1.9706, acc-0.4474
Iter-33530, train loss-1.9500, acc-0.5400, valid loss-1.9696, acc-0.4574, test loss-1.9705, acc-0.4474
Iter-33540, train loss-1.9441, acc-0.4600, valid loss-1.9695, acc-0.4574, test loss-1.9704, acc-0.4475
Iter-33550, train loss-2.0039, acc-0.4800, valid loss-1.9694, acc-0.4574, test loss-1.9703, acc-0.4474
Iter-33560, train loss-1.9845, acc-0.4400, valid loss-1.9693, acc-0.4578, test loss-1.9703, acc-0.4475
Iter-33570, train loss-1.9647, acc-0.5800, valid loss-1.9693, acc-0.4578, test loss-1.9702, acc-0.4475
Iter-33580, train loss-1.9901, acc-0.4600, valid loss-1.9692, acc-0.4576, test loss-1.9701, acc-0.4474
Iter-33590, train loss-1.9663, acc-0.4600, valid loss-1.9691, acc-0.4580, test loss-1.9700, acc-0.4474
Iter-33600, train loss-1.9712, acc-0.4200, valid loss-1.9690, acc-0.4578, test loss-1.9700, acc-0.4476
Iter-33610, train loss-1.9552, acc-0.4400, valid loss-1.9690, acc-0.4576, test loss-1.9699, acc-0.4476
Iter-33620, train loss-1.9954, acc-0.4600, valid loss-1.9689, acc-0.4576, test loss-1.9698, acc-0.4475
Iter-33630, train loss-1.9997, acc-0.5200, valid loss-1.9688, acc-0.4580, test loss-1.9697, acc-0.4477
Iter-33640, train loss-2.0578, acc-0.4400, valid loss-1.9688, acc-0.4578, test loss-1.9697, acc-0.4477
Iter-33650, train loss-1.9401, acc-0.5200, valid loss-1.9687, acc-0.4578, test loss-1.9696, acc-0.4477
Iter-33660, train loss-1.9829, acc-0.4000, valid loss-1.9686, acc-0.4578, test loss-1.9695, acc-0.4476
Iter-33670, train loss-1.9177, acc-0.4200, valid loss-1.9685, acc-0.4582, test loss-1.9694, acc-0.4479
Iter-33680, train loss-2.0360, acc-0.3800, valid loss-1.9684, acc-0.4582, test loss-1.9694, acc-0.4479
Iter-33690, train loss-2.0770, acc-0.3600, valid loss-1.9684, acc-0.4582, test loss-1.9693, acc-0.4478
Iter-33700, train loss-1.9656, acc-0.5000, valid loss-1.9683, acc-0.4576, test loss-1.9692, acc-0.4478
Iter-33710, train loss-1.9993, acc-0.5000, valid loss-1.9682, acc-0.4580, test loss-1.9691, acc-0.4479
Iter-33720, train loss-1.9510, acc-0.5200, valid loss-1.9681, acc-0.4576, test loss-1.9691, acc-0.4479
Iter-33730, train loss-2.0062, acc-0.4000, valid loss-1.9681, acc-0.4580, test loss-1.9690, acc-0.4479
Iter-33740, train loss-1.9784, acc-0.4400, valid loss-1.9680, acc-0.4582, test loss-1.9689, acc-0.4479
Iter-33750, train loss-1.9311, acc-0.5800, valid loss-1.9679, acc-0.4582, test loss-1.9688, acc-0.4478
Iter-33760, train loss-1.9203, acc-0.5000, valid loss-1.9678, acc-0.4580, test loss-1.9688, acc-0.4479
Iter-33770, train loss-1.9975, acc-0.4200, valid loss-1.9678, acc-0.4578, test loss-1.9687, acc-0.4478
Iter-33780, train loss-1.9629, acc-0.4400, valid loss-1.9677, acc-0.4582, test loss-1.9686, acc-0.4479
Iter-33790, train loss-1.9150, acc-0.5200, valid loss-1.9676, acc-0.4580, test loss-1.9685, acc-0.4478
Iter-33800, train loss-1.9438, acc-0.4800, valid loss-1.9675, acc-0.4580, test loss-1.9685, acc-0.4476
Iter-33810, train loss-1.9620, acc-0.5200, valid loss-1.9675, acc-0.4582, test loss-1.9684, acc-0.4479
Iter-33820, train loss-1.9803, acc-0.4800, valid loss-1.9674, acc-0.4580, test loss-1.9683, acc-0.4479
Iter-33830, train loss-2.0883, acc-0.3600, valid loss-1.9673, acc-0.4578, test loss-1.9682, acc-0.4479
Iter-33840, train loss-2.0506, acc-0.3800, valid loss-1.9672, acc-0.4580, test loss-1.9682, acc-0.4478
Iter-33850, train loss-1.9907, acc-0.4600, valid loss-1.9672, acc-0.4582, test loss-1.9681, acc-0.4478
Iter-33860, train loss-1.9143, acc-0.5200, valid loss-1.9671, acc-0.4580, test loss-1.9680, acc-0.4478
Iter-33870, train loss-1.9730, acc-0.4600, valid loss-1.9670, acc-0.4578, test loss-1.9679, acc-0.4478
Iter-33880, train loss-1.8472, acc-0.4800, valid loss-1.9669, acc-0.4580, test loss-1.9679, acc-0.4478
Iter-33890, train loss-1.9175, acc-0.5200, valid loss-1.9669, acc-0.4580, test loss-1.9678, acc-0.4478
Iter-33900, train loss-2.0295, acc-0.4000, valid loss-1.9668, acc-0.4578, test loss-1.9677, acc-0.4480
Iter-33910, train loss-1.9736, acc-0.4800, valid loss-1.9667, acc-0.4580, test loss-1.9676, acc-0.4476
Iter-33920, train loss-1.9605, acc-0.3600, valid loss-1.9666, acc-0.4584, test loss-1.9675, acc-0.4475
Iter-33930, train loss-2.0282, acc-0.3400, valid loss-1.9666, acc-0.4582, test loss-1.9675, acc-0.4475
Iter-33940, train loss-2.0471, acc-0.4000, valid loss-1.9665, acc-0.4586, test loss-1.9674, acc-0.4476
Iter-33950, train loss-1.9731, acc-0.4600, valid loss-1.9664, acc-0.4584, test loss-1.9673, acc-0.4477
Iter-33960, train loss-1.9893, acc-0.4400, valid loss-1.9663, acc-0.4586, test loss-1.9672, acc-0.4476
Iter-33970, train loss-1.8961, acc-0.5400, valid loss-1.9663, acc-0.4586, test loss-1.9672, acc-0.4480
Iter-33980, train loss-1.9472, acc-0.5000, valid loss-1.9662, acc-0.4588, test loss-1.9671, acc-0.4482
Iter-33990, train loss-2.0302, acc-0.4200, valid loss-1.9661, acc-0.4588, test loss-1.9670, acc-0.4481
Iter-34000, train loss-1.9752, acc-0.4600, valid loss-1.9660, acc-0.4588, test loss-1.9669, acc-0.4481
Iter-34010, train loss-2.0291, acc-0.4200, valid loss-1.9660, acc-0.4586, test loss-1.9669, acc-0.4481
Iter-34020, train loss-1.9136, acc-0.4600, valid loss-1.9659, acc-0.4586, test loss-1.9668, acc-0.4480
Iter-34030, train loss-1.9142, acc-0.5000, valid loss-1.9658, acc-0.4586, test loss-1.9667, acc-0.4480
Iter-34040, train loss-2.0868, acc-0.3400, valid loss-1.9657, acc-0.4586, test loss-1.9666, acc-0.4480
Iter-34050, train loss-1.9517, acc-0.4600, valid loss-1.9657, acc-0.4586, test loss-1.9666, acc-0.4482
Iter-34060, train loss-2.0027, acc-0.4600, valid loss-1.9656, acc-0.4586, test loss-1.9665, acc-0.4481
Iter-34070, train loss-1.9512, acc-0.5200, valid loss-1.9655, acc-0.4586, test loss-1.9664, acc-0.4482
Iter-34080, train loss-2.0173, acc-0.4000, valid loss-1.9654, acc-0.4588, test loss-1.9663, acc-0.4482
Iter-34090, train loss-1.9671, acc-0.4600, valid loss-1.9654, acc-0.4588, test loss-1.9663, acc-0.4481
Iter-34100, train loss-1.9922, acc-0.4200, valid loss-1.9653, acc-0.4588, test loss-1.9662, acc-0.4480
Iter-34110, train loss-2.0346, acc-0.3200, valid loss-1.9652, acc-0.4588, test loss-1.9661, acc-0.4479
Iter-34120, train loss-1.9894, acc-0.3400, valid loss-1.9651, acc-0.4586, test loss-1.9660, acc-0.4479
Iter-34130, train loss-1.9408, acc-0.5400, valid loss-1.9651, acc-0.4586, test loss-1.9660, acc-0.4479
Iter-34140, train loss-1.9623, acc-0.5000, valid loss-1.9650, acc-0.4586, test loss-1.9659, acc-0.4482
Iter-34150, train loss-1.9440, acc-0.4400, valid loss-1.9649, acc-0.4590, test loss-1.9658, acc-0.4483
Iter-34160, train loss-1.9566, acc-0.5000, valid loss-1.9648, acc-0.4590, test loss-1.9657, acc-0.4484
Iter-34170, train loss-1.8988, acc-0.6000, valid loss-1.9648, acc-0.4588, test loss-1.9657, acc-0.4485
Iter-34180, train loss-1.9754, acc-0.4800, valid loss-1.9647, acc-0.4588, test loss-1.9656, acc-0.4486
Iter-34190, train loss-1.9950, acc-0.4000, valid loss-1.9646, acc-0.4592, test loss-1.9655, acc-0.4486
Iter-34200, train loss-1.9291, acc-0.5600, valid loss-1.9646, acc-0.4592, test loss-1.9654, acc-0.4485
Iter-34210, train loss-1.9437, acc-0.4400, valid loss-1.9645, acc-0.4590, test loss-1.9654, acc-0.4485
Iter-34220, train loss-2.0093, acc-0.4200, valid loss-1.9644, acc-0.4590, test loss-1.9653, acc-0.4483
Iter-34230, train loss-1.9587, acc-0.4800, valid loss-1.9643, acc-0.4592, test loss-1.9652, acc-0.4485
Iter-34240, train loss-2.0161, acc-0.4000, valid loss-1.9643, acc-0.4592, test loss-1.9651, acc-0.4485
Iter-34250, train loss-1.9899, acc-0.5000, valid loss-1.9642, acc-0.4594, test loss-1.9651, acc-0.4486
Iter-34260, train loss-1.9760, acc-0.4600, valid loss-1.9641, acc-0.4592, test loss-1.9650, acc-0.4487
Iter-34270, train loss-2.0215, acc-0.4000, valid loss-1.9640, acc-0.4592, test loss-1.9649, acc-0.4487
Iter-34280, train loss-1.9733, acc-0.4200, valid loss-1.9640, acc-0.4594, test loss-1.9649, acc-0.4487
Iter-34290, train loss-1.9749, acc-0.3400, valid loss-1.9639, acc-0.4592, test loss-1.9648, acc-0.4487
Iter-34300, train loss-1.9732, acc-0.5200, valid loss-1.9638, acc-0.4596, test loss-1.9647, acc-0.4488
Iter-34310, train loss-1.9126, acc-0.5000, valid loss-1.9637, acc-0.4594, test loss-1.9646, acc-0.4487
Iter-34320, train loss-1.9816, acc-0.4400, valid loss-1.9637, acc-0.4594, test loss-1.9645, acc-0.4487
Iter-34330, train loss-1.9463, acc-0.4200, valid loss-1.9636, acc-0.4596, test loss-1.9645, acc-0.4487
Iter-34340, train loss-1.9801, acc-0.5400, valid loss-1.9635, acc-0.4592, test loss-1.9644, acc-0.4486
Iter-34350, train loss-1.9857, acc-0.5200, valid loss-1.9634, acc-0.4592, test loss-1.9643, acc-0.4486
Iter-34360, train loss-1.9862, acc-0.4000, valid loss-1.9634, acc-0.4590, test loss-1.9642, acc-0.4486
Iter-34370, train loss-1.9509, acc-0.5000, valid loss-1.9633, acc-0.4592, test loss-1.9642, acc-0.4488
Iter-34380, train loss-1.9521, acc-0.4600, valid loss-1.9632, acc-0.4592, test loss-1.9641, acc-0.4486
Iter-34390, train loss-1.8886, acc-0.5800, valid loss-1.9631, acc-0.4596, test loss-1.9640, acc-0.4484
Iter-34400, train loss-2.0227, acc-0.3800, valid loss-1.9631, acc-0.4594, test loss-1.9639, acc-0.4485
Iter-34410, train loss-1.9736, acc-0.4600, valid loss-1.9630, acc-0.4594, test loss-1.9639, acc-0.4484
Iter-34420, train loss-1.9192, acc-0.4800, valid loss-1.9629, acc-0.4596, test loss-1.9638, acc-0.4481
Iter-34430, train loss-1.9442, acc-0.5200, valid loss-1.9628, acc-0.4594, test loss-1.9637, acc-0.4482
Iter-34440, train loss-1.9759, acc-0.4800, valid loss-1.9628, acc-0.4594, test loss-1.9636, acc-0.4485
Iter-34450, train loss-2.0074, acc-0.5400, valid loss-1.9627, acc-0.4594, test loss-1.9636, acc-0.4487
Iter-34460, train loss-1.9708, acc-0.3600, valid loss-1.9626, acc-0.4592, test loss-1.9635, acc-0.4482
Iter-34470, train loss-1.9952, acc-0.5000, valid loss-1.9625, acc-0.4592, test loss-1.9634, acc-0.4483
Iter-34480, train loss-1.9621, acc-0.4400, valid loss-1.9625, acc-0.4594, test loss-1.9633, acc-0.4483
Iter-34490, train loss-1.9885, acc-0.4400, valid loss-1.9624, acc-0.4596, test loss-1.9633, acc-0.4481
Iter-34500, train loss-1.9945, acc-0.3800, valid loss-1.9623, acc-0.4600, test loss-1.9632, acc-0.4484
Iter-34510, train loss-1.9681, acc-0.4400, valid loss-1.9623, acc-0.4598, test loss-1.9631, acc-0.4484
Iter-34520, train loss-1.8795, acc-0.6000, valid loss-1.9622, acc-0.4598, test loss-1.9631, acc-0.4485
Iter-34530, train loss-1.9842, acc-0.4200, valid loss-1.9621, acc-0.4600, test loss-1.9630, acc-0.4483
Iter-34540, train loss-1.8955, acc-0.6000, valid loss-1.9620, acc-0.4602, test loss-1.9629, acc-0.4485
Iter-34550, train loss-1.9164, acc-0.4400, valid loss-1.9619, acc-0.4604, test loss-1.9628, acc-0.4486
Iter-34560, train loss-1.9938, acc-0.4600, valid loss-1.9619, acc-0.4604, test loss-1.9628, acc-0.4487
Iter-34570, train loss-1.9456, acc-0.5400, valid loss-1.9618, acc-0.4604, test loss-1.9627, acc-0.4487
Iter-34580, train loss-1.9394, acc-0.5200, valid loss-1.9617, acc-0.4606, test loss-1.9626, acc-0.4487
Iter-34590, train loss-1.9217, acc-0.5600, valid loss-1.9617, acc-0.4602, test loss-1.9625, acc-0.4486
Iter-34600, train loss-2.0340, acc-0.3400, valid loss-1.9616, acc-0.4600, test loss-1.9625, acc-0.4485
Iter-34610, train loss-1.9741, acc-0.4400, valid loss-1.9615, acc-0.4602, test loss-1.9624, acc-0.4487
Iter-34620, train loss-1.9895, acc-0.5000, valid loss-1.9614, acc-0.4602, test loss-1.9623, acc-0.4485
Iter-34630, train loss-1.9924, acc-0.4400, valid loss-1.9614, acc-0.4606, test loss-1.9623, acc-0.4488
Iter-34640, train loss-1.9823, acc-0.4200, valid loss-1.9613, acc-0.4602, test loss-1.9622, acc-0.4489
Iter-34650, train loss-1.9247, acc-0.5200, valid loss-1.9612, acc-0.4604, test loss-1.9621, acc-0.4491
Iter-34660, train loss-1.9996, acc-0.4200, valid loss-1.9611, acc-0.4608, test loss-1.9620, acc-0.4490
Iter-34670, train loss-1.9388, acc-0.3600, valid loss-1.9611, acc-0.4604, test loss-1.9620, acc-0.4491
Iter-34680, train loss-2.0296, acc-0.4000, valid loss-1.9610, acc-0.4604, test loss-1.9619, acc-0.4492
Iter-34690, train loss-1.9457, acc-0.5200, valid loss-1.9609, acc-0.4602, test loss-1.9618, acc-0.4491
Iter-34700, train loss-1.9768, acc-0.4400, valid loss-1.9608, acc-0.4604, test loss-1.9617, acc-0.4493
Iter-34710, train loss-2.0004, acc-0.4200, valid loss-1.9608, acc-0.4604, test loss-1.9617, acc-0.4493
Iter-34720, train loss-1.9627, acc-0.4000, valid loss-1.9607, acc-0.4604, test loss-1.9616, acc-0.4494
Iter-34730, train loss-1.9948, acc-0.3600, valid loss-1.9606, acc-0.4602, test loss-1.9615, acc-0.4494
Iter-34740, train loss-2.0197, acc-0.4200, valid loss-1.9605, acc-0.4608, test loss-1.9614, acc-0.4494
Iter-34750, train loss-1.8965, acc-0.4800, valid loss-1.9605, acc-0.4602, test loss-1.9614, acc-0.4495
Iter-34760, train loss-1.9936, acc-0.4600, valid loss-1.9604, acc-0.4600, test loss-1.9613, acc-0.4493
Iter-34770, train loss-2.0154, acc-0.3400, valid loss-1.9603, acc-0.4600, test loss-1.9612, acc-0.4493
Iter-34780, train loss-2.0226, acc-0.4000, valid loss-1.9602, acc-0.4604, test loss-1.9612, acc-0.4493
Iter-34790, train loss-1.9377, acc-0.6000, valid loss-1.9602, acc-0.4602, test loss-1.9611, acc-0.4493
Iter-34800, train loss-1.8884, acc-0.5600, valid loss-1.9601, acc-0.4602, test loss-1.9610, acc-0.4492
Iter-34810, train loss-2.0084, acc-0.3800, valid loss-1.9600, acc-0.4600, test loss-1.9609, acc-0.4492
Iter-34820, train loss-1.9368, acc-0.5200, valid loss-1.9599, acc-0.4600, test loss-1.9608, acc-0.4492
Iter-34830, train loss-1.8981, acc-0.5600, valid loss-1.9599, acc-0.4602, test loss-1.9608, acc-0.4490
Iter-34840, train loss-1.9959, acc-0.4600, valid loss-1.9598, acc-0.4602, test loss-1.9607, acc-0.4491
Iter-34850, train loss-1.9451, acc-0.4600, valid loss-1.9597, acc-0.4602, test loss-1.9606, acc-0.4489
Iter-34860, train loss-1.9341, acc-0.4600, valid loss-1.9596, acc-0.4602, test loss-1.9606, acc-0.4490
Iter-34870, train loss-1.9041, acc-0.5200, valid loss-1.9596, acc-0.4602, test loss-1.9605, acc-0.4491
Iter-34880, train loss-1.9488, acc-0.4800, valid loss-1.9595, acc-0.4600, test loss-1.9604, acc-0.4489
Iter-34890, train loss-2.0354, acc-0.3200, valid loss-1.9594, acc-0.4602, test loss-1.9603, acc-0.4490
Iter-34900, train loss-2.0101, acc-0.4600, valid loss-1.9593, acc-0.4602, test loss-1.9603, acc-0.4490
Iter-34910, train loss-1.9016, acc-0.5200, valid loss-1.9593, acc-0.4598, test loss-1.9602, acc-0.4491
Iter-34920, train loss-1.9133, acc-0.4600, valid loss-1.9592, acc-0.4598, test loss-1.9601, acc-0.4491
Iter-34930, train loss-1.9759, acc-0.4400, valid loss-1.9591, acc-0.4600, test loss-1.9600, acc-0.4491
Iter-34940, train loss-1.9829, acc-0.4600, valid loss-1.9590, acc-0.4604, test loss-1.9600, acc-0.4495
Iter-34950, train loss-1.9634, acc-0.5000, valid loss-1.9590, acc-0.4602, test loss-1.9599, acc-0.4491
Iter-34960, train loss-1.8942, acc-0.4600, valid loss-1.9589, acc-0.4602, test loss-1.9598, acc-0.4492
Iter-34970, train loss-1.9273, acc-0.5800, valid loss-1.9588, acc-0.4600, test loss-1.9597, acc-0.4494
Iter-34980, train loss-1.9582, acc-0.3400, valid loss-1.9587, acc-0.4598, test loss-1.9597, acc-0.4496
Iter-34990, train loss-1.9998, acc-0.4200, valid loss-1.9587, acc-0.4600, test loss-1.9596, acc-0.4498
Iter-35000, train loss-1.9517, acc-0.4800, valid loss-1.9586, acc-0.4602, test loss-1.9595, acc-0.4498
Iter-35010, train loss-1.9775, acc-0.4000, valid loss-1.9585, acc-0.4604, test loss-1.9594, acc-0.4498
Iter-35020, train loss-1.9974, acc-0.4400, valid loss-1.9584, acc-0.4602, test loss-1.9594, acc-0.4499
Iter-35030, train loss-1.9703, acc-0.5400, valid loss-1.9584, acc-0.4602, test loss-1.9593, acc-0.4498
Iter-35040, train loss-1.9756, acc-0.4000, valid loss-1.9583, acc-0.4604, test loss-1.9592, acc-0.4497
Iter-35050, train loss-1.9599, acc-0.4400, valid loss-1.9582, acc-0.4602, test loss-1.9591, acc-0.4500
Iter-35060, train loss-1.9817, acc-0.3800, valid loss-1.9582, acc-0.4602, test loss-1.9591, acc-0.4501
Iter-35070, train loss-2.0790, acc-0.3200, valid loss-1.9581, acc-0.4602, test loss-1.9590, acc-0.4501
Iter-35080, train loss-1.9921, acc-0.3600, valid loss-1.9580, acc-0.4604, test loss-1.9589, acc-0.4501
Iter-35090, train loss-1.9958, acc-0.3600, valid loss-1.9579, acc-0.4606, test loss-1.9589, acc-0.4501
Iter-35100, train loss-1.9075, acc-0.4400, valid loss-1.9579, acc-0.4604, test loss-1.9588, acc-0.4502
Iter-35110, train loss-1.9410, acc-0.4600, valid loss-1.9578, acc-0.4602, test loss-1.9587, acc-0.4502
Iter-35120, train loss-1.9278, acc-0.5000, valid loss-1.9577, acc-0.4604, test loss-1.9586, acc-0.4504
Iter-35130, train loss-1.9617, acc-0.4400, valid loss-1.9576, acc-0.4606, test loss-1.9586, acc-0.4502
Iter-35140, train loss-1.8795, acc-0.6200, valid loss-1.9575, acc-0.4604, test loss-1.9585, acc-0.4502
Iter-35150, train loss-1.9441, acc-0.4400, valid loss-1.9575, acc-0.4604, test loss-1.9584, acc-0.4500
Iter-35160, train loss-1.9685, acc-0.5200, valid loss-1.9574, acc-0.4606, test loss-1.9583, acc-0.4499
Iter-35170, train loss-1.9949, acc-0.5000, valid loss-1.9573, acc-0.4604, test loss-1.9583, acc-0.4501
Iter-35180, train loss-1.9919, acc-0.3800, valid loss-1.9573, acc-0.4608, test loss-1.9582, acc-0.4501
Iter-35190, train loss-1.9286, acc-0.4800, valid loss-1.9572, acc-0.4608, test loss-1.9581, acc-0.4503
Iter-35200, train loss-2.0206, acc-0.3400, valid loss-1.9571, acc-0.4608, test loss-1.9580, acc-0.4504
Iter-35210, train loss-1.9904, acc-0.3800, valid loss-1.9570, acc-0.4610, test loss-1.9580, acc-0.4504
Iter-35220, train loss-1.9721, acc-0.5400, valid loss-1.9570, acc-0.4610, test loss-1.9579, acc-0.4505
Iter-35230, train loss-2.0255, acc-0.3200, valid loss-1.9569, acc-0.4612, test loss-1.9578, acc-0.4505
Iter-35240, train loss-1.8916, acc-0.5600, valid loss-1.9568, acc-0.4614, test loss-1.9578, acc-0.4505
Iter-35250, train loss-1.9985, acc-0.3400, valid loss-1.9568, acc-0.4612, test loss-1.9577, acc-0.4507
Iter-35260, train loss-1.9683, acc-0.4200, valid loss-1.9567, acc-0.4614, test loss-1.9576, acc-0.4506
Iter-35270, train loss-1.9026, acc-0.5400, valid loss-1.9566, acc-0.4614, test loss-1.9575, acc-0.4507
Iter-35280, train loss-1.9376, acc-0.5000, valid loss-1.9565, acc-0.4614, test loss-1.9575, acc-0.4506
Iter-35290, train loss-1.9353, acc-0.5400, valid loss-1.9565, acc-0.4614, test loss-1.9574, acc-0.4504
Iter-35300, train loss-1.8684, acc-0.5800, valid loss-1.9564, acc-0.4612, test loss-1.9573, acc-0.4501
Iter-35310, train loss-1.9122, acc-0.5200, valid loss-1.9563, acc-0.4610, test loss-1.9572, acc-0.4503
Iter-35320, train loss-1.9557, acc-0.5000, valid loss-1.9562, acc-0.4610, test loss-1.9572, acc-0.4502
Iter-35330, train loss-1.9748, acc-0.4400, valid loss-1.9562, acc-0.4610, test loss-1.9571, acc-0.4503
Iter-35340, train loss-1.9864, acc-0.4000, valid loss-1.9561, acc-0.4610, test loss-1.9570, acc-0.4503
Iter-35350, train loss-1.9355, acc-0.4600, valid loss-1.9560, acc-0.4610, test loss-1.9569, acc-0.4503
Iter-35360, train loss-2.0078, acc-0.4600, valid loss-1.9559, acc-0.4612, test loss-1.9569, acc-0.4502
Iter-35370, train loss-1.9937, acc-0.3200, valid loss-1.9559, acc-0.4612, test loss-1.9568, acc-0.4502
Iter-35380, train loss-2.0729, acc-0.3800, valid loss-1.9558, acc-0.4610, test loss-1.9567, acc-0.4499
Iter-35390, train loss-1.9414, acc-0.4400, valid loss-1.9557, acc-0.4612, test loss-1.9567, acc-0.4499
Iter-35400, train loss-2.0121, acc-0.3600, valid loss-1.9557, acc-0.4610, test loss-1.9566, acc-0.4499
Iter-35410, train loss-1.9475, acc-0.5000, valid loss-1.9556, acc-0.4610, test loss-1.9565, acc-0.4500
Iter-35420, train loss-1.9930, acc-0.5200, valid loss-1.9555, acc-0.4612, test loss-1.9564, acc-0.4502
Iter-35430, train loss-1.9961, acc-0.4600, valid loss-1.9555, acc-0.4614, test loss-1.9564, acc-0.4502
Iter-35440, train loss-2.0030, acc-0.4000, valid loss-1.9554, acc-0.4614, test loss-1.9563, acc-0.4501
Iter-35450, train loss-1.9078, acc-0.5200, valid loss-1.9553, acc-0.4614, test loss-1.9562, acc-0.4501
Iter-35460, train loss-1.9334, acc-0.5000, valid loss-1.9552, acc-0.4614, test loss-1.9562, acc-0.4502
Iter-35470, train loss-2.0325, acc-0.4000, valid loss-1.9552, acc-0.4614, test loss-1.9561, acc-0.4502
Iter-35480, train loss-2.0562, acc-0.4200, valid loss-1.9551, acc-0.4616, test loss-1.9560, acc-0.4501
Iter-35490, train loss-1.9001, acc-0.5400, valid loss-1.9550, acc-0.4614, test loss-1.9559, acc-0.4502
Iter-35500, train loss-1.8942, acc-0.5000, valid loss-1.9549, acc-0.4616, test loss-1.9559, acc-0.4502
Iter-35510, train loss-1.9907, acc-0.3200, valid loss-1.9549, acc-0.4614, test loss-1.9558, acc-0.4504
Iter-35520, train loss-1.9359, acc-0.5000, valid loss-1.9548, acc-0.4614, test loss-1.9557, acc-0.4506
Iter-35530, train loss-1.9926, acc-0.3800, valid loss-1.9547, acc-0.4612, test loss-1.9556, acc-0.4505
Iter-35540, train loss-2.0161, acc-0.2800, valid loss-1.9546, acc-0.4614, test loss-1.9556, acc-0.4505
Iter-35550, train loss-1.9576, acc-0.4600, valid loss-1.9546, acc-0.4612, test loss-1.9555, acc-0.4504
Iter-35560, train loss-1.9030, acc-0.5200, valid loss-1.9545, acc-0.4612, test loss-1.9554, acc-0.4506
Iter-35570, train loss-2.0100, acc-0.3600, valid loss-1.9544, acc-0.4612, test loss-1.9554, acc-0.4503
Iter-35580, train loss-1.9618, acc-0.3600, valid loss-1.9544, acc-0.4614, test loss-1.9553, acc-0.4504
Iter-35590, train loss-1.9425, acc-0.4600, valid loss-1.9543, acc-0.4614, test loss-1.9552, acc-0.4505
Iter-35600, train loss-1.9660, acc-0.4600, valid loss-1.9542, acc-0.4614, test loss-1.9551, acc-0.4507
Iter-35610, train loss-1.9849, acc-0.5200, valid loss-1.9541, acc-0.4614, test loss-1.9551, acc-0.4507
Iter-35620, train loss-1.8869, acc-0.5400, valid loss-1.9541, acc-0.4616, test loss-1.9550, acc-0.4507
Iter-35630, train loss-1.8944, acc-0.5400, valid loss-1.9540, acc-0.4610, test loss-1.9549, acc-0.4505
Iter-35640, train loss-1.9471, acc-0.5000, valid loss-1.9539, acc-0.4614, test loss-1.9548, acc-0.4506
Iter-35650, train loss-2.0137, acc-0.3000, valid loss-1.9539, acc-0.4618, test loss-1.9548, acc-0.4507
Iter-35660, train loss-1.9788, acc-0.4000, valid loss-1.9538, acc-0.4616, test loss-1.9547, acc-0.4505
Iter-35670, train loss-1.9437, acc-0.5000, valid loss-1.9537, acc-0.4614, test loss-1.9546, acc-0.4507
Iter-35680, train loss-1.9772, acc-0.4000, valid loss-1.9536, acc-0.4618, test loss-1.9546, acc-0.4508
Iter-35690, train loss-2.0660, acc-0.3400, valid loss-1.9536, acc-0.4618, test loss-1.9545, acc-0.4508
Iter-35700, train loss-1.9856, acc-0.3400, valid loss-1.9535, acc-0.4618, test loss-1.9544, acc-0.4508
Iter-35710, train loss-1.9846, acc-0.5000, valid loss-1.9534, acc-0.4618, test loss-1.9543, acc-0.4507
Iter-35720, train loss-2.0058, acc-0.3400, valid loss-1.9534, acc-0.4618, test loss-1.9543, acc-0.4507
Iter-35730, train loss-2.0046, acc-0.4200, valid loss-1.9533, acc-0.4618, test loss-1.9542, acc-0.4508
Iter-35740, train loss-2.0328, acc-0.3600, valid loss-1.9532, acc-0.4620, test loss-1.9541, acc-0.4509
Iter-35750, train loss-2.0008, acc-0.5000, valid loss-1.9532, acc-0.4622, test loss-1.9541, acc-0.4509
Iter-35760, train loss-1.9281, acc-0.5000, valid loss-1.9531, acc-0.4620, test loss-1.9540, acc-0.4512
Iter-35770, train loss-1.9296, acc-0.4400, valid loss-1.9530, acc-0.4616, test loss-1.9539, acc-0.4512
Iter-35780, train loss-2.0036, acc-0.4400, valid loss-1.9529, acc-0.4616, test loss-1.9538, acc-0.4512
Iter-35790, train loss-1.9495, acc-0.4600, valid loss-1.9529, acc-0.4616, test loss-1.9538, acc-0.4513
Iter-35800, train loss-1.9390, acc-0.4800, valid loss-1.9528, acc-0.4616, test loss-1.9537, acc-0.4513
Iter-35810, train loss-1.9294, acc-0.4600, valid loss-1.9527, acc-0.4622, test loss-1.9536, acc-0.4514
Iter-35820, train loss-1.9788, acc-0.4600, valid loss-1.9526, acc-0.4622, test loss-1.9535, acc-0.4514
Iter-35830, train loss-2.0143, acc-0.3800, valid loss-1.9526, acc-0.4622, test loss-1.9535, acc-0.4512
Iter-35840, train loss-2.0666, acc-0.2800, valid loss-1.9525, acc-0.4622, test loss-1.9534, acc-0.4513
Iter-35850, train loss-1.9648, acc-0.4200, valid loss-1.9524, acc-0.4622, test loss-1.9533, acc-0.4512
Iter-35860, train loss-1.9268, acc-0.5200, valid loss-1.9523, acc-0.4622, test loss-1.9532, acc-0.4511
Iter-35870, train loss-1.9503, acc-0.4400, valid loss-1.9523, acc-0.4622, test loss-1.9532, acc-0.4509
Iter-35880, train loss-2.0459, acc-0.3200, valid loss-1.9522, acc-0.4622, test loss-1.9531, acc-0.4512
Iter-35890, train loss-2.0191, acc-0.4000, valid loss-1.9521, acc-0.4622, test loss-1.9530, acc-0.4510
Iter-35900, train loss-1.9301, acc-0.4800, valid loss-1.9520, acc-0.4622, test loss-1.9530, acc-0.4511
Iter-35910, train loss-2.0078, acc-0.2800, valid loss-1.9520, acc-0.4622, test loss-1.9529, acc-0.4511
Iter-35920, train loss-1.8678, acc-0.6200, valid loss-1.9519, acc-0.4624, test loss-1.9528, acc-0.4512
Iter-35930, train loss-1.8802, acc-0.5800, valid loss-1.9518, acc-0.4628, test loss-1.9527, acc-0.4514
Iter-35940, train loss-1.9284, acc-0.4800, valid loss-1.9518, acc-0.4626, test loss-1.9527, acc-0.4513
Iter-35950, train loss-2.0090, acc-0.4400, valid loss-1.9517, acc-0.4628, test loss-1.9526, acc-0.4513
Iter-35960, train loss-1.8903, acc-0.4400, valid loss-1.9516, acc-0.4626, test loss-1.9525, acc-0.4515
Iter-35970, train loss-1.9386, acc-0.4800, valid loss-1.9515, acc-0.4626, test loss-1.9524, acc-0.4516
Iter-35980, train loss-1.8893, acc-0.4600, valid loss-1.9515, acc-0.4622, test loss-1.9524, acc-0.4517
Iter-35990, train loss-1.9598, acc-0.5400, valid loss-1.9514, acc-0.4622, test loss-1.9523, acc-0.4517
Iter-36000, train loss-1.9771, acc-0.4000, valid loss-1.9513, acc-0.4624, test loss-1.9522, acc-0.4517
Iter-36010, train loss-1.9704, acc-0.4200, valid loss-1.9512, acc-0.4624, test loss-1.9521, acc-0.4517
Iter-36020, train loss-1.9719, acc-0.5000, valid loss-1.9512, acc-0.4622, test loss-1.9521, acc-0.4517
Iter-36030, train loss-1.9779, acc-0.4400, valid loss-1.9511, acc-0.4626, test loss-1.9520, acc-0.4517
Iter-36040, train loss-1.9335, acc-0.4400, valid loss-1.9510, acc-0.4626, test loss-1.9519, acc-0.4517
Iter-36050, train loss-1.9697, acc-0.4200, valid loss-1.9510, acc-0.4626, test loss-1.9519, acc-0.4517
Iter-36060, train loss-1.9717, acc-0.4600, valid loss-1.9509, acc-0.4624, test loss-1.9518, acc-0.4520
Iter-36070, train loss-1.8236, acc-0.6200, valid loss-1.9508, acc-0.4624, test loss-1.9517, acc-0.4519
Iter-36080, train loss-1.9821, acc-0.5200, valid loss-1.9507, acc-0.4624, test loss-1.9516, acc-0.4519
Iter-36090, train loss-1.9727, acc-0.4600, valid loss-1.9507, acc-0.4624, test loss-1.9516, acc-0.4519
Iter-36100, train loss-2.0038, acc-0.4200, valid loss-1.9506, acc-0.4624, test loss-1.9515, acc-0.4519
Iter-36110, train loss-1.9519, acc-0.4200, valid loss-1.9505, acc-0.4628, test loss-1.9514, acc-0.4519
Iter-36120, train loss-1.9478, acc-0.5000, valid loss-1.9505, acc-0.4626, test loss-1.9513, acc-0.4519
Iter-36130, train loss-2.0152, acc-0.4200, valid loss-1.9504, acc-0.4628, test loss-1.9513, acc-0.4519
Iter-36140, train loss-1.9499, acc-0.4600, valid loss-1.9503, acc-0.4628, test loss-1.9512, acc-0.4519
Iter-36150, train loss-1.9488, acc-0.4400, valid loss-1.9502, acc-0.4628, test loss-1.9511, acc-0.4519
Iter-36160, train loss-2.0171, acc-0.3600, valid loss-1.9502, acc-0.4630, test loss-1.9511, acc-0.4520
Iter-36170, train loss-1.9904, acc-0.3800, valid loss-1.9501, acc-0.4634, test loss-1.9510, acc-0.4522
Iter-36180, train loss-1.9675, acc-0.4600, valid loss-1.9500, acc-0.4632, test loss-1.9509, acc-0.4523
Iter-36190, train loss-2.0007, acc-0.3800, valid loss-1.9500, acc-0.4636, test loss-1.9508, acc-0.4524
Iter-36200, train loss-1.9860, acc-0.4800, valid loss-1.9499, acc-0.4636, test loss-1.9508, acc-0.4524
Iter-36210, train loss-2.0451, acc-0.2800, valid loss-1.9498, acc-0.4630, test loss-1.9507, acc-0.4523
Iter-36220, train loss-1.9820, acc-0.5600, valid loss-1.9497, acc-0.4632, test loss-1.9506, acc-0.4522
Iter-36230, train loss-1.9449, acc-0.4400, valid loss-1.9497, acc-0.4630, test loss-1.9506, acc-0.4521
Iter-36240, train loss-1.9507, acc-0.4600, valid loss-1.9496, acc-0.4630, test loss-1.9505, acc-0.4523
Iter-36250, train loss-2.0586, acc-0.2600, valid loss-1.9495, acc-0.4628, test loss-1.9504, acc-0.4523
Iter-36260, train loss-1.9884, acc-0.3800, valid loss-1.9495, acc-0.4630, test loss-1.9503, acc-0.4522
Iter-36270, train loss-1.9358, acc-0.4600, valid loss-1.9494, acc-0.4632, test loss-1.9503, acc-0.4521
Iter-36280, train loss-2.0493, acc-0.3600, valid loss-1.9493, acc-0.4630, test loss-1.9502, acc-0.4521
Iter-36290, train loss-1.9639, acc-0.4200, valid loss-1.9493, acc-0.4628, test loss-1.9501, acc-0.4521
Iter-36300, train loss-1.9616, acc-0.5000, valid loss-1.9492, acc-0.4630, test loss-1.9501, acc-0.4522
Iter-36310, train loss-1.9416, acc-0.5400, valid loss-1.9491, acc-0.4628, test loss-1.9500, acc-0.4521
Iter-36320, train loss-2.0452, acc-0.4000, valid loss-1.9490, acc-0.4628, test loss-1.9499, acc-0.4521
Iter-36330, train loss-1.9167, acc-0.4600, valid loss-1.9490, acc-0.4628, test loss-1.9499, acc-0.4520
Iter-36340, train loss-1.9862, acc-0.5000, valid loss-1.9489, acc-0.4628, test loss-1.9498, acc-0.4521
Iter-36350, train loss-1.9432, acc-0.4800, valid loss-1.9488, acc-0.4628, test loss-1.9497, acc-0.4521
Iter-36360, train loss-1.8794, acc-0.5600, valid loss-1.9488, acc-0.4628, test loss-1.9496, acc-0.4522
Iter-36370, train loss-1.9126, acc-0.5800, valid loss-1.9487, acc-0.4630, test loss-1.9496, acc-0.4522
Iter-36380, train loss-1.9583, acc-0.5400, valid loss-1.9486, acc-0.4632, test loss-1.9495, acc-0.4524
Iter-36390, train loss-2.0276, acc-0.4600, valid loss-1.9485, acc-0.4630, test loss-1.9494, acc-0.4524
Iter-36400, train loss-1.9112, acc-0.5400, valid loss-1.9485, acc-0.4632, test loss-1.9494, acc-0.4524
Iter-36410, train loss-1.9435, acc-0.4400, valid loss-1.9484, acc-0.4630, test loss-1.9493, acc-0.4524
Iter-36420, train loss-1.9400, acc-0.4400, valid loss-1.9483, acc-0.4630, test loss-1.9492, acc-0.4524
Iter-36430, train loss-1.9673, acc-0.4200, valid loss-1.9483, acc-0.4632, test loss-1.9491, acc-0.4524
Iter-36440, train loss-1.9496, acc-0.4400, valid loss-1.9482, acc-0.4634, test loss-1.9491, acc-0.4525
Iter-36450, train loss-1.9153, acc-0.5000, valid loss-1.9481, acc-0.4634, test loss-1.9490, acc-0.4526
Iter-36460, train loss-1.9922, acc-0.4800, valid loss-1.9480, acc-0.4632, test loss-1.9489, acc-0.4525
Iter-36470, train loss-1.9046, acc-0.4600, valid loss-1.9480, acc-0.4632, test loss-1.9489, acc-0.4526
Iter-36480, train loss-1.9409, acc-0.5000, valid loss-1.9479, acc-0.4632, test loss-1.9488, acc-0.4525
Iter-36490, train loss-1.9735, acc-0.4600, valid loss-1.9478, acc-0.4634, test loss-1.9487, acc-0.4525
Iter-36500, train loss-2.0541, acc-0.3400, valid loss-1.9478, acc-0.4636, test loss-1.9486, acc-0.4527
Iter-36510, train loss-1.9273, acc-0.4800, valid loss-1.9477, acc-0.4638, test loss-1.9486, acc-0.4526
Iter-36520, train loss-1.8940, acc-0.5400, valid loss-1.9476, acc-0.4636, test loss-1.9485, acc-0.4524
Iter-36530, train loss-1.9661, acc-0.4200, valid loss-1.9475, acc-0.4638, test loss-1.9484, acc-0.4527
Iter-36540, train loss-1.9325, acc-0.4600, valid loss-1.9475, acc-0.4638, test loss-1.9483, acc-0.4527
Iter-36550, train loss-1.9834, acc-0.3400, valid loss-1.9474, acc-0.4636, test loss-1.9483, acc-0.4526
Iter-36560, train loss-1.9369, acc-0.5600, valid loss-1.9473, acc-0.4638, test loss-1.9482, acc-0.4525
Iter-36570, train loss-1.9587, acc-0.4800, valid loss-1.9473, acc-0.4638, test loss-1.9481, acc-0.4527
Iter-36580, train loss-1.9420, acc-0.4200, valid loss-1.9472, acc-0.4638, test loss-1.9481, acc-0.4527
Iter-36590, train loss-1.8922, acc-0.4600, valid loss-1.9471, acc-0.4638, test loss-1.9480, acc-0.4526
Iter-36600, train loss-1.9568, acc-0.3800, valid loss-1.9470, acc-0.4636, test loss-1.9479, acc-0.4526
Iter-36610, train loss-1.9326, acc-0.5200, valid loss-1.9470, acc-0.4638, test loss-1.9478, acc-0.4526
Iter-36620, train loss-1.9497, acc-0.5400, valid loss-1.9469, acc-0.4638, test loss-1.9478, acc-0.4527
Iter-36630, train loss-1.9505, acc-0.4200, valid loss-1.9468, acc-0.4638, test loss-1.9477, acc-0.4527
Iter-36640, train loss-1.9920, acc-0.4200, valid loss-1.9467, acc-0.4642, test loss-1.9476, acc-0.4527
Iter-36650, train loss-1.9449, acc-0.4800, valid loss-1.9467, acc-0.4638, test loss-1.9475, acc-0.4527
Iter-36660, train loss-1.9272, acc-0.4200, valid loss-1.9466, acc-0.4638, test loss-1.9475, acc-0.4529
Iter-36670, train loss-1.8936, acc-0.4600, valid loss-1.9465, acc-0.4638, test loss-1.9474, acc-0.4530
Iter-36680, train loss-1.9632, acc-0.5200, valid loss-1.9465, acc-0.4638, test loss-1.9473, acc-0.4531
Iter-36690, train loss-2.0111, acc-0.4400, valid loss-1.9464, acc-0.4638, test loss-1.9473, acc-0.4530
Iter-36700, train loss-1.8450, acc-0.5800, valid loss-1.9463, acc-0.4638, test loss-1.9472, acc-0.4529
Iter-36710, train loss-1.9818, acc-0.4800, valid loss-1.9462, acc-0.4638, test loss-1.9471, acc-0.4529
Iter-36720, train loss-1.9301, acc-0.5400, valid loss-1.9462, acc-0.4636, test loss-1.9471, acc-0.4530
Iter-36730, train loss-2.0118, acc-0.3400, valid loss-1.9461, acc-0.4632, test loss-1.9470, acc-0.4530
Iter-36740, train loss-1.9189, acc-0.6000, valid loss-1.9460, acc-0.4636, test loss-1.9469, acc-0.4530
Iter-36750, train loss-2.0354, acc-0.3800, valid loss-1.9460, acc-0.4640, test loss-1.9468, acc-0.4530
Iter-36760, train loss-1.9646, acc-0.4400, valid loss-1.9459, acc-0.4636, test loss-1.9468, acc-0.4533
Iter-36770, train loss-2.0267, acc-0.3200, valid loss-1.9458, acc-0.4640, test loss-1.9467, acc-0.4529
Iter-36780, train loss-1.9046, acc-0.4200, valid loss-1.9458, acc-0.4638, test loss-1.9466, acc-0.4531
Iter-36790, train loss-1.9295, acc-0.5200, valid loss-1.9457, acc-0.4636, test loss-1.9466, acc-0.4534
Iter-36800, train loss-1.9388, acc-0.5800, valid loss-1.9456, acc-0.4636, test loss-1.9465, acc-0.4534
Iter-36810, train loss-1.9299, acc-0.5200, valid loss-1.9456, acc-0.4638, test loss-1.9464, acc-0.4535
Iter-36820, train loss-1.9218, acc-0.4400, valid loss-1.9455, acc-0.4636, test loss-1.9464, acc-0.4534
Iter-36830, train loss-1.9406, acc-0.4800, valid loss-1.9454, acc-0.4638, test loss-1.9463, acc-0.4535
Iter-36840, train loss-1.9087, acc-0.5600, valid loss-1.9453, acc-0.4638, test loss-1.9462, acc-0.4535
Iter-36850, train loss-1.9165, acc-0.4600, valid loss-1.9453, acc-0.4638, test loss-1.9461, acc-0.4535
Iter-36860, train loss-2.0064, acc-0.4200, valid loss-1.9452, acc-0.4638, test loss-1.9461, acc-0.4533
Iter-36870, train loss-2.0211, acc-0.4600, valid loss-1.9451, acc-0.4638, test loss-1.9460, acc-0.4533
Iter-36880, train loss-1.9388, acc-0.4800, valid loss-1.9451, acc-0.4638, test loss-1.9459, acc-0.4533
Iter-36890, train loss-2.0455, acc-0.3600, valid loss-1.9450, acc-0.4638, test loss-1.9459, acc-0.4535
Iter-36900, train loss-1.9041, acc-0.5600, valid loss-1.9449, acc-0.4638, test loss-1.9458, acc-0.4533
Iter-36910, train loss-1.9967, acc-0.4400, valid loss-1.9448, acc-0.4640, test loss-1.9457, acc-0.4535
Iter-36920, train loss-1.9713, acc-0.3800, valid loss-1.9448, acc-0.4638, test loss-1.9456, acc-0.4535
Iter-36930, train loss-1.9785, acc-0.4000, valid loss-1.9447, acc-0.4640, test loss-1.9456, acc-0.4534
Iter-36940, train loss-2.0178, acc-0.3600, valid loss-1.9446, acc-0.4642, test loss-1.9455, acc-0.4528
Iter-36950, train loss-2.0734, acc-0.3200, valid loss-1.9446, acc-0.4640, test loss-1.9454, acc-0.4534
Iter-36960, train loss-1.9046, acc-0.4800, valid loss-1.9445, acc-0.4640, test loss-1.9454, acc-0.4534
Iter-36970, train loss-1.9770, acc-0.4200, valid loss-1.9444, acc-0.4640, test loss-1.9453, acc-0.4537
Iter-36980, train loss-1.9650, acc-0.4200, valid loss-1.9444, acc-0.4640, test loss-1.9452, acc-0.4539
Iter-36990, train loss-1.9161, acc-0.5200, valid loss-1.9443, acc-0.4640, test loss-1.9452, acc-0.4537
Iter-37000, train loss-2.0076, acc-0.4200, valid loss-1.9442, acc-0.4640, test loss-1.9451, acc-0.4538
Iter-37010, train loss-1.8994, acc-0.4600, valid loss-1.9441, acc-0.4640, test loss-1.9450, acc-0.4540
Iter-37020, train loss-1.9373, acc-0.4200, valid loss-1.9441, acc-0.4640, test loss-1.9450, acc-0.4540
Iter-37030, train loss-1.9928, acc-0.3600, valid loss-1.9440, acc-0.4644, test loss-1.9449, acc-0.4537
Iter-37040, train loss-2.0415, acc-0.3000, valid loss-1.9439, acc-0.4646, test loss-1.9448, acc-0.4538
Iter-37050, train loss-1.8494, acc-0.5400, valid loss-1.9439, acc-0.4644, test loss-1.9447, acc-0.4538
Iter-37060, train loss-2.0278, acc-0.3000, valid loss-1.9438, acc-0.4642, test loss-1.9447, acc-0.4538
Iter-37070, train loss-1.9347, acc-0.4200, valid loss-1.9437, acc-0.4638, test loss-1.9446, acc-0.4540
Iter-37080, train loss-1.9029, acc-0.5200, valid loss-1.9436, acc-0.4640, test loss-1.9445, acc-0.4540
Iter-37090, train loss-1.9125, acc-0.4800, valid loss-1.9436, acc-0.4638, test loss-1.9445, acc-0.4539
Iter-37100, train loss-1.8572, acc-0.5600, valid loss-1.9435, acc-0.4640, test loss-1.9444, acc-0.4538
Iter-37110, train loss-1.9696, acc-0.4400, valid loss-1.9434, acc-0.4642, test loss-1.9443, acc-0.4538
Iter-37120, train loss-1.9151, acc-0.4400, valid loss-1.9433, acc-0.4640, test loss-1.9442, acc-0.4536
Iter-37130, train loss-1.9930, acc-0.3600, valid loss-1.9433, acc-0.4642, test loss-1.9442, acc-0.4537
Iter-37140, train loss-1.9438, acc-0.4400, valid loss-1.9432, acc-0.4642, test loss-1.9441, acc-0.4536
Iter-37150, train loss-1.9706, acc-0.3800, valid loss-1.9431, acc-0.4642, test loss-1.9440, acc-0.4537
Iter-37160, train loss-1.9730, acc-0.4000, valid loss-1.9431, acc-0.4642, test loss-1.9439, acc-0.4537
Iter-37170, train loss-1.9590, acc-0.4200, valid loss-1.9430, acc-0.4642, test loss-1.9439, acc-0.4538
Iter-37180, train loss-1.9142, acc-0.4200, valid loss-1.9429, acc-0.4640, test loss-1.9438, acc-0.4536
Iter-37190, train loss-1.9371, acc-0.4800, valid loss-1.9428, acc-0.4640, test loss-1.9437, acc-0.4539
Iter-37200, train loss-2.0404, acc-0.3800, valid loss-1.9428, acc-0.4640, test loss-1.9437, acc-0.4539
Iter-37210, train loss-1.9564, acc-0.5200, valid loss-1.9427, acc-0.4642, test loss-1.9436, acc-0.4538
Iter-37220, train loss-1.9917, acc-0.3400, valid loss-1.9426, acc-0.4642, test loss-1.9435, acc-0.4540
Iter-37230, train loss-1.9638, acc-0.4800, valid loss-1.9426, acc-0.4644, test loss-1.9435, acc-0.4540
Iter-37240, train loss-1.8957, acc-0.5000, valid loss-1.9425, acc-0.4644, test loss-1.9434, acc-0.4540
Iter-37250, train loss-1.9529, acc-0.4000, valid loss-1.9424, acc-0.4644, test loss-1.9433, acc-0.4541
Iter-37260, train loss-1.9773, acc-0.3200, valid loss-1.9424, acc-0.4646, test loss-1.9432, acc-0.4542
Iter-37270, train loss-1.9777, acc-0.4000, valid loss-1.9423, acc-0.4646, test loss-1.9432, acc-0.4544
Iter-37280, train loss-2.0235, acc-0.3400, valid loss-1.9422, acc-0.4646, test loss-1.9431, acc-0.4544
Iter-37290, train loss-1.9737, acc-0.5000, valid loss-1.9421, acc-0.4646, test loss-1.9430, acc-0.4545
Iter-37300, train loss-1.9906, acc-0.3800, valid loss-1.9421, acc-0.4646, test loss-1.9430, acc-0.4544
Iter-37310, train loss-1.8836, acc-0.5400, valid loss-1.9420, acc-0.4646, test loss-1.9429, acc-0.4546
Iter-37320, train loss-1.9963, acc-0.3800, valid loss-1.9419, acc-0.4646, test loss-1.9428, acc-0.4548
Iter-37330, train loss-1.9606, acc-0.4600, valid loss-1.9419, acc-0.4648, test loss-1.9427, acc-0.4547
Iter-37340, train loss-1.9418, acc-0.4800, valid loss-1.9418, acc-0.4646, test loss-1.9427, acc-0.4547
Iter-37350, train loss-1.9392, acc-0.4200, valid loss-1.9417, acc-0.4646, test loss-1.9426, acc-0.4550
Iter-37360, train loss-1.9300, acc-0.3800, valid loss-1.9416, acc-0.4646, test loss-1.9425, acc-0.4550
Iter-37370, train loss-1.9440, acc-0.4800, valid loss-1.9416, acc-0.4648, test loss-1.9425, acc-0.4550
Iter-37380, train loss-1.8734, acc-0.5200, valid loss-1.9415, acc-0.4648, test loss-1.9424, acc-0.4547
Iter-37390, train loss-1.9696, acc-0.5200, valid loss-1.9414, acc-0.4648, test loss-1.9423, acc-0.4549
Iter-37400, train loss-1.9371, acc-0.3800, valid loss-1.9414, acc-0.4648, test loss-1.9423, acc-0.4549
Iter-37410, train loss-1.9112, acc-0.4400, valid loss-1.9413, acc-0.4648, test loss-1.9422, acc-0.4549
Iter-37420, train loss-2.0167, acc-0.3800, valid loss-1.9412, acc-0.4648, test loss-1.9421, acc-0.4551
Iter-37430, train loss-2.0485, acc-0.2800, valid loss-1.9412, acc-0.4648, test loss-1.9420, acc-0.4550
Iter-37440, train loss-1.9945, acc-0.4000, valid loss-1.9411, acc-0.4648, test loss-1.9420, acc-0.4552
Iter-37450, train loss-1.9067, acc-0.4800, valid loss-1.9410, acc-0.4648, test loss-1.9419, acc-0.4553
Iter-37460, train loss-1.9515, acc-0.4200, valid loss-1.9409, acc-0.4648, test loss-1.9418, acc-0.4554
Iter-37470, train loss-1.9590, acc-0.4200, valid loss-1.9409, acc-0.4648, test loss-1.9418, acc-0.4555
Iter-37480, train loss-1.9341, acc-0.5000, valid loss-1.9408, acc-0.4646, test loss-1.9417, acc-0.4554
Iter-37490, train loss-1.9032, acc-0.4800, valid loss-1.9407, acc-0.4646, test loss-1.9416, acc-0.4556
Iter-37500, train loss-1.8488, acc-0.5400, valid loss-1.9407, acc-0.4648, test loss-1.9415, acc-0.4556
Iter-37510, train loss-1.9227, acc-0.4800, valid loss-1.9406, acc-0.4646, test loss-1.9415, acc-0.4556
Iter-37520, train loss-1.9293, acc-0.4400, valid loss-1.9405, acc-0.4646, test loss-1.9414, acc-0.4558
Iter-37530, train loss-1.9766, acc-0.5000, valid loss-1.9404, acc-0.4646, test loss-1.9413, acc-0.4560
Iter-37540, train loss-1.8921, acc-0.5200, valid loss-1.9404, acc-0.4650, test loss-1.9413, acc-0.4557
Iter-37550, train loss-1.9354, acc-0.4400, valid loss-1.9403, acc-0.4652, test loss-1.9412, acc-0.4558
Iter-37560, train loss-1.9056, acc-0.6000, valid loss-1.9402, acc-0.4652, test loss-1.9411, acc-0.4559
Iter-37570, train loss-1.9835, acc-0.3600, valid loss-1.9402, acc-0.4650, test loss-1.9411, acc-0.4559
Iter-37580, train loss-1.8922, acc-0.5200, valid loss-1.9401, acc-0.4650, test loss-1.9410, acc-0.4558
Iter-37590, train loss-1.9044, acc-0.4600, valid loss-1.9400, acc-0.4650, test loss-1.9409, acc-0.4557
Iter-37600, train loss-1.9754, acc-0.4400, valid loss-1.9400, acc-0.4650, test loss-1.9408, acc-0.4559
Iter-37610, train loss-1.9793, acc-0.3200, valid loss-1.9399, acc-0.4648, test loss-1.9408, acc-0.4558
Iter-37620, train loss-1.9027, acc-0.5600, valid loss-1.9398, acc-0.4648, test loss-1.9407, acc-0.4559
Iter-37630, train loss-1.9517, acc-0.5000, valid loss-1.9398, acc-0.4646, test loss-1.9406, acc-0.4559
Iter-37640, train loss-1.8986, acc-0.4600, valid loss-1.9397, acc-0.4648, test loss-1.9406, acc-0.4559
Iter-37650, train loss-1.9436, acc-0.5000, valid loss-1.9396, acc-0.4652, test loss-1.9405, acc-0.4559
Iter-37660, train loss-2.0429, acc-0.4200, valid loss-1.9395, acc-0.4648, test loss-1.9404, acc-0.4559
Iter-37670, train loss-1.9442, acc-0.4600, valid loss-1.9395, acc-0.4648, test loss-1.9404, acc-0.4555
Iter-37680, train loss-1.9222, acc-0.4800, valid loss-1.9394, acc-0.4648, test loss-1.9403, acc-0.4557
Iter-37690, train loss-1.9659, acc-0.4200, valid loss-1.9393, acc-0.4648, test loss-1.9402, acc-0.4558
Iter-37700, train loss-1.9396, acc-0.4200, valid loss-1.9393, acc-0.4648, test loss-1.9401, acc-0.4559
Iter-37710, train loss-1.8930, acc-0.4800, valid loss-1.9392, acc-0.4648, test loss-1.9401, acc-0.4559
Iter-37720, train loss-1.9554, acc-0.4400, valid loss-1.9391, acc-0.4650, test loss-1.9400, acc-0.4558
Iter-37730, train loss-1.9133, acc-0.5000, valid loss-1.9390, acc-0.4648, test loss-1.9399, acc-0.4559
Iter-37740, train loss-1.9580, acc-0.5400, valid loss-1.9390, acc-0.4650, test loss-1.9399, acc-0.4559
Iter-37750, train loss-1.8555, acc-0.6600, valid loss-1.9389, acc-0.4650, test loss-1.9398, acc-0.4560
Iter-37760, train loss-2.0212, acc-0.3600, valid loss-1.9388, acc-0.4654, test loss-1.9397, acc-0.4560
Iter-37770, train loss-2.0246, acc-0.4000, valid loss-1.9388, acc-0.4654, test loss-1.9396, acc-0.4560
Iter-37780, train loss-1.9817, acc-0.3800, valid loss-1.9387, acc-0.4652, test loss-1.9396, acc-0.4560
Iter-37790, train loss-2.0673, acc-0.3000, valid loss-1.9386, acc-0.4652, test loss-1.9395, acc-0.4558
Iter-37800, train loss-2.0664, acc-0.3400, valid loss-1.9386, acc-0.4652, test loss-1.9394, acc-0.4559
Iter-37810, train loss-1.9196, acc-0.5600, valid loss-1.9385, acc-0.4654, test loss-1.9394, acc-0.4557
Iter-37820, train loss-1.9517, acc-0.4200, valid loss-1.9384, acc-0.4654, test loss-1.9393, acc-0.4557
Iter-37830, train loss-1.9886, acc-0.4200, valid loss-1.9383, acc-0.4656, test loss-1.9392, acc-0.4557
Iter-37840, train loss-1.9797, acc-0.4200, valid loss-1.9383, acc-0.4656, test loss-1.9392, acc-0.4558
Iter-37850, train loss-1.9495, acc-0.4400, valid loss-1.9382, acc-0.4660, test loss-1.9391, acc-0.4558
Iter-37860, train loss-1.9803, acc-0.3400, valid loss-1.9381, acc-0.4658, test loss-1.9390, acc-0.4557
Iter-37870, train loss-1.9715, acc-0.3600, valid loss-1.9381, acc-0.4658, test loss-1.9390, acc-0.4558
Iter-37880, train loss-2.0443, acc-0.3600, valid loss-1.9380, acc-0.4662, test loss-1.9389, acc-0.4560
Iter-37890, train loss-1.9683, acc-0.4400, valid loss-1.9379, acc-0.4662, test loss-1.9388, acc-0.4562
Iter-37900, train loss-2.0102, acc-0.4000, valid loss-1.9379, acc-0.4664, test loss-1.9387, acc-0.4564
Iter-37910, train loss-1.9571, acc-0.4000, valid loss-1.9378, acc-0.4664, test loss-1.9387, acc-0.4564
Iter-37920, train loss-2.0296, acc-0.3000, valid loss-1.9377, acc-0.4660, test loss-1.9386, acc-0.4562
Iter-37930, train loss-1.9264, acc-0.4600, valid loss-1.9376, acc-0.4658, test loss-1.9385, acc-0.4561
Iter-37940, train loss-2.0341, acc-0.3800, valid loss-1.9376, acc-0.4660, test loss-1.9385, acc-0.4562
Iter-37950, train loss-1.9742, acc-0.4000, valid loss-1.9375, acc-0.4660, test loss-1.9384, acc-0.4562
Iter-37960, train loss-1.9475, acc-0.5000, valid loss-1.9374, acc-0.4658, test loss-1.9383, acc-0.4563
Iter-37970, train loss-1.9630, acc-0.5200, valid loss-1.9374, acc-0.4662, test loss-1.9383, acc-0.4561
Iter-37980, train loss-2.0160, acc-0.3800, valid loss-1.9373, acc-0.4662, test loss-1.9382, acc-0.4560
Iter-37990, train loss-1.9590, acc-0.4800, valid loss-1.9372, acc-0.4662, test loss-1.9381, acc-0.4560
Iter-38000, train loss-1.9276, acc-0.4600, valid loss-1.9372, acc-0.4664, test loss-1.9380, acc-0.4560
Iter-38010, train loss-1.8942, acc-0.4400, valid loss-1.9371, acc-0.4660, test loss-1.9380, acc-0.4560
Iter-38020, train loss-1.9585, acc-0.5200, valid loss-1.9370, acc-0.4662, test loss-1.9379, acc-0.4560
Iter-38030, train loss-1.9642, acc-0.3400, valid loss-1.9369, acc-0.4666, test loss-1.9378, acc-0.4560
Iter-38040, train loss-1.9263, acc-0.4200, valid loss-1.9369, acc-0.4668, test loss-1.9377, acc-0.4558
Iter-38050, train loss-1.9319, acc-0.5200, valid loss-1.9368, acc-0.4670, test loss-1.9377, acc-0.4560
Iter-38060, train loss-1.9516, acc-0.4800, valid loss-1.9367, acc-0.4670, test loss-1.9376, acc-0.4561
Iter-38070, train loss-1.8731, acc-0.4800, valid loss-1.9367, acc-0.4670, test loss-1.9375, acc-0.4561
Iter-38080, train loss-1.9352, acc-0.5200, valid loss-1.9366, acc-0.4666, test loss-1.9375, acc-0.4563
Iter-38090, train loss-1.8898, acc-0.6400, valid loss-1.9365, acc-0.4668, test loss-1.9374, acc-0.4563
Iter-38100, train loss-1.9351, acc-0.4200, valid loss-1.9365, acc-0.4668, test loss-1.9373, acc-0.4561
Iter-38110, train loss-1.9648, acc-0.3200, valid loss-1.9364, acc-0.4668, test loss-1.9373, acc-0.4560
Iter-38120, train loss-1.9407, acc-0.4800, valid loss-1.9363, acc-0.4666, test loss-1.9372, acc-0.4563
Iter-38130, train loss-1.9217, acc-0.5200, valid loss-1.9363, acc-0.4666, test loss-1.9371, acc-0.4562
Iter-38140, train loss-1.9753, acc-0.4400, valid loss-1.9362, acc-0.4670, test loss-1.9371, acc-0.4561
Iter-38150, train loss-1.8978, acc-0.5400, valid loss-1.9361, acc-0.4668, test loss-1.9370, acc-0.4563
Iter-38160, train loss-1.8973, acc-0.5200, valid loss-1.9361, acc-0.4666, test loss-1.9369, acc-0.4563
Iter-38170, train loss-1.9295, acc-0.4600, valid loss-1.9360, acc-0.4668, test loss-1.9369, acc-0.4565
Iter-38180, train loss-1.8886, acc-0.4600, valid loss-1.9359, acc-0.4666, test loss-1.9368, acc-0.4565
Iter-38190, train loss-1.9994, acc-0.4000, valid loss-1.9358, acc-0.4668, test loss-1.9367, acc-0.4562
Iter-38200, train loss-1.8688, acc-0.5800, valid loss-1.9358, acc-0.4666, test loss-1.9366, acc-0.4561
Iter-38210, train loss-1.9341, acc-0.4600, valid loss-1.9357, acc-0.4670, test loss-1.9366, acc-0.4561
Iter-38220, train loss-1.8886, acc-0.5000, valid loss-1.9356, acc-0.4668, test loss-1.9365, acc-0.4563
Iter-38230, train loss-1.9747, acc-0.4000, valid loss-1.9356, acc-0.4668, test loss-1.9364, acc-0.4567
Iter-38240, train loss-1.9177, acc-0.4000, valid loss-1.9355, acc-0.4666, test loss-1.9364, acc-0.4568
Iter-38250, train loss-2.0058, acc-0.4200, valid loss-1.9354, acc-0.4668, test loss-1.9363, acc-0.4566
Iter-38260, train loss-1.9220, acc-0.4800, valid loss-1.9354, acc-0.4668, test loss-1.9362, acc-0.4566
Iter-38270, train loss-1.9400, acc-0.4400, valid loss-1.9353, acc-0.4668, test loss-1.9361, acc-0.4567
Iter-38280, train loss-1.9285, acc-0.5400, valid loss-1.9352, acc-0.4666, test loss-1.9361, acc-0.4567
Iter-38290, train loss-2.0279, acc-0.3800, valid loss-1.9351, acc-0.4666, test loss-1.9360, acc-0.4566
Iter-38300, train loss-1.9219, acc-0.5000, valid loss-1.9351, acc-0.4666, test loss-1.9359, acc-0.4565
Iter-38310, train loss-2.0028, acc-0.3000, valid loss-1.9350, acc-0.4666, test loss-1.9359, acc-0.4566
Iter-38320, train loss-1.9171, acc-0.4000, valid loss-1.9349, acc-0.4668, test loss-1.9358, acc-0.4565
Iter-38330, train loss-1.9303, acc-0.4800, valid loss-1.9349, acc-0.4666, test loss-1.9357, acc-0.4567
Iter-38340, train loss-1.9535, acc-0.4400, valid loss-1.9348, acc-0.4666, test loss-1.9357, acc-0.4568
Iter-38350, train loss-1.9630, acc-0.4800, valid loss-1.9347, acc-0.4666, test loss-1.9356, acc-0.4568
Iter-38360, train loss-1.8637, acc-0.5600, valid loss-1.9347, acc-0.4666, test loss-1.9355, acc-0.4568
Iter-38370, train loss-1.9773, acc-0.4400, valid loss-1.9346, acc-0.4668, test loss-1.9355, acc-0.4571
Iter-38380, train loss-1.9088, acc-0.5200, valid loss-1.9345, acc-0.4668, test loss-1.9354, acc-0.4570
Iter-38390, train loss-2.0130, acc-0.3800, valid loss-1.9345, acc-0.4672, test loss-1.9353, acc-0.4570
Iter-38400, train loss-2.0120, acc-0.3400, valid loss-1.9344, acc-0.4668, test loss-1.9352, acc-0.4570
Iter-38410, train loss-1.9150, acc-0.4400, valid loss-1.9343, acc-0.4670, test loss-1.9352, acc-0.4571
Iter-38420, train loss-1.8962, acc-0.5000, valid loss-1.9343, acc-0.4672, test loss-1.9351, acc-0.4571
Iter-38430, train loss-1.9288, acc-0.4800, valid loss-1.9342, acc-0.4674, test loss-1.9350, acc-0.4571
Iter-38440, train loss-1.9898, acc-0.3800, valid loss-1.9341, acc-0.4674, test loss-1.9350, acc-0.4573
Iter-38450, train loss-1.9594, acc-0.3600, valid loss-1.9341, acc-0.4674, test loss-1.9349, acc-0.4575
Iter-38460, train loss-1.9311, acc-0.5200, valid loss-1.9340, acc-0.4676, test loss-1.9348, acc-0.4575
Iter-38470, train loss-1.8929, acc-0.4400, valid loss-1.9339, acc-0.4676, test loss-1.9348, acc-0.4574
Iter-38480, train loss-1.9907, acc-0.3400, valid loss-1.9339, acc-0.4676, test loss-1.9347, acc-0.4573
Iter-38490, train loss-1.9266, acc-0.5000, valid loss-1.9338, acc-0.4674, test loss-1.9346, acc-0.4574
Iter-38500, train loss-1.8794, acc-0.4800, valid loss-1.9337, acc-0.4676, test loss-1.9346, acc-0.4574
Iter-38510, train loss-1.8848, acc-0.5000, valid loss-1.9336, acc-0.4672, test loss-1.9345, acc-0.4573
Iter-38520, train loss-1.9928, acc-0.3600, valid loss-1.9336, acc-0.4676, test loss-1.9344, acc-0.4573
Iter-38530, train loss-1.9154, acc-0.5000, valid loss-1.9335, acc-0.4670, test loss-1.9343, acc-0.4576
Iter-38540, train loss-1.8870, acc-0.4800, valid loss-1.9334, acc-0.4676, test loss-1.9343, acc-0.4579
Iter-38550, train loss-1.8770, acc-0.5400, valid loss-1.9334, acc-0.4672, test loss-1.9342, acc-0.4578
Iter-38560, train loss-1.8994, acc-0.4800, valid loss-1.9333, acc-0.4672, test loss-1.9341, acc-0.4576
Iter-38570, train loss-1.8945, acc-0.5200, valid loss-1.9332, acc-0.4674, test loss-1.9341, acc-0.4578
Iter-38580, train loss-1.9502, acc-0.5000, valid loss-1.9332, acc-0.4674, test loss-1.9340, acc-0.4578
Iter-38590, train loss-1.9552, acc-0.4400, valid loss-1.9331, acc-0.4676, test loss-1.9339, acc-0.4580
Iter-38600, train loss-1.8737, acc-0.5200, valid loss-1.9330, acc-0.4676, test loss-1.9339, acc-0.4578
Iter-38610, train loss-1.8895, acc-0.4000, valid loss-1.9330, acc-0.4678, test loss-1.9338, acc-0.4578
Iter-38620, train loss-1.8849, acc-0.6000, valid loss-1.9329, acc-0.4676, test loss-1.9337, acc-0.4577
Iter-38630, train loss-1.9252, acc-0.4200, valid loss-1.9328, acc-0.4678, test loss-1.9336, acc-0.4576
Iter-38640, train loss-1.9627, acc-0.4000, valid loss-1.9327, acc-0.4678, test loss-1.9336, acc-0.4577
Iter-38650, train loss-1.9809, acc-0.5000, valid loss-1.9327, acc-0.4676, test loss-1.9335, acc-0.4579
Iter-38660, train loss-1.9249, acc-0.4400, valid loss-1.9326, acc-0.4678, test loss-1.9334, acc-0.4580
Iter-38670, train loss-1.9606, acc-0.4800, valid loss-1.9325, acc-0.4678, test loss-1.9334, acc-0.4581
Iter-38680, train loss-1.9431, acc-0.4600, valid loss-1.9325, acc-0.4676, test loss-1.9333, acc-0.4583
Iter-38690, train loss-1.9467, acc-0.4600, valid loss-1.9324, acc-0.4676, test loss-1.9333, acc-0.4581
Iter-38700, train loss-1.9372, acc-0.4600, valid loss-1.9323, acc-0.4676, test loss-1.9332, acc-0.4583
Iter-38710, train loss-1.9573, acc-0.5000, valid loss-1.9323, acc-0.4676, test loss-1.9331, acc-0.4581
Iter-38720, train loss-1.9855, acc-0.4200, valid loss-1.9322, acc-0.4678, test loss-1.9330, acc-0.4581
Iter-38730, train loss-1.8716, acc-0.5400, valid loss-1.9321, acc-0.4676, test loss-1.9330, acc-0.4584
Iter-38740, train loss-1.9088, acc-0.5800, valid loss-1.9321, acc-0.4674, test loss-1.9329, acc-0.4583
Iter-38750, train loss-1.9299, acc-0.4800, valid loss-1.9320, acc-0.4674, test loss-1.9328, acc-0.4583
Iter-38760, train loss-1.8999, acc-0.4800, valid loss-1.9319, acc-0.4678, test loss-1.9328, acc-0.4584
Iter-38770, train loss-2.0177, acc-0.3800, valid loss-1.9319, acc-0.4676, test loss-1.9327, acc-0.4584
Iter-38780, train loss-1.9027, acc-0.5000, valid loss-1.9318, acc-0.4676, test loss-1.9326, acc-0.4583
Iter-38790, train loss-1.9184, acc-0.4400, valid loss-1.9317, acc-0.4674, test loss-1.9326, acc-0.4582
Iter-38800, train loss-2.0161, acc-0.4000, valid loss-1.9317, acc-0.4678, test loss-1.9325, acc-0.4583
Iter-38810, train loss-1.9636, acc-0.3400, valid loss-1.9316, acc-0.4674, test loss-1.9324, acc-0.4584
Iter-38820, train loss-1.9104, acc-0.5400, valid loss-1.9315, acc-0.4674, test loss-1.9323, acc-0.4583
Iter-38830, train loss-1.9092, acc-0.5800, valid loss-1.9315, acc-0.4676, test loss-1.9323, acc-0.4585
Iter-38840, train loss-1.9202, acc-0.5800, valid loss-1.9314, acc-0.4678, test loss-1.9322, acc-0.4584
Iter-38850, train loss-1.8653, acc-0.6000, valid loss-1.9313, acc-0.4680, test loss-1.9321, acc-0.4585
Iter-38860, train loss-1.9420, acc-0.4600, valid loss-1.9312, acc-0.4680, test loss-1.9321, acc-0.4584
Iter-38870, train loss-1.9158, acc-0.4800, valid loss-1.9312, acc-0.4682, test loss-1.9320, acc-0.4584
Iter-38880, train loss-1.9609, acc-0.4600, valid loss-1.9311, acc-0.4684, test loss-1.9319, acc-0.4586
Iter-38890, train loss-2.0601, acc-0.2800, valid loss-1.9310, acc-0.4682, test loss-1.9319, acc-0.4588
Iter-38900, train loss-2.0520, acc-0.3200, valid loss-1.9310, acc-0.4684, test loss-1.9318, acc-0.4588
Iter-38910, train loss-1.9907, acc-0.4200, valid loss-1.9309, acc-0.4686, test loss-1.9317, acc-0.4589
Iter-38920, train loss-1.9344, acc-0.4400, valid loss-1.9308, acc-0.4686, test loss-1.9317, acc-0.4591
Iter-38930, train loss-1.9903, acc-0.4600, valid loss-1.9308, acc-0.4684, test loss-1.9316, acc-0.4590
Iter-38940, train loss-1.9634, acc-0.4000, valid loss-1.9307, acc-0.4682, test loss-1.9315, acc-0.4589
Iter-38950, train loss-1.9184, acc-0.5200, valid loss-1.9306, acc-0.4680, test loss-1.9315, acc-0.4590
Iter-38960, train loss-1.9177, acc-0.5000, valid loss-1.9306, acc-0.4678, test loss-1.9314, acc-0.4589
Iter-38970, train loss-1.8552, acc-0.5400, valid loss-1.9305, acc-0.4684, test loss-1.9313, acc-0.4588
Iter-38980, train loss-1.8984, acc-0.5200, valid loss-1.9304, acc-0.4682, test loss-1.9312, acc-0.4589
Iter-38990, train loss-1.9711, acc-0.4400, valid loss-1.9303, acc-0.4682, test loss-1.9312, acc-0.4591
Iter-39000, train loss-1.9519, acc-0.5200, valid loss-1.9303, acc-0.4682, test loss-1.9311, acc-0.4589
Iter-39010, train loss-1.9045, acc-0.4800, valid loss-1.9302, acc-0.4682, test loss-1.9310, acc-0.4587
Iter-39020, train loss-1.9427, acc-0.4200, valid loss-1.9301, acc-0.4682, test loss-1.9310, acc-0.4590
Iter-39030, train loss-1.8954, acc-0.4400, valid loss-1.9301, acc-0.4682, test loss-1.9309, acc-0.4588
Iter-39040, train loss-1.9814, acc-0.3800, valid loss-1.9300, acc-0.4684, test loss-1.9308, acc-0.4586
Iter-39050, train loss-1.9176, acc-0.4400, valid loss-1.9299, acc-0.4682, test loss-1.9308, acc-0.4586
Iter-39060, train loss-1.8891, acc-0.5000, valid loss-1.9299, acc-0.4684, test loss-1.9307, acc-0.4588
Iter-39070, train loss-2.0054, acc-0.3400, valid loss-1.9298, acc-0.4682, test loss-1.9306, acc-0.4588
Iter-39080, train loss-1.9676, acc-0.4400, valid loss-1.9297, acc-0.4684, test loss-1.9306, acc-0.4586
Iter-39090, train loss-1.9945, acc-0.3800, valid loss-1.9297, acc-0.4684, test loss-1.9305, acc-0.4586
Iter-39100, train loss-1.9515, acc-0.4800, valid loss-1.9296, acc-0.4686, test loss-1.9304, acc-0.4586
Iter-39110, train loss-1.9235, acc-0.4800, valid loss-1.9295, acc-0.4686, test loss-1.9304, acc-0.4588
Iter-39120, train loss-1.9899, acc-0.4000, valid loss-1.9294, acc-0.4686, test loss-1.9303, acc-0.4590
Iter-39130, train loss-1.9553, acc-0.5000, valid loss-1.9294, acc-0.4686, test loss-1.9302, acc-0.4587
Iter-39140, train loss-2.0056, acc-0.3800, valid loss-1.9293, acc-0.4686, test loss-1.9301, acc-0.4588
Iter-39150, train loss-1.9981, acc-0.4600, valid loss-1.9292, acc-0.4684, test loss-1.9301, acc-0.4590
Iter-39160, train loss-1.8880, acc-0.5400, valid loss-1.9292, acc-0.4686, test loss-1.9300, acc-0.4591
Iter-39170, train loss-1.9415, acc-0.4200, valid loss-1.9291, acc-0.4684, test loss-1.9299, acc-0.4590
Iter-39180, train loss-1.9091, acc-0.4400, valid loss-1.9290, acc-0.4684, test loss-1.9299, acc-0.4591
Iter-39190, train loss-1.9253, acc-0.3600, valid loss-1.9290, acc-0.4684, test loss-1.9298, acc-0.4589
Iter-39200, train loss-1.9373, acc-0.5600, valid loss-1.9289, acc-0.4684, test loss-1.9297, acc-0.4590
Iter-39210, train loss-1.9347, acc-0.4800, valid loss-1.9288, acc-0.4684, test loss-1.9297, acc-0.4591
Iter-39220, train loss-1.9415, acc-0.4400, valid loss-1.9287, acc-0.4684, test loss-1.9296, acc-0.4589
Iter-39230, train loss-1.9719, acc-0.3800, valid loss-1.9287, acc-0.4684, test loss-1.9295, acc-0.4592
Iter-39240, train loss-1.9839, acc-0.4200, valid loss-1.9286, acc-0.4684, test loss-1.9295, acc-0.4590
Iter-39250, train loss-1.9076, acc-0.4800, valid loss-1.9285, acc-0.4684, test loss-1.9294, acc-0.4589
Iter-39260, train loss-1.9660, acc-0.4600, valid loss-1.9285, acc-0.4684, test loss-1.9293, acc-0.4589
Iter-39270, train loss-1.9396, acc-0.4600, valid loss-1.9284, acc-0.4684, test loss-1.9293, acc-0.4591
Iter-39280, train loss-1.9080, acc-0.4600, valid loss-1.9283, acc-0.4684, test loss-1.9292, acc-0.4587
Iter-39290, train loss-1.9872, acc-0.4600, valid loss-1.9283, acc-0.4684, test loss-1.9291, acc-0.4585
Iter-39300, train loss-1.8790, acc-0.5200, valid loss-1.9282, acc-0.4684, test loss-1.9291, acc-0.4583
Iter-39310, train loss-1.9980, acc-0.4000, valid loss-1.9281, acc-0.4690, test loss-1.9290, acc-0.4587
Iter-39320, train loss-1.9431, acc-0.3400, valid loss-1.9281, acc-0.4692, test loss-1.9289, acc-0.4586
Iter-39330, train loss-1.9576, acc-0.3200, valid loss-1.9280, acc-0.4688, test loss-1.9289, acc-0.4588
Iter-39340, train loss-2.0054, acc-0.4000, valid loss-1.9279, acc-0.4694, test loss-1.9288, acc-0.4590
Iter-39350, train loss-1.9574, acc-0.3400, valid loss-1.9279, acc-0.4694, test loss-1.9287, acc-0.4587
Iter-39360, train loss-1.8852, acc-0.4800, valid loss-1.9278, acc-0.4694, test loss-1.9287, acc-0.4588
Iter-39370, train loss-1.9959, acc-0.3800, valid loss-1.9277, acc-0.4694, test loss-1.9286, acc-0.4588
Iter-39380, train loss-1.9081, acc-0.5600, valid loss-1.9277, acc-0.4694, test loss-1.9285, acc-0.4588
Iter-39390, train loss-1.9193, acc-0.5200, valid loss-1.9276, acc-0.4694, test loss-1.9285, acc-0.4589
Iter-39400, train loss-1.9063, acc-0.4400, valid loss-1.9275, acc-0.4698, test loss-1.9284, acc-0.4587
Iter-39410, train loss-1.8957, acc-0.4800, valid loss-1.9274, acc-0.4696, test loss-1.9283, acc-0.4586
Iter-39420, train loss-1.9223, acc-0.4600, valid loss-1.9274, acc-0.4696, test loss-1.9282, acc-0.4586
Iter-39430, train loss-1.9494, acc-0.4800, valid loss-1.9273, acc-0.4698, test loss-1.9282, acc-0.4586
Iter-39440, train loss-1.8893, acc-0.5200, valid loss-1.9272, acc-0.4696, test loss-1.9281, acc-0.4586
Iter-39450, train loss-1.8140, acc-0.5200, valid loss-1.9272, acc-0.4696, test loss-1.9280, acc-0.4587
Iter-39460, train loss-1.9199, acc-0.5200, valid loss-1.9271, acc-0.4698, test loss-1.9280, acc-0.4588
Iter-39470, train loss-1.9164, acc-0.4800, valid loss-1.9270, acc-0.4698, test loss-1.9279, acc-0.4591
Iter-39480, train loss-1.9370, acc-0.5000, valid loss-1.9270, acc-0.4698, test loss-1.9278, acc-0.4592
Iter-39490, train loss-1.9509, acc-0.3800, valid loss-1.9269, acc-0.4698, test loss-1.9278, acc-0.4588
Iter-39500, train loss-1.8350, acc-0.5200, valid loss-1.9268, acc-0.4700, test loss-1.9277, acc-0.4587
Iter-39510, train loss-2.0143, acc-0.3800, valid loss-1.9268, acc-0.4700, test loss-1.9276, acc-0.4589
Iter-39520, train loss-1.8916, acc-0.5200, valid loss-1.9267, acc-0.4700, test loss-1.9276, acc-0.4588
Iter-39530, train loss-1.9847, acc-0.4000, valid loss-1.9266, acc-0.4696, test loss-1.9275, acc-0.4590
Iter-39540, train loss-1.9631, acc-0.3800, valid loss-1.9266, acc-0.4702, test loss-1.9274, acc-0.4590
Iter-39550, train loss-1.9289, acc-0.5200, valid loss-1.9265, acc-0.4700, test loss-1.9274, acc-0.4589
Iter-39560, train loss-1.9954, acc-0.3600, valid loss-1.9264, acc-0.4700, test loss-1.9273, acc-0.4589
Iter-39570, train loss-1.9769, acc-0.4000, valid loss-1.9264, acc-0.4702, test loss-1.9272, acc-0.4588
Iter-39580, train loss-1.9591, acc-0.4200, valid loss-1.9263, acc-0.4702, test loss-1.9272, acc-0.4587
Iter-39590, train loss-1.9079, acc-0.5200, valid loss-1.9262, acc-0.4704, test loss-1.9271, acc-0.4588
Iter-39600, train loss-1.9202, acc-0.4800, valid loss-1.9261, acc-0.4700, test loss-1.9270, acc-0.4587
Iter-39610, train loss-2.0295, acc-0.3200, valid loss-1.9261, acc-0.4706, test loss-1.9270, acc-0.4587
Iter-39620, train loss-1.9862, acc-0.4000, valid loss-1.9260, acc-0.4706, test loss-1.9269, acc-0.4588
Iter-39630, train loss-1.9860, acc-0.3600, valid loss-1.9259, acc-0.4706, test loss-1.9268, acc-0.4589
Iter-39640, train loss-1.8783, acc-0.5400, valid loss-1.9259, acc-0.4706, test loss-1.9268, acc-0.4588
Iter-39650, train loss-1.9530, acc-0.4800, valid loss-1.9258, acc-0.4704, test loss-1.9267, acc-0.4589
Iter-39660, train loss-1.9933, acc-0.3600, valid loss-1.9257, acc-0.4708, test loss-1.9266, acc-0.4589
Iter-39670, train loss-1.9067, acc-0.5600, valid loss-1.9257, acc-0.4704, test loss-1.9265, acc-0.4589
Iter-39680, train loss-1.8695, acc-0.4800, valid loss-1.9256, acc-0.4700, test loss-1.9265, acc-0.4590
Iter-39690, train loss-2.0062, acc-0.3800, valid loss-1.9255, acc-0.4702, test loss-1.9264, acc-0.4588
Iter-39700, train loss-1.8831, acc-0.4400, valid loss-1.9255, acc-0.4704, test loss-1.9263, acc-0.4588
Iter-39710, train loss-1.9507, acc-0.4400, valid loss-1.9254, acc-0.4704, test loss-1.9263, acc-0.4589
Iter-39720, train loss-1.8749, acc-0.5000, valid loss-1.9253, acc-0.4706, test loss-1.9262, acc-0.4591
Iter-39730, train loss-1.9523, acc-0.3600, valid loss-1.9253, acc-0.4706, test loss-1.9261, acc-0.4591
Iter-39740, train loss-1.9335, acc-0.3800, valid loss-1.9252, acc-0.4708, test loss-1.9261, acc-0.4591
Iter-39750, train loss-2.0120, acc-0.4200, valid loss-1.9251, acc-0.4708, test loss-1.9260, acc-0.4592
Iter-39760, train loss-1.9191, acc-0.4200, valid loss-1.9251, acc-0.4706, test loss-1.9259, acc-0.4590
Iter-39770, train loss-1.9846, acc-0.4600, valid loss-1.9250, acc-0.4708, test loss-1.9259, acc-0.4593
Iter-39780, train loss-1.9128, acc-0.4200, valid loss-1.9249, acc-0.4708, test loss-1.9258, acc-0.4593
Iter-39790, train loss-1.9010, acc-0.5200, valid loss-1.9249, acc-0.4710, test loss-1.9257, acc-0.4593
Iter-39800, train loss-1.9782, acc-0.3000, valid loss-1.9248, acc-0.4708, test loss-1.9257, acc-0.4595
Iter-39810, train loss-1.9580, acc-0.5000, valid loss-1.9247, acc-0.4712, test loss-1.9256, acc-0.4593
Iter-39820, train loss-1.9266, acc-0.4600, valid loss-1.9247, acc-0.4712, test loss-1.9255, acc-0.4594
Iter-39830, train loss-2.0094, acc-0.4200, valid loss-1.9246, acc-0.4712, test loss-1.9255, acc-0.4593
Iter-39840, train loss-1.9950, acc-0.4000, valid loss-1.9245, acc-0.4710, test loss-1.9254, acc-0.4597
Iter-39850, train loss-1.9779, acc-0.3400, valid loss-1.9245, acc-0.4710, test loss-1.9253, acc-0.4595
Iter-39860, train loss-1.8942, acc-0.4400, valid loss-1.9244, acc-0.4708, test loss-1.9253, acc-0.4595
Iter-39870, train loss-1.9621, acc-0.4200, valid loss-1.9243, acc-0.4710, test loss-1.9252, acc-0.4595
Iter-39880, train loss-1.8897, acc-0.5000, valid loss-1.9242, acc-0.4710, test loss-1.9251, acc-0.4593
Iter-39890, train loss-1.9059, acc-0.5200, valid loss-1.9242, acc-0.4710, test loss-1.9251, acc-0.4597
Iter-39900, train loss-1.9462, acc-0.4600, valid loss-1.9241, acc-0.4710, test loss-1.9250, acc-0.4597
Iter-39910, train loss-1.9265, acc-0.5200, valid loss-1.9240, acc-0.4712, test loss-1.9249, acc-0.4597
Iter-39920, train loss-2.0058, acc-0.3400, valid loss-1.9240, acc-0.4710, test loss-1.9249, acc-0.4599
Iter-39930, train loss-1.9045, acc-0.4200, valid loss-1.9239, acc-0.4712, test loss-1.9248, acc-0.4601
Iter-39940, train loss-1.9066, acc-0.4400, valid loss-1.9238, acc-0.4714, test loss-1.9247, acc-0.4599
Iter-39950, train loss-1.8992, acc-0.5200, valid loss-1.9238, acc-0.4714, test loss-1.9247, acc-0.4600
Iter-39960, train loss-1.9859, acc-0.3800, valid loss-1.9237, acc-0.4714, test loss-1.9246, acc-0.4600
Iter-39970, train loss-1.8922, acc-0.4400, valid loss-1.9236, acc-0.4714, test loss-1.9245, acc-0.4601
Iter-39980, train loss-1.9325, acc-0.4200, valid loss-1.9236, acc-0.4714, test loss-1.9245, acc-0.4600
Iter-39990, train loss-1.8954, acc-0.5600, valid loss-1.9235, acc-0.4714, test loss-1.9244, acc-0.4599
Iter-40000, train loss-1.9085, acc-0.5400, valid loss-1.9234, acc-0.4714, test loss-1.9243, acc-0.4600
Iter-40010, train loss-1.8894, acc-0.4600, valid loss-1.9234, acc-0.4712, test loss-1.9243, acc-0.4600
Iter-40020, train loss-1.9480, acc-0.4400, valid loss-1.9233, acc-0.4714, test loss-1.9242, acc-0.4601
Iter-40030, train loss-1.9046, acc-0.5200, valid loss-1.9232, acc-0.4712, test loss-1.9241, acc-0.4601
Iter-40040, train loss-1.9385, acc-0.5400, valid loss-1.9232, acc-0.4712, test loss-1.9241, acc-0.4603
Iter-40050, train loss-1.9930, acc-0.3800, valid loss-1.9231, acc-0.4712, test loss-1.9240, acc-0.4602
Iter-40060, train loss-1.8930, acc-0.5000, valid loss-1.9230, acc-0.4714, test loss-1.9239, acc-0.4604
Iter-40070, train loss-1.9142, acc-0.5000, valid loss-1.9230, acc-0.4714, test loss-1.9239, acc-0.4603
Iter-40080, train loss-1.9649, acc-0.5000, valid loss-1.9229, acc-0.4714, test loss-1.9238, acc-0.4603
Iter-40090, train loss-1.9654, acc-0.4600, valid loss-1.9228, acc-0.4716, test loss-1.9237, acc-0.4604
Iter-40100, train loss-1.9578, acc-0.4600, valid loss-1.9228, acc-0.4716, test loss-1.9237, acc-0.4603
Iter-40110, train loss-1.8623, acc-0.5400, valid loss-1.9227, acc-0.4716, test loss-1.9236, acc-0.4604
Iter-40120, train loss-1.9404, acc-0.5400, valid loss-1.9226, acc-0.4718, test loss-1.9235, acc-0.4605
Iter-40130, train loss-1.9257, acc-0.5000, valid loss-1.9226, acc-0.4714, test loss-1.9234, acc-0.4608
Iter-40140, train loss-1.8686, acc-0.5600, valid loss-1.9225, acc-0.4718, test loss-1.9234, acc-0.4606
Iter-40150, train loss-1.8957, acc-0.5000, valid loss-1.9224, acc-0.4716, test loss-1.9233, acc-0.4605
Iter-40160, train loss-1.8544, acc-0.5400, valid loss-1.9224, acc-0.4716, test loss-1.9232, acc-0.4606
Iter-40170, train loss-1.8820, acc-0.6400, valid loss-1.9223, acc-0.4716, test loss-1.9232, acc-0.4608
Iter-40180, train loss-1.9878, acc-0.3200, valid loss-1.9222, acc-0.4716, test loss-1.9231, acc-0.4605
Iter-40190, train loss-1.9289, acc-0.4200, valid loss-1.9222, acc-0.4716, test loss-1.9230, acc-0.4603
Iter-40200, train loss-1.9053, acc-0.4800, valid loss-1.9221, acc-0.4716, test loss-1.9230, acc-0.4604
Iter-40210, train loss-1.9420, acc-0.5200, valid loss-1.9220, acc-0.4718, test loss-1.9229, acc-0.4609
Iter-40220, train loss-1.8893, acc-0.4800, valid loss-1.9220, acc-0.4718, test loss-1.9228, acc-0.4608
Iter-40230, train loss-1.9316, acc-0.5600, valid loss-1.9219, acc-0.4718, test loss-1.9228, acc-0.4607
Iter-40240, train loss-1.9572, acc-0.4400, valid loss-1.9218, acc-0.4720, test loss-1.9227, acc-0.4608
Iter-40250, train loss-1.9740, acc-0.3800, valid loss-1.9217, acc-0.4720, test loss-1.9226, acc-0.4610
Iter-40260, train loss-1.9325, acc-0.4400, valid loss-1.9217, acc-0.4720, test loss-1.9226, acc-0.4609
Iter-40270, train loss-1.9350, acc-0.4600, valid loss-1.9216, acc-0.4720, test loss-1.9225, acc-0.4610
Iter-40280, train loss-1.9128, acc-0.5000, valid loss-1.9215, acc-0.4720, test loss-1.9224, acc-0.4611
Iter-40290, train loss-1.9566, acc-0.3600, valid loss-1.9215, acc-0.4720, test loss-1.9224, acc-0.4611
Iter-40300, train loss-1.8846, acc-0.5800, valid loss-1.9214, acc-0.4718, test loss-1.9223, acc-0.4609
Iter-40310, train loss-1.8876, acc-0.5000, valid loss-1.9213, acc-0.4720, test loss-1.9222, acc-0.4610
Iter-40320, train loss-1.8893, acc-0.5000, valid loss-1.9213, acc-0.4722, test loss-1.9222, acc-0.4609
Iter-40330, train loss-1.8805, acc-0.5400, valid loss-1.9212, acc-0.4720, test loss-1.9221, acc-0.4610
Iter-40340, train loss-1.9249, acc-0.4200, valid loss-1.9211, acc-0.4720, test loss-1.9220, acc-0.4612
Iter-40350, train loss-1.9580, acc-0.4400, valid loss-1.9211, acc-0.4718, test loss-1.9220, acc-0.4615
Iter-40360, train loss-1.9674, acc-0.4800, valid loss-1.9210, acc-0.4716, test loss-1.9219, acc-0.4612
Iter-40370, train loss-1.9361, acc-0.4000, valid loss-1.9209, acc-0.4720, test loss-1.9218, acc-0.4614
Iter-40380, train loss-1.9967, acc-0.4800, valid loss-1.9209, acc-0.4720, test loss-1.9218, acc-0.4613
Iter-40390, train loss-2.0055, acc-0.3800, valid loss-1.9208, acc-0.4718, test loss-1.9217, acc-0.4612
Iter-40400, train loss-1.9025, acc-0.4800, valid loss-1.9207, acc-0.4718, test loss-1.9216, acc-0.4613
Iter-40410, train loss-1.8837, acc-0.4600, valid loss-1.9207, acc-0.4720, test loss-1.9216, acc-0.4614
Iter-40420, train loss-1.9780, acc-0.3600, valid loss-1.9206, acc-0.4718, test loss-1.9215, acc-0.4617
Iter-40430, train loss-1.9226, acc-0.4600, valid loss-1.9205, acc-0.4722, test loss-1.9214, acc-0.4618
Iter-40440, train loss-1.9336, acc-0.4800, valid loss-1.9205, acc-0.4720, test loss-1.9214, acc-0.4618
Iter-40450, train loss-1.8989, acc-0.4000, valid loss-1.9204, acc-0.4722, test loss-1.9213, acc-0.4616
Iter-40460, train loss-1.9542, acc-0.4600, valid loss-1.9203, acc-0.4720, test loss-1.9212, acc-0.4616
Iter-40470, train loss-2.0434, acc-0.3800, valid loss-1.9203, acc-0.4720, test loss-1.9212, acc-0.4617
Iter-40480, train loss-1.9172, acc-0.5400, valid loss-1.9202, acc-0.4720, test loss-1.9211, acc-0.4616
Iter-40490, train loss-1.8650, acc-0.5000, valid loss-1.9201, acc-0.4720, test loss-1.9210, acc-0.4615
Iter-40500, train loss-1.9316, acc-0.4600, valid loss-1.9201, acc-0.4720, test loss-1.9210, acc-0.4615
Iter-40510, train loss-1.9742, acc-0.4600, valid loss-1.9200, acc-0.4720, test loss-1.9209, acc-0.4617
Iter-40520, train loss-1.9591, acc-0.3800, valid loss-1.9199, acc-0.4720, test loss-1.9208, acc-0.4619
Iter-40530, train loss-1.9846, acc-0.3400, valid loss-1.9199, acc-0.4720, test loss-1.9208, acc-0.4620
Iter-40540, train loss-1.8443, acc-0.5800, valid loss-1.9198, acc-0.4722, test loss-1.9207, acc-0.4620
Iter-40550, train loss-1.9922, acc-0.3000, valid loss-1.9197, acc-0.4720, test loss-1.9206, acc-0.4620
Iter-40560, train loss-1.8364, acc-0.5600, valid loss-1.9197, acc-0.4722, test loss-1.9206, acc-0.4619
Iter-40570, train loss-1.9232, acc-0.5200, valid loss-1.9196, acc-0.4720, test loss-1.9205, acc-0.4618
Iter-40580, train loss-1.9175, acc-0.5000, valid loss-1.9195, acc-0.4720, test loss-1.9204, acc-0.4619
Iter-40590, train loss-1.9553, acc-0.4400, valid loss-1.9195, acc-0.4720, test loss-1.9204, acc-0.4623
Iter-40600, train loss-1.8035, acc-0.6600, valid loss-1.9194, acc-0.4720, test loss-1.9203, acc-0.4622
Iter-40610, train loss-1.9045, acc-0.6200, valid loss-1.9193, acc-0.4720, test loss-1.9202, acc-0.4620
Iter-40620, train loss-1.9531, acc-0.4600, valid loss-1.9193, acc-0.4720, test loss-1.9202, acc-0.4622
Iter-40630, train loss-1.9121, acc-0.5800, valid loss-1.9192, acc-0.4720, test loss-1.9201, acc-0.4622
Iter-40640, train loss-1.9879, acc-0.4000, valid loss-1.9191, acc-0.4722, test loss-1.9200, acc-0.4620
Iter-40650, train loss-1.9612, acc-0.4000, valid loss-1.9191, acc-0.4720, test loss-1.9200, acc-0.4624
Iter-40660, train loss-1.9562, acc-0.3400, valid loss-1.9190, acc-0.4720, test loss-1.9199, acc-0.4622
Iter-40670, train loss-1.9404, acc-0.3600, valid loss-1.9189, acc-0.4722, test loss-1.9198, acc-0.4623
Iter-40680, train loss-1.9473, acc-0.5600, valid loss-1.9189, acc-0.4722, test loss-1.9198, acc-0.4622
Iter-40690, train loss-1.8242, acc-0.5600, valid loss-1.9188, acc-0.4722, test loss-1.9197, acc-0.4626
Iter-40700, train loss-1.9955, acc-0.3000, valid loss-1.9187, acc-0.4722, test loss-1.9196, acc-0.4622
Iter-40710, train loss-1.9525, acc-0.5000, valid loss-1.9187, acc-0.4722, test loss-1.9196, acc-0.4623
Iter-40720, train loss-1.8956, acc-0.5000, valid loss-1.9186, acc-0.4720, test loss-1.9195, acc-0.4622
Iter-40730, train loss-1.9585, acc-0.4400, valid loss-1.9185, acc-0.4720, test loss-1.9194, acc-0.4624
Iter-40740, train loss-1.9536, acc-0.5400, valid loss-1.9185, acc-0.4722, test loss-1.9194, acc-0.4629
Iter-40750, train loss-1.9632, acc-0.4200, valid loss-1.9184, acc-0.4720, test loss-1.9193, acc-0.4628
Iter-40760, train loss-1.9798, acc-0.3800, valid loss-1.9183, acc-0.4724, test loss-1.9192, acc-0.4627
Iter-40770, train loss-1.9111, acc-0.3800, valid loss-1.9183, acc-0.4724, test loss-1.9192, acc-0.4628
Iter-40780, train loss-1.9521, acc-0.4600, valid loss-1.9182, acc-0.4722, test loss-1.9191, acc-0.4631
Iter-40790, train loss-1.9451, acc-0.4600, valid loss-1.9181, acc-0.4724, test loss-1.9190, acc-0.4630
Iter-40800, train loss-1.9381, acc-0.4400, valid loss-1.9181, acc-0.4722, test loss-1.9190, acc-0.4631
Iter-40810, train loss-2.0042, acc-0.4000, valid loss-1.9180, acc-0.4720, test loss-1.9189, acc-0.4630
Iter-40820, train loss-1.8604, acc-0.5000, valid loss-1.9179, acc-0.4724, test loss-1.9188, acc-0.4630
Iter-40830, train loss-1.8547, acc-0.4400, valid loss-1.9179, acc-0.4726, test loss-1.9188, acc-0.4628
Iter-40840, train loss-1.9660, acc-0.4400, valid loss-1.9178, acc-0.4726, test loss-1.9187, acc-0.4627
Iter-40850, train loss-2.0262, acc-0.3600, valid loss-1.9177, acc-0.4726, test loss-1.9186, acc-0.4626
Iter-40860, train loss-1.9082, acc-0.4600, valid loss-1.9177, acc-0.4724, test loss-1.9186, acc-0.4629
Iter-40870, train loss-1.9095, acc-0.4400, valid loss-1.9176, acc-0.4726, test loss-1.9185, acc-0.4628
Iter-40880, train loss-1.9067, acc-0.4600, valid loss-1.9175, acc-0.4730, test loss-1.9184, acc-0.4630
Iter-40890, train loss-1.8933, acc-0.4000, valid loss-1.9175, acc-0.4730, test loss-1.9184, acc-0.4629
Iter-40900, train loss-1.9072, acc-0.4800, valid loss-1.9174, acc-0.4728, test loss-1.9183, acc-0.4627
Iter-40910, train loss-1.8913, acc-0.4200, valid loss-1.9173, acc-0.4726, test loss-1.9182, acc-0.4629
Iter-40920, train loss-1.8454, acc-0.5200, valid loss-1.9173, acc-0.4730, test loss-1.9182, acc-0.4628
Iter-40930, train loss-1.8973, acc-0.4600, valid loss-1.9172, acc-0.4732, test loss-1.9181, acc-0.4627
Iter-40940, train loss-1.8572, acc-0.5600, valid loss-1.9171, acc-0.4734, test loss-1.9180, acc-0.4628
Iter-40950, train loss-1.9556, acc-0.4800, valid loss-1.9171, acc-0.4732, test loss-1.9180, acc-0.4628
Iter-40960, train loss-1.8660, acc-0.5200, valid loss-1.9170, acc-0.4730, test loss-1.9179, acc-0.4628
Iter-40970, train loss-1.8464, acc-0.5400, valid loss-1.9169, acc-0.4728, test loss-1.9178, acc-0.4628
Iter-40980, train loss-1.8948, acc-0.5400, valid loss-1.9169, acc-0.4730, test loss-1.9178, acc-0.4628
Iter-40990, train loss-1.9215, acc-0.4200, valid loss-1.9168, acc-0.4730, test loss-1.9177, acc-0.4629
Iter-41000, train loss-1.9442, acc-0.4600, valid loss-1.9167, acc-0.4730, test loss-1.9176, acc-0.4628
Iter-41010, train loss-1.8533, acc-0.5000, valid loss-1.9167, acc-0.4730, test loss-1.9176, acc-0.4629
Iter-41020, train loss-1.9759, acc-0.4000, valid loss-1.9166, acc-0.4730, test loss-1.9175, acc-0.4629
Iter-41030, train loss-1.9381, acc-0.4400, valid loss-1.9165, acc-0.4728, test loss-1.9174, acc-0.4627
Iter-41040, train loss-1.9233, acc-0.4400, valid loss-1.9165, acc-0.4728, test loss-1.9174, acc-0.4630
Iter-41050, train loss-1.9488, acc-0.4400, valid loss-1.9164, acc-0.4732, test loss-1.9173, acc-0.4628
Iter-41060, train loss-1.9275, acc-0.4600, valid loss-1.9163, acc-0.4728, test loss-1.9172, acc-0.4628
Iter-41070, train loss-1.8911, acc-0.5200, valid loss-1.9163, acc-0.4730, test loss-1.9172, acc-0.4629
Iter-41080, train loss-1.9766, acc-0.5000, valid loss-1.9162, acc-0.4730, test loss-1.9171, acc-0.4629
Iter-41090, train loss-1.9369, acc-0.4600, valid loss-1.9161, acc-0.4730, test loss-1.9170, acc-0.4629
Iter-41100, train loss-2.0241, acc-0.3600, valid loss-1.9161, acc-0.4732, test loss-1.9170, acc-0.4631
Iter-41110, train loss-1.8755, acc-0.5400, valid loss-1.9160, acc-0.4730, test loss-1.9169, acc-0.4631
Iter-41120, train loss-1.9377, acc-0.5000, valid loss-1.9159, acc-0.4732, test loss-1.9168, acc-0.4628
Iter-41130, train loss-1.9647, acc-0.4400, valid loss-1.9159, acc-0.4732, test loss-1.9168, acc-0.4629
Iter-41140, train loss-1.8652, acc-0.5200, valid loss-1.9158, acc-0.4732, test loss-1.9167, acc-0.4629
Iter-41150, train loss-1.9725, acc-0.3800, valid loss-1.9157, acc-0.4732, test loss-1.9166, acc-0.4630
Iter-41160, train loss-1.9630, acc-0.4200, valid loss-1.9157, acc-0.4732, test loss-1.9166, acc-0.4629
Iter-41170, train loss-1.9565, acc-0.4200, valid loss-1.9156, acc-0.4734, test loss-1.9165, acc-0.4630
Iter-41180, train loss-1.8045, acc-0.6000, valid loss-1.9155, acc-0.4734, test loss-1.9164, acc-0.4630
Iter-41190, train loss-1.9608, acc-0.4600, valid loss-1.9155, acc-0.4734, test loss-1.9164, acc-0.4631
Iter-41200, train loss-1.9077, acc-0.5200, valid loss-1.9154, acc-0.4734, test loss-1.9163, acc-0.4631
Iter-41210, train loss-1.9492, acc-0.3400, valid loss-1.9153, acc-0.4732, test loss-1.9162, acc-0.4632
Iter-41220, train loss-1.9031, acc-0.5600, valid loss-1.9153, acc-0.4734, test loss-1.9162, acc-0.4633
Iter-41230, train loss-1.8603, acc-0.4400, valid loss-1.9152, acc-0.4730, test loss-1.9161, acc-0.4633
Iter-41240, train loss-1.9390, acc-0.3800, valid loss-1.9151, acc-0.4730, test loss-1.9160, acc-0.4633
Iter-41250, train loss-1.9327, acc-0.4400, valid loss-1.9151, acc-0.4732, test loss-1.9160, acc-0.4635
Iter-41260, train loss-1.9836, acc-0.3600, valid loss-1.9150, acc-0.4732, test loss-1.9159, acc-0.4635
Iter-41270, train loss-1.9090, acc-0.4400, valid loss-1.9149, acc-0.4732, test loss-1.9158, acc-0.4637
Iter-41280, train loss-1.9046, acc-0.4400, valid loss-1.9149, acc-0.4730, test loss-1.9158, acc-0.4636
Iter-41290, train loss-1.9507, acc-0.5000, valid loss-1.9148, acc-0.4732, test loss-1.9157, acc-0.4634
Iter-41300, train loss-1.9184, acc-0.5400, valid loss-1.9148, acc-0.4730, test loss-1.9156, acc-0.4635
Iter-41310, train loss-1.9708, acc-0.3400, valid loss-1.9147, acc-0.4730, test loss-1.9156, acc-0.4638
Iter-41320, train loss-1.9453, acc-0.4800, valid loss-1.9146, acc-0.4730, test loss-1.9155, acc-0.4638
Iter-41330, train loss-1.8910, acc-0.4200, valid loss-1.9146, acc-0.4730, test loss-1.9154, acc-0.4637
Iter-41340, train loss-1.9259, acc-0.4000, valid loss-1.9145, acc-0.4730, test loss-1.9154, acc-0.4639
Iter-41350, train loss-1.8929, acc-0.4400, valid loss-1.9144, acc-0.4732, test loss-1.9153, acc-0.4636
Iter-41360, train loss-2.0034, acc-0.5000, valid loss-1.9144, acc-0.4732, test loss-1.9152, acc-0.4636
Iter-41370, train loss-1.9191, acc-0.3800, valid loss-1.9143, acc-0.4732, test loss-1.9152, acc-0.4635
Iter-41380, train loss-1.8558, acc-0.5200, valid loss-1.9142, acc-0.4730, test loss-1.9151, acc-0.4637
Iter-41390, train loss-1.9867, acc-0.4000, valid loss-1.9142, acc-0.4730, test loss-1.9151, acc-0.4637
Iter-41400, train loss-1.9359, acc-0.5000, valid loss-1.9141, acc-0.4730, test loss-1.9150, acc-0.4636
Iter-41410, train loss-1.9150, acc-0.4800, valid loss-1.9140, acc-0.4730, test loss-1.9149, acc-0.4638
Iter-41420, train loss-1.9295, acc-0.4400, valid loss-1.9140, acc-0.4730, test loss-1.9148, acc-0.4635
Iter-41430, train loss-1.9399, acc-0.4800, valid loss-1.9139, acc-0.4732, test loss-1.9148, acc-0.4636
Iter-41440, train loss-1.9300, acc-0.4800, valid loss-1.9138, acc-0.4730, test loss-1.9147, acc-0.4637
Iter-41450, train loss-1.9114, acc-0.5400, valid loss-1.9138, acc-0.4734, test loss-1.9147, acc-0.4639
Iter-41460, train loss-1.8880, acc-0.5600, valid loss-1.9137, acc-0.4732, test loss-1.9146, acc-0.4638
Iter-41470, train loss-1.9051, acc-0.5000, valid loss-1.9136, acc-0.4734, test loss-1.9145, acc-0.4638
Iter-41480, train loss-1.9090, acc-0.4200, valid loss-1.9135, acc-0.4734, test loss-1.9144, acc-0.4638
Iter-41490, train loss-1.9052, acc-0.4000, valid loss-1.9135, acc-0.4734, test loss-1.9144, acc-0.4639
Iter-41500, train loss-1.9823, acc-0.4200, valid loss-1.9134, acc-0.4732, test loss-1.9143, acc-0.4637
Iter-41510, train loss-1.9332, acc-0.3400, valid loss-1.9134, acc-0.4732, test loss-1.9143, acc-0.4637
Iter-41520, train loss-1.8748, acc-0.5400, valid loss-1.9133, acc-0.4732, test loss-1.9142, acc-0.4641
Iter-41530, train loss-1.9665, acc-0.4600, valid loss-1.9132, acc-0.4734, test loss-1.9141, acc-0.4640
Iter-41540, train loss-1.8903, acc-0.5800, valid loss-1.9132, acc-0.4734, test loss-1.9141, acc-0.4640
Iter-41550, train loss-1.8467, acc-0.6000, valid loss-1.9131, acc-0.4732, test loss-1.9140, acc-0.4643
Iter-41560, train loss-1.8928, acc-0.4600, valid loss-1.9130, acc-0.4734, test loss-1.9139, acc-0.4642
Iter-41570, train loss-1.8411, acc-0.5400, valid loss-1.9130, acc-0.4732, test loss-1.9139, acc-0.4640
Iter-41580, train loss-1.9709, acc-0.3800, valid loss-1.9129, acc-0.4732, test loss-1.9138, acc-0.4641
Iter-41590, train loss-1.8156, acc-0.5400, valid loss-1.9128, acc-0.4734, test loss-1.9137, acc-0.4643
Iter-41600, train loss-1.8982, acc-0.4000, valid loss-1.9128, acc-0.4730, test loss-1.9137, acc-0.4644
Iter-41610, train loss-1.8966, acc-0.4800, valid loss-1.9127, acc-0.4728, test loss-1.9136, acc-0.4642
Iter-41620, train loss-1.8882, acc-0.4800, valid loss-1.9126, acc-0.4730, test loss-1.9135, acc-0.4643
Iter-41630, train loss-1.8940, acc-0.4800, valid loss-1.9126, acc-0.4732, test loss-1.9135, acc-0.4642
Iter-41640, train loss-1.9337, acc-0.5000, valid loss-1.9125, acc-0.4732, test loss-1.9134, acc-0.4642
Iter-41650, train loss-1.9361, acc-0.4600, valid loss-1.9124, acc-0.4732, test loss-1.9133, acc-0.4643
Iter-41660, train loss-1.9352, acc-0.5200, valid loss-1.9124, acc-0.4734, test loss-1.9133, acc-0.4643
Iter-41670, train loss-1.8716, acc-0.5000, valid loss-1.9123, acc-0.4732, test loss-1.9132, acc-0.4644
Iter-41680, train loss-1.9164, acc-0.5000, valid loss-1.9122, acc-0.4732, test loss-1.9131, acc-0.4644
Iter-41690, train loss-1.8915, acc-0.5400, valid loss-1.9122, acc-0.4732, test loss-1.9131, acc-0.4643
Iter-41700, train loss-1.9170, acc-0.5000, valid loss-1.9121, acc-0.4734, test loss-1.9130, acc-0.4642
Iter-41710, train loss-1.9364, acc-0.4800, valid loss-1.9120, acc-0.4732, test loss-1.9129, acc-0.4642
Iter-41720, train loss-1.9749, acc-0.4200, valid loss-1.9120, acc-0.4732, test loss-1.9129, acc-0.4642
Iter-41730, train loss-1.8745, acc-0.5200, valid loss-1.9119, acc-0.4732, test loss-1.9128, acc-0.4643
Iter-41740, train loss-1.8951, acc-0.5200, valid loss-1.9118, acc-0.4730, test loss-1.9127, acc-0.4643
Iter-41750, train loss-1.8654, acc-0.4400, valid loss-1.9118, acc-0.4732, test loss-1.9127, acc-0.4643
Iter-41760, train loss-1.8963, acc-0.4600, valid loss-1.9117, acc-0.4732, test loss-1.9126, acc-0.4647
Iter-41770, train loss-1.8669, acc-0.5000, valid loss-1.9116, acc-0.4734, test loss-1.9125, acc-0.4647
Iter-41780, train loss-1.9233, acc-0.4800, valid loss-1.9116, acc-0.4732, test loss-1.9125, acc-0.4646
Iter-41790, train loss-1.8801, acc-0.4600, valid loss-1.9115, acc-0.4734, test loss-1.9124, acc-0.4647
Iter-41800, train loss-1.9343, acc-0.5000, valid loss-1.9114, acc-0.4734, test loss-1.9123, acc-0.4647
Iter-41810, train loss-1.9044, acc-0.4600, valid loss-1.9114, acc-0.4734, test loss-1.9123, acc-0.4648
Iter-41820, train loss-1.9529, acc-0.3200, valid loss-1.9113, acc-0.4734, test loss-1.9122, acc-0.4648
Iter-41830, train loss-1.8581, acc-0.5400, valid loss-1.9112, acc-0.4734, test loss-1.9121, acc-0.4648
Iter-41840, train loss-1.9396, acc-0.4000, valid loss-1.9112, acc-0.4736, test loss-1.9121, acc-0.4648
Iter-41850, train loss-1.8532, acc-0.5000, valid loss-1.9111, acc-0.4736, test loss-1.9120, acc-0.4649
Iter-41860, train loss-1.9863, acc-0.3200, valid loss-1.9111, acc-0.4736, test loss-1.9119, acc-0.4647
Iter-41870, train loss-1.9153, acc-0.4000, valid loss-1.9110, acc-0.4736, test loss-1.9119, acc-0.4648
Iter-41880, train loss-1.8714, acc-0.6200, valid loss-1.9109, acc-0.4734, test loss-1.9118, acc-0.4648
Iter-41890, train loss-1.9649, acc-0.4000, valid loss-1.9108, acc-0.4734, test loss-1.9117, acc-0.4650
Iter-41900, train loss-1.8964, acc-0.5200, valid loss-1.9108, acc-0.4734, test loss-1.9117, acc-0.4648
Iter-41910, train loss-1.8591, acc-0.5800, valid loss-1.9107, acc-0.4734, test loss-1.9116, acc-0.4649
Iter-41920, train loss-1.9326, acc-0.3800, valid loss-1.9106, acc-0.4736, test loss-1.9116, acc-0.4649
Iter-41930, train loss-1.9717, acc-0.4000, valid loss-1.9106, acc-0.4736, test loss-1.9115, acc-0.4648
Iter-41940, train loss-1.9400, acc-0.3800, valid loss-1.9105, acc-0.4736, test loss-1.9114, acc-0.4651
Iter-41950, train loss-2.0266, acc-0.4200, valid loss-1.9104, acc-0.4736, test loss-1.9114, acc-0.4648
Iter-41960, train loss-1.9016, acc-0.5800, valid loss-1.9104, acc-0.4736, test loss-1.9113, acc-0.4651
Iter-41970, train loss-1.8748, acc-0.5000, valid loss-1.9103, acc-0.4736, test loss-1.9112, acc-0.4651
Iter-41980, train loss-1.9360, acc-0.4600, valid loss-1.9102, acc-0.4736, test loss-1.9112, acc-0.4650
Iter-41990, train loss-1.8956, acc-0.4200, valid loss-1.9102, acc-0.4736, test loss-1.9111, acc-0.4652
Iter-42000, train loss-1.8859, acc-0.4400, valid loss-1.9101, acc-0.4736, test loss-1.9110, acc-0.4653
Iter-42010, train loss-1.8541, acc-0.5800, valid loss-1.9101, acc-0.4736, test loss-1.9110, acc-0.4654
Iter-42020, train loss-1.9500, acc-0.4200, valid loss-1.9100, acc-0.4736, test loss-1.9109, acc-0.4653
Iter-42030, train loss-1.9167, acc-0.4400, valid loss-1.9099, acc-0.4736, test loss-1.9108, acc-0.4656
Iter-42040, train loss-1.8937, acc-0.4600, valid loss-1.9099, acc-0.4736, test loss-1.9108, acc-0.4656
Iter-42050, train loss-1.9141, acc-0.4000, valid loss-1.9098, acc-0.4736, test loss-1.9107, acc-0.4658
Iter-42060, train loss-1.8861, acc-0.4400, valid loss-1.9097, acc-0.4736, test loss-1.9107, acc-0.4654
Iter-42070, train loss-1.8730, acc-0.5200, valid loss-1.9097, acc-0.4734, test loss-1.9106, acc-0.4655
Iter-42080, train loss-1.9513, acc-0.4600, valid loss-1.9096, acc-0.4736, test loss-1.9105, acc-0.4652
Iter-42090, train loss-1.9493, acc-0.5400, valid loss-1.9095, acc-0.4736, test loss-1.9105, acc-0.4653
Iter-42100, train loss-1.9025, acc-0.5000, valid loss-1.9095, acc-0.4734, test loss-1.9104, acc-0.4655
Iter-42110, train loss-1.8663, acc-0.4400, valid loss-1.9094, acc-0.4734, test loss-1.9103, acc-0.4656
Iter-42120, train loss-1.9660, acc-0.4400, valid loss-1.9093, acc-0.4734, test loss-1.9103, acc-0.4653
Iter-42130, train loss-1.9074, acc-0.4400, valid loss-1.9093, acc-0.4736, test loss-1.9102, acc-0.4654
Iter-42140, train loss-1.9525, acc-0.3600, valid loss-1.9092, acc-0.4736, test loss-1.9101, acc-0.4655
Iter-42150, train loss-1.9023, acc-0.5400, valid loss-1.9091, acc-0.4736, test loss-1.9101, acc-0.4652
Iter-42160, train loss-1.8495, acc-0.6200, valid loss-1.9091, acc-0.4736, test loss-1.9100, acc-0.4651
Iter-42170, train loss-1.8439, acc-0.5600, valid loss-1.9090, acc-0.4734, test loss-1.9099, acc-0.4651
Iter-42180, train loss-1.9862, acc-0.3400, valid loss-1.9090, acc-0.4734, test loss-1.9099, acc-0.4652
Iter-42190, train loss-1.8482, acc-0.5800, valid loss-1.9089, acc-0.4734, test loss-1.9098, acc-0.4653
Iter-42200, train loss-1.9458, acc-0.3800, valid loss-1.9088, acc-0.4734, test loss-1.9097, acc-0.4655
Iter-42210, train loss-1.9562, acc-0.3800, valid loss-1.9088, acc-0.4734, test loss-1.9097, acc-0.4654
Iter-42220, train loss-1.9895, acc-0.4000, valid loss-1.9087, acc-0.4734, test loss-1.9096, acc-0.4656
Iter-42230, train loss-1.9220, acc-0.4600, valid loss-1.9086, acc-0.4732, test loss-1.9095, acc-0.4658
Iter-42240, train loss-1.8228, acc-0.6000, valid loss-1.9086, acc-0.4732, test loss-1.9095, acc-0.4656
Iter-42250, train loss-1.9492, acc-0.4600, valid loss-1.9085, acc-0.4732, test loss-1.9094, acc-0.4656
Iter-42260, train loss-1.8781, acc-0.4000, valid loss-1.9084, acc-0.4732, test loss-1.9093, acc-0.4657
Iter-42270, train loss-1.9472, acc-0.3600, valid loss-1.9084, acc-0.4732, test loss-1.9093, acc-0.4655
Iter-42280, train loss-1.8791, acc-0.4800, valid loss-1.9083, acc-0.4736, test loss-1.9092, acc-0.4657
Iter-42290, train loss-1.8703, acc-0.4800, valid loss-1.9082, acc-0.4736, test loss-1.9091, acc-0.4658
Iter-42300, train loss-1.8994, acc-0.4600, valid loss-1.9082, acc-0.4736, test loss-1.9091, acc-0.4656
Iter-42310, train loss-1.8867, acc-0.5400, valid loss-1.9081, acc-0.4736, test loss-1.9090, acc-0.4655
Iter-42320, train loss-1.9482, acc-0.4800, valid loss-1.9081, acc-0.4736, test loss-1.9089, acc-0.4657
Iter-42330, train loss-1.7944, acc-0.6000, valid loss-1.9080, acc-0.4734, test loss-1.9089, acc-0.4655
Iter-42340, train loss-1.9327, acc-0.4600, valid loss-1.9079, acc-0.4738, test loss-1.9088, acc-0.4655
Iter-42350, train loss-1.9513, acc-0.4200, valid loss-1.9078, acc-0.4738, test loss-1.9087, acc-0.4654
Iter-42360, train loss-1.9843, acc-0.4800, valid loss-1.9078, acc-0.4738, test loss-1.9087, acc-0.4654
Iter-42370, train loss-1.8935, acc-0.4200, valid loss-1.9077, acc-0.4736, test loss-1.9086, acc-0.4657
Iter-42380, train loss-2.0016, acc-0.4200, valid loss-1.9077, acc-0.4738, test loss-1.9085, acc-0.4657
Iter-42390, train loss-1.8575, acc-0.5400, valid loss-1.9076, acc-0.4738, test loss-1.9085, acc-0.4658
Iter-42400, train loss-1.8905, acc-0.4600, valid loss-1.9075, acc-0.4738, test loss-1.9084, acc-0.4659
Iter-42410, train loss-1.8711, acc-0.5600, valid loss-1.9075, acc-0.4738, test loss-1.9084, acc-0.4660
Iter-42420, train loss-1.9659, acc-0.4800, valid loss-1.9074, acc-0.4738, test loss-1.9083, acc-0.4656
Iter-42430, train loss-1.9162, acc-0.4200, valid loss-1.9073, acc-0.4736, test loss-1.9082, acc-0.4658
Iter-42440, train loss-1.9252, acc-0.4600, valid loss-1.9073, acc-0.4736, test loss-1.9082, acc-0.4659
Iter-42450, train loss-1.9009, acc-0.4800, valid loss-1.9072, acc-0.4736, test loss-1.9081, acc-0.4659
Iter-42460, train loss-1.9927, acc-0.3800, valid loss-1.9071, acc-0.4736, test loss-1.9080, acc-0.4659
Iter-42470, train loss-1.8760, acc-0.5200, valid loss-1.9071, acc-0.4736, test loss-1.9080, acc-0.4660
Iter-42480, train loss-1.9076, acc-0.4000, valid loss-1.9070, acc-0.4736, test loss-1.9079, acc-0.4660
Iter-42490, train loss-1.9409, acc-0.5200, valid loss-1.9069, acc-0.4736, test loss-1.9078, acc-0.4658
Iter-42500, train loss-1.9152, acc-0.4800, valid loss-1.9069, acc-0.4736, test loss-1.9078, acc-0.4661
Iter-42510, train loss-1.8947, acc-0.5000, valid loss-1.9068, acc-0.4736, test loss-1.9077, acc-0.4663
Iter-42520, train loss-1.9777, acc-0.3200, valid loss-1.9067, acc-0.4736, test loss-1.9076, acc-0.4663
Iter-42530, train loss-1.9622, acc-0.3800, valid loss-1.9067, acc-0.4736, test loss-1.9076, acc-0.4661
Iter-42540, train loss-1.9073, acc-0.4600, valid loss-1.9066, acc-0.4736, test loss-1.9075, acc-0.4665
Iter-42550, train loss-1.8776, acc-0.4800, valid loss-1.9065, acc-0.4736, test loss-1.9074, acc-0.4663
Iter-42560, train loss-1.9368, acc-0.4400, valid loss-1.9065, acc-0.4736, test loss-1.9074, acc-0.4663
Iter-42570, train loss-1.9119, acc-0.4400, valid loss-1.9064, acc-0.4736, test loss-1.9073, acc-0.4662
Iter-42580, train loss-1.9777, acc-0.3000, valid loss-1.9064, acc-0.4736, test loss-1.9072, acc-0.4663
Iter-42590, train loss-1.9049, acc-0.5000, valid loss-1.9063, acc-0.4736, test loss-1.9072, acc-0.4663
Iter-42600, train loss-1.9034, acc-0.4200, valid loss-1.9062, acc-0.4736, test loss-1.9071, acc-0.4665
Iter-42610, train loss-1.8620, acc-0.5000, valid loss-1.9062, acc-0.4736, test loss-1.9070, acc-0.4666
Iter-42620, train loss-1.8646, acc-0.4800, valid loss-1.9061, acc-0.4734, test loss-1.9070, acc-0.4668
Iter-42630, train loss-1.8687, acc-0.5000, valid loss-1.9060, acc-0.4736, test loss-1.9069, acc-0.4668
Iter-42640, train loss-1.9878, acc-0.4600, valid loss-1.9060, acc-0.4736, test loss-1.9069, acc-0.4670
Iter-42650, train loss-1.9307, acc-0.4400, valid loss-1.9059, acc-0.4736, test loss-1.9068, acc-0.4672
Iter-42660, train loss-1.9735, acc-0.3600, valid loss-1.9058, acc-0.4738, test loss-1.9067, acc-0.4672
Iter-42670, train loss-1.8891, acc-0.5800, valid loss-1.9058, acc-0.4738, test loss-1.9067, acc-0.4670
Iter-42680, train loss-1.9228, acc-0.5200, valid loss-1.9057, acc-0.4738, test loss-1.9066, acc-0.4672
Iter-42690, train loss-1.8927, acc-0.4600, valid loss-1.9056, acc-0.4740, test loss-1.9065, acc-0.4673
Iter-42700, train loss-1.8590, acc-0.5400, valid loss-1.9056, acc-0.4740, test loss-1.9065, acc-0.4674
Iter-42710, train loss-2.0088, acc-0.4600, valid loss-1.9055, acc-0.4740, test loss-1.9064, acc-0.4673
Iter-42720, train loss-1.9767, acc-0.4000, valid loss-1.9055, acc-0.4740, test loss-1.9063, acc-0.4672
Iter-42730, train loss-1.9275, acc-0.4800, valid loss-1.9054, acc-0.4738, test loss-1.9063, acc-0.4672
Iter-42740, train loss-1.8916, acc-0.4600, valid loss-1.9053, acc-0.4740, test loss-1.9062, acc-0.4672
Iter-42750, train loss-1.9263, acc-0.4200, valid loss-1.9053, acc-0.4740, test loss-1.9061, acc-0.4674
Iter-42760, train loss-1.9346, acc-0.5200, valid loss-1.9052, acc-0.4740, test loss-1.9061, acc-0.4674
Iter-42770, train loss-1.9466, acc-0.4400, valid loss-1.9051, acc-0.4740, test loss-1.9060, acc-0.4673
Iter-42780, train loss-1.9195, acc-0.4200, valid loss-1.9051, acc-0.4740, test loss-1.9060, acc-0.4674
Iter-42790, train loss-1.9713, acc-0.4600, valid loss-1.9050, acc-0.4740, test loss-1.9059, acc-0.4674
Iter-42800, train loss-1.9174, acc-0.5200, valid loss-1.9049, acc-0.4740, test loss-1.9058, acc-0.4674
Iter-42810, train loss-1.9660, acc-0.3400, valid loss-1.9049, acc-0.4740, test loss-1.9058, acc-0.4674
Iter-42820, train loss-1.9791, acc-0.3400, valid loss-1.9048, acc-0.4742, test loss-1.9057, acc-0.4674
Iter-42830, train loss-1.8225, acc-0.5800, valid loss-1.9047, acc-0.4744, test loss-1.9056, acc-0.4676
Iter-42840, train loss-1.9164, acc-0.5000, valid loss-1.9047, acc-0.4744, test loss-1.9056, acc-0.4677
Iter-42850, train loss-1.9214, acc-0.4400, valid loss-1.9046, acc-0.4742, test loss-1.9055, acc-0.4676
Iter-42860, train loss-1.8878, acc-0.5000, valid loss-1.9046, acc-0.4742, test loss-1.9054, acc-0.4677
Iter-42870, train loss-1.9614, acc-0.4600, valid loss-1.9045, acc-0.4740, test loss-1.9054, acc-0.4677
Iter-42880, train loss-1.8459, acc-0.5200, valid loss-1.9044, acc-0.4740, test loss-1.9053, acc-0.4677
Iter-42890, train loss-1.8939, acc-0.5400, valid loss-1.9044, acc-0.4738, test loss-1.9052, acc-0.4676
Iter-42900, train loss-1.9427, acc-0.4000, valid loss-1.9043, acc-0.4740, test loss-1.9052, acc-0.4677
Iter-42910, train loss-1.9768, acc-0.4200, valid loss-1.9042, acc-0.4742, test loss-1.9051, acc-0.4675
Iter-42920, train loss-1.8552, acc-0.6400, valid loss-1.9042, acc-0.4740, test loss-1.9051, acc-0.4676
Iter-42930, train loss-1.8696, acc-0.5800, valid loss-1.9041, acc-0.4742, test loss-1.9050, acc-0.4675
Iter-42940, train loss-1.9383, acc-0.5200, valid loss-1.9040, acc-0.4740, test loss-1.9049, acc-0.4675
Iter-42950, train loss-1.8648, acc-0.5200, valid loss-1.9039, acc-0.4740, test loss-1.9049, acc-0.4674
Iter-42960, train loss-1.8841, acc-0.3800, valid loss-1.9039, acc-0.4740, test loss-1.9048, acc-0.4674
Iter-42970, train loss-1.9225, acc-0.4800, valid loss-1.9038, acc-0.4742, test loss-1.9047, acc-0.4673
Iter-42980, train loss-1.9418, acc-0.4600, valid loss-1.9038, acc-0.4742, test loss-1.9047, acc-0.4673
Iter-42990, train loss-1.8776, acc-0.4800, valid loss-1.9037, acc-0.4744, test loss-1.9046, acc-0.4676
Iter-43000, train loss-1.8096, acc-0.5400, valid loss-1.9036, acc-0.4742, test loss-1.9045, acc-0.4676
Iter-43010, train loss-1.9091, acc-0.4200, valid loss-1.9036, acc-0.4742, test loss-1.9045, acc-0.4674
Iter-43020, train loss-1.8953, acc-0.4600, valid loss-1.9035, acc-0.4744, test loss-1.9044, acc-0.4676
Iter-43030, train loss-1.9348, acc-0.4400, valid loss-1.9034, acc-0.4742, test loss-1.9043, acc-0.4676
Iter-43040, train loss-1.8574, acc-0.4600, valid loss-1.9034, acc-0.4742, test loss-1.9043, acc-0.4675
Iter-43050, train loss-1.9532, acc-0.3800, valid loss-1.9033, acc-0.4742, test loss-1.9042, acc-0.4677
Iter-43060, train loss-1.9296, acc-0.4600, valid loss-1.9032, acc-0.4742, test loss-1.9042, acc-0.4677
Iter-43070, train loss-1.8853, acc-0.6200, valid loss-1.9032, acc-0.4744, test loss-1.9041, acc-0.4677
Iter-43080, train loss-1.8477, acc-0.5600, valid loss-1.9031, acc-0.4744, test loss-1.9040, acc-0.4679
Iter-43090, train loss-1.9780, acc-0.3000, valid loss-1.9031, acc-0.4744, test loss-1.9040, acc-0.4678
Iter-43100, train loss-1.9467, acc-0.4600, valid loss-1.9030, acc-0.4742, test loss-1.9039, acc-0.4677
Iter-43110, train loss-1.9439, acc-0.4200, valid loss-1.9029, acc-0.4744, test loss-1.9038, acc-0.4677
Iter-43120, train loss-1.9442, acc-0.4200, valid loss-1.9029, acc-0.4742, test loss-1.9038, acc-0.4678
Iter-43130, train loss-1.9795, acc-0.4200, valid loss-1.9028, acc-0.4742, test loss-1.9037, acc-0.4678
Iter-43140, train loss-1.8353, acc-0.5200, valid loss-1.9027, acc-0.4744, test loss-1.9036, acc-0.4679
Iter-43150, train loss-1.8880, acc-0.4800, valid loss-1.9027, acc-0.4742, test loss-1.9036, acc-0.4680
Iter-43160, train loss-1.8802, acc-0.3800, valid loss-1.9026, acc-0.4742, test loss-1.9035, acc-0.4681
Iter-43170, train loss-1.8883, acc-0.4400, valid loss-1.9025, acc-0.4742, test loss-1.9035, acc-0.4681
Iter-43180, train loss-1.8998, acc-0.5000, valid loss-1.9025, acc-0.4742, test loss-1.9034, acc-0.4681
Iter-43190, train loss-1.8713, acc-0.4800, valid loss-1.9024, acc-0.4742, test loss-1.9033, acc-0.4680
Iter-43200, train loss-1.8637, acc-0.4200, valid loss-1.9024, acc-0.4742, test loss-1.9033, acc-0.4680
Iter-43210, train loss-1.9255, acc-0.4400, valid loss-1.9023, acc-0.4742, test loss-1.9032, acc-0.4680
Iter-43220, train loss-1.8471, acc-0.5000, valid loss-1.9022, acc-0.4742, test loss-1.9031, acc-0.4681
Iter-43230, train loss-1.8660, acc-0.4200, valid loss-1.9022, acc-0.4742, test loss-1.9031, acc-0.4681
Iter-43240, train loss-1.8643, acc-0.5400, valid loss-1.9021, acc-0.4742, test loss-1.9030, acc-0.4681
Iter-43250, train loss-1.9474, acc-0.4200, valid loss-1.9020, acc-0.4742, test loss-1.9029, acc-0.4681
Iter-43260, train loss-1.9284, acc-0.4800, valid loss-1.9020, acc-0.4740, test loss-1.9029, acc-0.4681
Iter-43270, train loss-1.9299, acc-0.4200, valid loss-1.9019, acc-0.4742, test loss-1.9028, acc-0.4681
Iter-43280, train loss-1.9813, acc-0.3800, valid loss-1.9018, acc-0.4740, test loss-1.9027, acc-0.4681
Iter-43290, train loss-1.9469, acc-0.4000, valid loss-1.9018, acc-0.4740, test loss-1.9027, acc-0.4681
Iter-43300, train loss-1.9098, acc-0.4000, valid loss-1.9017, acc-0.4740, test loss-1.9026, acc-0.4681
Iter-43310, train loss-1.9190, acc-0.4000, valid loss-1.9016, acc-0.4740, test loss-1.9026, acc-0.4681
Iter-43320, train loss-1.8921, acc-0.5000, valid loss-1.9016, acc-0.4740, test loss-1.9025, acc-0.4680
Iter-43330, train loss-1.9348, acc-0.3800, valid loss-1.9015, acc-0.4740, test loss-1.9024, acc-0.4682
Iter-43340, train loss-1.9468, acc-0.5400, valid loss-1.9015, acc-0.4740, test loss-1.9024, acc-0.4682
Iter-43350, train loss-1.9530, acc-0.3800, valid loss-1.9014, acc-0.4742, test loss-1.9023, acc-0.4683
Iter-43360, train loss-1.9940, acc-0.3800, valid loss-1.9013, acc-0.4740, test loss-1.9022, acc-0.4684
Iter-43370, train loss-1.8927, acc-0.4200, valid loss-1.9013, acc-0.4738, test loss-1.9022, acc-0.4688
Iter-43380, train loss-1.8734, acc-0.5400, valid loss-1.9012, acc-0.4736, test loss-1.9021, acc-0.4685
Iter-43390, train loss-1.8903, acc-0.5800, valid loss-1.9011, acc-0.4740, test loss-1.9020, acc-0.4686
Iter-43400, train loss-1.9820, acc-0.3600, valid loss-1.9011, acc-0.4738, test loss-1.9020, acc-0.4686
Iter-43410, train loss-1.8893, acc-0.4400, valid loss-1.9010, acc-0.4740, test loss-1.9019, acc-0.4686
Iter-43420, train loss-1.9778, acc-0.4000, valid loss-1.9010, acc-0.4742, test loss-1.9018, acc-0.4686
Iter-43430, train loss-1.9612, acc-0.3800, valid loss-1.9009, acc-0.4742, test loss-1.9018, acc-0.4689
Iter-43440, train loss-1.9656, acc-0.3800, valid loss-1.9008, acc-0.4742, test loss-1.9017, acc-0.4687
Iter-43450, train loss-1.8659, acc-0.5000, valid loss-1.9008, acc-0.4742, test loss-1.9017, acc-0.4687
Iter-43460, train loss-1.9053, acc-0.4000, valid loss-1.9007, acc-0.4744, test loss-1.9016, acc-0.4690
Iter-43470, train loss-1.8779, acc-0.6000, valid loss-1.9006, acc-0.4744, test loss-1.9015, acc-0.4689
Iter-43480, train loss-1.9123, acc-0.5000, valid loss-1.9006, acc-0.4740, test loss-1.9015, acc-0.4690
Iter-43490, train loss-1.8417, acc-0.5800, valid loss-1.9005, acc-0.4738, test loss-1.9014, acc-0.4690
Iter-43500, train loss-1.8664, acc-0.4600, valid loss-1.9004, acc-0.4738, test loss-1.9013, acc-0.4690
Iter-43510, train loss-1.9408, acc-0.5200, valid loss-1.9004, acc-0.4738, test loss-1.9013, acc-0.4690
Iter-43520, train loss-1.9425, acc-0.3600, valid loss-1.9003, acc-0.4740, test loss-1.9012, acc-0.4689
Iter-43530, train loss-1.9632, acc-0.4000, valid loss-1.9003, acc-0.4736, test loss-1.9012, acc-0.4691
Iter-43540, train loss-1.8547, acc-0.5400, valid loss-1.9002, acc-0.4736, test loss-1.9011, acc-0.4690
Iter-43550, train loss-1.9370, acc-0.4000, valid loss-1.9001, acc-0.4740, test loss-1.9010, acc-0.4689
Iter-43560, train loss-1.9597, acc-0.4200, valid loss-1.9001, acc-0.4736, test loss-1.9010, acc-0.4690
Iter-43570, train loss-1.9680, acc-0.3800, valid loss-1.9000, acc-0.4738, test loss-1.9009, acc-0.4693
Iter-43580, train loss-2.0147, acc-0.3000, valid loss-1.8999, acc-0.4736, test loss-1.9008, acc-0.4692
Iter-43590, train loss-1.8662, acc-0.5200, valid loss-1.8999, acc-0.4738, test loss-1.9008, acc-0.4693
Iter-43600, train loss-1.9344, acc-0.4000, valid loss-1.8998, acc-0.4738, test loss-1.9007, acc-0.4691
Iter-43610, train loss-1.8578, acc-0.5200, valid loss-1.8997, acc-0.4738, test loss-1.9007, acc-0.4692
Iter-43620, train loss-1.9273, acc-0.4000, valid loss-1.8997, acc-0.4738, test loss-1.9006, acc-0.4692
Iter-43630, train loss-1.8806, acc-0.4800, valid loss-1.8996, acc-0.4738, test loss-1.9005, acc-0.4691
Iter-43640, train loss-1.8971, acc-0.4800, valid loss-1.8995, acc-0.4738, test loss-1.9005, acc-0.4693
Iter-43650, train loss-1.9045, acc-0.4400, valid loss-1.8995, acc-0.4740, test loss-1.9004, acc-0.4695
Iter-43660, train loss-1.9014, acc-0.5200, valid loss-1.8994, acc-0.4740, test loss-1.9003, acc-0.4694
Iter-43670, train loss-1.8711, acc-0.5000, valid loss-1.8994, acc-0.4738, test loss-1.9003, acc-0.4695
Iter-43680, train loss-1.9331, acc-0.4800, valid loss-1.8993, acc-0.4740, test loss-1.9002, acc-0.4695
Iter-43690, train loss-1.8230, acc-0.5600, valid loss-1.8992, acc-0.4740, test loss-1.9001, acc-0.4695
Iter-43700, train loss-1.8917, acc-0.5800, valid loss-1.8992, acc-0.4742, test loss-1.9001, acc-0.4697
Iter-43710, train loss-1.9030, acc-0.5400, valid loss-1.8991, acc-0.4740, test loss-1.9000, acc-0.4695
Iter-43720, train loss-1.9405, acc-0.4400, valid loss-1.8991, acc-0.4738, test loss-1.8999, acc-0.4695
Iter-43730, train loss-1.9455, acc-0.4600, valid loss-1.8990, acc-0.4736, test loss-1.8999, acc-0.4694
Iter-43740, train loss-1.8873, acc-0.5200, valid loss-1.8989, acc-0.4738, test loss-1.8998, acc-0.4694
Iter-43750, train loss-1.8550, acc-0.5400, valid loss-1.8989, acc-0.4738, test loss-1.8997, acc-0.4695
Iter-43760, train loss-1.9565, acc-0.4000, valid loss-1.8988, acc-0.4740, test loss-1.8997, acc-0.4695
Iter-43770, train loss-2.0101, acc-0.3400, valid loss-1.8987, acc-0.4738, test loss-1.8996, acc-0.4694
Iter-43780, train loss-1.8644, acc-0.5000, valid loss-1.8987, acc-0.4736, test loss-1.8996, acc-0.4694
Iter-43790, train loss-1.9229, acc-0.4600, valid loss-1.8986, acc-0.4736, test loss-1.8995, acc-0.4694
Iter-43800, train loss-1.9613, acc-0.4600, valid loss-1.8985, acc-0.4736, test loss-1.8994, acc-0.4694
Iter-43810, train loss-1.8447, acc-0.5600, valid loss-1.8985, acc-0.4736, test loss-1.8994, acc-0.4692
Iter-43820, train loss-1.8842, acc-0.5400, valid loss-1.8984, acc-0.4738, test loss-1.8993, acc-0.4694
Iter-43830, train loss-1.9355, acc-0.4200, valid loss-1.8984, acc-0.4736, test loss-1.8993, acc-0.4695
Iter-43840, train loss-1.9434, acc-0.4000, valid loss-1.8983, acc-0.4738, test loss-1.8992, acc-0.4696
Iter-43850, train loss-1.9254, acc-0.4800, valid loss-1.8982, acc-0.4736, test loss-1.8991, acc-0.4693
Iter-43860, train loss-1.8782, acc-0.6000, valid loss-1.8982, acc-0.4736, test loss-1.8991, acc-0.4695
Iter-43870, train loss-1.8255, acc-0.5800, valid loss-1.8981, acc-0.4742, test loss-1.8990, acc-0.4693
Iter-43880, train loss-1.8753, acc-0.5600, valid loss-1.8980, acc-0.4742, test loss-1.8989, acc-0.4696
Iter-43890, train loss-2.0260, acc-0.4000, valid loss-1.8980, acc-0.4740, test loss-1.8989, acc-0.4696
Iter-43900, train loss-1.9540, acc-0.4200, valid loss-1.8979, acc-0.4740, test loss-1.8988, acc-0.4696
Iter-43910, train loss-1.8923, acc-0.5000, valid loss-1.8979, acc-0.4744, test loss-1.8987, acc-0.4696
Iter-43920, train loss-1.8712, acc-0.4800, valid loss-1.8978, acc-0.4742, test loss-1.8987, acc-0.4696
Iter-43930, train loss-1.8474, acc-0.4800, valid loss-1.8977, acc-0.4742, test loss-1.8986, acc-0.4697
Iter-43940, train loss-1.8708, acc-0.4600, valid loss-1.8977, acc-0.4746, test loss-1.8986, acc-0.4697
Iter-43950, train loss-1.8620, acc-0.4200, valid loss-1.8976, acc-0.4742, test loss-1.8985, acc-0.4697
Iter-43960, train loss-1.8410, acc-0.4800, valid loss-1.8975, acc-0.4748, test loss-1.8984, acc-0.4693
Iter-43970, train loss-1.9075, acc-0.3800, valid loss-1.8975, acc-0.4750, test loss-1.8984, acc-0.4692
Iter-43980, train loss-1.8947, acc-0.5000, valid loss-1.8974, acc-0.4744, test loss-1.8983, acc-0.4695
Iter-43990, train loss-1.7232, acc-0.5800, valid loss-1.8973, acc-0.4750, test loss-1.8982, acc-0.4692
Iter-44000, train loss-1.9455, acc-0.3800, valid loss-1.8973, acc-0.4748, test loss-1.8982, acc-0.4693
Iter-44010, train loss-1.9328, acc-0.4400, valid loss-1.8972, acc-0.4746, test loss-1.8981, acc-0.4696
Iter-44020, train loss-1.9457, acc-0.3400, valid loss-1.8972, acc-0.4744, test loss-1.8980, acc-0.4697
Iter-44030, train loss-1.9658, acc-0.4600, valid loss-1.8971, acc-0.4748, test loss-1.8980, acc-0.4697
Iter-44040, train loss-1.9517, acc-0.4600, valid loss-1.8970, acc-0.4752, test loss-1.8979, acc-0.4697
Iter-44050, train loss-1.9672, acc-0.3800, valid loss-1.8970, acc-0.4752, test loss-1.8979, acc-0.4698
Iter-44060, train loss-1.8421, acc-0.5000, valid loss-1.8969, acc-0.4752, test loss-1.8978, acc-0.4698
Iter-44070, train loss-1.9265, acc-0.4200, valid loss-1.8968, acc-0.4752, test loss-1.8977, acc-0.4696
Iter-44080, train loss-1.9168, acc-0.5200, valid loss-1.8968, acc-0.4752, test loss-1.8977, acc-0.4699
Iter-44090, train loss-1.9100, acc-0.4200, valid loss-1.8967, acc-0.4752, test loss-1.8976, acc-0.4698
Iter-44100, train loss-1.8632, acc-0.5200, valid loss-1.8967, acc-0.4752, test loss-1.8975, acc-0.4700
Iter-44110, train loss-1.8663, acc-0.5200, valid loss-1.8966, acc-0.4752, test loss-1.8975, acc-0.4701
Iter-44120, train loss-1.8771, acc-0.5000, valid loss-1.8965, acc-0.4752, test loss-1.8974, acc-0.4701
Iter-44130, train loss-1.8359, acc-0.5400, valid loss-1.8965, acc-0.4752, test loss-1.8974, acc-0.4701
Iter-44140, train loss-1.9263, acc-0.4400, valid loss-1.8964, acc-0.4752, test loss-1.8973, acc-0.4700
Iter-44150, train loss-1.8612, acc-0.5200, valid loss-1.8964, acc-0.4748, test loss-1.8972, acc-0.4701
Iter-44160, train loss-1.8357, acc-0.5200, valid loss-1.8963, acc-0.4752, test loss-1.8972, acc-0.4701
Iter-44170, train loss-1.8239, acc-0.5800, valid loss-1.8962, acc-0.4752, test loss-1.8971, acc-0.4701
Iter-44180, train loss-1.9480, acc-0.4400, valid loss-1.8962, acc-0.4752, test loss-1.8971, acc-0.4699
Iter-44190, train loss-1.9401, acc-0.4600, valid loss-1.8961, acc-0.4752, test loss-1.8970, acc-0.4700
Iter-44200, train loss-1.8133, acc-0.5800, valid loss-1.8960, acc-0.4752, test loss-1.8969, acc-0.4701
Iter-44210, train loss-1.9209, acc-0.4600, valid loss-1.8960, acc-0.4752, test loss-1.8969, acc-0.4701
Iter-44220, train loss-1.9228, acc-0.3800, valid loss-1.8959, acc-0.4756, test loss-1.8968, acc-0.4699
Iter-44230, train loss-1.9156, acc-0.5000, valid loss-1.8958, acc-0.4756, test loss-1.8967, acc-0.4700
Iter-44240, train loss-1.9171, acc-0.4400, valid loss-1.8958, acc-0.4756, test loss-1.8967, acc-0.4704
Iter-44250, train loss-1.8264, acc-0.5800, valid loss-1.8957, acc-0.4754, test loss-1.8966, acc-0.4704
Iter-44260, train loss-1.7765, acc-0.5600, valid loss-1.8957, acc-0.4752, test loss-1.8965, acc-0.4703
Iter-44270, train loss-1.7846, acc-0.5200, valid loss-1.8956, acc-0.4754, test loss-1.8965, acc-0.4705
Iter-44280, train loss-1.8909, acc-0.5200, valid loss-1.8955, acc-0.4754, test loss-1.8964, acc-0.4703
Iter-44290, train loss-1.8023, acc-0.5600, valid loss-1.8955, acc-0.4754, test loss-1.8964, acc-0.4701
Iter-44300, train loss-1.8984, acc-0.4600, valid loss-1.8954, acc-0.4756, test loss-1.8963, acc-0.4702
Iter-44310, train loss-1.8516, acc-0.5200, valid loss-1.8954, acc-0.4756, test loss-1.8962, acc-0.4703
Iter-44320, train loss-1.9250, acc-0.4200, valid loss-1.8953, acc-0.4756, test loss-1.8962, acc-0.4702
Iter-44330, train loss-1.7491, acc-0.6200, valid loss-1.8952, acc-0.4754, test loss-1.8961, acc-0.4703
Iter-44340, train loss-1.9672, acc-0.5400, valid loss-1.8952, acc-0.4754, test loss-1.8960, acc-0.4702
Iter-44350, train loss-1.9889, acc-0.3600, valid loss-1.8951, acc-0.4754, test loss-1.8960, acc-0.4704
Iter-44360, train loss-1.9327, acc-0.4800, valid loss-1.8950, acc-0.4754, test loss-1.8959, acc-0.4705
Iter-44370, train loss-1.9801, acc-0.3800, valid loss-1.8950, acc-0.4752, test loss-1.8959, acc-0.4705
Iter-44380, train loss-1.8801, acc-0.5200, valid loss-1.8949, acc-0.4752, test loss-1.8958, acc-0.4705
Iter-44390, train loss-1.9471, acc-0.5200, valid loss-1.8949, acc-0.4754, test loss-1.8957, acc-0.4705
Iter-44400, train loss-1.8483, acc-0.5400, valid loss-1.8948, acc-0.4754, test loss-1.8957, acc-0.4703
Iter-44410, train loss-1.9662, acc-0.4800, valid loss-1.8947, acc-0.4754, test loss-1.8956, acc-0.4705
Iter-44420, train loss-1.9332, acc-0.4000, valid loss-1.8947, acc-0.4754, test loss-1.8955, acc-0.4704
Iter-44430, train loss-1.8959, acc-0.4400, valid loss-1.8946, acc-0.4754, test loss-1.8955, acc-0.4703
Iter-44440, train loss-1.8977, acc-0.5200, valid loss-1.8946, acc-0.4754, test loss-1.8954, acc-0.4703
Iter-44450, train loss-1.9260, acc-0.4200, valid loss-1.8945, acc-0.4756, test loss-1.8954, acc-0.4705
Iter-44460, train loss-1.8067, acc-0.5400, valid loss-1.8944, acc-0.4756, test loss-1.8953, acc-0.4705
Iter-44470, train loss-1.9044, acc-0.4600, valid loss-1.8944, acc-0.4756, test loss-1.8952, acc-0.4704
Iter-44480, train loss-1.9108, acc-0.5000, valid loss-1.8943, acc-0.4752, test loss-1.8952, acc-0.4705
Iter-44490, train loss-1.8348, acc-0.5200, valid loss-1.8942, acc-0.4752, test loss-1.8951, acc-0.4704
Iter-44500, train loss-1.8780, acc-0.5400, valid loss-1.8942, acc-0.4754, test loss-1.8950, acc-0.4705
Iter-44510, train loss-1.9833, acc-0.3400, valid loss-1.8941, acc-0.4756, test loss-1.8950, acc-0.4704
Iter-44520, train loss-1.9145, acc-0.4400, valid loss-1.8940, acc-0.4756, test loss-1.8949, acc-0.4704
Iter-44530, train loss-1.9592, acc-0.4000, valid loss-1.8940, acc-0.4758, test loss-1.8948, acc-0.4705
Iter-44540, train loss-1.9511, acc-0.5600, valid loss-1.8939, acc-0.4758, test loss-1.8948, acc-0.4705
Iter-44550, train loss-1.8954, acc-0.4400, valid loss-1.8939, acc-0.4762, test loss-1.8947, acc-0.4705
Iter-44560, train loss-1.8064, acc-0.5400, valid loss-1.8938, acc-0.4762, test loss-1.8947, acc-0.4703
Iter-44570, train loss-1.9048, acc-0.4400, valid loss-1.8937, acc-0.4758, test loss-1.8946, acc-0.4705
Iter-44580, train loss-1.9984, acc-0.4200, valid loss-1.8937, acc-0.4758, test loss-1.8945, acc-0.4704
Iter-44590, train loss-1.9201, acc-0.5000, valid loss-1.8936, acc-0.4758, test loss-1.8945, acc-0.4705
Iter-44600, train loss-1.9096, acc-0.4200, valid loss-1.8936, acc-0.4758, test loss-1.8944, acc-0.4705
Iter-44610, train loss-1.8998, acc-0.4800, valid loss-1.8935, acc-0.4756, test loss-1.8944, acc-0.4705
Iter-44620, train loss-1.8864, acc-0.4600, valid loss-1.8934, acc-0.4758, test loss-1.8943, acc-0.4705
Iter-44630, train loss-1.8968, acc-0.4000, valid loss-1.8934, acc-0.4758, test loss-1.8942, acc-0.4705
Iter-44640, train loss-1.8999, acc-0.5200, valid loss-1.8933, acc-0.4758, test loss-1.8942, acc-0.4705
Iter-44650, train loss-1.9462, acc-0.3600, valid loss-1.8932, acc-0.4758, test loss-1.8941, acc-0.4704
Iter-44660, train loss-1.9074, acc-0.4600, valid loss-1.8932, acc-0.4760, test loss-1.8940, acc-0.4706
Iter-44670, train loss-1.9496, acc-0.3600, valid loss-1.8931, acc-0.4758, test loss-1.8940, acc-0.4705
Iter-44680, train loss-1.8720, acc-0.5600, valid loss-1.8931, acc-0.4758, test loss-1.8939, acc-0.4703
Iter-44690, train loss-1.8671, acc-0.5600, valid loss-1.8930, acc-0.4756, test loss-1.8939, acc-0.4705
Iter-44700, train loss-1.8938, acc-0.5000, valid loss-1.8929, acc-0.4756, test loss-1.8938, acc-0.4705
Iter-44710, train loss-1.9588, acc-0.3800, valid loss-1.8929, acc-0.4756, test loss-1.8937, acc-0.4703
Iter-44720, train loss-1.9152, acc-0.3800, valid loss-1.8928, acc-0.4758, test loss-1.8937, acc-0.4705
Iter-44730, train loss-1.9446, acc-0.4600, valid loss-1.8928, acc-0.4760, test loss-1.8936, acc-0.4706
Iter-44740, train loss-1.8684, acc-0.4800, valid loss-1.8927, acc-0.4758, test loss-1.8936, acc-0.4704
Iter-44750, train loss-1.9749, acc-0.3600, valid loss-1.8926, acc-0.4762, test loss-1.8935, acc-0.4706
Iter-44760, train loss-1.8642, acc-0.4400, valid loss-1.8926, acc-0.4762, test loss-1.8934, acc-0.4706
Iter-44770, train loss-1.8278, acc-0.5200, valid loss-1.8925, acc-0.4762, test loss-1.8934, acc-0.4707
Iter-44780, train loss-1.9040, acc-0.5000, valid loss-1.8924, acc-0.4760, test loss-1.8933, acc-0.4707
Iter-44790, train loss-1.9270, acc-0.4800, valid loss-1.8924, acc-0.4762, test loss-1.8932, acc-0.4707
Iter-44800, train loss-1.8904, acc-0.4800, valid loss-1.8923, acc-0.4760, test loss-1.8932, acc-0.4708
Iter-44810, train loss-1.9170, acc-0.3800, valid loss-1.8923, acc-0.4760, test loss-1.8931, acc-0.4707
Iter-44820, train loss-1.8655, acc-0.6200, valid loss-1.8922, acc-0.4762, test loss-1.8931, acc-0.4710
Iter-44830, train loss-1.8075, acc-0.5200, valid loss-1.8921, acc-0.4764, test loss-1.8930, acc-0.4708
Iter-44840, train loss-1.9139, acc-0.4600, valid loss-1.8921, acc-0.4762, test loss-1.8929, acc-0.4709
Iter-44850, train loss-1.8280, acc-0.5400, valid loss-1.8920, acc-0.4762, test loss-1.8929, acc-0.4709
Iter-44860, train loss-1.9242, acc-0.5000, valid loss-1.8919, acc-0.4760, test loss-1.8928, acc-0.4709
Iter-44870, train loss-1.9030, acc-0.4400, valid loss-1.8919, acc-0.4762, test loss-1.8928, acc-0.4710
Iter-44880, train loss-1.9224, acc-0.4800, valid loss-1.8918, acc-0.4762, test loss-1.8927, acc-0.4711
Iter-44890, train loss-1.8230, acc-0.5000, valid loss-1.8918, acc-0.4762, test loss-1.8926, acc-0.4711
Iter-44900, train loss-1.8710, acc-0.5600, valid loss-1.8917, acc-0.4764, test loss-1.8926, acc-0.4710
Iter-44910, train loss-1.9079, acc-0.4800, valid loss-1.8916, acc-0.4764, test loss-1.8925, acc-0.4709
Iter-44920, train loss-1.9338, acc-0.4200, valid loss-1.8916, acc-0.4764, test loss-1.8924, acc-0.4709
Iter-44930, train loss-1.8769, acc-0.4600, valid loss-1.8915, acc-0.4764, test loss-1.8924, acc-0.4709
Iter-44940, train loss-1.8663, acc-0.4400, valid loss-1.8914, acc-0.4764, test loss-1.8923, acc-0.4709
Iter-44950, train loss-1.9034, acc-0.3600, valid loss-1.8914, acc-0.4764, test loss-1.8922, acc-0.4709
Iter-44960, train loss-1.7633, acc-0.5800, valid loss-1.8913, acc-0.4764, test loss-1.8922, acc-0.4709
Iter-44970, train loss-1.8978, acc-0.4400, valid loss-1.8912, acc-0.4764, test loss-1.8921, acc-0.4709
Iter-44980, train loss-1.8248, acc-0.5600, valid loss-1.8912, acc-0.4764, test loss-1.8920, acc-0.4710
Iter-44990, train loss-1.9761, acc-0.3600, valid loss-1.8911, acc-0.4760, test loss-1.8920, acc-0.4711
Iter-45000, train loss-1.9272, acc-0.3600, valid loss-1.8910, acc-0.4760, test loss-1.8919, acc-0.4711
Iter-45010, train loss-1.8681, acc-0.5200, valid loss-1.8910, acc-0.4762, test loss-1.8919, acc-0.4710
Iter-45020, train loss-1.9039, acc-0.4800, valid loss-1.8909, acc-0.4766, test loss-1.8918, acc-0.4709
Iter-45030, train loss-1.9387, acc-0.3400, valid loss-1.8909, acc-0.4766, test loss-1.8917, acc-0.4709
Iter-45040, train loss-1.9599, acc-0.5000, valid loss-1.8908, acc-0.4766, test loss-1.8917, acc-0.4709
Iter-45050, train loss-1.9265, acc-0.3600, valid loss-1.8907, acc-0.4766, test loss-1.8916, acc-0.4710
Iter-45060, train loss-1.8536, acc-0.4800, valid loss-1.8907, acc-0.4768, test loss-1.8916, acc-0.4710
Iter-45070, train loss-1.9156, acc-0.4400, valid loss-1.8906, acc-0.4768, test loss-1.8915, acc-0.4710
Iter-45080, train loss-1.9352, acc-0.4600, valid loss-1.8906, acc-0.4772, test loss-1.8914, acc-0.4710
Iter-45090, train loss-1.9374, acc-0.4600, valid loss-1.8905, acc-0.4770, test loss-1.8914, acc-0.4710
Iter-45100, train loss-1.8253, acc-0.4600, valid loss-1.8904, acc-0.4770, test loss-1.8913, acc-0.4710
Iter-45110, train loss-1.9338, acc-0.5000, valid loss-1.8904, acc-0.4768, test loss-1.8912, acc-0.4711
Iter-45120, train loss-1.9189, acc-0.4600, valid loss-1.8903, acc-0.4772, test loss-1.8912, acc-0.4709
Iter-45130, train loss-1.9528, acc-0.3800, valid loss-1.8902, acc-0.4772, test loss-1.8911, acc-0.4710
Iter-45140, train loss-1.7662, acc-0.6200, valid loss-1.8902, acc-0.4772, test loss-1.8911, acc-0.4711
Iter-45150, train loss-1.8441, acc-0.5800, valid loss-1.8901, acc-0.4772, test loss-1.8910, acc-0.4709
Iter-45160, train loss-1.8413, acc-0.5800, valid loss-1.8900, acc-0.4772, test loss-1.8909, acc-0.4709
Iter-45170, train loss-1.8551, acc-0.4200, valid loss-1.8900, acc-0.4772, test loss-1.8909, acc-0.4710
Iter-45180, train loss-2.0214, acc-0.3200, valid loss-1.8899, acc-0.4772, test loss-1.8908, acc-0.4709
Iter-45190, train loss-1.9011, acc-0.4400, valid loss-1.8899, acc-0.4772, test loss-1.8907, acc-0.4709
Iter-45200, train loss-1.9099, acc-0.4400, valid loss-1.8898, acc-0.4772, test loss-1.8907, acc-0.4709
Iter-45210, train loss-1.8666, acc-0.5200, valid loss-1.8897, acc-0.4774, test loss-1.8906, acc-0.4709
Iter-45220, train loss-1.7743, acc-0.5800, valid loss-1.8897, acc-0.4774, test loss-1.8906, acc-0.4709
Iter-45230, train loss-1.8584, acc-0.5000, valid loss-1.8896, acc-0.4774, test loss-1.8905, acc-0.4711
Iter-45240, train loss-1.8867, acc-0.5200, valid loss-1.8895, acc-0.4774, test loss-1.8904, acc-0.4711
Iter-45250, train loss-1.9373, acc-0.5000, valid loss-1.8895, acc-0.4774, test loss-1.8904, acc-0.4711
Iter-45260, train loss-1.9038, acc-0.5400, valid loss-1.8894, acc-0.4770, test loss-1.8903, acc-0.4710
Iter-45270, train loss-1.9289, acc-0.4000, valid loss-1.8894, acc-0.4770, test loss-1.8902, acc-0.4710
Iter-45280, train loss-1.9546, acc-0.3600, valid loss-1.8893, acc-0.4770, test loss-1.8902, acc-0.4711
Iter-45290, train loss-1.9043, acc-0.4400, valid loss-1.8892, acc-0.4774, test loss-1.8901, acc-0.4709
Iter-45300, train loss-1.8328, acc-0.5000, valid loss-1.8892, acc-0.4770, test loss-1.8901, acc-0.4710
Iter-45310, train loss-1.8914, acc-0.4400, valid loss-1.8891, acc-0.4770, test loss-1.8900, acc-0.4710
Iter-45320, train loss-2.0221, acc-0.2800, valid loss-1.8890, acc-0.4772, test loss-1.8899, acc-0.4708
Iter-45330, train loss-1.9115, acc-0.5000, valid loss-1.8890, acc-0.4770, test loss-1.8899, acc-0.4709
Iter-45340, train loss-1.8768, acc-0.4400, valid loss-1.8889, acc-0.4768, test loss-1.8898, acc-0.4709
Iter-45350, train loss-1.8262, acc-0.4600, valid loss-1.8888, acc-0.4768, test loss-1.8897, acc-0.4709
Iter-45360, train loss-1.8721, acc-0.5400, valid loss-1.8888, acc-0.4770, test loss-1.8897, acc-0.4709
Iter-45370, train loss-1.9245, acc-0.3800, valid loss-1.8887, acc-0.4770, test loss-1.8896, acc-0.4709
Iter-45380, train loss-1.8224, acc-0.5600, valid loss-1.8887, acc-0.4766, test loss-1.8896, acc-0.4707
Iter-45390, train loss-1.8683, acc-0.4000, valid loss-1.8886, acc-0.4768, test loss-1.8895, acc-0.4707
Iter-45400, train loss-1.9784, acc-0.3600, valid loss-1.8885, acc-0.4766, test loss-1.8894, acc-0.4708
Iter-45410, train loss-1.9660, acc-0.3800, valid loss-1.8885, acc-0.4766, test loss-1.8894, acc-0.4707
Iter-45420, train loss-1.9666, acc-0.4000, valid loss-1.8884, acc-0.4768, test loss-1.8893, acc-0.4707
Iter-45430, train loss-1.9064, acc-0.5600, valid loss-1.8884, acc-0.4768, test loss-1.8892, acc-0.4708
Iter-45440, train loss-1.9549, acc-0.4200, valid loss-1.8883, acc-0.4770, test loss-1.8892, acc-0.4709
Iter-45450, train loss-1.9489, acc-0.5000, valid loss-1.8882, acc-0.4770, test loss-1.8891, acc-0.4709
Iter-45460, train loss-1.9646, acc-0.4600, valid loss-1.8882, acc-0.4770, test loss-1.8891, acc-0.4709
Iter-45470, train loss-1.9126, acc-0.4200, valid loss-1.8881, acc-0.4770, test loss-1.8890, acc-0.4709
Iter-45480, train loss-1.8447, acc-0.5400, valid loss-1.8880, acc-0.4768, test loss-1.8889, acc-0.4707
Iter-45490, train loss-1.9410, acc-0.4400, valid loss-1.8880, acc-0.4768, test loss-1.8889, acc-0.4707
Iter-45500, train loss-1.9424, acc-0.3800, valid loss-1.8879, acc-0.4768, test loss-1.8888, acc-0.4707
Iter-45510, train loss-1.8865, acc-0.4800, valid loss-1.8879, acc-0.4768, test loss-1.8887, acc-0.4707
Iter-45520, train loss-1.8255, acc-0.5800, valid loss-1.8878, acc-0.4768, test loss-1.8887, acc-0.4707
Iter-45530, train loss-1.9123, acc-0.5000, valid loss-1.8877, acc-0.4770, test loss-1.8886, acc-0.4707
Iter-45540, train loss-1.8878, acc-0.4800, valid loss-1.8877, acc-0.4770, test loss-1.8886, acc-0.4707
Iter-45550, train loss-1.9104, acc-0.3600, valid loss-1.8876, acc-0.4768, test loss-1.8885, acc-0.4707
Iter-45560, train loss-1.8741, acc-0.5400, valid loss-1.8875, acc-0.4768, test loss-1.8884, acc-0.4707
Iter-45570, train loss-1.9950, acc-0.4000, valid loss-1.8875, acc-0.4768, test loss-1.8884, acc-0.4707
Iter-45580, train loss-1.8742, acc-0.5200, valid loss-1.8874, acc-0.4770, test loss-1.8883, acc-0.4707
Iter-45590, train loss-1.8816, acc-0.5000, valid loss-1.8874, acc-0.4770, test loss-1.8883, acc-0.4707
Iter-45600, train loss-1.9250, acc-0.4000, valid loss-1.8873, acc-0.4770, test loss-1.8882, acc-0.4707
Iter-45610, train loss-1.8855, acc-0.4200, valid loss-1.8872, acc-0.4770, test loss-1.8881, acc-0.4707
Iter-45620, train loss-1.9057, acc-0.4200, valid loss-1.8872, acc-0.4772, test loss-1.8881, acc-0.4708
Iter-45630, train loss-1.8794, acc-0.4200, valid loss-1.8871, acc-0.4772, test loss-1.8880, acc-0.4709
Iter-45640, train loss-1.8980, acc-0.5600, valid loss-1.8870, acc-0.4772, test loss-1.8879, acc-0.4710
Iter-45650, train loss-1.8368, acc-0.4800, valid loss-1.8870, acc-0.4774, test loss-1.8879, acc-0.4710
Iter-45660, train loss-1.9432, acc-0.4000, valid loss-1.8869, acc-0.4774, test loss-1.8878, acc-0.4710
Iter-45670, train loss-1.9544, acc-0.3800, valid loss-1.8869, acc-0.4776, test loss-1.8878, acc-0.4710
Iter-45680, train loss-1.8224, acc-0.6800, valid loss-1.8868, acc-0.4780, test loss-1.8877, acc-0.4710
Iter-45690, train loss-1.9514, acc-0.4800, valid loss-1.8867, acc-0.4780, test loss-1.8876, acc-0.4710
Iter-45700, train loss-1.8302, acc-0.5000, valid loss-1.8867, acc-0.4780, test loss-1.8876, acc-0.4710
Iter-45710, train loss-1.8617, acc-0.4800, valid loss-1.8866, acc-0.4780, test loss-1.8875, acc-0.4710
Iter-45720, train loss-1.8855, acc-0.5200, valid loss-1.8866, acc-0.4782, test loss-1.8874, acc-0.4711
Iter-45730, train loss-1.9541, acc-0.3800, valid loss-1.8865, acc-0.4782, test loss-1.8874, acc-0.4711
Iter-45740, train loss-1.8579, acc-0.5400, valid loss-1.8864, acc-0.4784, test loss-1.8873, acc-0.4710
Iter-45750, train loss-1.8073, acc-0.5400, valid loss-1.8864, acc-0.4786, test loss-1.8873, acc-0.4712
Iter-45760, train loss-1.8509, acc-0.3800, valid loss-1.8863, acc-0.4786, test loss-1.8872, acc-0.4710
Iter-45770, train loss-1.9269, acc-0.5000, valid loss-1.8862, acc-0.4782, test loss-1.8871, acc-0.4711
Iter-45780, train loss-1.9778, acc-0.4000, valid loss-1.8862, acc-0.4784, test loss-1.8871, acc-0.4712
Iter-45790, train loss-1.9478, acc-0.4400, valid loss-1.8861, acc-0.4786, test loss-1.8870, acc-0.4713
Iter-45800, train loss-1.8612, acc-0.4600, valid loss-1.8861, acc-0.4786, test loss-1.8869, acc-0.4713
Iter-45810, train loss-1.9716, acc-0.4200, valid loss-1.8860, acc-0.4786, test loss-1.8869, acc-0.4713
Iter-45820, train loss-2.0306, acc-0.3600, valid loss-1.8859, acc-0.4786, test loss-1.8868, acc-0.4713
Iter-45830, train loss-1.9443, acc-0.5600, valid loss-1.8859, acc-0.4784, test loss-1.8868, acc-0.4713
Iter-45840, train loss-1.8721, acc-0.4800, valid loss-1.8858, acc-0.4784, test loss-1.8867, acc-0.4715
Iter-45850, train loss-1.9304, acc-0.4000, valid loss-1.8858, acc-0.4784, test loss-1.8866, acc-0.4717
Iter-45860, train loss-1.8939, acc-0.5400, valid loss-1.8857, acc-0.4782, test loss-1.8866, acc-0.4716
Iter-45870, train loss-1.8431, acc-0.5400, valid loss-1.8856, acc-0.4782, test loss-1.8865, acc-0.4717
Iter-45880, train loss-1.8050, acc-0.5400, valid loss-1.8856, acc-0.4782, test loss-1.8865, acc-0.4716
Iter-45890, train loss-1.8580, acc-0.4600, valid loss-1.8855, acc-0.4780, test loss-1.8864, acc-0.4714
Iter-45900, train loss-1.8988, acc-0.4400, valid loss-1.8854, acc-0.4784, test loss-1.8863, acc-0.4716
Iter-45910, train loss-1.8377, acc-0.5800, valid loss-1.8854, acc-0.4782, test loss-1.8863, acc-0.4717
Iter-45920, train loss-1.8616, acc-0.5800, valid loss-1.8853, acc-0.4784, test loss-1.8862, acc-0.4716
Iter-45930, train loss-1.8843, acc-0.4600, valid loss-1.8853, acc-0.4786, test loss-1.8862, acc-0.4716
Iter-45940, train loss-1.9718, acc-0.3600, valid loss-1.8852, acc-0.4786, test loss-1.8861, acc-0.4716
Iter-45950, train loss-1.9258, acc-0.4600, valid loss-1.8852, acc-0.4786, test loss-1.8860, acc-0.4716
Iter-45960, train loss-1.9094, acc-0.5000, valid loss-1.8851, acc-0.4786, test loss-1.8860, acc-0.4716
Iter-45970, train loss-1.9739, acc-0.4400, valid loss-1.8850, acc-0.4782, test loss-1.8859, acc-0.4716
Iter-45980, train loss-1.9248, acc-0.4800, valid loss-1.8850, acc-0.4782, test loss-1.8859, acc-0.4716
Iter-45990, train loss-1.9150, acc-0.4000, valid loss-1.8849, acc-0.4782, test loss-1.8858, acc-0.4717
Iter-46000, train loss-1.9634, acc-0.4200, valid loss-1.8848, acc-0.4780, test loss-1.8857, acc-0.4720
Iter-46010, train loss-1.8768, acc-0.5200, valid loss-1.8848, acc-0.4780, test loss-1.8857, acc-0.4719
Iter-46020, train loss-1.9149, acc-0.5200, valid loss-1.8847, acc-0.4782, test loss-1.8856, acc-0.4723
Iter-46030, train loss-1.8639, acc-0.4400, valid loss-1.8847, acc-0.4780, test loss-1.8855, acc-0.4721
Iter-46040, train loss-1.8795, acc-0.5200, valid loss-1.8846, acc-0.4780, test loss-1.8855, acc-0.4721
Iter-46050, train loss-1.8273, acc-0.5800, valid loss-1.8845, acc-0.4782, test loss-1.8854, acc-0.4723
Iter-46060, train loss-1.8962, acc-0.4600, valid loss-1.8845, acc-0.4784, test loss-1.8854, acc-0.4720
Iter-46070, train loss-1.8468, acc-0.5800, valid loss-1.8844, acc-0.4784, test loss-1.8853, acc-0.4725
Iter-46080, train loss-1.8492, acc-0.5600, valid loss-1.8844, acc-0.4784, test loss-1.8852, acc-0.4726
Iter-46090, train loss-1.9363, acc-0.4600, valid loss-1.8843, acc-0.4784, test loss-1.8852, acc-0.4725
Iter-46100, train loss-1.8722, acc-0.5600, valid loss-1.8842, acc-0.4784, test loss-1.8851, acc-0.4723
Iter-46110, train loss-1.9316, acc-0.5800, valid loss-1.8842, acc-0.4784, test loss-1.8851, acc-0.4726
Iter-46120, train loss-1.9165, acc-0.5200, valid loss-1.8841, acc-0.4784, test loss-1.8850, acc-0.4726
Iter-46130, train loss-1.8751, acc-0.4600, valid loss-1.8841, acc-0.4784, test loss-1.8850, acc-0.4725
Iter-46140, train loss-1.8693, acc-0.4200, valid loss-1.8840, acc-0.4784, test loss-1.8849, acc-0.4726
Iter-46150, train loss-1.8949, acc-0.4800, valid loss-1.8839, acc-0.4784, test loss-1.8848, acc-0.4728
Iter-46160, train loss-1.8526, acc-0.4800, valid loss-1.8839, acc-0.4784, test loss-1.8848, acc-0.4728
Iter-46170, train loss-1.8650, acc-0.5400, valid loss-1.8838, acc-0.4786, test loss-1.8847, acc-0.4728
Iter-46180, train loss-1.9723, acc-0.3600, valid loss-1.8838, acc-0.4786, test loss-1.8847, acc-0.4727
Iter-46190, train loss-1.8740, acc-0.4000, valid loss-1.8837, acc-0.4786, test loss-1.8846, acc-0.4729
Iter-46200, train loss-1.8836, acc-0.5000, valid loss-1.8836, acc-0.4786, test loss-1.8845, acc-0.4727
Iter-46210, train loss-1.9284, acc-0.4200, valid loss-1.8836, acc-0.4786, test loss-1.8845, acc-0.4728
Iter-46220, train loss-1.8846, acc-0.4000, valid loss-1.8835, acc-0.4784, test loss-1.8844, acc-0.4728
Iter-46230, train loss-1.9346, acc-0.4200, valid loss-1.8835, acc-0.4784, test loss-1.8843, acc-0.4729
Iter-46240, train loss-1.8501, acc-0.4800, valid loss-1.8834, acc-0.4786, test loss-1.8843, acc-0.4727
Iter-46250, train loss-1.9182, acc-0.4200, valid loss-1.8833, acc-0.4790, test loss-1.8842, acc-0.4729
Iter-46260, train loss-1.8843, acc-0.4800, valid loss-1.8833, acc-0.4794, test loss-1.8842, acc-0.4731
Iter-46270, train loss-1.9073, acc-0.5400, valid loss-1.8832, acc-0.4794, test loss-1.8841, acc-0.4729
Iter-46280, train loss-1.8493, acc-0.4800, valid loss-1.8832, acc-0.4794, test loss-1.8840, acc-0.4730
Iter-46290, train loss-1.8472, acc-0.5200, valid loss-1.8831, acc-0.4792, test loss-1.8840, acc-0.4729
Iter-46300, train loss-1.9352, acc-0.4200, valid loss-1.8830, acc-0.4786, test loss-1.8839, acc-0.4730
Iter-46310, train loss-1.8580, acc-0.4800, valid loss-1.8830, acc-0.4790, test loss-1.8839, acc-0.4731
Iter-46320, train loss-1.9334, acc-0.4000, valid loss-1.8829, acc-0.4790, test loss-1.8838, acc-0.4731
Iter-46330, train loss-1.9207, acc-0.4400, valid loss-1.8828, acc-0.4794, test loss-1.8837, acc-0.4731
Iter-46340, train loss-1.9642, acc-0.3800, valid loss-1.8828, acc-0.4794, test loss-1.8837, acc-0.4733
Iter-46350, train loss-1.8069, acc-0.5200, valid loss-1.8827, acc-0.4794, test loss-1.8836, acc-0.4734
Iter-46360, train loss-1.8716, acc-0.3200, valid loss-1.8827, acc-0.4794, test loss-1.8835, acc-0.4734
Iter-46370, train loss-1.8948, acc-0.5400, valid loss-1.8826, acc-0.4794, test loss-1.8835, acc-0.4734
Iter-46380, train loss-1.8780, acc-0.4600, valid loss-1.8825, acc-0.4794, test loss-1.8834, acc-0.4732
Iter-46390, train loss-1.8297, acc-0.4800, valid loss-1.8825, acc-0.4792, test loss-1.8834, acc-0.4732
Iter-46400, train loss-1.9043, acc-0.4800, valid loss-1.8824, acc-0.4790, test loss-1.8833, acc-0.4731
Iter-46410, train loss-1.8549, acc-0.5400, valid loss-1.8823, acc-0.4792, test loss-1.8832, acc-0.4730
Iter-46420, train loss-1.8370, acc-0.5600, valid loss-1.8823, acc-0.4792, test loss-1.8832, acc-0.4732
Iter-46430, train loss-1.9171, acc-0.5200, valid loss-1.8822, acc-0.4792, test loss-1.8831, acc-0.4733
Iter-46440, train loss-1.7942, acc-0.5000, valid loss-1.8821, acc-0.4790, test loss-1.8830, acc-0.4730
Iter-46450, train loss-1.8491, acc-0.5200, valid loss-1.8821, acc-0.4790, test loss-1.8830, acc-0.4732
Iter-46460, train loss-2.0002, acc-0.2800, valid loss-1.8820, acc-0.4790, test loss-1.8829, acc-0.4731
Iter-46470, train loss-1.8701, acc-0.5200, valid loss-1.8820, acc-0.4790, test loss-1.8829, acc-0.4731
Iter-46480, train loss-1.9672, acc-0.3200, valid loss-1.8819, acc-0.4794, test loss-1.8828, acc-0.4734
Iter-46490, train loss-1.9847, acc-0.3000, valid loss-1.8818, acc-0.4794, test loss-1.8827, acc-0.4734
Iter-46500, train loss-1.9430, acc-0.5200, valid loss-1.8818, acc-0.4794, test loss-1.8827, acc-0.4736
Iter-46510, train loss-1.9657, acc-0.4000, valid loss-1.8817, acc-0.4794, test loss-1.8826, acc-0.4734
Iter-46520, train loss-1.9664, acc-0.3600, valid loss-1.8817, acc-0.4792, test loss-1.8826, acc-0.4735
Iter-46530, train loss-1.8281, acc-0.6000, valid loss-1.8816, acc-0.4794, test loss-1.8825, acc-0.4737
Iter-46540, train loss-1.8977, acc-0.4000, valid loss-1.8815, acc-0.4792, test loss-1.8824, acc-0.4736
Iter-46550, train loss-1.9611, acc-0.3800, valid loss-1.8815, acc-0.4794, test loss-1.8824, acc-0.4736
Iter-46560, train loss-1.9764, acc-0.3400, valid loss-1.8814, acc-0.4794, test loss-1.8823, acc-0.4737
Iter-46570, train loss-1.9528, acc-0.4200, valid loss-1.8814, acc-0.4794, test loss-1.8823, acc-0.4735
Iter-46580, train loss-1.9368, acc-0.4200, valid loss-1.8813, acc-0.4792, test loss-1.8822, acc-0.4736
Iter-46590, train loss-1.9116, acc-0.5200, valid loss-1.8812, acc-0.4792, test loss-1.8821, acc-0.4736
Iter-46600, train loss-1.8523, acc-0.5200, valid loss-1.8812, acc-0.4792, test loss-1.8821, acc-0.4737
Iter-46610, train loss-1.9410, acc-0.4400, valid loss-1.8811, acc-0.4792, test loss-1.8820, acc-0.4738
Iter-46620, train loss-1.8880, acc-0.5400, valid loss-1.8811, acc-0.4794, test loss-1.8819, acc-0.4738
Iter-46630, train loss-1.8586, acc-0.4800, valid loss-1.8810, acc-0.4794, test loss-1.8819, acc-0.4738
Iter-46640, train loss-1.7280, acc-0.6400, valid loss-1.8809, acc-0.4792, test loss-1.8818, acc-0.4737
Iter-46650, train loss-1.8504, acc-0.5800, valid loss-1.8809, acc-0.4794, test loss-1.8818, acc-0.4738
Iter-46660, train loss-1.8433, acc-0.6000, valid loss-1.8808, acc-0.4794, test loss-1.8817, acc-0.4737
Iter-46670, train loss-1.8165, acc-0.4800, valid loss-1.8808, acc-0.4794, test loss-1.8816, acc-0.4737
Iter-46680, train loss-1.8379, acc-0.5000, valid loss-1.8807, acc-0.4796, test loss-1.8816, acc-0.4737
Iter-46690, train loss-1.8941, acc-0.4400, valid loss-1.8806, acc-0.4796, test loss-1.8815, acc-0.4737
Iter-46700, train loss-1.9542, acc-0.4200, valid loss-1.8806, acc-0.4796, test loss-1.8815, acc-0.4737
Iter-46710, train loss-1.8939, acc-0.4800, valid loss-1.8805, acc-0.4796, test loss-1.8814, acc-0.4737
Iter-46720, train loss-1.9563, acc-0.4400, valid loss-1.8805, acc-0.4798, test loss-1.8813, acc-0.4736
Iter-46730, train loss-1.9490, acc-0.3800, valid loss-1.8804, acc-0.4796, test loss-1.8813, acc-0.4739
Iter-46740, train loss-1.8610, acc-0.6000, valid loss-1.8803, acc-0.4796, test loss-1.8812, acc-0.4739
Iter-46750, train loss-1.8793, acc-0.4800, valid loss-1.8803, acc-0.4796, test loss-1.8812, acc-0.4737
Iter-46760, train loss-1.9664, acc-0.4400, valid loss-1.8802, acc-0.4796, test loss-1.8811, acc-0.4741
Iter-46770, train loss-1.9941, acc-0.4400, valid loss-1.8802, acc-0.4796, test loss-1.8810, acc-0.4739
Iter-46780, train loss-1.9077, acc-0.4600, valid loss-1.8801, acc-0.4794, test loss-1.8810, acc-0.4741
Iter-46790, train loss-1.9592, acc-0.3600, valid loss-1.8800, acc-0.4794, test loss-1.8809, acc-0.4742
Iter-46800, train loss-1.8487, acc-0.5200, valid loss-1.8800, acc-0.4796, test loss-1.8809, acc-0.4741
Iter-46810, train loss-1.8256, acc-0.5200, valid loss-1.8799, acc-0.4794, test loss-1.8808, acc-0.4743
Iter-46820, train loss-1.8846, acc-0.4800, valid loss-1.8799, acc-0.4794, test loss-1.8807, acc-0.4743
Iter-46830, train loss-1.8526, acc-0.4600, valid loss-1.8798, acc-0.4794, test loss-1.8807, acc-0.4741
Iter-46840, train loss-1.9404, acc-0.5000, valid loss-1.8797, acc-0.4794, test loss-1.8806, acc-0.4744
Iter-46850, train loss-1.8710, acc-0.4600, valid loss-1.8797, acc-0.4798, test loss-1.8806, acc-0.4747
Iter-46860, train loss-1.9100, acc-0.4400, valid loss-1.8796, acc-0.4798, test loss-1.8805, acc-0.4747
Iter-46870, train loss-1.9704, acc-0.4400, valid loss-1.8796, acc-0.4802, test loss-1.8804, acc-0.4747
Iter-46880, train loss-1.8909, acc-0.4400, valid loss-1.8795, acc-0.4804, test loss-1.8804, acc-0.4748
Iter-46890, train loss-1.7774, acc-0.5400, valid loss-1.8794, acc-0.4804, test loss-1.8803, acc-0.4750
Iter-46900, train loss-1.9359, acc-0.4400, valid loss-1.8794, acc-0.4804, test loss-1.8803, acc-0.4748
Iter-46910, train loss-1.8845, acc-0.4800, valid loss-1.8793, acc-0.4802, test loss-1.8802, acc-0.4748
Iter-46920, train loss-1.9808, acc-0.5400, valid loss-1.8793, acc-0.4800, test loss-1.8801, acc-0.4748
Iter-46930, train loss-1.9140, acc-0.4800, valid loss-1.8792, acc-0.4802, test loss-1.8801, acc-0.4747
Iter-46940, train loss-1.8428, acc-0.4400, valid loss-1.8791, acc-0.4798, test loss-1.8800, acc-0.4744
Iter-46950, train loss-1.9088, acc-0.4800, valid loss-1.8791, acc-0.4802, test loss-1.8800, acc-0.4746
Iter-46960, train loss-1.8963, acc-0.4400, valid loss-1.8790, acc-0.4798, test loss-1.8799, acc-0.4746
Iter-46970, train loss-1.9625, acc-0.4400, valid loss-1.8790, acc-0.4798, test loss-1.8798, acc-0.4747
Iter-46980, train loss-1.8853, acc-0.3800, valid loss-1.8789, acc-0.4800, test loss-1.8798, acc-0.4748
Iter-46990, train loss-1.8724, acc-0.4600, valid loss-1.8789, acc-0.4802, test loss-1.8797, acc-0.4748
Iter-47000, train loss-1.9012, acc-0.4400, valid loss-1.8788, acc-0.4804, test loss-1.8797, acc-0.4748
Iter-47010, train loss-1.9037, acc-0.4200, valid loss-1.8787, acc-0.4802, test loss-1.8796, acc-0.4750
Iter-47020, train loss-1.8246, acc-0.5400, valid loss-1.8787, acc-0.4802, test loss-1.8795, acc-0.4749
Iter-47030, train loss-1.8833, acc-0.4600, valid loss-1.8786, acc-0.4802, test loss-1.8795, acc-0.4748
Iter-47040, train loss-1.8576, acc-0.5000, valid loss-1.8786, acc-0.4802, test loss-1.8794, acc-0.4749
Iter-47050, train loss-1.9136, acc-0.5000, valid loss-1.8785, acc-0.4802, test loss-1.8794, acc-0.4748
Iter-47060, train loss-1.8832, acc-0.4800, valid loss-1.8784, acc-0.4802, test loss-1.8793, acc-0.4746
Iter-47070, train loss-1.8360, acc-0.5400, valid loss-1.8784, acc-0.4804, test loss-1.8792, acc-0.4749
Iter-47080, train loss-1.8476, acc-0.5200, valid loss-1.8783, acc-0.4804, test loss-1.8792, acc-0.4750
Iter-47090, train loss-1.9529, acc-0.5200, valid loss-1.8783, acc-0.4804, test loss-1.8791, acc-0.4747
Iter-47100, train loss-1.9211, acc-0.4200, valid loss-1.8782, acc-0.4804, test loss-1.8791, acc-0.4749
Iter-47110, train loss-1.8145, acc-0.5400, valid loss-1.8781, acc-0.4804, test loss-1.8790, acc-0.4750
Iter-47120, train loss-1.8337, acc-0.4600, valid loss-1.8781, acc-0.4804, test loss-1.8789, acc-0.4750
Iter-47130, train loss-1.9006, acc-0.4000, valid loss-1.8780, acc-0.4806, test loss-1.8789, acc-0.4752
Iter-47140, train loss-1.8982, acc-0.5800, valid loss-1.8780, acc-0.4804, test loss-1.8788, acc-0.4748
Iter-47150, train loss-1.7816, acc-0.5800, valid loss-1.8779, acc-0.4804, test loss-1.8788, acc-0.4749
Iter-47160, train loss-1.9124, acc-0.3800, valid loss-1.8778, acc-0.4804, test loss-1.8787, acc-0.4750
Iter-47170, train loss-1.9071, acc-0.3400, valid loss-1.8778, acc-0.4802, test loss-1.8786, acc-0.4751
Iter-47180, train loss-1.8941, acc-0.5200, valid loss-1.8777, acc-0.4802, test loss-1.8786, acc-0.4748
Iter-47190, train loss-1.8582, acc-0.5200, valid loss-1.8777, acc-0.4804, test loss-1.8785, acc-0.4749
Iter-47200, train loss-2.0266, acc-0.3400, valid loss-1.8776, acc-0.4806, test loss-1.8784, acc-0.4751
Iter-47210, train loss-1.8486, acc-0.4800, valid loss-1.8775, acc-0.4806, test loss-1.8784, acc-0.4751
Iter-47220, train loss-1.8604, acc-0.5000, valid loss-1.8775, acc-0.4808, test loss-1.8783, acc-0.4749
Iter-47230, train loss-1.9116, acc-0.4400, valid loss-1.8774, acc-0.4806, test loss-1.8783, acc-0.4751
Iter-47240, train loss-1.8314, acc-0.5200, valid loss-1.8774, acc-0.4806, test loss-1.8782, acc-0.4751
Iter-47250, train loss-1.8983, acc-0.4000, valid loss-1.8773, acc-0.4806, test loss-1.8781, acc-0.4751
Iter-47260, train loss-1.8353, acc-0.5800, valid loss-1.8772, acc-0.4806, test loss-1.8781, acc-0.4751
Iter-47270, train loss-1.8742, acc-0.5200, valid loss-1.8772, acc-0.4806, test loss-1.8780, acc-0.4751
Iter-47280, train loss-1.9251, acc-0.4200, valid loss-1.8771, acc-0.4806, test loss-1.8780, acc-0.4752
Iter-47290, train loss-1.8908, acc-0.4200, valid loss-1.8771, acc-0.4808, test loss-1.8779, acc-0.4751
Iter-47300, train loss-1.8987, acc-0.3600, valid loss-1.8770, acc-0.4810, test loss-1.8779, acc-0.4752
Iter-47310, train loss-1.8351, acc-0.5600, valid loss-1.8769, acc-0.4808, test loss-1.8778, acc-0.4753
Iter-47320, train loss-1.8549, acc-0.4800, valid loss-1.8769, acc-0.4810, test loss-1.8777, acc-0.4753
Iter-47330, train loss-1.9417, acc-0.3600, valid loss-1.8768, acc-0.4810, test loss-1.8777, acc-0.4755
Iter-47340, train loss-1.8081, acc-0.5200, valid loss-1.8768, acc-0.4810, test loss-1.8776, acc-0.4752
Iter-47350, train loss-1.8584, acc-0.5000, valid loss-1.8767, acc-0.4812, test loss-1.8775, acc-0.4756
Iter-47360, train loss-1.8235, acc-0.5200, valid loss-1.8767, acc-0.4812, test loss-1.8775, acc-0.4757
Iter-47370, train loss-1.8320, acc-0.5600, valid loss-1.8766, acc-0.4812, test loss-1.8774, acc-0.4760
Iter-47380, train loss-1.8922, acc-0.5000, valid loss-1.8765, acc-0.4812, test loss-1.8774, acc-0.4759
Iter-47390, train loss-1.9981, acc-0.3400, valid loss-1.8765, acc-0.4812, test loss-1.8773, acc-0.4759
Iter-47400, train loss-1.9210, acc-0.4600, valid loss-1.8764, acc-0.4812, test loss-1.8772, acc-0.4760
Iter-47410, train loss-1.8882, acc-0.5000, valid loss-1.8764, acc-0.4812, test loss-1.8772, acc-0.4760
Iter-47420, train loss-1.8844, acc-0.5600, valid loss-1.8763, acc-0.4814, test loss-1.8771, acc-0.4760
Iter-47430, train loss-1.8258, acc-0.4600, valid loss-1.8762, acc-0.4814, test loss-1.8771, acc-0.4759
Iter-47440, train loss-1.9060, acc-0.4000, valid loss-1.8762, acc-0.4814, test loss-1.8770, acc-0.4760
Iter-47450, train loss-1.9344, acc-0.4600, valid loss-1.8761, acc-0.4814, test loss-1.8769, acc-0.4760
Iter-47460, train loss-1.8221, acc-0.5800, valid loss-1.8761, acc-0.4814, test loss-1.8769, acc-0.4758
Iter-47470, train loss-1.7825, acc-0.5600, valid loss-1.8760, acc-0.4814, test loss-1.8768, acc-0.4758
Iter-47480, train loss-1.9821, acc-0.4000, valid loss-1.8759, acc-0.4814, test loss-1.8768, acc-0.4757
Iter-47490, train loss-1.8769, acc-0.5200, valid loss-1.8759, acc-0.4814, test loss-1.8767, acc-0.4759
Iter-47500, train loss-1.9302, acc-0.4000, valid loss-1.8758, acc-0.4816, test loss-1.8766, acc-0.4757
Iter-47510, train loss-1.9469, acc-0.2800, valid loss-1.8758, acc-0.4816, test loss-1.8766, acc-0.4758
Iter-47520, train loss-1.8771, acc-0.4600, valid loss-1.8757, acc-0.4814, test loss-1.8765, acc-0.4759
Iter-47530, train loss-1.8927, acc-0.5000, valid loss-1.8756, acc-0.4816, test loss-1.8765, acc-0.4760
Iter-47540, train loss-1.8519, acc-0.4200, valid loss-1.8756, acc-0.4814, test loss-1.8764, acc-0.4761
Iter-47550, train loss-1.9633, acc-0.4400, valid loss-1.8755, acc-0.4814, test loss-1.8763, acc-0.4760
Iter-47560, train loss-1.9611, acc-0.3600, valid loss-1.8754, acc-0.4816, test loss-1.8763, acc-0.4759
Iter-47570, train loss-1.9966, acc-0.3200, valid loss-1.8754, acc-0.4814, test loss-1.8762, acc-0.4758
Iter-47580, train loss-1.8739, acc-0.4400, valid loss-1.8753, acc-0.4816, test loss-1.8762, acc-0.4757
Iter-47590, train loss-1.9426, acc-0.4000, valid loss-1.8753, acc-0.4816, test loss-1.8761, acc-0.4758
Iter-47600, train loss-1.8313, acc-0.5600, valid loss-1.8752, acc-0.4818, test loss-1.8761, acc-0.4759
Iter-47610, train loss-1.8944, acc-0.4800, valid loss-1.8752, acc-0.4816, test loss-1.8760, acc-0.4760
Iter-47620, train loss-1.9037, acc-0.4600, valid loss-1.8751, acc-0.4818, test loss-1.8759, acc-0.4762
Iter-47630, train loss-1.8611, acc-0.5400, valid loss-1.8751, acc-0.4818, test loss-1.8759, acc-0.4762
Iter-47640, train loss-1.8378, acc-0.4600, valid loss-1.8750, acc-0.4818, test loss-1.8758, acc-0.4762
Iter-47650, train loss-1.9267, acc-0.4400, valid loss-1.8749, acc-0.4824, test loss-1.8758, acc-0.4763
Iter-47660, train loss-1.9255, acc-0.5000, valid loss-1.8749, acc-0.4824, test loss-1.8757, acc-0.4763
Iter-47670, train loss-1.9134, acc-0.4800, valid loss-1.8748, acc-0.4820, test loss-1.8756, acc-0.4761
Iter-47680, train loss-1.8539, acc-0.4600, valid loss-1.8748, acc-0.4824, test loss-1.8756, acc-0.4762
Iter-47690, train loss-1.9755, acc-0.4000, valid loss-1.8747, acc-0.4826, test loss-1.8755, acc-0.4762
Iter-47700, train loss-1.8685, acc-0.5600, valid loss-1.8746, acc-0.4824, test loss-1.8755, acc-0.4762
Iter-47710, train loss-1.8688, acc-0.5200, valid loss-1.8746, acc-0.4824, test loss-1.8754, acc-0.4764
Iter-47720, train loss-1.9162, acc-0.4800, valid loss-1.8745, acc-0.4826, test loss-1.8753, acc-0.4764
Iter-47730, train loss-1.9798, acc-0.4400, valid loss-1.8745, acc-0.4824, test loss-1.8753, acc-0.4766
Iter-47740, train loss-1.8747, acc-0.5000, valid loss-1.8744, acc-0.4824, test loss-1.8752, acc-0.4766
Iter-47750, train loss-1.8485, acc-0.5000, valid loss-1.8743, acc-0.4822, test loss-1.8752, acc-0.4765
Iter-47760, train loss-1.8836, acc-0.5400, valid loss-1.8743, acc-0.4822, test loss-1.8751, acc-0.4766
Iter-47770, train loss-1.9160, acc-0.4600, valid loss-1.8742, acc-0.4824, test loss-1.8751, acc-0.4767
Iter-47780, train loss-1.9571, acc-0.4000, valid loss-1.8742, acc-0.4824, test loss-1.8750, acc-0.4767
Iter-47790, train loss-1.8097, acc-0.5400, valid loss-1.8741, acc-0.4824, test loss-1.8749, acc-0.4767
Iter-47800, train loss-1.9366, acc-0.3400, valid loss-1.8740, acc-0.4824, test loss-1.8749, acc-0.4766
Iter-47810, train loss-1.8292, acc-0.5400, valid loss-1.8740, acc-0.4824, test loss-1.8748, acc-0.4766
Iter-47820, train loss-1.8628, acc-0.5600, valid loss-1.8739, acc-0.4824, test loss-1.8747, acc-0.4765
Iter-47830, train loss-1.9511, acc-0.4000, valid loss-1.8739, acc-0.4822, test loss-1.8747, acc-0.4765
Iter-47840, train loss-1.8693, acc-0.4600, valid loss-1.8738, acc-0.4824, test loss-1.8746, acc-0.4765
Iter-47850, train loss-1.9465, acc-0.4600, valid loss-1.8737, acc-0.4824, test loss-1.8746, acc-0.4765
Iter-47860, train loss-1.8357, acc-0.5600, valid loss-1.8737, acc-0.4820, test loss-1.8745, acc-0.4764
Iter-47870, train loss-1.8413, acc-0.5200, valid loss-1.8736, acc-0.4822, test loss-1.8745, acc-0.4765
Iter-47880, train loss-1.7780, acc-0.5800, valid loss-1.8736, acc-0.4822, test loss-1.8744, acc-0.4765
Iter-47890, train loss-1.9004, acc-0.4600, valid loss-1.8735, acc-0.4822, test loss-1.8743, acc-0.4764
Iter-47900, train loss-1.8978, acc-0.4200, valid loss-1.8734, acc-0.4822, test loss-1.8743, acc-0.4766
Iter-47910, train loss-1.7670, acc-0.6000, valid loss-1.8734, acc-0.4822, test loss-1.8742, acc-0.4765
Iter-47920, train loss-1.8684, acc-0.4200, valid loss-1.8733, acc-0.4822, test loss-1.8742, acc-0.4765
Iter-47930, train loss-1.9299, acc-0.4000, valid loss-1.8733, acc-0.4822, test loss-1.8741, acc-0.4764
Iter-47940, train loss-1.8907, acc-0.4000, valid loss-1.8732, acc-0.4822, test loss-1.8740, acc-0.4763
Iter-47950, train loss-1.9321, acc-0.4400, valid loss-1.8731, acc-0.4822, test loss-1.8740, acc-0.4764
Iter-47960, train loss-1.8453, acc-0.5400, valid loss-1.8731, acc-0.4820, test loss-1.8739, acc-0.4764
Iter-47970, train loss-1.8234, acc-0.5000, valid loss-1.8730, acc-0.4822, test loss-1.8739, acc-0.4765
Iter-47980, train loss-1.8479, acc-0.5600, valid loss-1.8730, acc-0.4824, test loss-1.8738, acc-0.4766
Iter-47990, train loss-1.8733, acc-0.4800, valid loss-1.8729, acc-0.4824, test loss-1.8737, acc-0.4767
Iter-48000, train loss-1.8268, acc-0.5600, valid loss-1.8728, acc-0.4824, test loss-1.8737, acc-0.4768
Iter-48010, train loss-1.8500, acc-0.5200, valid loss-1.8728, acc-0.4824, test loss-1.8736, acc-0.4768
Iter-48020, train loss-1.8624, acc-0.4600, valid loss-1.8727, acc-0.4820, test loss-1.8736, acc-0.4768
Iter-48030, train loss-1.8124, acc-0.4600, valid loss-1.8727, acc-0.4820, test loss-1.8735, acc-0.4767
Iter-48040, train loss-1.9083, acc-0.4400, valid loss-1.8726, acc-0.4820, test loss-1.8734, acc-0.4767
Iter-48050, train loss-1.8800, acc-0.4200, valid loss-1.8725, acc-0.4820, test loss-1.8734, acc-0.4768
Iter-48060, train loss-1.8668, acc-0.4800, valid loss-1.8725, acc-0.4822, test loss-1.8733, acc-0.4767
Iter-48070, train loss-1.9207, acc-0.4600, valid loss-1.8724, acc-0.4824, test loss-1.8733, acc-0.4768
Iter-48080, train loss-1.9617, acc-0.3600, valid loss-1.8724, acc-0.4822, test loss-1.8732, acc-0.4768
Iter-48090, train loss-1.8443, acc-0.4800, valid loss-1.8723, acc-0.4824, test loss-1.8731, acc-0.4768
Iter-48100, train loss-1.9732, acc-0.3800, valid loss-1.8722, acc-0.4824, test loss-1.8731, acc-0.4768
Iter-48110, train loss-1.8236, acc-0.5000, valid loss-1.8722, acc-0.4824, test loss-1.8730, acc-0.4767
Iter-48120, train loss-1.9022, acc-0.4400, valid loss-1.8721, acc-0.4822, test loss-1.8730, acc-0.4769
Iter-48130, train loss-1.8420, acc-0.4200, valid loss-1.8721, acc-0.4824, test loss-1.8729, acc-0.4769
Iter-48140, train loss-1.8678, acc-0.4600, valid loss-1.8720, acc-0.4824, test loss-1.8728, acc-0.4768
Iter-48150, train loss-1.8546, acc-0.5600, valid loss-1.8719, acc-0.4824, test loss-1.8728, acc-0.4767
Iter-48160, train loss-1.9589, acc-0.4600, valid loss-1.8719, acc-0.4824, test loss-1.8727, acc-0.4768
Iter-48170, train loss-1.9493, acc-0.3800, valid loss-1.8718, acc-0.4824, test loss-1.8727, acc-0.4768
Iter-48180, train loss-1.8734, acc-0.4400, valid loss-1.8718, acc-0.4828, test loss-1.8726, acc-0.4768
Iter-48190, train loss-1.8729, acc-0.5600, valid loss-1.8717, acc-0.4826, test loss-1.8725, acc-0.4769
Iter-48200, train loss-1.8670, acc-0.4400, valid loss-1.8716, acc-0.4826, test loss-1.8725, acc-0.4769
Iter-48210, train loss-1.9608, acc-0.3600, valid loss-1.8716, acc-0.4828, test loss-1.8724, acc-0.4768
Iter-48220, train loss-1.8537, acc-0.5200, valid loss-1.8715, acc-0.4828, test loss-1.8724, acc-0.4769
Iter-48230, train loss-1.8099, acc-0.6400, valid loss-1.8715, acc-0.4828, test loss-1.8723, acc-0.4769
Iter-48240, train loss-1.8863, acc-0.4600, valid loss-1.8714, acc-0.4826, test loss-1.8722, acc-0.4771
Iter-48250, train loss-1.8740, acc-0.4800, valid loss-1.8714, acc-0.4826, test loss-1.8722, acc-0.4769
Iter-48260, train loss-1.9056, acc-0.4800, valid loss-1.8713, acc-0.4826, test loss-1.8721, acc-0.4770
Iter-48270, train loss-1.8995, acc-0.4800, valid loss-1.8712, acc-0.4826, test loss-1.8721, acc-0.4770
Iter-48280, train loss-1.9375, acc-0.3800, valid loss-1.8712, acc-0.4826, test loss-1.8720, acc-0.4769
Iter-48290, train loss-1.8543, acc-0.5400, valid loss-1.8711, acc-0.4828, test loss-1.8720, acc-0.4770
Iter-48300, train loss-1.9031, acc-0.5000, valid loss-1.8711, acc-0.4828, test loss-1.8719, acc-0.4769
Iter-48310, train loss-1.8966, acc-0.4600, valid loss-1.8710, acc-0.4826, test loss-1.8718, acc-0.4769
Iter-48320, train loss-1.8096, acc-0.6000, valid loss-1.8710, acc-0.4832, test loss-1.8718, acc-0.4768
Iter-48330, train loss-1.8897, acc-0.4000, valid loss-1.8709, acc-0.4828, test loss-1.8717, acc-0.4769
Iter-48340, train loss-1.9652, acc-0.3600, valid loss-1.8708, acc-0.4828, test loss-1.8717, acc-0.4769
Iter-48350, train loss-1.9541, acc-0.4800, valid loss-1.8708, acc-0.4830, test loss-1.8716, acc-0.4768
Iter-48360, train loss-1.8402, acc-0.5200, valid loss-1.8707, acc-0.4830, test loss-1.8715, acc-0.4768
Iter-48370, train loss-1.8583, acc-0.4600, valid loss-1.8707, acc-0.4824, test loss-1.8715, acc-0.4769
Iter-48380, train loss-1.9146, acc-0.4000, valid loss-1.8706, acc-0.4826, test loss-1.8714, acc-0.4769
Iter-48390, train loss-1.8976, acc-0.4400, valid loss-1.8705, acc-0.4830, test loss-1.8714, acc-0.4769
Iter-48400, train loss-1.8869, acc-0.4600, valid loss-1.8705, acc-0.4826, test loss-1.8713, acc-0.4769
Iter-48410, train loss-1.8644, acc-0.5000, valid loss-1.8704, acc-0.4826, test loss-1.8713, acc-0.4769
Iter-48420, train loss-1.8431, acc-0.5600, valid loss-1.8704, acc-0.4828, test loss-1.8712, acc-0.4769
Iter-48430, train loss-1.9971, acc-0.3000, valid loss-1.8703, acc-0.4830, test loss-1.8711, acc-0.4770
Iter-48440, train loss-1.9417, acc-0.3400, valid loss-1.8702, acc-0.4832, test loss-1.8711, acc-0.4770
Iter-48450, train loss-1.8903, acc-0.4600, valid loss-1.8702, acc-0.4832, test loss-1.8710, acc-0.4770
Iter-48460, train loss-1.7249, acc-0.6600, valid loss-1.8701, acc-0.4832, test loss-1.8710, acc-0.4770
Iter-48470, train loss-1.9024, acc-0.3800, valid loss-1.8701, acc-0.4832, test loss-1.8709, acc-0.4770
Iter-48480, train loss-1.8586, acc-0.5400, valid loss-1.8700, acc-0.4830, test loss-1.8708, acc-0.4771
Iter-48490, train loss-1.8909, acc-0.4200, valid loss-1.8700, acc-0.4832, test loss-1.8708, acc-0.4772
Iter-48500, train loss-1.8915, acc-0.5000, valid loss-1.8699, acc-0.4832, test loss-1.8707, acc-0.4772
Iter-48510, train loss-1.8988, acc-0.4600, valid loss-1.8698, acc-0.4832, test loss-1.8707, acc-0.4772
Iter-48520, train loss-1.8624, acc-0.4800, valid loss-1.8698, acc-0.4832, test loss-1.8706, acc-0.4772
Iter-48530, train loss-1.8703, acc-0.5400, valid loss-1.8697, acc-0.4830, test loss-1.8705, acc-0.4772
Iter-48540, train loss-1.8600, acc-0.5200, valid loss-1.8697, acc-0.4830, test loss-1.8705, acc-0.4772
Iter-48550, train loss-1.8798, acc-0.4600, valid loss-1.8696, acc-0.4834, test loss-1.8704, acc-0.4771
Iter-48560, train loss-1.8621, acc-0.5400, valid loss-1.8695, acc-0.4838, test loss-1.8704, acc-0.4772
Iter-48570, train loss-1.9222, acc-0.3600, valid loss-1.8695, acc-0.4838, test loss-1.8703, acc-0.4772
Iter-48580, train loss-1.9268, acc-0.4800, valid loss-1.8694, acc-0.4836, test loss-1.8703, acc-0.4774
Iter-48590, train loss-1.9089, acc-0.5400, valid loss-1.8694, acc-0.4836, test loss-1.8702, acc-0.4776
Iter-48600, train loss-1.9661, acc-0.3200, valid loss-1.8693, acc-0.4836, test loss-1.8701, acc-0.4774
Iter-48610, train loss-1.7731, acc-0.5400, valid loss-1.8693, acc-0.4834, test loss-1.8701, acc-0.4774
Iter-48620, train loss-1.8773, acc-0.4600, valid loss-1.8692, acc-0.4834, test loss-1.8700, acc-0.4776
Iter-48630, train loss-1.8778, acc-0.5600, valid loss-1.8691, acc-0.4834, test loss-1.8700, acc-0.4774
Iter-48640, train loss-1.8615, acc-0.4200, valid loss-1.8691, acc-0.4834, test loss-1.8699, acc-0.4774
Iter-48650, train loss-1.8676, acc-0.4800, valid loss-1.8690, acc-0.4834, test loss-1.8698, acc-0.4776
Iter-48660, train loss-1.8691, acc-0.4800, valid loss-1.8690, acc-0.4834, test loss-1.8698, acc-0.4775
Iter-48670, train loss-1.8164, acc-0.4400, valid loss-1.8689, acc-0.4834, test loss-1.8697, acc-0.4775
Iter-48680, train loss-1.9067, acc-0.3800, valid loss-1.8688, acc-0.4834, test loss-1.8697, acc-0.4774
Iter-48690, train loss-1.9085, acc-0.4200, valid loss-1.8688, acc-0.4834, test loss-1.8696, acc-0.4775
Iter-48700, train loss-1.9242, acc-0.4600, valid loss-1.8687, acc-0.4834, test loss-1.8695, acc-0.4777
Iter-48710, train loss-1.8475, acc-0.4800, valid loss-1.8687, acc-0.4834, test loss-1.8695, acc-0.4778
Iter-48720, train loss-1.9646, acc-0.4000, valid loss-1.8686, acc-0.4834, test loss-1.8694, acc-0.4780
Iter-48730, train loss-1.8860, acc-0.4800, valid loss-1.8686, acc-0.4834, test loss-1.8694, acc-0.4781
Iter-48740, train loss-1.9974, acc-0.3200, valid loss-1.8685, acc-0.4836, test loss-1.8693, acc-0.4781
Iter-48750, train loss-1.9543, acc-0.3600, valid loss-1.8684, acc-0.4838, test loss-1.8693, acc-0.4781
Iter-48760, train loss-1.8975, acc-0.3200, valid loss-1.8684, acc-0.4838, test loss-1.8692, acc-0.4779
Iter-48770, train loss-1.8538, acc-0.4600, valid loss-1.8683, acc-0.4838, test loss-1.8691, acc-0.4780
Iter-48780, train loss-1.8150, acc-0.5000, valid loss-1.8683, acc-0.4836, test loss-1.8691, acc-0.4781
Iter-48790, train loss-1.8083, acc-0.5000, valid loss-1.8682, acc-0.4838, test loss-1.8690, acc-0.4781
Iter-48800, train loss-1.8712, acc-0.4600, valid loss-1.8682, acc-0.4840, test loss-1.8690, acc-0.4782
Iter-48810, train loss-1.8651, acc-0.5000, valid loss-1.8681, acc-0.4842, test loss-1.8689, acc-0.4781
Iter-48820, train loss-1.8747, acc-0.4600, valid loss-1.8680, acc-0.4842, test loss-1.8688, acc-0.4782
Iter-48830, train loss-1.8616, acc-0.5000, valid loss-1.8680, acc-0.4842, test loss-1.8688, acc-0.4781
Iter-48840, train loss-1.9383, acc-0.4200, valid loss-1.8679, acc-0.4844, test loss-1.8687, acc-0.4781
Iter-48850, train loss-1.8943, acc-0.4400, valid loss-1.8678, acc-0.4844, test loss-1.8687, acc-0.4781
Iter-48860, train loss-1.9268, acc-0.3000, valid loss-1.8678, acc-0.4844, test loss-1.8686, acc-0.4781
Iter-48870, train loss-1.9155, acc-0.4800, valid loss-1.8677, acc-0.4842, test loss-1.8686, acc-0.4782
Iter-48880, train loss-1.9366, acc-0.3800, valid loss-1.8677, acc-0.4842, test loss-1.8685, acc-0.4782
Iter-48890, train loss-1.8895, acc-0.4600, valid loss-1.8676, acc-0.4842, test loss-1.8684, acc-0.4782
Iter-48900, train loss-1.8699, acc-0.4400, valid loss-1.8676, acc-0.4838, test loss-1.8684, acc-0.4782
Iter-48910, train loss-2.0292, acc-0.4000, valid loss-1.8675, acc-0.4840, test loss-1.8683, acc-0.4785
Iter-48920, train loss-1.8563, acc-0.6400, valid loss-1.8674, acc-0.4842, test loss-1.8683, acc-0.4784
Iter-48930, train loss-1.8392, acc-0.6200, valid loss-1.8674, acc-0.4842, test loss-1.8682, acc-0.4784
Iter-48940, train loss-1.8071, acc-0.5400, valid loss-1.8673, acc-0.4842, test loss-1.8682, acc-0.4783
Iter-48950, train loss-1.8985, acc-0.4400, valid loss-1.8673, acc-0.4842, test loss-1.8681, acc-0.4783
Iter-48960, train loss-1.8286, acc-0.5000, valid loss-1.8672, acc-0.4842, test loss-1.8680, acc-0.4782
Iter-48970, train loss-1.8411, acc-0.4800, valid loss-1.8671, acc-0.4842, test loss-1.8680, acc-0.4783
Iter-48980, train loss-1.9615, acc-0.3400, valid loss-1.8671, acc-0.4840, test loss-1.8679, acc-0.4785
Iter-48990, train loss-1.8500, acc-0.4200, valid loss-1.8670, acc-0.4840, test loss-1.8679, acc-0.4785
Iter-49000, train loss-1.7913, acc-0.6400, valid loss-1.8670, acc-0.4836, test loss-1.8678, acc-0.4784
Iter-49010, train loss-1.8411, acc-0.5000, valid loss-1.8669, acc-0.4838, test loss-1.8677, acc-0.4785
Iter-49020, train loss-1.8534, acc-0.5200, valid loss-1.8668, acc-0.4838, test loss-1.8677, acc-0.4785
Iter-49030, train loss-1.8668, acc-0.5400, valid loss-1.8668, acc-0.4838, test loss-1.8676, acc-0.4784
Iter-49040, train loss-1.8572, acc-0.3800, valid loss-1.8667, acc-0.4838, test loss-1.8676, acc-0.4784
Iter-49050, train loss-1.9290, acc-0.4400, valid loss-1.8667, acc-0.4840, test loss-1.8675, acc-0.4784
Iter-49060, train loss-1.9391, acc-0.4200, valid loss-1.8666, acc-0.4840, test loss-1.8675, acc-0.4784
Iter-49070, train loss-1.8726, acc-0.4600, valid loss-1.8666, acc-0.4838, test loss-1.8674, acc-0.4784
Iter-49080, train loss-1.8831, acc-0.4000, valid loss-1.8665, acc-0.4840, test loss-1.8673, acc-0.4788
Iter-49090, train loss-1.9398, acc-0.4000, valid loss-1.8664, acc-0.4840, test loss-1.8673, acc-0.4789
Iter-49100, train loss-1.9037, acc-0.5000, valid loss-1.8664, acc-0.4840, test loss-1.8672, acc-0.4787
Iter-49110, train loss-1.8364, acc-0.5400, valid loss-1.8663, acc-0.4840, test loss-1.8672, acc-0.4789
Iter-49120, train loss-1.8282, acc-0.5200, valid loss-1.8663, acc-0.4842, test loss-1.8671, acc-0.4789
Iter-49130, train loss-1.8542, acc-0.5400, valid loss-1.8662, acc-0.4840, test loss-1.8670, acc-0.4787
Iter-49140, train loss-1.9178, acc-0.4200, valid loss-1.8661, acc-0.4842, test loss-1.8670, acc-0.4788
Iter-49150, train loss-1.8593, acc-0.4800, valid loss-1.8661, acc-0.4840, test loss-1.8669, acc-0.4786
Iter-49160, train loss-1.8574, acc-0.5000, valid loss-1.8660, acc-0.4840, test loss-1.8669, acc-0.4786
Iter-49170, train loss-1.8223, acc-0.5000, valid loss-1.8660, acc-0.4842, test loss-1.8668, acc-0.4788
Iter-49180, train loss-1.8262, acc-0.5000, valid loss-1.8659, acc-0.4842, test loss-1.8667, acc-0.4789
Iter-49190, train loss-1.9118, acc-0.4800, valid loss-1.8658, acc-0.4844, test loss-1.8667, acc-0.4788
Iter-49200, train loss-1.8444, acc-0.5000, valid loss-1.8658, acc-0.4844, test loss-1.8666, acc-0.4788
Iter-49210, train loss-1.8189, acc-0.5400, valid loss-1.8657, acc-0.4844, test loss-1.8666, acc-0.4788
Iter-49220, train loss-1.8269, acc-0.4800, valid loss-1.8657, acc-0.4844, test loss-1.8665, acc-0.4788
Iter-49230, train loss-1.9993, acc-0.3600, valid loss-1.8656, acc-0.4844, test loss-1.8665, acc-0.4789
Iter-49240, train loss-1.9025, acc-0.3800, valid loss-1.8656, acc-0.4844, test loss-1.8664, acc-0.4787
Iter-49250, train loss-1.8046, acc-0.4200, valid loss-1.8655, acc-0.4844, test loss-1.8663, acc-0.4787
Iter-49260, train loss-1.7990, acc-0.6200, valid loss-1.8654, acc-0.4844, test loss-1.8663, acc-0.4790
Iter-49270, train loss-1.8391, acc-0.5200, valid loss-1.8654, acc-0.4844, test loss-1.8662, acc-0.4789
Iter-49280, train loss-1.8234, acc-0.5600, valid loss-1.8653, acc-0.4844, test loss-1.8662, acc-0.4790
Iter-49290, train loss-1.7741, acc-0.5400, valid loss-1.8653, acc-0.4844, test loss-1.8661, acc-0.4790
Iter-49300, train loss-1.8579, acc-0.4400, valid loss-1.8652, acc-0.4844, test loss-1.8660, acc-0.4790
Iter-49310, train loss-1.9187, acc-0.4400, valid loss-1.8652, acc-0.4844, test loss-1.8660, acc-0.4791
Iter-49320, train loss-1.9051, acc-0.4600, valid loss-1.8651, acc-0.4844, test loss-1.8659, acc-0.4790
Iter-49330, train loss-1.8191, acc-0.5200, valid loss-1.8650, acc-0.4842, test loss-1.8659, acc-0.4789
Iter-49340, train loss-1.8917, acc-0.4600, valid loss-1.8650, acc-0.4842, test loss-1.8658, acc-0.4790
Iter-49350, train loss-1.8446, acc-0.4800, valid loss-1.8649, acc-0.4842, test loss-1.8658, acc-0.4790
Iter-49360, train loss-1.8412, acc-0.4600, valid loss-1.8649, acc-0.4844, test loss-1.8657, acc-0.4790
Iter-49370, train loss-1.8151, acc-0.5000, valid loss-1.8648, acc-0.4844, test loss-1.8656, acc-0.4791
Iter-49380, train loss-1.8601, acc-0.4800, valid loss-1.8647, acc-0.4844, test loss-1.8656, acc-0.4791
Iter-49390, train loss-1.8851, acc-0.4200, valid loss-1.8647, acc-0.4844, test loss-1.8655, acc-0.4790
Iter-49400, train loss-1.8667, acc-0.4200, valid loss-1.8646, acc-0.4846, test loss-1.8655, acc-0.4791
Iter-49410, train loss-1.8997, acc-0.4400, valid loss-1.8646, acc-0.4846, test loss-1.8654, acc-0.4793
Iter-49420, train loss-1.8812, acc-0.4800, valid loss-1.8645, acc-0.4846, test loss-1.8653, acc-0.4792
Iter-49430, train loss-1.9026, acc-0.3600, valid loss-1.8645, acc-0.4846, test loss-1.8653, acc-0.4792
Iter-49440, train loss-1.8637, acc-0.4800, valid loss-1.8644, acc-0.4846, test loss-1.8652, acc-0.4792
Iter-49450, train loss-1.8491, acc-0.5200, valid loss-1.8643, acc-0.4846, test loss-1.8652, acc-0.4793
Iter-49460, train loss-1.9708, acc-0.4600, valid loss-1.8643, acc-0.4846, test loss-1.8651, acc-0.4792
Iter-49470, train loss-1.8837, acc-0.5200, valid loss-1.8642, acc-0.4848, test loss-1.8651, acc-0.4794
Iter-49480, train loss-2.0113, acc-0.4000, valid loss-1.8642, acc-0.4850, test loss-1.8650, acc-0.4794
Iter-49490, train loss-1.7956, acc-0.5400, valid loss-1.8641, acc-0.4850, test loss-1.8649, acc-0.4792
Iter-49500, train loss-1.8802, acc-0.4200, valid loss-1.8641, acc-0.4852, test loss-1.8649, acc-0.4792
Iter-49510, train loss-1.9326, acc-0.4400, valid loss-1.8640, acc-0.4848, test loss-1.8648, acc-0.4794
Iter-49520, train loss-1.8116, acc-0.5200, valid loss-1.8640, acc-0.4848, test loss-1.8648, acc-0.4795
Iter-49530, train loss-1.8763, acc-0.5400, valid loss-1.8639, acc-0.4848, test loss-1.8647, acc-0.4796
Iter-49540, train loss-1.9844, acc-0.4400, valid loss-1.8638, acc-0.4848, test loss-1.8647, acc-0.4797
Iter-49550, train loss-1.8460, acc-0.6000, valid loss-1.8638, acc-0.4848, test loss-1.8646, acc-0.4796
Iter-49560, train loss-1.8384, acc-0.4600, valid loss-1.8637, acc-0.4850, test loss-1.8645, acc-0.4795
Iter-49570, train loss-1.8131, acc-0.6000, valid loss-1.8637, acc-0.4850, test loss-1.8645, acc-0.4794
Iter-49580, train loss-1.9093, acc-0.3800, valid loss-1.8636, acc-0.4848, test loss-1.8644, acc-0.4794
Iter-49590, train loss-1.8514, acc-0.5000, valid loss-1.8636, acc-0.4848, test loss-1.8644, acc-0.4795
Iter-49600, train loss-1.8114, acc-0.5200, valid loss-1.8635, acc-0.4846, test loss-1.8643, acc-0.4795
Iter-49610, train loss-1.8204, acc-0.5000, valid loss-1.8634, acc-0.4846, test loss-1.8643, acc-0.4794
Iter-49620, train loss-1.9432, acc-0.3800, valid loss-1.8634, acc-0.4846, test loss-1.8642, acc-0.4793
Iter-49630, train loss-1.9273, acc-0.4600, valid loss-1.8633, acc-0.4846, test loss-1.8641, acc-0.4794
Iter-49640, train loss-1.8988, acc-0.4600, valid loss-1.8633, acc-0.4846, test loss-1.8641, acc-0.4795
Iter-49650, train loss-1.8318, acc-0.5400, valid loss-1.8632, acc-0.4846, test loss-1.8640, acc-0.4796
Iter-49660, train loss-1.9706, acc-0.4400, valid loss-1.8631, acc-0.4846, test loss-1.8640, acc-0.4794
Iter-49670, train loss-1.8947, acc-0.4800, valid loss-1.8631, acc-0.4844, test loss-1.8639, acc-0.4797
Iter-49680, train loss-1.8767, acc-0.4000, valid loss-1.8630, acc-0.4844, test loss-1.8639, acc-0.4797
Iter-49690, train loss-1.9151, acc-0.4000, valid loss-1.8630, acc-0.4844, test loss-1.8638, acc-0.4797
Iter-49700, train loss-1.8192, acc-0.5200, valid loss-1.8629, acc-0.4844, test loss-1.8637, acc-0.4799
Iter-49710, train loss-1.7732, acc-0.5200, valid loss-1.8629, acc-0.4844, test loss-1.8637, acc-0.4800
Iter-49720, train loss-1.8360, acc-0.4400, valid loss-1.8628, acc-0.4846, test loss-1.8636, acc-0.4801
Iter-49730, train loss-1.8959, acc-0.4000, valid loss-1.8627, acc-0.4846, test loss-1.8636, acc-0.4801
Iter-49740, train loss-1.8633, acc-0.4200, valid loss-1.8627, acc-0.4848, test loss-1.8635, acc-0.4801
Iter-49750, train loss-1.9640, acc-0.3800, valid loss-1.8626, acc-0.4848, test loss-1.8635, acc-0.4800
Iter-49760, train loss-1.9523, acc-0.5000, valid loss-1.8626, acc-0.4848, test loss-1.8634, acc-0.4802
Iter-49770, train loss-1.7736, acc-0.5600, valid loss-1.8625, acc-0.4848, test loss-1.8633, acc-0.4802
Iter-49780, train loss-1.8902, acc-0.4800, valid loss-1.8625, acc-0.4846, test loss-1.8633, acc-0.4802
Iter-49790, train loss-1.8325, acc-0.5200, valid loss-1.8624, acc-0.4848, test loss-1.8632, acc-0.4804
Iter-49800, train loss-1.9176, acc-0.4000, valid loss-1.8623, acc-0.4848, test loss-1.8632, acc-0.4802
Iter-49810, train loss-1.8622, acc-0.4400, valid loss-1.8623, acc-0.4848, test loss-1.8631, acc-0.4802
Iter-49820, train loss-1.8706, acc-0.4200, valid loss-1.8622, acc-0.4850, test loss-1.8631, acc-0.4802
Iter-49830, train loss-1.7959, acc-0.5400, valid loss-1.8622, acc-0.4850, test loss-1.8630, acc-0.4801
Iter-49840, train loss-1.8837, acc-0.5600, valid loss-1.8621, acc-0.4848, test loss-1.8629, acc-0.4801
Iter-49850, train loss-1.8754, acc-0.4600, valid loss-1.8620, acc-0.4848, test loss-1.8629, acc-0.4802
Iter-49860, train loss-1.8777, acc-0.5200, valid loss-1.8620, acc-0.4850, test loss-1.8628, acc-0.4801
Iter-49870, train loss-1.8575, acc-0.5000, valid loss-1.8619, acc-0.4850, test loss-1.8628, acc-0.4801
Iter-49880, train loss-1.8623, acc-0.5600, valid loss-1.8619, acc-0.4852, test loss-1.8627, acc-0.4801
Iter-49890, train loss-1.8355, acc-0.5400, valid loss-1.8618, acc-0.4852, test loss-1.8627, acc-0.4801
Iter-49900, train loss-1.8292, acc-0.4600, valid loss-1.8617, acc-0.4852, test loss-1.8626, acc-0.4801
Iter-49910, train loss-1.7955, acc-0.4600, valid loss-1.8617, acc-0.4854, test loss-1.8625, acc-0.4802
Iter-49920, train loss-1.9687, acc-0.3800, valid loss-1.8616, acc-0.4854, test loss-1.8625, acc-0.4803
Iter-49930, train loss-1.8651, acc-0.5000, valid loss-1.8616, acc-0.4854, test loss-1.8624, acc-0.4803
Iter-49940, train loss-1.8467, acc-0.4600, valid loss-1.8615, acc-0.4854, test loss-1.8624, acc-0.4804
Iter-49950, train loss-1.8965, acc-0.4400, valid loss-1.8615, acc-0.4856, test loss-1.8623, acc-0.4806
Iter-49960, train loss-1.7958, acc-0.6200, valid loss-1.8614, acc-0.4856, test loss-1.8622, acc-0.4804
Iter-49970, train loss-1.8031, acc-0.5400, valid loss-1.8613, acc-0.4858, test loss-1.8622, acc-0.4804
Iter-49980, train loss-1.8654, acc-0.4600, valid loss-1.8613, acc-0.4860, test loss-1.8621, acc-0.4804
Iter-49990, train loss-1.9674, acc-0.3000, valid loss-1.8612, acc-0.4858, test loss-1.8621, acc-0.4805
Iter-50000, train loss-1.8472, acc-0.4600, valid loss-1.8612, acc-0.4856, test loss-1.8620, acc-0.4806
Iter-50010, train loss-1.8545, acc-0.4600, valid loss-1.8611, acc-0.4856, test loss-1.8620, acc-0.4803
Iter-50020, train loss-1.9748, acc-0.3400, valid loss-1.8611, acc-0.4854, test loss-1.8619, acc-0.4804
Iter-50030, train loss-1.7848, acc-0.5400, valid loss-1.8610, acc-0.4854, test loss-1.8618, acc-0.4804
Iter-50040, train loss-1.8400, acc-0.4800, valid loss-1.8609, acc-0.4854, test loss-1.8618, acc-0.4804
Iter-50050, train loss-1.9063, acc-0.4400, valid loss-1.8609, acc-0.4854, test loss-1.8617, acc-0.4806
Iter-50060, train loss-1.9164, acc-0.4600, valid loss-1.8608, acc-0.4854, test loss-1.8617, acc-0.4805
Iter-50070, train loss-1.8312, acc-0.5400, valid loss-1.8608, acc-0.4854, test loss-1.8616, acc-0.4809
Iter-50080, train loss-1.8206, acc-0.4600, valid loss-1.8607, acc-0.4854, test loss-1.8615, acc-0.4809
Iter-50090, train loss-1.8592, acc-0.4800, valid loss-1.8607, acc-0.4854, test loss-1.8615, acc-0.4809
Iter-50100, train loss-1.8691, acc-0.4400, valid loss-1.8606, acc-0.4854, test loss-1.8614, acc-0.4808
Iter-50110, train loss-1.8909, acc-0.3200, valid loss-1.8605, acc-0.4858, test loss-1.8614, acc-0.4809
Iter-50120, train loss-1.7856, acc-0.4600, valid loss-1.8605, acc-0.4858, test loss-1.8613, acc-0.4807
Iter-50130, train loss-1.8851, acc-0.4400, valid loss-1.8604, acc-0.4858, test loss-1.8613, acc-0.4808
Iter-50140, train loss-1.8647, acc-0.4400, valid loss-1.8604, acc-0.4858, test loss-1.8612, acc-0.4810
Iter-50150, train loss-1.9109, acc-0.4400, valid loss-1.8603, acc-0.4858, test loss-1.8611, acc-0.4808
Iter-50160, train loss-1.8586, acc-0.5000, valid loss-1.8603, acc-0.4856, test loss-1.8611, acc-0.4808
Iter-50170, train loss-1.9192, acc-0.4600, valid loss-1.8602, acc-0.4856, test loss-1.8610, acc-0.4807
Iter-50180, train loss-1.9250, acc-0.4000, valid loss-1.8601, acc-0.4858, test loss-1.8610, acc-0.4807
Iter-50190, train loss-1.8651, acc-0.4200, valid loss-1.8601, acc-0.4858, test loss-1.8609, acc-0.4809
Iter-50200, train loss-1.8844, acc-0.4800, valid loss-1.8600, acc-0.4858, test loss-1.8609, acc-0.4810
Iter-50210, train loss-1.8589, acc-0.5000, valid loss-1.8600, acc-0.4858, test loss-1.8608, acc-0.4809
Iter-50220, train loss-1.7921, acc-0.6000, valid loss-1.8599, acc-0.4856, test loss-1.8607, acc-0.4808
Iter-50230, train loss-1.9497, acc-0.3800, valid loss-1.8599, acc-0.4858, test loss-1.8607, acc-0.4810
Iter-50240, train loss-1.8515, acc-0.5000, valid loss-1.8598, acc-0.4858, test loss-1.8606, acc-0.4812
Iter-50250, train loss-1.8567, acc-0.4400, valid loss-1.8598, acc-0.4860, test loss-1.8606, acc-0.4814
Iter-50260, train loss-1.9331, acc-0.4200, valid loss-1.8597, acc-0.4860, test loss-1.8605, acc-0.4812
Iter-50270, train loss-1.9281, acc-0.4000, valid loss-1.8596, acc-0.4860, test loss-1.8605, acc-0.4813
Iter-50280, train loss-1.8310, acc-0.4400, valid loss-1.8596, acc-0.4860, test loss-1.8604, acc-0.4813
Iter-50290, train loss-1.9511, acc-0.3600, valid loss-1.8595, acc-0.4858, test loss-1.8603, acc-0.4814
Iter-50300, train loss-1.9081, acc-0.4200, valid loss-1.8595, acc-0.4860, test loss-1.8603, acc-0.4814
Iter-50310, train loss-1.8742, acc-0.3800, valid loss-1.8594, acc-0.4860, test loss-1.8602, acc-0.4814
Iter-50320, train loss-1.8568, acc-0.5600, valid loss-1.8594, acc-0.4860, test loss-1.8602, acc-0.4813
Iter-50330, train loss-1.8914, acc-0.4800, valid loss-1.8593, acc-0.4860, test loss-1.8601, acc-0.4813
Iter-50340, train loss-1.9254, acc-0.3600, valid loss-1.8592, acc-0.4860, test loss-1.8601, acc-0.4814
Iter-50350, train loss-1.8594, acc-0.5200, valid loss-1.8592, acc-0.4858, test loss-1.8600, acc-0.4812
Iter-50360, train loss-1.8208, acc-0.5800, valid loss-1.8591, acc-0.4858, test loss-1.8599, acc-0.4814
Iter-50370, train loss-1.8069, acc-0.4400, valid loss-1.8591, acc-0.4858, test loss-1.8599, acc-0.4814
Iter-50380, train loss-1.7888, acc-0.5600, valid loss-1.8590, acc-0.4860, test loss-1.8598, acc-0.4815
Iter-50390, train loss-1.8827, acc-0.4200, valid loss-1.8589, acc-0.4860, test loss-1.8598, acc-0.4812
Iter-50400, train loss-1.8666, acc-0.3600, valid loss-1.8589, acc-0.4862, test loss-1.8597, acc-0.4813
Iter-50410, train loss-1.8788, acc-0.4200, valid loss-1.8588, acc-0.4860, test loss-1.8597, acc-0.4812
Iter-50420, train loss-1.7879, acc-0.5800, valid loss-1.8588, acc-0.4860, test loss-1.8596, acc-0.4814
Iter-50430, train loss-1.8848, acc-0.4600, valid loss-1.8587, acc-0.4858, test loss-1.8595, acc-0.4816
Iter-50440, train loss-1.8853, acc-0.4600, valid loss-1.8587, acc-0.4858, test loss-1.8595, acc-0.4815
Iter-50450, train loss-1.8578, acc-0.4400, valid loss-1.8586, acc-0.4858, test loss-1.8594, acc-0.4815
Iter-50460, train loss-1.8683, acc-0.4400, valid loss-1.8585, acc-0.4858, test loss-1.8594, acc-0.4814
Iter-50470, train loss-1.9783, acc-0.3400, valid loss-1.8585, acc-0.4860, test loss-1.8593, acc-0.4814
Iter-50480, train loss-1.9444, acc-0.4400, valid loss-1.8584, acc-0.4860, test loss-1.8593, acc-0.4814
Iter-50490, train loss-1.8965, acc-0.3600, valid loss-1.8584, acc-0.4862, test loss-1.8592, acc-0.4813
Iter-50500, train loss-1.8172, acc-0.4600, valid loss-1.8583, acc-0.4860, test loss-1.8591, acc-0.4814
Iter-50510, train loss-1.8980, acc-0.4800, valid loss-1.8582, acc-0.4860, test loss-1.8591, acc-0.4814
Iter-50520, train loss-1.8382, acc-0.4400, valid loss-1.8582, acc-0.4860, test loss-1.8590, acc-0.4815
Iter-50530, train loss-1.7886, acc-0.6400, valid loss-1.8581, acc-0.4860, test loss-1.8590, acc-0.4814
Iter-50540, train loss-1.8988, acc-0.4400, valid loss-1.8581, acc-0.4860, test loss-1.8589, acc-0.4814
Iter-50550, train loss-1.7449, acc-0.5800, valid loss-1.8580, acc-0.4860, test loss-1.8589, acc-0.4814
Iter-50560, train loss-1.8558, acc-0.5000, valid loss-1.8580, acc-0.4860, test loss-1.8588, acc-0.4814
Iter-50570, train loss-1.8340, acc-0.5800, valid loss-1.8579, acc-0.4860, test loss-1.8587, acc-0.4815
Iter-50580, train loss-1.7955, acc-0.5000, valid loss-1.8578, acc-0.4862, test loss-1.8587, acc-0.4815
Iter-50590, train loss-1.8242, acc-0.5200, valid loss-1.8578, acc-0.4860, test loss-1.8586, acc-0.4815
Iter-50600, train loss-1.9442, acc-0.3400, valid loss-1.8577, acc-0.4860, test loss-1.8586, acc-0.4815
Iter-50610, train loss-1.7958, acc-0.5200, valid loss-1.8577, acc-0.4860, test loss-1.8585, acc-0.4814
Iter-50620, train loss-1.8857, acc-0.4400, valid loss-1.8576, acc-0.4858, test loss-1.8584, acc-0.4815
Iter-50630, train loss-1.8870, acc-0.4800, valid loss-1.8575, acc-0.4860, test loss-1.8584, acc-0.4814
Iter-50640, train loss-1.8353, acc-0.5600, valid loss-1.8575, acc-0.4858, test loss-1.8583, acc-0.4814
Iter-50650, train loss-1.8398, acc-0.5000, valid loss-1.8574, acc-0.4856, test loss-1.8583, acc-0.4815
Iter-50660, train loss-1.9203, acc-0.4200, valid loss-1.8574, acc-0.4858, test loss-1.8582, acc-0.4816
Iter-50670, train loss-1.8456, acc-0.4200, valid loss-1.8573, acc-0.4856, test loss-1.8581, acc-0.4815
Iter-50680, train loss-1.8761, acc-0.4400, valid loss-1.8572, acc-0.4856, test loss-1.8581, acc-0.4815
Iter-50690, train loss-1.9316, acc-0.4000, valid loss-1.8572, acc-0.4856, test loss-1.8580, acc-0.4813
Iter-50700, train loss-1.8530, acc-0.5200, valid loss-1.8571, acc-0.4854, test loss-1.8580, acc-0.4813
Iter-50710, train loss-1.8839, acc-0.4600, valid loss-1.8571, acc-0.4858, test loss-1.8579, acc-0.4813
Iter-50720, train loss-1.8564, acc-0.5000, valid loss-1.8570, acc-0.4856, test loss-1.8579, acc-0.4813
Iter-50730, train loss-1.8694, acc-0.5000, valid loss-1.8570, acc-0.4858, test loss-1.8578, acc-0.4814
Iter-50740, train loss-1.8670, acc-0.4400, valid loss-1.8569, acc-0.4856, test loss-1.8578, acc-0.4816
Iter-50750, train loss-1.9506, acc-0.4000, valid loss-1.8569, acc-0.4858, test loss-1.8577, acc-0.4817
Iter-50760, train loss-1.9603, acc-0.4200, valid loss-1.8568, acc-0.4858, test loss-1.8576, acc-0.4818
Iter-50770, train loss-1.8423, acc-0.5000, valid loss-1.8567, acc-0.4856, test loss-1.8576, acc-0.4816
Iter-50780, train loss-1.9362, acc-0.3800, valid loss-1.8567, acc-0.4860, test loss-1.8575, acc-0.4816
Iter-50790, train loss-1.9313, acc-0.3600, valid loss-1.8566, acc-0.4864, test loss-1.8575, acc-0.4817
Iter-50800, train loss-1.9381, acc-0.4000, valid loss-1.8566, acc-0.4862, test loss-1.8574, acc-0.4817
Iter-50810, train loss-1.8015, acc-0.5600, valid loss-1.8565, acc-0.4864, test loss-1.8574, acc-0.4817
Iter-50820, train loss-1.9289, acc-0.4000, valid loss-1.8565, acc-0.4864, test loss-1.8573, acc-0.4817
Iter-50830, train loss-1.8956, acc-0.5200, valid loss-1.8564, acc-0.4860, test loss-1.8572, acc-0.4817
Iter-50840, train loss-1.9096, acc-0.4600, valid loss-1.8564, acc-0.4862, test loss-1.8572, acc-0.4817
Iter-50850, train loss-1.8469, acc-0.5000, valid loss-1.8563, acc-0.4860, test loss-1.8571, acc-0.4818
Iter-50860, train loss-1.8693, acc-0.5000, valid loss-1.8562, acc-0.4862, test loss-1.8571, acc-0.4815
Iter-50870, train loss-1.8584, acc-0.4800, valid loss-1.8562, acc-0.4860, test loss-1.8570, acc-0.4817
Iter-50880, train loss-1.9086, acc-0.4000, valid loss-1.8561, acc-0.4862, test loss-1.8570, acc-0.4821
Iter-50890, train loss-1.8276, acc-0.4800, valid loss-1.8561, acc-0.4862, test loss-1.8569, acc-0.4817
Iter-50900, train loss-1.9000, acc-0.5200, valid loss-1.8560, acc-0.4860, test loss-1.8569, acc-0.4816
Iter-50910, train loss-1.8513, acc-0.4400, valid loss-1.8560, acc-0.4860, test loss-1.8568, acc-0.4815
Iter-50920, train loss-1.8172, acc-0.5200, valid loss-1.8559, acc-0.4860, test loss-1.8567, acc-0.4815
Iter-50930, train loss-1.8662, acc-0.4800, valid loss-1.8558, acc-0.4860, test loss-1.8567, acc-0.4816
Iter-50940, train loss-1.8821, acc-0.4000, valid loss-1.8558, acc-0.4858, test loss-1.8566, acc-0.4814
Iter-50950, train loss-1.8815, acc-0.4600, valid loss-1.8557, acc-0.4856, test loss-1.8566, acc-0.4814
Iter-50960, train loss-1.9205, acc-0.3800, valid loss-1.8557, acc-0.4854, test loss-1.8565, acc-0.4813
Iter-50970, train loss-1.8084, acc-0.6200, valid loss-1.8556, acc-0.4854, test loss-1.8564, acc-0.4813
Iter-50980, train loss-1.8735, acc-0.4000, valid loss-1.8555, acc-0.4854, test loss-1.8564, acc-0.4813
Iter-50990, train loss-1.8409, acc-0.6000, valid loss-1.8555, acc-0.4858, test loss-1.8563, acc-0.4816
Iter-51000, train loss-1.8647, acc-0.5000, valid loss-1.8554, acc-0.4860, test loss-1.8563, acc-0.4817
Iter-51010, train loss-1.8690, acc-0.4600, valid loss-1.8554, acc-0.4860, test loss-1.8562, acc-0.4819
Iter-51020, train loss-1.8712, acc-0.4000, valid loss-1.8553, acc-0.4856, test loss-1.8562, acc-0.4817
Iter-51030, train loss-1.8557, acc-0.4600, valid loss-1.8553, acc-0.4860, test loss-1.8561, acc-0.4817
Iter-51040, train loss-1.8546, acc-0.4200, valid loss-1.8552, acc-0.4864, test loss-1.8560, acc-0.4816
Iter-51050, train loss-1.9305, acc-0.4000, valid loss-1.8551, acc-0.4866, test loss-1.8560, acc-0.4816
Iter-51060, train loss-1.8682, acc-0.5800, valid loss-1.8551, acc-0.4866, test loss-1.8559, acc-0.4815
Iter-51070, train loss-1.9703, acc-0.3000, valid loss-1.8550, acc-0.4868, test loss-1.8559, acc-0.4817
Iter-51080, train loss-1.8240, acc-0.6400, valid loss-1.8550, acc-0.4866, test loss-1.8558, acc-0.4816
Iter-51090, train loss-1.8512, acc-0.4000, valid loss-1.8549, acc-0.4868, test loss-1.8558, acc-0.4817
Iter-51100, train loss-1.8008, acc-0.5000, valid loss-1.8549, acc-0.4864, test loss-1.8557, acc-0.4816
Iter-51110, train loss-1.8783, acc-0.4400, valid loss-1.8548, acc-0.4860, test loss-1.8556, acc-0.4815
Iter-51120, train loss-1.8532, acc-0.5200, valid loss-1.8547, acc-0.4864, test loss-1.8556, acc-0.4814
Iter-51130, train loss-1.9605, acc-0.4400, valid loss-1.8547, acc-0.4862, test loss-1.8555, acc-0.4815
Iter-51140, train loss-1.8982, acc-0.4600, valid loss-1.8546, acc-0.4862, test loss-1.8555, acc-0.4817
Iter-51150, train loss-1.8111, acc-0.4400, valid loss-1.8546, acc-0.4866, test loss-1.8554, acc-0.4819
Iter-51160, train loss-1.8741, acc-0.4200, valid loss-1.8545, acc-0.4866, test loss-1.8554, acc-0.4817
Iter-51170, train loss-1.8560, acc-0.5600, valid loss-1.8545, acc-0.4868, test loss-1.8553, acc-0.4817
Iter-51180, train loss-1.8881, acc-0.4400, valid loss-1.8544, acc-0.4868, test loss-1.8552, acc-0.4818
Iter-51190, train loss-1.9097, acc-0.4800, valid loss-1.8544, acc-0.4872, test loss-1.8552, acc-0.4820
Iter-51200, train loss-1.8604, acc-0.5200, valid loss-1.8543, acc-0.4870, test loss-1.8551, acc-0.4818
Iter-51210, train loss-1.9399, acc-0.4400, valid loss-1.8543, acc-0.4870, test loss-1.8551, acc-0.4818
Iter-51220, train loss-1.8995, acc-0.5000, valid loss-1.8542, acc-0.4870, test loss-1.8550, acc-0.4818
Iter-51230, train loss-1.9020, acc-0.4800, valid loss-1.8541, acc-0.4870, test loss-1.8550, acc-0.4818
Iter-51240, train loss-1.8055, acc-0.5200, valid loss-1.8541, acc-0.4872, test loss-1.8549, acc-0.4818
Iter-51250, train loss-1.9421, acc-0.4000, valid loss-1.8540, acc-0.4872, test loss-1.8549, acc-0.4818
Iter-51260, train loss-1.8584, acc-0.5400, valid loss-1.8540, acc-0.4872, test loss-1.8548, acc-0.4818
Iter-51270, train loss-1.8527, acc-0.5000, valid loss-1.8539, acc-0.4872, test loss-1.8548, acc-0.4818
Iter-51280, train loss-1.8125, acc-0.5600, valid loss-1.8539, acc-0.4870, test loss-1.8547, acc-0.4820
Iter-51290, train loss-1.9339, acc-0.4600, valid loss-1.8538, acc-0.4870, test loss-1.8546, acc-0.4819
Iter-51300, train loss-1.8106, acc-0.5200, valid loss-1.8538, acc-0.4874, test loss-1.8546, acc-0.4822
Iter-51310, train loss-1.9077, acc-0.4800, valid loss-1.8537, acc-0.4872, test loss-1.8545, acc-0.4821
Iter-51320, train loss-1.8366, acc-0.5200, valid loss-1.8537, acc-0.4872, test loss-1.8545, acc-0.4820
Iter-51330, train loss-1.8834, acc-0.5000, valid loss-1.8536, acc-0.4874, test loss-1.8544, acc-0.4821
Iter-51340, train loss-1.8535, acc-0.3800, valid loss-1.8535, acc-0.4874, test loss-1.8544, acc-0.4821
Iter-51350, train loss-1.9064, acc-0.4400, valid loss-1.8535, acc-0.4874, test loss-1.8543, acc-0.4822
Iter-51360, train loss-1.9010, acc-0.3800, valid loss-1.8534, acc-0.4874, test loss-1.8542, acc-0.4824
Iter-51370, train loss-1.9143, acc-0.3800, valid loss-1.8534, acc-0.4874, test loss-1.8542, acc-0.4823
Iter-51380, train loss-1.8537, acc-0.5400, valid loss-1.8533, acc-0.4874, test loss-1.8541, acc-0.4824
Iter-51390, train loss-1.8693, acc-0.4800, valid loss-1.8533, acc-0.4874, test loss-1.8541, acc-0.4824
Iter-51400, train loss-1.7489, acc-0.6400, valid loss-1.8532, acc-0.4874, test loss-1.8540, acc-0.4824
Iter-51410, train loss-1.8841, acc-0.4400, valid loss-1.8532, acc-0.4876, test loss-1.8540, acc-0.4824
Iter-51420, train loss-1.8361, acc-0.5000, valid loss-1.8531, acc-0.4878, test loss-1.8539, acc-0.4825
Iter-51430, train loss-1.8008, acc-0.5600, valid loss-1.8530, acc-0.4878, test loss-1.8539, acc-0.4825
Iter-51440, train loss-1.9064, acc-0.5400, valid loss-1.8530, acc-0.4876, test loss-1.8538, acc-0.4826
Iter-51450, train loss-1.8621, acc-0.5400, valid loss-1.8529, acc-0.4876, test loss-1.8538, acc-0.4827
Iter-51460, train loss-1.8520, acc-0.4800, valid loss-1.8529, acc-0.4876, test loss-1.8537, acc-0.4825
Iter-51470, train loss-1.7764, acc-0.6000, valid loss-1.8528, acc-0.4874, test loss-1.8536, acc-0.4826
Iter-51480, train loss-1.7908, acc-0.5800, valid loss-1.8528, acc-0.4876, test loss-1.8536, acc-0.4826
Iter-51490, train loss-1.9980, acc-0.4200, valid loss-1.8527, acc-0.4876, test loss-1.8535, acc-0.4826
Iter-51500, train loss-1.7873, acc-0.5200, valid loss-1.8526, acc-0.4876, test loss-1.8535, acc-0.4825
Iter-51510, train loss-1.7843, acc-0.5000, valid loss-1.8526, acc-0.4876, test loss-1.8534, acc-0.4825
Iter-51520, train loss-1.8677, acc-0.4400, valid loss-1.8525, acc-0.4876, test loss-1.8534, acc-0.4826
Iter-51530, train loss-1.9387, acc-0.3800, valid loss-1.8525, acc-0.4876, test loss-1.8533, acc-0.4826
Iter-51540, train loss-1.8192, acc-0.5000, valid loss-1.8524, acc-0.4878, test loss-1.8532, acc-0.4826
Iter-51550, train loss-1.8544, acc-0.5000, valid loss-1.8524, acc-0.4876, test loss-1.8532, acc-0.4826
Iter-51560, train loss-1.7127, acc-0.6600, valid loss-1.8523, acc-0.4876, test loss-1.8531, acc-0.4828
Iter-51570, train loss-1.8743, acc-0.4800, valid loss-1.8523, acc-0.4876, test loss-1.8531, acc-0.4828
Iter-51580, train loss-1.8228, acc-0.5000, valid loss-1.8522, acc-0.4876, test loss-1.8530, acc-0.4829
Iter-51590, train loss-1.8216, acc-0.6000, valid loss-1.8521, acc-0.4878, test loss-1.8530, acc-0.4829
Iter-51600, train loss-1.9503, acc-0.4200, valid loss-1.8521, acc-0.4878, test loss-1.8529, acc-0.4828
Iter-51610, train loss-1.7591, acc-0.5200, valid loss-1.8520, acc-0.4878, test loss-1.8529, acc-0.4827
Iter-51620, train loss-1.8312, acc-0.4800, valid loss-1.8520, acc-0.4878, test loss-1.8528, acc-0.4827
Iter-51630, train loss-1.7774, acc-0.5600, valid loss-1.8519, acc-0.4876, test loss-1.8527, acc-0.4827
Iter-51640, train loss-1.9071, acc-0.4400, valid loss-1.8519, acc-0.4878, test loss-1.8527, acc-0.4827
Iter-51650, train loss-1.8981, acc-0.4800, valid loss-1.8518, acc-0.4878, test loss-1.8526, acc-0.4830
Iter-51660, train loss-1.7925, acc-0.5000, valid loss-1.8518, acc-0.4878, test loss-1.8526, acc-0.4828
Iter-51670, train loss-1.8121, acc-0.6000, valid loss-1.8517, acc-0.4880, test loss-1.8525, acc-0.4829
Iter-51680, train loss-1.8652, acc-0.5000, valid loss-1.8516, acc-0.4882, test loss-1.8525, acc-0.4828
Iter-51690, train loss-1.8037, acc-0.5400, valid loss-1.8516, acc-0.4878, test loss-1.8524, acc-0.4830
Iter-51700, train loss-1.8306, acc-0.4000, valid loss-1.8515, acc-0.4882, test loss-1.8523, acc-0.4828
Iter-51710, train loss-1.8577, acc-0.4800, valid loss-1.8515, acc-0.4878, test loss-1.8523, acc-0.4828
Iter-51720, train loss-1.8899, acc-0.4400, valid loss-1.8514, acc-0.4880, test loss-1.8522, acc-0.4828
Iter-51730, train loss-1.9464, acc-0.3600, valid loss-1.8514, acc-0.4882, test loss-1.8522, acc-0.4829
Iter-51740, train loss-1.8442, acc-0.5000, valid loss-1.8513, acc-0.4882, test loss-1.8521, acc-0.4829
Iter-51750, train loss-1.8092, acc-0.5400, valid loss-1.8513, acc-0.4882, test loss-1.8521, acc-0.4832
Iter-51760, train loss-1.9053, acc-0.4200, valid loss-1.8512, acc-0.4882, test loss-1.8520, acc-0.4833
Iter-51770, train loss-1.8751, acc-0.3800, valid loss-1.8511, acc-0.4884, test loss-1.8520, acc-0.4831
Iter-51780, train loss-1.8653, acc-0.4400, valid loss-1.8511, acc-0.4884, test loss-1.8519, acc-0.4833
Iter-51790, train loss-1.8021, acc-0.5600, valid loss-1.8510, acc-0.4884, test loss-1.8519, acc-0.4833
Iter-51800, train loss-1.8896, acc-0.3800, valid loss-1.8510, acc-0.4884, test loss-1.8518, acc-0.4832
Iter-51810, train loss-1.9091, acc-0.4600, valid loss-1.8509, acc-0.4884, test loss-1.8517, acc-0.4834
Iter-51820, train loss-1.7913, acc-0.5800, valid loss-1.8509, acc-0.4886, test loss-1.8517, acc-0.4832
Iter-51830, train loss-1.9107, acc-0.4200, valid loss-1.8508, acc-0.4886, test loss-1.8516, acc-0.4833
Iter-51840, train loss-1.8282, acc-0.5000, valid loss-1.8507, acc-0.4884, test loss-1.8516, acc-0.4834
Iter-51850, train loss-2.0028, acc-0.3400, valid loss-1.8507, acc-0.4884, test loss-1.8515, acc-0.4836
Iter-51860, train loss-1.8402, acc-0.5000, valid loss-1.8506, acc-0.4886, test loss-1.8515, acc-0.4837
Iter-51870, train loss-1.7604, acc-0.5200, valid loss-1.8506, acc-0.4886, test loss-1.8514, acc-0.4836
Iter-51880, train loss-1.8255, acc-0.5400, valid loss-1.8505, acc-0.4884, test loss-1.8513, acc-0.4833
Iter-51890, train loss-1.8693, acc-0.5000, valid loss-1.8505, acc-0.4886, test loss-1.8513, acc-0.4832
Iter-51900, train loss-1.8178, acc-0.4600, valid loss-1.8504, acc-0.4886, test loss-1.8512, acc-0.4832
Iter-51910, train loss-1.8844, acc-0.4400, valid loss-1.8503, acc-0.4884, test loss-1.8512, acc-0.4833
Iter-51920, train loss-1.9418, acc-0.4400, valid loss-1.8503, acc-0.4884, test loss-1.8511, acc-0.4832
Iter-51930, train loss-1.8002, acc-0.5600, valid loss-1.8502, acc-0.4884, test loss-1.8511, acc-0.4831
Iter-51940, train loss-1.8523, acc-0.5400, valid loss-1.8502, acc-0.4886, test loss-1.8510, acc-0.4835
Iter-51950, train loss-1.8837, acc-0.4200, valid loss-1.8501, acc-0.4886, test loss-1.8510, acc-0.4835
Iter-51960, train loss-1.7517, acc-0.6200, valid loss-1.8501, acc-0.4884, test loss-1.8509, acc-0.4834
Iter-51970, train loss-1.8619, acc-0.5200, valid loss-1.8500, acc-0.4884, test loss-1.8508, acc-0.4838
Iter-51980, train loss-1.8575, acc-0.4200, valid loss-1.8500, acc-0.4886, test loss-1.8508, acc-0.4839
Iter-51990, train loss-1.8257, acc-0.4600, valid loss-1.8499, acc-0.4886, test loss-1.8507, acc-0.4838
Iter-52000, train loss-1.7257, acc-0.5800, valid loss-1.8498, acc-0.4886, test loss-1.8507, acc-0.4840
Iter-52010, train loss-1.8474, acc-0.5200, valid loss-1.8498, acc-0.4886, test loss-1.8506, acc-0.4838
Iter-52020, train loss-1.8726, acc-0.5400, valid loss-1.8497, acc-0.4884, test loss-1.8506, acc-0.4836
Iter-52030, train loss-1.8804, acc-0.4400, valid loss-1.8497, acc-0.4882, test loss-1.8505, acc-0.4838
Iter-52040, train loss-1.8884, acc-0.4600, valid loss-1.8496, acc-0.4882, test loss-1.8505, acc-0.4839
Iter-52050, train loss-1.8636, acc-0.5600, valid loss-1.8496, acc-0.4880, test loss-1.8504, acc-0.4834
Iter-52060, train loss-1.9230, acc-0.3400, valid loss-1.8495, acc-0.4882, test loss-1.8503, acc-0.4837
Iter-52070, train loss-1.9481, acc-0.3000, valid loss-1.8494, acc-0.4882, test loss-1.8503, acc-0.4837
Iter-52080, train loss-1.9036, acc-0.4200, valid loss-1.8494, acc-0.4882, test loss-1.8502, acc-0.4840
Iter-52090, train loss-1.8692, acc-0.5200, valid loss-1.8493, acc-0.4880, test loss-1.8502, acc-0.4840
Iter-52100, train loss-1.8562, acc-0.5600, valid loss-1.8493, acc-0.4878, test loss-1.8501, acc-0.4840
Iter-52110, train loss-1.8816, acc-0.4200, valid loss-1.8492, acc-0.4882, test loss-1.8501, acc-0.4843
Iter-52120, train loss-1.7775, acc-0.4600, valid loss-1.8492, acc-0.4880, test loss-1.8500, acc-0.4843
Iter-52130, train loss-1.9420, acc-0.4400, valid loss-1.8491, acc-0.4878, test loss-1.8499, acc-0.4842
Iter-52140, train loss-1.8326, acc-0.5200, valid loss-1.8490, acc-0.4880, test loss-1.8499, acc-0.4842
Iter-52150, train loss-1.8750, acc-0.5600, valid loss-1.8490, acc-0.4878, test loss-1.8498, acc-0.4839
Iter-52160, train loss-1.7854, acc-0.4600, valid loss-1.8489, acc-0.4882, test loss-1.8498, acc-0.4840
Iter-52170, train loss-1.8581, acc-0.5000, valid loss-1.8489, acc-0.4880, test loss-1.8497, acc-0.4841
Iter-52180, train loss-1.8278, acc-0.5600, valid loss-1.8488, acc-0.4882, test loss-1.8497, acc-0.4840
Iter-52190, train loss-1.9363, acc-0.4200, valid loss-1.8488, acc-0.4884, test loss-1.8496, acc-0.4841
Iter-52200, train loss-1.7823, acc-0.5200, valid loss-1.8487, acc-0.4882, test loss-1.8496, acc-0.4840
Iter-52210, train loss-1.8429, acc-0.5600, valid loss-1.8487, acc-0.4882, test loss-1.8495, acc-0.4839
Iter-52220, train loss-1.8615, acc-0.4600, valid loss-1.8486, acc-0.4884, test loss-1.8494, acc-0.4838
Iter-52230, train loss-1.9515, acc-0.4200, valid loss-1.8485, acc-0.4886, test loss-1.8494, acc-0.4838
Iter-52240, train loss-1.6957, acc-0.6600, valid loss-1.8485, acc-0.4888, test loss-1.8493, acc-0.4840
Iter-52250, train loss-1.8707, acc-0.5600, valid loss-1.8484, acc-0.4888, test loss-1.8493, acc-0.4838
Iter-52260, train loss-1.8084, acc-0.5600, valid loss-1.8484, acc-0.4886, test loss-1.8492, acc-0.4839
Iter-52270, train loss-1.9199, acc-0.4000, valid loss-1.8483, acc-0.4888, test loss-1.8492, acc-0.4839
Iter-52280, train loss-1.8901, acc-0.4800, valid loss-1.8483, acc-0.4884, test loss-1.8491, acc-0.4840
Iter-52290, train loss-1.9071, acc-0.4200, valid loss-1.8482, acc-0.4888, test loss-1.8491, acc-0.4839
Iter-52300, train loss-1.8411, acc-0.4400, valid loss-1.8482, acc-0.4888, test loss-1.8490, acc-0.4840
Iter-52310, train loss-1.9619, acc-0.3800, valid loss-1.8481, acc-0.4888, test loss-1.8489, acc-0.4838
Iter-52320, train loss-1.9286, acc-0.4000, valid loss-1.8481, acc-0.4886, test loss-1.8489, acc-0.4837
Iter-52330, train loss-1.8518, acc-0.4800, valid loss-1.8480, acc-0.4886, test loss-1.8488, acc-0.4835
Iter-52340, train loss-1.8390, acc-0.4600, valid loss-1.8479, acc-0.4886, test loss-1.8488, acc-0.4837
Iter-52350, train loss-1.7818, acc-0.5400, valid loss-1.8479, acc-0.4888, test loss-1.8487, acc-0.4836
Iter-52360, train loss-1.8187, acc-0.5400, valid loss-1.8478, acc-0.4888, test loss-1.8487, acc-0.4838
Iter-52370, train loss-1.8834, acc-0.4800, valid loss-1.8478, acc-0.4888, test loss-1.8486, acc-0.4836
Iter-52380, train loss-1.9158, acc-0.4200, valid loss-1.8477, acc-0.4886, test loss-1.8486, acc-0.4837
Iter-52390, train loss-1.8359, acc-0.5200, valid loss-1.8477, acc-0.4888, test loss-1.8485, acc-0.4838
Iter-52400, train loss-1.8685, acc-0.3600, valid loss-1.8476, acc-0.4892, test loss-1.8484, acc-0.4839
Iter-52410, train loss-1.8277, acc-0.5000, valid loss-1.8476, acc-0.4890, test loss-1.8484, acc-0.4839
Iter-52420, train loss-1.8497, acc-0.5000, valid loss-1.8475, acc-0.4892, test loss-1.8483, acc-0.4840
Iter-52430, train loss-1.8144, acc-0.5400, valid loss-1.8474, acc-0.4892, test loss-1.8483, acc-0.4841
Iter-52440, train loss-1.8339, acc-0.4800, valid loss-1.8474, acc-0.4892, test loss-1.8482, acc-0.4842
Iter-52450, train loss-1.8593, acc-0.4400, valid loss-1.8473, acc-0.4892, test loss-1.8482, acc-0.4843
Iter-52460, train loss-1.7888, acc-0.4600, valid loss-1.8473, acc-0.4894, test loss-1.8481, acc-0.4845
Iter-52470, train loss-1.8182, acc-0.5600, valid loss-1.8472, acc-0.4896, test loss-1.8481, acc-0.4843
Iter-52480, train loss-1.7874, acc-0.4800, valid loss-1.8472, acc-0.4896, test loss-1.8480, acc-0.4845
Iter-52490, train loss-1.8078, acc-0.4800, valid loss-1.8471, acc-0.4896, test loss-1.8479, acc-0.4846
Iter-52500, train loss-1.7924, acc-0.4800, valid loss-1.8471, acc-0.4896, test loss-1.8479, acc-0.4841
Iter-52510, train loss-1.8212, acc-0.4800, valid loss-1.8470, acc-0.4896, test loss-1.8478, acc-0.4844
Iter-52520, train loss-1.8754, acc-0.4800, valid loss-1.8470, acc-0.4896, test loss-1.8478, acc-0.4844
Iter-52530, train loss-1.8912, acc-0.4200, valid loss-1.8469, acc-0.4898, test loss-1.8477, acc-0.4843
Iter-52540, train loss-1.7511, acc-0.6000, valid loss-1.8468, acc-0.4898, test loss-1.8477, acc-0.4841
Iter-52550, train loss-1.8167, acc-0.4800, valid loss-1.8468, acc-0.4898, test loss-1.8476, acc-0.4842
Iter-52560, train loss-1.8660, acc-0.4600, valid loss-1.8467, acc-0.4894, test loss-1.8476, acc-0.4841
Iter-52570, train loss-1.9139, acc-0.3400, valid loss-1.8467, acc-0.4896, test loss-1.8475, acc-0.4841
Iter-52580, train loss-1.7901, acc-0.5400, valid loss-1.8466, acc-0.4898, test loss-1.8474, acc-0.4841
Iter-52590, train loss-1.8669, acc-0.4400, valid loss-1.8466, acc-0.4898, test loss-1.8474, acc-0.4842
Iter-52600, train loss-1.8647, acc-0.5200, valid loss-1.8465, acc-0.4898, test loss-1.8473, acc-0.4841
Iter-52610, train loss-1.8307, acc-0.5600, valid loss-1.8465, acc-0.4896, test loss-1.8473, acc-0.4842
Iter-52620, train loss-1.8332, acc-0.5000, valid loss-1.8464, acc-0.4896, test loss-1.8472, acc-0.4842
Iter-52630, train loss-1.9939, acc-0.2800, valid loss-1.8464, acc-0.4898, test loss-1.8472, acc-0.4842
Iter-52640, train loss-1.8646, acc-0.5600, valid loss-1.8463, acc-0.4898, test loss-1.8471, acc-0.4843
Iter-52650, train loss-1.7744, acc-0.5600, valid loss-1.8462, acc-0.4900, test loss-1.8471, acc-0.4842
Iter-52660, train loss-1.9269, acc-0.4200, valid loss-1.8462, acc-0.4900, test loss-1.8470, acc-0.4842
Iter-52670, train loss-1.7671, acc-0.6600, valid loss-1.8461, acc-0.4898, test loss-1.8470, acc-0.4844
Iter-52680, train loss-1.8374, acc-0.4800, valid loss-1.8461, acc-0.4898, test loss-1.8469, acc-0.4845
Iter-52690, train loss-1.7240, acc-0.5600, valid loss-1.8460, acc-0.4898, test loss-1.8468, acc-0.4843
Iter-52700, train loss-1.8829, acc-0.4000, valid loss-1.8460, acc-0.4896, test loss-1.8468, acc-0.4843
Iter-52710, train loss-1.9342, acc-0.3800, valid loss-1.8459, acc-0.4896, test loss-1.8467, acc-0.4844
Iter-52720, train loss-1.8896, acc-0.4000, valid loss-1.8459, acc-0.4896, test loss-1.8467, acc-0.4845
Iter-52730, train loss-1.9089, acc-0.4000, valid loss-1.8458, acc-0.4898, test loss-1.8466, acc-0.4845
Iter-52740, train loss-1.8385, acc-0.4600, valid loss-1.8457, acc-0.4900, test loss-1.8466, acc-0.4845
Iter-52750, train loss-1.7917, acc-0.5600, valid loss-1.8457, acc-0.4902, test loss-1.8465, acc-0.4844
Iter-52760, train loss-1.9422, acc-0.4400, valid loss-1.8456, acc-0.4902, test loss-1.8465, acc-0.4844
Iter-52770, train loss-1.9135, acc-0.4600, valid loss-1.8456, acc-0.4902, test loss-1.8464, acc-0.4844
Iter-52780, train loss-1.9069, acc-0.4800, valid loss-1.8455, acc-0.4902, test loss-1.8463, acc-0.4844
Iter-52790, train loss-1.8643, acc-0.4200, valid loss-1.8455, acc-0.4902, test loss-1.8463, acc-0.4844
Iter-52800, train loss-1.8384, acc-0.5000, valid loss-1.8454, acc-0.4900, test loss-1.8462, acc-0.4843
Iter-52810, train loss-1.8598, acc-0.4000, valid loss-1.8453, acc-0.4902, test loss-1.8462, acc-0.4844
Iter-52820, train loss-1.8329, acc-0.4800, valid loss-1.8453, acc-0.4904, test loss-1.8461, acc-0.4844
Iter-52830, train loss-1.8773, acc-0.5200, valid loss-1.8452, acc-0.4902, test loss-1.8461, acc-0.4843
Iter-52840, train loss-1.8283, acc-0.4600, valid loss-1.8452, acc-0.4902, test loss-1.8460, acc-0.4843
Iter-52850, train loss-1.9684, acc-0.4000, valid loss-1.8451, acc-0.4902, test loss-1.8460, acc-0.4843
Iter-52860, train loss-1.8261, acc-0.5000, valid loss-1.8451, acc-0.4902, test loss-1.8459, acc-0.4843
Iter-52870, train loss-1.9340, acc-0.3400, valid loss-1.8450, acc-0.4900, test loss-1.8458, acc-0.4843
Iter-52880, train loss-1.9400, acc-0.4000, valid loss-1.8450, acc-0.4900, test loss-1.8458, acc-0.4843
Iter-52890, train loss-1.8939, acc-0.4000, valid loss-1.8449, acc-0.4902, test loss-1.8457, acc-0.4843
Iter-52900, train loss-1.8680, acc-0.5200, valid loss-1.8448, acc-0.4900, test loss-1.8457, acc-0.4843
Iter-52910, train loss-1.8996, acc-0.5000, valid loss-1.8448, acc-0.4898, test loss-1.8456, acc-0.4844
Iter-52920, train loss-1.7275, acc-0.5200, valid loss-1.8447, acc-0.4900, test loss-1.8456, acc-0.4844
Iter-52930, train loss-1.9672, acc-0.4000, valid loss-1.8447, acc-0.4900, test loss-1.8455, acc-0.4843
Iter-52940, train loss-1.7966, acc-0.5800, valid loss-1.8446, acc-0.4900, test loss-1.8455, acc-0.4843
Iter-52950, train loss-1.7637, acc-0.5200, valid loss-1.8446, acc-0.4904, test loss-1.8454, acc-0.4843
Iter-52960, train loss-1.8050, acc-0.5000, valid loss-1.8445, acc-0.4902, test loss-1.8453, acc-0.4842
Iter-52970, train loss-1.9334, acc-0.4000, valid loss-1.8445, acc-0.4904, test loss-1.8453, acc-0.4844
Iter-52980, train loss-1.8814, acc-0.5000, valid loss-1.8444, acc-0.4906, test loss-1.8452, acc-0.4844
Iter-52990, train loss-1.7428, acc-0.5600, valid loss-1.8443, acc-0.4904, test loss-1.8452, acc-0.4842
Iter-53000, train loss-1.8993, acc-0.4400, valid loss-1.8443, acc-0.4904, test loss-1.8451, acc-0.4842
Iter-53010, train loss-1.8577, acc-0.4200, valid loss-1.8442, acc-0.4904, test loss-1.8451, acc-0.4843
Iter-53020, train loss-1.8718, acc-0.4000, valid loss-1.8442, acc-0.4904, test loss-1.8450, acc-0.4844
Iter-53030, train loss-1.9306, acc-0.3200, valid loss-1.8441, acc-0.4904, test loss-1.8450, acc-0.4844
Iter-53040, train loss-1.8650, acc-0.4600, valid loss-1.8441, acc-0.4902, test loss-1.8449, acc-0.4844
Iter-53050, train loss-1.8987, acc-0.4400, valid loss-1.8440, acc-0.4904, test loss-1.8448, acc-0.4844
Iter-53060, train loss-1.8624, acc-0.4600, valid loss-1.8439, acc-0.4906, test loss-1.8448, acc-0.4843
Iter-53070, train loss-1.8240, acc-0.5200, valid loss-1.8439, acc-0.4906, test loss-1.8447, acc-0.4847
Iter-53080, train loss-1.9056, acc-0.4400, valid loss-1.8438, acc-0.4908, test loss-1.8447, acc-0.4846
Iter-53090, train loss-1.7985, acc-0.5600, valid loss-1.8438, acc-0.4904, test loss-1.8446, acc-0.4844
Iter-53100, train loss-1.8752, acc-0.5200, valid loss-1.8437, acc-0.4908, test loss-1.8446, acc-0.4845
Iter-53110, train loss-1.8624, acc-0.5200, valid loss-1.8437, acc-0.4908, test loss-1.8445, acc-0.4845
Iter-53120, train loss-1.9338, acc-0.4200, valid loss-1.8436, acc-0.4906, test loss-1.8445, acc-0.4843
Iter-53130, train loss-1.9349, acc-0.3400, valid loss-1.8436, acc-0.4910, test loss-1.8444, acc-0.4843
Iter-53140, train loss-1.8055, acc-0.4200, valid loss-1.8435, acc-0.4904, test loss-1.8443, acc-0.4844
Iter-53150, train loss-1.8311, acc-0.4200, valid loss-1.8434, acc-0.4904, test loss-1.8443, acc-0.4843
Iter-53160, train loss-1.8216, acc-0.5000, valid loss-1.8434, acc-0.4906, test loss-1.8442, acc-0.4844
Iter-53170, train loss-1.8676, acc-0.5200, valid loss-1.8433, acc-0.4906, test loss-1.8442, acc-0.4845
Iter-53180, train loss-1.7494, acc-0.5600, valid loss-1.8433, acc-0.4898, test loss-1.8441, acc-0.4845
Iter-53190, train loss-1.8448, acc-0.4200, valid loss-1.8432, acc-0.4896, test loss-1.8441, acc-0.4844
Iter-53200, train loss-1.8269, acc-0.5000, valid loss-1.8432, acc-0.4900, test loss-1.8440, acc-0.4844
Iter-53210, train loss-1.9972, acc-0.4200, valid loss-1.8431, acc-0.4898, test loss-1.8439, acc-0.4843
Iter-53220, train loss-1.7998, acc-0.4800, valid loss-1.8430, acc-0.4900, test loss-1.8439, acc-0.4845
Iter-53230, train loss-1.8721, acc-0.4200, valid loss-1.8430, acc-0.4894, test loss-1.8438, acc-0.4844
Iter-53240, train loss-1.7702, acc-0.4800, valid loss-1.8429, acc-0.4894, test loss-1.8438, acc-0.4844
Iter-53250, train loss-1.7641, acc-0.6000, valid loss-1.8429, acc-0.4896, test loss-1.8437, acc-0.4847
Iter-53260, train loss-1.7868, acc-0.4800, valid loss-1.8428, acc-0.4894, test loss-1.8437, acc-0.4845
Iter-53270, train loss-1.8257, acc-0.5200, valid loss-1.8428, acc-0.4894, test loss-1.8436, acc-0.4846
Iter-53280, train loss-1.8623, acc-0.5200, valid loss-1.8427, acc-0.4898, test loss-1.8436, acc-0.4845
Iter-53290, train loss-1.8846, acc-0.4800, valid loss-1.8427, acc-0.4898, test loss-1.8435, acc-0.4845
Iter-53300, train loss-1.8572, acc-0.5000, valid loss-1.8426, acc-0.4900, test loss-1.8435, acc-0.4843
Iter-53310, train loss-1.8974, acc-0.4400, valid loss-1.8426, acc-0.4900, test loss-1.8434, acc-0.4843
Iter-53320, train loss-1.8065, acc-0.4600, valid loss-1.8425, acc-0.4904, test loss-1.8433, acc-0.4841
Iter-53330, train loss-1.8868, acc-0.4000, valid loss-1.8424, acc-0.4902, test loss-1.8433, acc-0.4846
Iter-53340, train loss-1.8934, acc-0.4800, valid loss-1.8424, acc-0.4904, test loss-1.8432, acc-0.4843
Iter-53350, train loss-1.8214, acc-0.4600, valid loss-1.8423, acc-0.4902, test loss-1.8432, acc-0.4844
Iter-53360, train loss-1.8939, acc-0.4800, valid loss-1.8423, acc-0.4902, test loss-1.8431, acc-0.4844
Iter-53370, train loss-1.7535, acc-0.6000, valid loss-1.8422, acc-0.4900, test loss-1.8431, acc-0.4843
Iter-53380, train loss-1.8351, acc-0.4800, valid loss-1.8422, acc-0.4902, test loss-1.8430, acc-0.4840
Iter-53390, train loss-1.8913, acc-0.4400, valid loss-1.8421, acc-0.4900, test loss-1.8430, acc-0.4844
Iter-53400, train loss-1.8811, acc-0.4000, valid loss-1.8421, acc-0.4902, test loss-1.8429, acc-0.4844
Iter-53410, train loss-1.8608, acc-0.4400, valid loss-1.8420, acc-0.4902, test loss-1.8429, acc-0.4844
Iter-53420, train loss-1.8381, acc-0.5800, valid loss-1.8420, acc-0.4904, test loss-1.8428, acc-0.4846
Iter-53430, train loss-1.9114, acc-0.4200, valid loss-1.8419, acc-0.4900, test loss-1.8427, acc-0.4847
Iter-53440, train loss-1.8286, acc-0.4600, valid loss-1.8419, acc-0.4900, test loss-1.8427, acc-0.4849
Iter-53450, train loss-1.8954, acc-0.4400, valid loss-1.8418, acc-0.4902, test loss-1.8426, acc-0.4850
Iter-53460, train loss-1.8993, acc-0.3800, valid loss-1.8417, acc-0.4904, test loss-1.8426, acc-0.4850
Iter-53470, train loss-1.8014, acc-0.5200, valid loss-1.8417, acc-0.4906, test loss-1.8425, acc-0.4850
Iter-53480, train loss-1.7900, acc-0.5800, valid loss-1.8416, acc-0.4902, test loss-1.8425, acc-0.4850
Iter-53490, train loss-1.7962, acc-0.6000, valid loss-1.8416, acc-0.4906, test loss-1.8424, acc-0.4851
Iter-53500, train loss-1.8473, acc-0.5200, valid loss-1.8415, acc-0.4902, test loss-1.8424, acc-0.4851
Iter-53510, train loss-1.8137, acc-0.5000, valid loss-1.8415, acc-0.4900, test loss-1.8423, acc-0.4852
Iter-53520, train loss-1.8363, acc-0.5000, valid loss-1.8414, acc-0.4904, test loss-1.8423, acc-0.4849
Iter-53530, train loss-1.7723, acc-0.5800, valid loss-1.8414, acc-0.4904, test loss-1.8422, acc-0.4849
Iter-53540, train loss-1.8279, acc-0.5200, valid loss-1.8413, acc-0.4906, test loss-1.8421, acc-0.4848
Iter-53550, train loss-1.9044, acc-0.4200, valid loss-1.8413, acc-0.4904, test loss-1.8421, acc-0.4849
Iter-53560, train loss-1.7947, acc-0.4600, valid loss-1.8412, acc-0.4904, test loss-1.8420, acc-0.4848
Iter-53570, train loss-1.9068, acc-0.4600, valid loss-1.8411, acc-0.4904, test loss-1.8420, acc-0.4851
Iter-53580, train loss-1.8222, acc-0.6000, valid loss-1.8411, acc-0.4904, test loss-1.8419, acc-0.4851
Iter-53590, train loss-1.8785, acc-0.5400, valid loss-1.8410, acc-0.4904, test loss-1.8419, acc-0.4853
Iter-53600, train loss-1.9198, acc-0.4400, valid loss-1.8410, acc-0.4902, test loss-1.8418, acc-0.4853
Iter-53610, train loss-1.9703, acc-0.3200, valid loss-1.8409, acc-0.4904, test loss-1.8418, acc-0.4851
Iter-53620, train loss-1.8453, acc-0.5000, valid loss-1.8409, acc-0.4902, test loss-1.8417, acc-0.4851
Iter-53630, train loss-1.7936, acc-0.5200, valid loss-1.8408, acc-0.4904, test loss-1.8417, acc-0.4854
Iter-53640, train loss-1.8672, acc-0.4400, valid loss-1.8408, acc-0.4904, test loss-1.8416, acc-0.4855
Iter-53650, train loss-1.8358, acc-0.4400, valid loss-1.8407, acc-0.4906, test loss-1.8415, acc-0.4855
Iter-53660, train loss-1.7730, acc-0.5400, valid loss-1.8407, acc-0.4904, test loss-1.8415, acc-0.4855
Iter-53670, train loss-1.8861, acc-0.3800, valid loss-1.8406, acc-0.4902, test loss-1.8414, acc-0.4855
Iter-53680, train loss-1.8571, acc-0.4400, valid loss-1.8406, acc-0.4902, test loss-1.8414, acc-0.4854
Iter-53690, train loss-1.8666, acc-0.4000, valid loss-1.8405, acc-0.4902, test loss-1.8413, acc-0.4854
Iter-53700, train loss-1.8851, acc-0.4600, valid loss-1.8404, acc-0.4902, test loss-1.8413, acc-0.4854
Iter-53710, train loss-1.8833, acc-0.5000, valid loss-1.8404, acc-0.4902, test loss-1.8412, acc-0.4853
Iter-53720, train loss-1.8365, acc-0.5400, valid loss-1.8403, acc-0.4904, test loss-1.8412, acc-0.4854
Iter-53730, train loss-1.8098, acc-0.4800, valid loss-1.8403, acc-0.4904, test loss-1.8411, acc-0.4853
Iter-53740, train loss-1.7921, acc-0.5200, valid loss-1.8402, acc-0.4906, test loss-1.8411, acc-0.4853
Iter-53750, train loss-1.8527, acc-0.5000, valid loss-1.8402, acc-0.4906, test loss-1.8410, acc-0.4853
Iter-53760, train loss-1.7794, acc-0.5000, valid loss-1.8401, acc-0.4904, test loss-1.8410, acc-0.4852
Iter-53770, train loss-1.8856, acc-0.4000, valid loss-1.8401, acc-0.4904, test loss-1.8409, acc-0.4853
Iter-53780, train loss-1.8079, acc-0.5000, valid loss-1.8400, acc-0.4906, test loss-1.8408, acc-0.4854
Iter-53790, train loss-1.9151, acc-0.2400, valid loss-1.8400, acc-0.4904, test loss-1.8408, acc-0.4854
Iter-53800, train loss-1.8573, acc-0.4400, valid loss-1.8399, acc-0.4906, test loss-1.8407, acc-0.4853
Iter-53810, train loss-1.8752, acc-0.5000, valid loss-1.8398, acc-0.4906, test loss-1.8407, acc-0.4854
Iter-53820, train loss-1.8571, acc-0.4800, valid loss-1.8398, acc-0.4906, test loss-1.8406, acc-0.4854
Iter-53830, train loss-1.8936, acc-0.4600, valid loss-1.8397, acc-0.4908, test loss-1.8406, acc-0.4857
Iter-53840, train loss-1.8685, acc-0.5000, valid loss-1.8397, acc-0.4906, test loss-1.8405, acc-0.4857
Iter-53850, train loss-1.7956, acc-0.5000, valid loss-1.8396, acc-0.4906, test loss-1.8405, acc-0.4859
Iter-53860, train loss-1.8338, acc-0.5200, valid loss-1.8396, acc-0.4908, test loss-1.8404, acc-0.4859
Iter-53870, train loss-1.8126, acc-0.5200, valid loss-1.8395, acc-0.4910, test loss-1.8404, acc-0.4860
Iter-53880, train loss-1.8292, acc-0.4800, valid loss-1.8395, acc-0.4908, test loss-1.8403, acc-0.4860
Iter-53890, train loss-1.9832, acc-0.3600, valid loss-1.8394, acc-0.4906, test loss-1.8402, acc-0.4859
Iter-53900, train loss-1.8574, acc-0.5000, valid loss-1.8394, acc-0.4906, test loss-1.8402, acc-0.4859
Iter-53910, train loss-1.7465, acc-0.5600, valid loss-1.8393, acc-0.4906, test loss-1.8401, acc-0.4857
Iter-53920, train loss-1.8120, acc-0.4600, valid loss-1.8393, acc-0.4906, test loss-1.8401, acc-0.4860
Iter-53930, train loss-1.8510, acc-0.4200, valid loss-1.8392, acc-0.4908, test loss-1.8400, acc-0.4858
Iter-53940, train loss-1.8440, acc-0.4600, valid loss-1.8392, acc-0.4908, test loss-1.8400, acc-0.4859
Iter-53950, train loss-1.9005, acc-0.4600, valid loss-1.8391, acc-0.4910, test loss-1.8399, acc-0.4859
Iter-53960, train loss-1.8416, acc-0.5800, valid loss-1.8390, acc-0.4908, test loss-1.8399, acc-0.4860
Iter-53970, train loss-1.8778, acc-0.4000, valid loss-1.8390, acc-0.4910, test loss-1.8398, acc-0.4862
Iter-53980, train loss-1.9022, acc-0.4000, valid loss-1.8389, acc-0.4910, test loss-1.8398, acc-0.4860
Iter-53990, train loss-1.8865, acc-0.4400, valid loss-1.8389, acc-0.4912, test loss-1.8397, acc-0.4860
Iter-54000, train loss-1.7976, acc-0.5600, valid loss-1.8388, acc-0.4912, test loss-1.8397, acc-0.4860
Iter-54010, train loss-1.8884, acc-0.4800, valid loss-1.8388, acc-0.4912, test loss-1.8396, acc-0.4860
Iter-54020, train loss-1.7644, acc-0.5800, valid loss-1.8387, acc-0.4912, test loss-1.8396, acc-0.4861
Iter-54030, train loss-1.7800, acc-0.5200, valid loss-1.8387, acc-0.4912, test loss-1.8395, acc-0.4862
Iter-54040, train loss-1.9939, acc-0.3400, valid loss-1.8386, acc-0.4912, test loss-1.8394, acc-0.4861
Iter-54050, train loss-1.8591, acc-0.3800, valid loss-1.8386, acc-0.4912, test loss-1.8394, acc-0.4860
Iter-54060, train loss-1.8437, acc-0.4400, valid loss-1.8385, acc-0.4912, test loss-1.8393, acc-0.4861
Iter-54070, train loss-1.8260, acc-0.4600, valid loss-1.8384, acc-0.4912, test loss-1.8393, acc-0.4861
Iter-54080, train loss-1.7741, acc-0.6000, valid loss-1.8384, acc-0.4912, test loss-1.8392, acc-0.4861
Iter-54090, train loss-1.8542, acc-0.4000, valid loss-1.8383, acc-0.4912, test loss-1.8392, acc-0.4860
Iter-54100, train loss-1.7591, acc-0.6000, valid loss-1.8383, acc-0.4914, test loss-1.8391, acc-0.4860
Iter-54110, train loss-1.8764, acc-0.4400, valid loss-1.8382, acc-0.4912, test loss-1.8391, acc-0.4861
Iter-54120, train loss-1.8191, acc-0.5200, valid loss-1.8382, acc-0.4914, test loss-1.8390, acc-0.4863
Iter-54130, train loss-1.8310, acc-0.4800, valid loss-1.8381, acc-0.4910, test loss-1.8390, acc-0.4862
Iter-54140, train loss-1.8933, acc-0.4200, valid loss-1.8381, acc-0.4912, test loss-1.8389, acc-0.4862
Iter-54150, train loss-1.8472, acc-0.4800, valid loss-1.8380, acc-0.4912, test loss-1.8388, acc-0.4863
Iter-54160, train loss-1.9211, acc-0.3800, valid loss-1.8379, acc-0.4914, test loss-1.8388, acc-0.4864
Iter-54170, train loss-1.7989, acc-0.5800, valid loss-1.8379, acc-0.4912, test loss-1.8387, acc-0.4865
Iter-54180, train loss-1.8099, acc-0.5400, valid loss-1.8378, acc-0.4916, test loss-1.8387, acc-0.4864
Iter-54190, train loss-1.8940, acc-0.3800, valid loss-1.8378, acc-0.4912, test loss-1.8386, acc-0.4861
Iter-54200, train loss-1.7261, acc-0.5800, valid loss-1.8377, acc-0.4912, test loss-1.8386, acc-0.4861
Iter-54210, train loss-1.8269, acc-0.4400, valid loss-1.8377, acc-0.4914, test loss-1.8385, acc-0.4864
Iter-54220, train loss-1.8021, acc-0.4600, valid loss-1.8376, acc-0.4916, test loss-1.8385, acc-0.4863
Iter-54230, train loss-1.9428, acc-0.4200, valid loss-1.8376, acc-0.4916, test loss-1.8384, acc-0.4865
Iter-54240, train loss-1.8698, acc-0.4400, valid loss-1.8375, acc-0.4914, test loss-1.8384, acc-0.4865
Iter-54250, train loss-1.9333, acc-0.3400, valid loss-1.8375, acc-0.4916, test loss-1.8383, acc-0.4863
Iter-54260, train loss-1.9442, acc-0.4000, valid loss-1.8374, acc-0.4916, test loss-1.8383, acc-0.4866
Iter-54270, train loss-1.9034, acc-0.4400, valid loss-1.8374, acc-0.4918, test loss-1.8382, acc-0.4863
Iter-54280, train loss-1.7976, acc-0.5400, valid loss-1.8373, acc-0.4916, test loss-1.8381, acc-0.4865
Iter-54290, train loss-1.8628, acc-0.5000, valid loss-1.8373, acc-0.4914, test loss-1.8381, acc-0.4864
Iter-54300, train loss-1.8255, acc-0.5000, valid loss-1.8372, acc-0.4916, test loss-1.8380, acc-0.4865
Iter-54310, train loss-1.8922, acc-0.4200, valid loss-1.8372, acc-0.4916, test loss-1.8380, acc-0.4865
Iter-54320, train loss-1.8431, acc-0.4800, valid loss-1.8371, acc-0.4918, test loss-1.8379, acc-0.4867
Iter-54330, train loss-1.9181, acc-0.4400, valid loss-1.8370, acc-0.4918, test loss-1.8379, acc-0.4866
Iter-54340, train loss-1.9333, acc-0.4000, valid loss-1.8370, acc-0.4918, test loss-1.8378, acc-0.4867
Iter-54350, train loss-1.8789, acc-0.4200, valid loss-1.8369, acc-0.4916, test loss-1.8378, acc-0.4867
Iter-54360, train loss-1.8870, acc-0.4800, valid loss-1.8369, acc-0.4916, test loss-1.8377, acc-0.4868
Iter-54370, train loss-1.7807, acc-0.4800, valid loss-1.8368, acc-0.4914, test loss-1.8377, acc-0.4869
Iter-54380, train loss-1.8464, acc-0.4800, valid loss-1.8368, acc-0.4914, test loss-1.8376, acc-0.4868
Iter-54390, train loss-1.9325, acc-0.4400, valid loss-1.8367, acc-0.4916, test loss-1.8376, acc-0.4868
Iter-54400, train loss-1.7870, acc-0.5000, valid loss-1.8367, acc-0.4916, test loss-1.8375, acc-0.4867
Iter-54410, train loss-1.8221, acc-0.4400, valid loss-1.8366, acc-0.4918, test loss-1.8374, acc-0.4868
Iter-54420, train loss-1.8418, acc-0.4200, valid loss-1.8365, acc-0.4918, test loss-1.8374, acc-0.4869
Iter-54430, train loss-1.9261, acc-0.4200, valid loss-1.8365, acc-0.4916, test loss-1.8373, acc-0.4869
Iter-54440, train loss-1.8188, acc-0.5000, valid loss-1.8364, acc-0.4918, test loss-1.8373, acc-0.4869
Iter-54450, train loss-1.8277, acc-0.4800, valid loss-1.8364, acc-0.4918, test loss-1.8372, acc-0.4869
Iter-54460, train loss-1.8777, acc-0.4600, valid loss-1.8363, acc-0.4920, test loss-1.8372, acc-0.4868
Iter-54470, train loss-1.8845, acc-0.4400, valid loss-1.8363, acc-0.4920, test loss-1.8371, acc-0.4870
Iter-54480, train loss-1.8083, acc-0.4800, valid loss-1.8362, acc-0.4920, test loss-1.8371, acc-0.4871
Iter-54490, train loss-1.8411, acc-0.5400, valid loss-1.8362, acc-0.4920, test loss-1.8370, acc-0.4871
Iter-54500, train loss-1.8176, acc-0.4400, valid loss-1.8361, acc-0.4920, test loss-1.8370, acc-0.4871
Iter-54510, train loss-1.8487, acc-0.4000, valid loss-1.8361, acc-0.4922, test loss-1.8369, acc-0.4870
Iter-54520, train loss-1.8397, acc-0.5800, valid loss-1.8360, acc-0.4922, test loss-1.8369, acc-0.4869
Iter-54530, train loss-1.7495, acc-0.5600, valid loss-1.8359, acc-0.4920, test loss-1.8368, acc-0.4868
Iter-54540, train loss-1.8305, acc-0.5200, valid loss-1.8359, acc-0.4920, test loss-1.8367, acc-0.4868
Iter-54550, train loss-1.8745, acc-0.4200, valid loss-1.8358, acc-0.4920, test loss-1.8367, acc-0.4868
Iter-54560, train loss-1.8076, acc-0.4600, valid loss-1.8358, acc-0.4920, test loss-1.8366, acc-0.4868
Iter-54570, train loss-1.8107, acc-0.5600, valid loss-1.8357, acc-0.4920, test loss-1.8366, acc-0.4868
Iter-54580, train loss-1.7414, acc-0.5600, valid loss-1.8357, acc-0.4922, test loss-1.8365, acc-0.4870
Iter-54590, train loss-1.8129, acc-0.5000, valid loss-1.8356, acc-0.4922, test loss-1.8365, acc-0.4869
Iter-54600, train loss-1.8626, acc-0.5200, valid loss-1.8356, acc-0.4922, test loss-1.8364, acc-0.4870
Iter-54610, train loss-1.7197, acc-0.5800, valid loss-1.8355, acc-0.4924, test loss-1.8364, acc-0.4870
Iter-54620, train loss-1.7628, acc-0.6400, valid loss-1.8355, acc-0.4922, test loss-1.8363, acc-0.4869
Iter-54630, train loss-1.7949, acc-0.5400, valid loss-1.8354, acc-0.4924, test loss-1.8363, acc-0.4870
Iter-54640, train loss-1.8340, acc-0.4200, valid loss-1.8354, acc-0.4922, test loss-1.8362, acc-0.4870
Iter-54650, train loss-1.7274, acc-0.6400, valid loss-1.8353, acc-0.4924, test loss-1.8361, acc-0.4870
Iter-54660, train loss-1.8529, acc-0.5000, valid loss-1.8352, acc-0.4922, test loss-1.8361, acc-0.4872
Iter-54670, train loss-1.8623, acc-0.4800, valid loss-1.8352, acc-0.4922, test loss-1.8360, acc-0.4872
Iter-54680, train loss-1.8762, acc-0.4400, valid loss-1.8351, acc-0.4924, test loss-1.8360, acc-0.4872
Iter-54690, train loss-1.8431, acc-0.6000, valid loss-1.8351, acc-0.4924, test loss-1.8359, acc-0.4872
Iter-54700, train loss-1.8218, acc-0.4800, valid loss-1.8350, acc-0.4924, test loss-1.8359, acc-0.4871
Iter-54710, train loss-1.8061, acc-0.5000, valid loss-1.8350, acc-0.4922, test loss-1.8358, acc-0.4872
Iter-54720, train loss-1.7556, acc-0.6000, valid loss-1.8349, acc-0.4924, test loss-1.8358, acc-0.4872
Iter-54730, train loss-1.9528, acc-0.3600, valid loss-1.8349, acc-0.4922, test loss-1.8357, acc-0.4872
Iter-54740, train loss-1.7750, acc-0.5400, valid loss-1.8348, acc-0.4926, test loss-1.8357, acc-0.4873
Iter-54750, train loss-1.8064, acc-0.4600, valid loss-1.8348, acc-0.4924, test loss-1.8356, acc-0.4874
Iter-54760, train loss-1.8559, acc-0.5000, valid loss-1.8347, acc-0.4924, test loss-1.8356, acc-0.4874
Iter-54770, train loss-1.7286, acc-0.6000, valid loss-1.8346, acc-0.4926, test loss-1.8355, acc-0.4875
Iter-54780, train loss-1.8896, acc-0.4800, valid loss-1.8346, acc-0.4926, test loss-1.8355, acc-0.4874
Iter-54790, train loss-1.8103, acc-0.5400, valid loss-1.8345, acc-0.4924, test loss-1.8354, acc-0.4874
Iter-54800, train loss-1.8098, acc-0.5000, valid loss-1.8345, acc-0.4924, test loss-1.8353, acc-0.4874
Iter-54810, train loss-1.8731, acc-0.4600, valid loss-1.8344, acc-0.4924, test loss-1.8353, acc-0.4874
Iter-54820, train loss-1.8773, acc-0.5000, valid loss-1.8344, acc-0.4920, test loss-1.8352, acc-0.4873
Iter-54830, train loss-1.8173, acc-0.5800, valid loss-1.8343, acc-0.4920, test loss-1.8352, acc-0.4872
Iter-54840, train loss-1.8669, acc-0.4800, valid loss-1.8343, acc-0.4920, test loss-1.8351, acc-0.4872
Iter-54850, train loss-1.8014, acc-0.5000, valid loss-1.8342, acc-0.4920, test loss-1.8351, acc-0.4872
Iter-54860, train loss-1.9176, acc-0.4200, valid loss-1.8342, acc-0.4922, test loss-1.8350, acc-0.4873
Iter-54870, train loss-1.8847, acc-0.4200, valid loss-1.8341, acc-0.4922, test loss-1.8350, acc-0.4872
Iter-54880, train loss-1.8266, acc-0.5200, valid loss-1.8341, acc-0.4922, test loss-1.8349, acc-0.4873
Iter-54890, train loss-1.8463, acc-0.5000, valid loss-1.8340, acc-0.4922, test loss-1.8349, acc-0.4874
Iter-54900, train loss-1.9159, acc-0.4600, valid loss-1.8339, acc-0.4922, test loss-1.8348, acc-0.4875
Iter-54910, train loss-1.8399, acc-0.4800, valid loss-1.8339, acc-0.4922, test loss-1.8347, acc-0.4876
Iter-54920, train loss-1.8190, acc-0.4800, valid loss-1.8338, acc-0.4922, test loss-1.8347, acc-0.4877
Iter-54930, train loss-1.8644, acc-0.4600, valid loss-1.8338, acc-0.4922, test loss-1.8346, acc-0.4877
Iter-54940, train loss-1.9059, acc-0.3800, valid loss-1.8337, acc-0.4922, test loss-1.8346, acc-0.4878
Iter-54950, train loss-1.7855, acc-0.5600, valid loss-1.8337, acc-0.4924, test loss-1.8345, acc-0.4878
Iter-54960, train loss-1.7738, acc-0.5200, valid loss-1.8336, acc-0.4924, test loss-1.8345, acc-0.4877
Iter-54970, train loss-1.9327, acc-0.4400, valid loss-1.8336, acc-0.4922, test loss-1.8344, acc-0.4878
Iter-54980, train loss-1.8495, acc-0.4200, valid loss-1.8335, acc-0.4924, test loss-1.8344, acc-0.4877
Iter-54990, train loss-1.8450, acc-0.4200, valid loss-1.8335, acc-0.4924, test loss-1.8343, acc-0.4878
Iter-55000, train loss-1.8437, acc-0.5000, valid loss-1.8334, acc-0.4924, test loss-1.8343, acc-0.4878
Iter-55010, train loss-1.8447, acc-0.5800, valid loss-1.8334, acc-0.4926, test loss-1.8342, acc-0.4878
Iter-55020, train loss-1.8410, acc-0.5000, valid loss-1.8333, acc-0.4926, test loss-1.8342, acc-0.4879
Iter-55030, train loss-1.8048, acc-0.5200, valid loss-1.8333, acc-0.4922, test loss-1.8341, acc-0.4877
Iter-55040, train loss-1.8829, acc-0.3400, valid loss-1.8332, acc-0.4922, test loss-1.8340, acc-0.4879
Iter-55050, train loss-1.8069, acc-0.4800, valid loss-1.8331, acc-0.4924, test loss-1.8340, acc-0.4879
Iter-55060, train loss-1.7853, acc-0.5000, valid loss-1.8331, acc-0.4922, test loss-1.8339, acc-0.4878
Iter-55070, train loss-1.7721, acc-0.5800, valid loss-1.8330, acc-0.4924, test loss-1.8339, acc-0.4878
Iter-55080, train loss-1.8644, acc-0.4400, valid loss-1.8330, acc-0.4924, test loss-1.8338, acc-0.4878
Iter-55090, train loss-1.8223, acc-0.5600, valid loss-1.8329, acc-0.4924, test loss-1.8338, acc-0.4878
Iter-55100, train loss-1.7759, acc-0.5200, valid loss-1.8329, acc-0.4926, test loss-1.8337, acc-0.4879
Iter-55110, train loss-1.8339, acc-0.5800, valid loss-1.8328, acc-0.4930, test loss-1.8337, acc-0.4879
Iter-55120, train loss-1.7689, acc-0.5000, valid loss-1.8328, acc-0.4928, test loss-1.8336, acc-0.4879
Iter-55130, train loss-1.8262, acc-0.5200, valid loss-1.8327, acc-0.4930, test loss-1.8336, acc-0.4881
Iter-55140, train loss-1.7968, acc-0.4800, valid loss-1.8327, acc-0.4928, test loss-1.8335, acc-0.4879
Iter-55150, train loss-1.9305, acc-0.4800, valid loss-1.8326, acc-0.4928, test loss-1.8335, acc-0.4880
Iter-55160, train loss-1.8356, acc-0.4200, valid loss-1.8326, acc-0.4928, test loss-1.8334, acc-0.4880
Iter-55170, train loss-1.8068, acc-0.5800, valid loss-1.8325, acc-0.4928, test loss-1.8333, acc-0.4881
Iter-55180, train loss-1.8815, acc-0.4000, valid loss-1.8325, acc-0.4928, test loss-1.8333, acc-0.4879
Iter-55190, train loss-1.8514, acc-0.4800, valid loss-1.8324, acc-0.4928, test loss-1.8332, acc-0.4879
Iter-55200, train loss-1.8548, acc-0.4200, valid loss-1.8323, acc-0.4928, test loss-1.8332, acc-0.4881
Iter-55210, train loss-1.8449, acc-0.4600, valid loss-1.8323, acc-0.4928, test loss-1.8331, acc-0.4880
Iter-55220, train loss-1.8447, acc-0.4800, valid loss-1.8322, acc-0.4926, test loss-1.8331, acc-0.4881
Iter-55230, train loss-1.8256, acc-0.5400, valid loss-1.8322, acc-0.4926, test loss-1.8330, acc-0.4881
Iter-55240, train loss-1.9014, acc-0.3200, valid loss-1.8321, acc-0.4926, test loss-1.8330, acc-0.4880
Iter-55250, train loss-1.8917, acc-0.5200, valid loss-1.8321, acc-0.4926, test loss-1.8329, acc-0.4882
Iter-55260, train loss-1.8482, acc-0.5400, valid loss-1.8320, acc-0.4926, test loss-1.8329, acc-0.4882
Iter-55270, train loss-1.8275, acc-0.5000, valid loss-1.8320, acc-0.4928, test loss-1.8328, acc-0.4881
Iter-55280, train loss-1.7773, acc-0.5000, valid loss-1.8319, acc-0.4928, test loss-1.8328, acc-0.4881
Iter-55290, train loss-1.7561, acc-0.6200, valid loss-1.8319, acc-0.4932, test loss-1.8327, acc-0.4882
Iter-55300, train loss-1.8292, acc-0.5000, valid loss-1.8318, acc-0.4930, test loss-1.8327, acc-0.4882
Iter-55310, train loss-1.9174, acc-0.3800, valid loss-1.8318, acc-0.4932, test loss-1.8326, acc-0.4883
Iter-55320, train loss-1.7532, acc-0.5600, valid loss-1.8317, acc-0.4928, test loss-1.8325, acc-0.4884
Iter-55330, train loss-1.7847, acc-0.5200, valid loss-1.8316, acc-0.4930, test loss-1.8325, acc-0.4884
Iter-55340, train loss-1.7892, acc-0.5200, valid loss-1.8316, acc-0.4928, test loss-1.8324, acc-0.4885
Iter-55350, train loss-1.9074, acc-0.4400, valid loss-1.8315, acc-0.4930, test loss-1.8324, acc-0.4884
Iter-55360, train loss-1.8495, acc-0.4400, valid loss-1.8315, acc-0.4930, test loss-1.8323, acc-0.4881
Iter-55370, train loss-1.9684, acc-0.4000, valid loss-1.8314, acc-0.4930, test loss-1.8323, acc-0.4882
Iter-55380, train loss-1.9530, acc-0.4400, valid loss-1.8314, acc-0.4928, test loss-1.8322, acc-0.4881
Iter-55390, train loss-1.7966, acc-0.3800, valid loss-1.8313, acc-0.4930, test loss-1.8322, acc-0.4883
Iter-55400, train loss-1.8226, acc-0.4200, valid loss-1.8313, acc-0.4930, test loss-1.8321, acc-0.4882
Iter-55410, train loss-1.7994, acc-0.5600, valid loss-1.8312, acc-0.4928, test loss-1.8321, acc-0.4881
Iter-55420, train loss-1.8751, acc-0.4000, valid loss-1.8312, acc-0.4928, test loss-1.8320, acc-0.4881
Iter-55430, train loss-1.7749, acc-0.5200, valid loss-1.8311, acc-0.4930, test loss-1.8320, acc-0.4882
Iter-55440, train loss-1.8425, acc-0.4200, valid loss-1.8311, acc-0.4930, test loss-1.8319, acc-0.4881
Iter-55450, train loss-1.8203, acc-0.4400, valid loss-1.8310, acc-0.4930, test loss-1.8319, acc-0.4884
Iter-55460, train loss-1.8923, acc-0.4400, valid loss-1.8310, acc-0.4932, test loss-1.8318, acc-0.4884
Iter-55470, train loss-1.8437, acc-0.5000, valid loss-1.8309, acc-0.4932, test loss-1.8318, acc-0.4883
Iter-55480, train loss-1.8218, acc-0.5000, valid loss-1.8309, acc-0.4934, test loss-1.8317, acc-0.4883
Iter-55490, train loss-1.8169, acc-0.5600, valid loss-1.8308, acc-0.4934, test loss-1.8317, acc-0.4885
Iter-55500, train loss-1.8047, acc-0.5600, valid loss-1.8308, acc-0.4934, test loss-1.8316, acc-0.4881
Iter-55510, train loss-1.8223, acc-0.5000, valid loss-1.8307, acc-0.4934, test loss-1.8316, acc-0.4883
Iter-55520, train loss-1.8081, acc-0.5400, valid loss-1.8306, acc-0.4934, test loss-1.8315, acc-0.4884
Iter-55530, train loss-1.8966, acc-0.4400, valid loss-1.8306, acc-0.4934, test loss-1.8315, acc-0.4883
Iter-55540, train loss-1.8616, acc-0.4400, valid loss-1.8305, acc-0.4932, test loss-1.8314, acc-0.4883
Iter-55550, train loss-1.8150, acc-0.5000, valid loss-1.8305, acc-0.4936, test loss-1.8313, acc-0.4883
Iter-55560, train loss-1.8347, acc-0.5800, valid loss-1.8304, acc-0.4936, test loss-1.8313, acc-0.4883
Iter-55570, train loss-1.8787, acc-0.4800, valid loss-1.8304, acc-0.4936, test loss-1.8312, acc-0.4884
Iter-55580, train loss-1.8020, acc-0.5000, valid loss-1.8303, acc-0.4936, test loss-1.8312, acc-0.4884
Iter-55590, train loss-1.8327, acc-0.3800, valid loss-1.8303, acc-0.4934, test loss-1.8311, acc-0.4883
Iter-55600, train loss-1.8631, acc-0.4400, valid loss-1.8302, acc-0.4936, test loss-1.8311, acc-0.4885
Iter-55610, train loss-1.9153, acc-0.3400, valid loss-1.8302, acc-0.4938, test loss-1.8310, acc-0.4884
Iter-55620, train loss-1.7528, acc-0.6000, valid loss-1.8301, acc-0.4936, test loss-1.8310, acc-0.4884
Iter-55630, train loss-1.9046, acc-0.4600, valid loss-1.8301, acc-0.4936, test loss-1.8309, acc-0.4884
Iter-55640, train loss-1.8699, acc-0.4600, valid loss-1.8300, acc-0.4936, test loss-1.8309, acc-0.4884
Iter-55650, train loss-1.8350, acc-0.5000, valid loss-1.8300, acc-0.4932, test loss-1.8308, acc-0.4884
Iter-55660, train loss-1.8198, acc-0.4600, valid loss-1.8299, acc-0.4932, test loss-1.8308, acc-0.4884
Iter-55670, train loss-1.7540, acc-0.4800, valid loss-1.8299, acc-0.4930, test loss-1.8307, acc-0.4885
Iter-55680, train loss-1.8575, acc-0.4200, valid loss-1.8298, acc-0.4932, test loss-1.8307, acc-0.4883
Iter-55690, train loss-1.8612, acc-0.4400, valid loss-1.8298, acc-0.4942, test loss-1.8306, acc-0.4883
Iter-55700, train loss-1.7766, acc-0.4600, valid loss-1.8297, acc-0.4942, test loss-1.8306, acc-0.4885
Iter-55710, train loss-1.8196, acc-0.4600, valid loss-1.8297, acc-0.4942, test loss-1.8305, acc-0.4886
Iter-55720, train loss-1.8586, acc-0.4800, valid loss-1.8296, acc-0.4942, test loss-1.8305, acc-0.4886
Iter-55730, train loss-1.7683, acc-0.6200, valid loss-1.8296, acc-0.4940, test loss-1.8304, acc-0.4884
Iter-55740, train loss-1.8098, acc-0.4400, valid loss-1.8295, acc-0.4942, test loss-1.8304, acc-0.4887
Iter-55750, train loss-1.8514, acc-0.4800, valid loss-1.8294, acc-0.4940, test loss-1.8303, acc-0.4887
Iter-55760, train loss-1.8482, acc-0.4400, valid loss-1.8294, acc-0.4940, test loss-1.8303, acc-0.4887
Iter-55770, train loss-1.8329, acc-0.5200, valid loss-1.8293, acc-0.4940, test loss-1.8302, acc-0.4886
Iter-55780, train loss-1.8240, acc-0.3600, valid loss-1.8293, acc-0.4940, test loss-1.8302, acc-0.4888
Iter-55790, train loss-1.9130, acc-0.4000, valid loss-1.8292, acc-0.4942, test loss-1.8301, acc-0.4887
Iter-55800, train loss-1.7709, acc-0.6200, valid loss-1.8292, acc-0.4942, test loss-1.8301, acc-0.4886
Iter-55810, train loss-1.7836, acc-0.4800, valid loss-1.8291, acc-0.4940, test loss-1.8300, acc-0.4886
Iter-55820, train loss-1.7415, acc-0.6000, valid loss-1.8291, acc-0.4942, test loss-1.8300, acc-0.4887
Iter-55830, train loss-1.8276, acc-0.4800, valid loss-1.8290, acc-0.4942, test loss-1.8299, acc-0.4888
Iter-55840, train loss-1.8133, acc-0.5400, valid loss-1.8290, acc-0.4942, test loss-1.8298, acc-0.4889
Iter-55850, train loss-1.8932, acc-0.4200, valid loss-1.8289, acc-0.4940, test loss-1.8298, acc-0.4888
Iter-55860, train loss-1.9381, acc-0.3800, valid loss-1.8289, acc-0.4940, test loss-1.8297, acc-0.4889
Iter-55870, train loss-1.8884, acc-0.4800, valid loss-1.8288, acc-0.4940, test loss-1.8297, acc-0.4889
Iter-55880, train loss-1.8452, acc-0.4600, valid loss-1.8288, acc-0.4942, test loss-1.8296, acc-0.4889
Iter-55890, train loss-1.8845, acc-0.4400, valid loss-1.8287, acc-0.4942, test loss-1.8296, acc-0.4889
Iter-55900, train loss-1.8017, acc-0.4600, valid loss-1.8287, acc-0.4942, test loss-1.8295, acc-0.4889
Iter-55910, train loss-1.8668, acc-0.4400, valid loss-1.8286, acc-0.4942, test loss-1.8295, acc-0.4889
Iter-55920, train loss-1.7524, acc-0.6400, valid loss-1.8286, acc-0.4942, test loss-1.8294, acc-0.4889
Iter-55930, train loss-1.9545, acc-0.3400, valid loss-1.8285, acc-0.4942, test loss-1.8294, acc-0.4889
Iter-55940, train loss-1.7676, acc-0.6400, valid loss-1.8284, acc-0.4942, test loss-1.8293, acc-0.4889
Iter-55950, train loss-1.8435, acc-0.4800, valid loss-1.8284, acc-0.4944, test loss-1.8293, acc-0.4889
Iter-55960, train loss-1.8831, acc-0.4200, valid loss-1.8283, acc-0.4944, test loss-1.8292, acc-0.4889
Iter-55970, train loss-1.8416, acc-0.4400, valid loss-1.8283, acc-0.4944, test loss-1.8292, acc-0.4889
Iter-55980, train loss-1.8862, acc-0.5800, valid loss-1.8282, acc-0.4944, test loss-1.8291, acc-0.4886
Iter-55990, train loss-1.8525, acc-0.4600, valid loss-1.8282, acc-0.4944, test loss-1.8291, acc-0.4887
Iter-56000, train loss-1.8448, acc-0.4800, valid loss-1.8281, acc-0.4944, test loss-1.8290, acc-0.4887
Iter-56010, train loss-1.8498, acc-0.5000, valid loss-1.8281, acc-0.4944, test loss-1.8290, acc-0.4888
Iter-56020, train loss-1.9140, acc-0.3800, valid loss-1.8280, acc-0.4944, test loss-1.8289, acc-0.4887
Iter-56030, train loss-1.8577, acc-0.3800, valid loss-1.8280, acc-0.4944, test loss-1.8288, acc-0.4888
Iter-56040, train loss-1.7854, acc-0.5800, valid loss-1.8279, acc-0.4946, test loss-1.8288, acc-0.4887
Iter-56050, train loss-1.9608, acc-0.2800, valid loss-1.8279, acc-0.4946, test loss-1.8287, acc-0.4889
Iter-56060, train loss-1.7885, acc-0.4600, valid loss-1.8278, acc-0.4946, test loss-1.8287, acc-0.4887
Iter-56070, train loss-1.7683, acc-0.5600, valid loss-1.8278, acc-0.4946, test loss-1.8286, acc-0.4887
Iter-56080, train loss-1.8609, acc-0.4600, valid loss-1.8277, acc-0.4946, test loss-1.8286, acc-0.4887
Iter-56090, train loss-1.8170, acc-0.4800, valid loss-1.8277, acc-0.4946, test loss-1.8285, acc-0.4888
Iter-56100, train loss-1.8474, acc-0.5000, valid loss-1.8276, acc-0.4944, test loss-1.8285, acc-0.4890
Iter-56110, train loss-1.8767, acc-0.4000, valid loss-1.8276, acc-0.4944, test loss-1.8284, acc-0.4891
Iter-56120, train loss-1.7955, acc-0.5400, valid loss-1.8275, acc-0.4946, test loss-1.8284, acc-0.4890
Iter-56130, train loss-1.7960, acc-0.5200, valid loss-1.8275, acc-0.4946, test loss-1.8283, acc-0.4893
Iter-56140, train loss-1.8474, acc-0.4400, valid loss-1.8274, acc-0.4946, test loss-1.8283, acc-0.4892
Iter-56150, train loss-1.7934, acc-0.5000, valid loss-1.8274, acc-0.4948, test loss-1.8282, acc-0.4892
Iter-56160, train loss-1.7942, acc-0.4800, valid loss-1.8273, acc-0.4948, test loss-1.8282, acc-0.4892
Iter-56170, train loss-1.8251, acc-0.4000, valid loss-1.8272, acc-0.4948, test loss-1.8281, acc-0.4891
Iter-56180, train loss-1.8094, acc-0.5000, valid loss-1.8272, acc-0.4948, test loss-1.8281, acc-0.4892
Iter-56190, train loss-1.8778, acc-0.4800, valid loss-1.8271, acc-0.4948, test loss-1.8280, acc-0.4891
Iter-56200, train loss-1.8233, acc-0.4000, valid loss-1.8271, acc-0.4948, test loss-1.8280, acc-0.4890
Iter-56210, train loss-1.8756, acc-0.3800, valid loss-1.8270, acc-0.4948, test loss-1.8279, acc-0.4893
Iter-56220, train loss-1.7717, acc-0.5600, valid loss-1.8270, acc-0.4948, test loss-1.8279, acc-0.4891
Iter-56230, train loss-1.9308, acc-0.3200, valid loss-1.8269, acc-0.4948, test loss-1.8278, acc-0.4893
Iter-56240, train loss-1.7458, acc-0.5000, valid loss-1.8269, acc-0.4948, test loss-1.8278, acc-0.4896
Iter-56250, train loss-1.8220, acc-0.4800, valid loss-1.8268, acc-0.4946, test loss-1.8277, acc-0.4895
Iter-56260, train loss-1.8510, acc-0.4000, valid loss-1.8268, acc-0.4946, test loss-1.8276, acc-0.4894
Iter-56270, train loss-1.8025, acc-0.4400, valid loss-1.8267, acc-0.4948, test loss-1.8276, acc-0.4895
Iter-56280, train loss-1.8283, acc-0.4200, valid loss-1.8267, acc-0.4946, test loss-1.8275, acc-0.4895
Iter-56290, train loss-1.8019, acc-0.5800, valid loss-1.8266, acc-0.4948, test loss-1.8275, acc-0.4894
Iter-56300, train loss-1.9032, acc-0.4000, valid loss-1.8266, acc-0.4948, test loss-1.8274, acc-0.4896
Iter-56310, train loss-1.7732, acc-0.4600, valid loss-1.8265, acc-0.4948, test loss-1.8274, acc-0.4896
Iter-56320, train loss-1.9267, acc-0.3600, valid loss-1.8265, acc-0.4948, test loss-1.8273, acc-0.4895
Iter-56330, train loss-1.8769, acc-0.4600, valid loss-1.8264, acc-0.4948, test loss-1.8273, acc-0.4895
Iter-56340, train loss-1.8529, acc-0.4600, valid loss-1.8264, acc-0.4948, test loss-1.8272, acc-0.4894
Iter-56350, train loss-1.7739, acc-0.6000, valid loss-1.8263, acc-0.4948, test loss-1.8272, acc-0.4894
Iter-56360, train loss-1.8633, acc-0.4400, valid loss-1.8263, acc-0.4948, test loss-1.8271, acc-0.4894
Iter-56370, train loss-1.8579, acc-0.4400, valid loss-1.8262, acc-0.4948, test loss-1.8271, acc-0.4896
Iter-56380, train loss-1.7250, acc-0.5200, valid loss-1.8261, acc-0.4948, test loss-1.8270, acc-0.4896
Iter-56390, train loss-1.8585, acc-0.4600, valid loss-1.8261, acc-0.4948, test loss-1.8270, acc-0.4896
Iter-56400, train loss-1.8801, acc-0.4400, valid loss-1.8260, acc-0.4948, test loss-1.8269, acc-0.4895
Iter-56410, train loss-1.8018, acc-0.5200, valid loss-1.8260, acc-0.4948, test loss-1.8269, acc-0.4897
Iter-56420, train loss-1.8410, acc-0.4400, valid loss-1.8259, acc-0.4948, test loss-1.8268, acc-0.4898
Iter-56430, train loss-1.8541, acc-0.5000, valid loss-1.8259, acc-0.4948, test loss-1.8268, acc-0.4898
Iter-56440, train loss-1.7827, acc-0.4200, valid loss-1.8258, acc-0.4948, test loss-1.8267, acc-0.4897
Iter-56450, train loss-1.8108, acc-0.4600, valid loss-1.8258, acc-0.4950, test loss-1.8266, acc-0.4898
Iter-56460, train loss-1.8840, acc-0.5400, valid loss-1.8257, acc-0.4950, test loss-1.8266, acc-0.4898
Iter-56470, train loss-1.8850, acc-0.4800, valid loss-1.8257, acc-0.4950, test loss-1.8265, acc-0.4898
Iter-56480, train loss-1.7892, acc-0.5000, valid loss-1.8256, acc-0.4950, test loss-1.8265, acc-0.4897
Iter-56490, train loss-1.7438, acc-0.5800, valid loss-1.8256, acc-0.4952, test loss-1.8264, acc-0.4898
Iter-56500, train loss-1.8707, acc-0.4800, valid loss-1.8255, acc-0.4952, test loss-1.8264, acc-0.4898
Iter-56510, train loss-1.8078, acc-0.4400, valid loss-1.8255, acc-0.4952, test loss-1.8263, acc-0.4900
Iter-56520, train loss-1.9321, acc-0.4000, valid loss-1.8254, acc-0.4948, test loss-1.8263, acc-0.4900
Iter-56530, train loss-1.8435, acc-0.4400, valid loss-1.8254, acc-0.4948, test loss-1.8262, acc-0.4901
Iter-56540, train loss-1.7216, acc-0.6200, valid loss-1.8253, acc-0.4948, test loss-1.8262, acc-0.4901
Iter-56550, train loss-1.8631, acc-0.4200, valid loss-1.8253, acc-0.4950, test loss-1.8261, acc-0.4899
Iter-56560, train loss-1.7905, acc-0.5000, valid loss-1.8252, acc-0.4950, test loss-1.8261, acc-0.4899
Iter-56570, train loss-1.8437, acc-0.4800, valid loss-1.8251, acc-0.4950, test loss-1.8260, acc-0.4899
Iter-56580, train loss-1.9000, acc-0.4000, valid loss-1.8251, acc-0.4948, test loss-1.8260, acc-0.4897
Iter-56590, train loss-1.8776, acc-0.4400, valid loss-1.8250, acc-0.4948, test loss-1.8259, acc-0.4897
Iter-56600, train loss-1.9161, acc-0.4000, valid loss-1.8250, acc-0.4948, test loss-1.8259, acc-0.4895
Iter-56610, train loss-1.7816, acc-0.5200, valid loss-1.8249, acc-0.4948, test loss-1.8258, acc-0.4896
Iter-56620, train loss-1.8696, acc-0.4200, valid loss-1.8249, acc-0.4950, test loss-1.8258, acc-0.4898
Iter-56630, train loss-1.8661, acc-0.4600, valid loss-1.8248, acc-0.4948, test loss-1.8257, acc-0.4899
Iter-56640, train loss-1.8617, acc-0.4800, valid loss-1.8248, acc-0.4948, test loss-1.8257, acc-0.4900
Iter-56650, train loss-1.8675, acc-0.4600, valid loss-1.8247, acc-0.4950, test loss-1.8256, acc-0.4899
Iter-56660, train loss-1.8855, acc-0.5000, valid loss-1.8247, acc-0.4950, test loss-1.8256, acc-0.4899
Iter-56670, train loss-1.8919, acc-0.4800, valid loss-1.8246, acc-0.4948, test loss-1.8255, acc-0.4899
Iter-56680, train loss-1.7861, acc-0.5400, valid loss-1.8246, acc-0.4948, test loss-1.8254, acc-0.4900
Iter-56690, train loss-1.9573, acc-0.4000, valid loss-1.8245, acc-0.4948, test loss-1.8254, acc-0.4900
Iter-56700, train loss-1.8411, acc-0.5200, valid loss-1.8245, acc-0.4950, test loss-1.8253, acc-0.4900
Iter-56710, train loss-1.8805, acc-0.4400, valid loss-1.8244, acc-0.4950, test loss-1.8253, acc-0.4900
Iter-56720, train loss-1.8400, acc-0.4800, valid loss-1.8244, acc-0.4952, test loss-1.8252, acc-0.4900
Iter-56730, train loss-1.7740, acc-0.5400, valid loss-1.8243, acc-0.4950, test loss-1.8252, acc-0.4901
Iter-56740, train loss-1.8214, acc-0.5200, valid loss-1.8243, acc-0.4950, test loss-1.8251, acc-0.4901
Iter-56750, train loss-1.9162, acc-0.4400, valid loss-1.8242, acc-0.4948, test loss-1.8251, acc-0.4900
Iter-56760, train loss-1.7819, acc-0.5400, valid loss-1.8242, acc-0.4950, test loss-1.8250, acc-0.4901
Iter-56770, train loss-1.8227, acc-0.4800, valid loss-1.8241, acc-0.4952, test loss-1.8250, acc-0.4900
Iter-56780, train loss-1.7848, acc-0.5000, valid loss-1.8241, acc-0.4952, test loss-1.8249, acc-0.4902
Iter-56790, train loss-1.8384, acc-0.5600, valid loss-1.8240, acc-0.4950, test loss-1.8249, acc-0.4902
Iter-56800, train loss-1.9302, acc-0.3600, valid loss-1.8240, acc-0.4952, test loss-1.8248, acc-0.4901
Iter-56810, train loss-1.8289, acc-0.4600, valid loss-1.8239, acc-0.4952, test loss-1.8248, acc-0.4901
Iter-56820, train loss-1.8355, acc-0.4600, valid loss-1.8239, acc-0.4952, test loss-1.8247, acc-0.4902
Iter-56830, train loss-1.9793, acc-0.4800, valid loss-1.8238, acc-0.4952, test loss-1.8247, acc-0.4902
Iter-56840, train loss-1.8088, acc-0.5600, valid loss-1.8238, acc-0.4952, test loss-1.8246, acc-0.4901
Iter-56850, train loss-1.9120, acc-0.4000, valid loss-1.8237, acc-0.4954, test loss-1.8246, acc-0.4902
Iter-56860, train loss-1.8308, acc-0.4800, valid loss-1.8237, acc-0.4952, test loss-1.8245, acc-0.4900
Iter-56870, train loss-1.8200, acc-0.5000, valid loss-1.8236, acc-0.4948, test loss-1.8245, acc-0.4902
Iter-56880, train loss-1.8608, acc-0.5000, valid loss-1.8235, acc-0.4952, test loss-1.8244, acc-0.4902
Iter-56890, train loss-1.8141, acc-0.5200, valid loss-1.8235, acc-0.4950, test loss-1.8244, acc-0.4900
Iter-56900, train loss-1.8456, acc-0.4000, valid loss-1.8234, acc-0.4950, test loss-1.8243, acc-0.4901
Iter-56910, train loss-1.7813, acc-0.5000, valid loss-1.8234, acc-0.4952, test loss-1.8242, acc-0.4901
Iter-56920, train loss-1.8827, acc-0.4000, valid loss-1.8233, acc-0.4954, test loss-1.8242, acc-0.4900
Iter-56930, train loss-1.8114, acc-0.4400, valid loss-1.8233, acc-0.4954, test loss-1.8241, acc-0.4900
Iter-56940, train loss-1.8542, acc-0.4200, valid loss-1.8232, acc-0.4954, test loss-1.8241, acc-0.4900
Iter-56950, train loss-1.7942, acc-0.5200, valid loss-1.8232, acc-0.4954, test loss-1.8240, acc-0.4900
Iter-56960, train loss-1.8212, acc-0.5400, valid loss-1.8231, acc-0.4956, test loss-1.8240, acc-0.4901
Iter-56970, train loss-1.8378, acc-0.4400, valid loss-1.8231, acc-0.4952, test loss-1.8239, acc-0.4901
Iter-56980, train loss-1.7396, acc-0.5400, valid loss-1.8230, acc-0.4954, test loss-1.8239, acc-0.4901
Iter-56990, train loss-1.9113, acc-0.4200, valid loss-1.8230, acc-0.4952, test loss-1.8238, acc-0.4903
Iter-57000, train loss-1.8605, acc-0.4800, valid loss-1.8229, acc-0.4954, test loss-1.8238, acc-0.4903
Iter-57010, train loss-1.9910, acc-0.3800, valid loss-1.8229, acc-0.4956, test loss-1.8237, acc-0.4904
Iter-57020, train loss-1.8936, acc-0.3800, valid loss-1.8228, acc-0.4954, test loss-1.8237, acc-0.4904
Iter-57030, train loss-1.9280, acc-0.4400, valid loss-1.8228, acc-0.4954, test loss-1.8236, acc-0.4904
Iter-57040, train loss-1.6975, acc-0.7000, valid loss-1.8227, acc-0.4954, test loss-1.8236, acc-0.4904
Iter-57050, train loss-1.8171, acc-0.5400, valid loss-1.8227, acc-0.4954, test loss-1.8235, acc-0.4904
Iter-57060, train loss-1.8457, acc-0.4600, valid loss-1.8226, acc-0.4954, test loss-1.8235, acc-0.4904
Iter-57070, train loss-1.8864, acc-0.4800, valid loss-1.8226, acc-0.4958, test loss-1.8234, acc-0.4904
Iter-57080, train loss-1.8814, acc-0.3800, valid loss-1.8225, acc-0.4956, test loss-1.8234, acc-0.4904
Iter-57090, train loss-1.8491, acc-0.4400, valid loss-1.8225, acc-0.4956, test loss-1.8233, acc-0.4901
Iter-57100, train loss-1.8200, acc-0.4800, valid loss-1.8224, acc-0.4956, test loss-1.8233, acc-0.4901
Iter-57110, train loss-1.8322, acc-0.5000, valid loss-1.8224, acc-0.4956, test loss-1.8232, acc-0.4901
Iter-57120, train loss-1.8561, acc-0.5200, valid loss-1.8223, acc-0.4956, test loss-1.8232, acc-0.4900
Iter-57130, train loss-1.8360, acc-0.4400, valid loss-1.8222, acc-0.4956, test loss-1.8231, acc-0.4900
Iter-57140, train loss-1.8967, acc-0.4800, valid loss-1.8222, acc-0.4954, test loss-1.8231, acc-0.4900
Iter-57150, train loss-1.8280, acc-0.5200, valid loss-1.8221, acc-0.4954, test loss-1.8230, acc-0.4901
Iter-57160, train loss-1.8163, acc-0.5400, valid loss-1.8221, acc-0.4954, test loss-1.8230, acc-0.4900
Iter-57170, train loss-1.9122, acc-0.4800, valid loss-1.8220, acc-0.4954, test loss-1.8229, acc-0.4899
Iter-57180, train loss-1.8522, acc-0.5400, valid loss-1.8220, acc-0.4954, test loss-1.8229, acc-0.4899
Iter-57190, train loss-1.9110, acc-0.3400, valid loss-1.8219, acc-0.4954, test loss-1.8228, acc-0.4901
Iter-57200, train loss-1.8038, acc-0.4800, valid loss-1.8219, acc-0.4954, test loss-1.8227, acc-0.4900
Iter-57210, train loss-1.7702, acc-0.5600, valid loss-1.8218, acc-0.4954, test loss-1.8227, acc-0.4900
Iter-57220, train loss-1.8361, acc-0.3800, valid loss-1.8218, acc-0.4952, test loss-1.8226, acc-0.4900
Iter-57230, train loss-1.8067, acc-0.5800, valid loss-1.8217, acc-0.4952, test loss-1.8226, acc-0.4902
Iter-57240, train loss-1.8147, acc-0.6000, valid loss-1.8217, acc-0.4954, test loss-1.8225, acc-0.4903
Iter-57250, train loss-1.9148, acc-0.4000, valid loss-1.8216, acc-0.4952, test loss-1.8225, acc-0.4903
Iter-57260, train loss-1.8284, acc-0.4400, valid loss-1.8216, acc-0.4954, test loss-1.8224, acc-0.4903
Iter-57270, train loss-1.8614, acc-0.4800, valid loss-1.8215, acc-0.4954, test loss-1.8224, acc-0.4903
Iter-57280, train loss-1.9017, acc-0.3800, valid loss-1.8215, acc-0.4954, test loss-1.8223, acc-0.4903
Iter-57290, train loss-1.8831, acc-0.4800, valid loss-1.8214, acc-0.4954, test loss-1.8223, acc-0.4902
Iter-57300, train loss-1.7746, acc-0.5000, valid loss-1.8213, acc-0.4952, test loss-1.8222, acc-0.4902
Iter-57310, train loss-1.7142, acc-0.6400, valid loss-1.8213, acc-0.4954, test loss-1.8222, acc-0.4903
Iter-57320, train loss-1.8384, acc-0.4600, valid loss-1.8212, acc-0.4950, test loss-1.8221, acc-0.4902
Iter-57330, train loss-1.8510, acc-0.5200, valid loss-1.8212, acc-0.4958, test loss-1.8221, acc-0.4902
Iter-57340, train loss-1.8914, acc-0.4400, valid loss-1.8211, acc-0.4950, test loss-1.8220, acc-0.4902
Iter-57350, train loss-1.8795, acc-0.4800, valid loss-1.8211, acc-0.4954, test loss-1.8220, acc-0.4903
Iter-57360, train loss-1.8592, acc-0.4800, valid loss-1.8210, acc-0.4956, test loss-1.8219, acc-0.4903
Iter-57370, train loss-1.8178, acc-0.5200, valid loss-1.8210, acc-0.4954, test loss-1.8219, acc-0.4903
Iter-57380, train loss-1.8730, acc-0.5000, valid loss-1.8209, acc-0.4956, test loss-1.8218, acc-0.4903
Iter-57390, train loss-1.8480, acc-0.4000, valid loss-1.8209, acc-0.4958, test loss-1.8218, acc-0.4903
Iter-57400, train loss-1.8917, acc-0.3600, valid loss-1.8208, acc-0.4958, test loss-1.8217, acc-0.4903
Iter-57410, train loss-1.9217, acc-0.5000, valid loss-1.8208, acc-0.4954, test loss-1.8217, acc-0.4903
Iter-57420, train loss-1.8494, acc-0.5200, valid loss-1.8207, acc-0.4954, test loss-1.8216, acc-0.4903
Iter-57430, train loss-1.9175, acc-0.4600, valid loss-1.8207, acc-0.4956, test loss-1.8216, acc-0.4904
Iter-57440, train loss-1.7961, acc-0.4400, valid loss-1.8206, acc-0.4956, test loss-1.8215, acc-0.4904
Iter-57450, train loss-1.8139, acc-0.5600, valid loss-1.8206, acc-0.4956, test loss-1.8215, acc-0.4904
Iter-57460, train loss-1.8164, acc-0.4600, valid loss-1.8205, acc-0.4956, test loss-1.8214, acc-0.4903
Iter-57470, train loss-1.7902, acc-0.5000, valid loss-1.8205, acc-0.4958, test loss-1.8214, acc-0.4903
Iter-57480, train loss-1.7565, acc-0.5400, valid loss-1.8204, acc-0.4958, test loss-1.8213, acc-0.4903
Iter-57490, train loss-1.8294, acc-0.5600, valid loss-1.8203, acc-0.4958, test loss-1.8213, acc-0.4903
Iter-57500, train loss-1.7992, acc-0.5000, valid loss-1.8203, acc-0.4958, test loss-1.8212, acc-0.4904
Iter-57510, train loss-1.7703, acc-0.5000, valid loss-1.8202, acc-0.4958, test loss-1.8212, acc-0.4904
Iter-57520, train loss-1.8087, acc-0.4800, valid loss-1.8202, acc-0.4956, test loss-1.8211, acc-0.4903
Iter-57530, train loss-1.8413, acc-0.4800, valid loss-1.8201, acc-0.4960, test loss-1.8210, acc-0.4904
Iter-57540, train loss-1.7926, acc-0.5200, valid loss-1.8201, acc-0.4958, test loss-1.8210, acc-0.4904
Iter-57550, train loss-1.8325, acc-0.5200, valid loss-1.8200, acc-0.4958, test loss-1.8209, acc-0.4905
Iter-57560, train loss-1.8166, acc-0.4000, valid loss-1.8200, acc-0.4958, test loss-1.8209, acc-0.4905
Iter-57570, train loss-1.9736, acc-0.3600, valid loss-1.8199, acc-0.4958, test loss-1.8208, acc-0.4906
Iter-57580, train loss-1.8403, acc-0.4200, valid loss-1.8199, acc-0.4960, test loss-1.8208, acc-0.4907
Iter-57590, train loss-1.8242, acc-0.4600, valid loss-1.8198, acc-0.4960, test loss-1.8207, acc-0.4904
Iter-57600, train loss-1.8853, acc-0.3800, valid loss-1.8198, acc-0.4958, test loss-1.8207, acc-0.4905
Iter-57610, train loss-1.8446, acc-0.4200, valid loss-1.8197, acc-0.4960, test loss-1.8206, acc-0.4904
Iter-57620, train loss-1.7985, acc-0.5800, valid loss-1.8197, acc-0.4960, test loss-1.8206, acc-0.4904
Iter-57630, train loss-1.9781, acc-0.3800, valid loss-1.8196, acc-0.4960, test loss-1.8205, acc-0.4904
Iter-57640, train loss-2.0100, acc-0.3400, valid loss-1.8196, acc-0.4960, test loss-1.8205, acc-0.4906
Iter-57650, train loss-1.7131, acc-0.5600, valid loss-1.8195, acc-0.4958, test loss-1.8204, acc-0.4907
Iter-57660, train loss-1.8089, acc-0.4400, valid loss-1.8195, acc-0.4956, test loss-1.8204, acc-0.4905
Iter-57670, train loss-1.7981, acc-0.5000, valid loss-1.8194, acc-0.4956, test loss-1.8203, acc-0.4907
Iter-57680, train loss-1.8892, acc-0.4400, valid loss-1.8194, acc-0.4956, test loss-1.8203, acc-0.4903
Iter-57690, train loss-1.7814, acc-0.5000, valid loss-1.8193, acc-0.4956, test loss-1.8202, acc-0.4905
Iter-57700, train loss-1.9436, acc-0.4200, valid loss-1.8193, acc-0.4954, test loss-1.8202, acc-0.4905
Iter-57710, train loss-1.8398, acc-0.4600, valid loss-1.8192, acc-0.4954, test loss-1.8201, acc-0.4905
Iter-57720, train loss-1.9042, acc-0.4200, valid loss-1.8192, acc-0.4954, test loss-1.8201, acc-0.4905
Iter-57730, train loss-1.8251, acc-0.5000, valid loss-1.8191, acc-0.4960, test loss-1.8200, acc-0.4907
Iter-57740, train loss-1.7546, acc-0.6000, valid loss-1.8191, acc-0.4954, test loss-1.8200, acc-0.4907
Iter-57750, train loss-1.8657, acc-0.3400, valid loss-1.8190, acc-0.4956, test loss-1.8199, acc-0.4907
Iter-57760, train loss-1.8238, acc-0.5600, valid loss-1.8190, acc-0.4956, test loss-1.8199, acc-0.4907
Iter-57770, train loss-1.7442, acc-0.5000, valid loss-1.8189, acc-0.4958, test loss-1.8198, acc-0.4907
Iter-57780, train loss-1.8949, acc-0.4600, valid loss-1.8189, acc-0.4958, test loss-1.8198, acc-0.4908
Iter-57790, train loss-1.8370, acc-0.5000, valid loss-1.8188, acc-0.4960, test loss-1.8197, acc-0.4906
Iter-57800, train loss-1.9071, acc-0.4600, valid loss-1.8188, acc-0.4964, test loss-1.8197, acc-0.4908
Iter-57810, train loss-1.8447, acc-0.4800, valid loss-1.8187, acc-0.4964, test loss-1.8196, acc-0.4908
Iter-57820, train loss-1.7387, acc-0.5800, valid loss-1.8187, acc-0.4968, test loss-1.8196, acc-0.4907
Iter-57830, train loss-1.7473, acc-0.5200, valid loss-1.8186, acc-0.4964, test loss-1.8195, acc-0.4905
Iter-57840, train loss-1.8333, acc-0.5200, valid loss-1.8186, acc-0.4962, test loss-1.8195, acc-0.4905
Iter-57850, train loss-1.8991, acc-0.4200, valid loss-1.8185, acc-0.4962, test loss-1.8194, acc-0.4904
Iter-57860, train loss-1.8798, acc-0.4600, valid loss-1.8184, acc-0.4964, test loss-1.8194, acc-0.4906
Iter-57870, train loss-1.8689, acc-0.3800, valid loss-1.8184, acc-0.4964, test loss-1.8193, acc-0.4906
Iter-57880, train loss-1.9147, acc-0.3200, valid loss-1.8183, acc-0.4962, test loss-1.8193, acc-0.4906
Iter-57890, train loss-1.8524, acc-0.4400, valid loss-1.8183, acc-0.4964, test loss-1.8192, acc-0.4907
Iter-57900, train loss-1.9396, acc-0.3800, valid loss-1.8182, acc-0.4964, test loss-1.8191, acc-0.4907
Iter-57910, train loss-1.8550, acc-0.4400, valid loss-1.8182, acc-0.4962, test loss-1.8191, acc-0.4907
Iter-57920, train loss-1.7943, acc-0.4200, valid loss-1.8181, acc-0.4960, test loss-1.8190, acc-0.4907
Iter-57930, train loss-1.7732, acc-0.5000, valid loss-1.8181, acc-0.4962, test loss-1.8190, acc-0.4907
Iter-57940, train loss-1.7587, acc-0.6200, valid loss-1.8180, acc-0.4964, test loss-1.8189, acc-0.4908
Iter-57950, train loss-1.8397, acc-0.4800, valid loss-1.8180, acc-0.4964, test loss-1.8189, acc-0.4906
Iter-57960, train loss-1.8292, acc-0.4800, valid loss-1.8179, acc-0.4964, test loss-1.8188, acc-0.4906
Iter-57970, train loss-1.7942, acc-0.4600, valid loss-1.8179, acc-0.4962, test loss-1.8188, acc-0.4907
Iter-57980, train loss-1.7705, acc-0.4800, valid loss-1.8178, acc-0.4964, test loss-1.8187, acc-0.4906
Iter-57990, train loss-1.8249, acc-0.4400, valid loss-1.8178, acc-0.4962, test loss-1.8187, acc-0.4908
Iter-58000, train loss-1.7943, acc-0.6000, valid loss-1.8177, acc-0.4964, test loss-1.8186, acc-0.4906
Iter-58010, train loss-1.8102, acc-0.3600, valid loss-1.8177, acc-0.4964, test loss-1.8186, acc-0.4907
Iter-58020, train loss-1.8565, acc-0.4800, valid loss-1.8176, acc-0.4964, test loss-1.8185, acc-0.4906
Iter-58030, train loss-1.7831, acc-0.5200, valid loss-1.8176, acc-0.4964, test loss-1.8185, acc-0.4906
Iter-58040, train loss-1.8704, acc-0.4600, valid loss-1.8175, acc-0.4964, test loss-1.8184, acc-0.4906
Iter-58050, train loss-1.7200, acc-0.5200, valid loss-1.8175, acc-0.4964, test loss-1.8184, acc-0.4907
Iter-58060, train loss-1.8294, acc-0.5400, valid loss-1.8174, acc-0.4964, test loss-1.8183, acc-0.4907
Iter-58070, train loss-1.8047, acc-0.5200, valid loss-1.8174, acc-0.4964, test loss-1.8183, acc-0.4907
Iter-58080, train loss-1.8255, acc-0.5800, valid loss-1.8173, acc-0.4964, test loss-1.8182, acc-0.4907
Iter-58090, train loss-1.8766, acc-0.5000, valid loss-1.8173, acc-0.4964, test loss-1.8182, acc-0.4906
Iter-58100, train loss-1.8267, acc-0.5000, valid loss-1.8172, acc-0.4964, test loss-1.8181, acc-0.4905
Iter-58110, train loss-1.8531, acc-0.3800, valid loss-1.8172, acc-0.4964, test loss-1.8181, acc-0.4906
Iter-58120, train loss-1.8075, acc-0.4200, valid loss-1.8171, acc-0.4964, test loss-1.8180, acc-0.4906
Iter-58130, train loss-1.6974, acc-0.4600, valid loss-1.8171, acc-0.4964, test loss-1.8180, acc-0.4906
Iter-58140, train loss-1.8338, acc-0.4600, valid loss-1.8170, acc-0.4964, test loss-1.8179, acc-0.4907
Iter-58150, train loss-1.9263, acc-0.4200, valid loss-1.8170, acc-0.4964, test loss-1.8179, acc-0.4907
Iter-58160, train loss-1.8480, acc-0.5000, valid loss-1.8169, acc-0.4964, test loss-1.8178, acc-0.4908
Iter-58170, train loss-1.8556, acc-0.4200, valid loss-1.8169, acc-0.4964, test loss-1.8178, acc-0.4908
Iter-58180, train loss-1.8054, acc-0.4800, valid loss-1.8168, acc-0.4964, test loss-1.8177, acc-0.4908
Iter-58190, train loss-1.9029, acc-0.4000, valid loss-1.8168, acc-0.4966, test loss-1.8177, acc-0.4908
Iter-58200, train loss-1.8269, acc-0.5400, valid loss-1.8167, acc-0.4966, test loss-1.8176, acc-0.4910
Iter-58210, train loss-1.9818, acc-0.3800, valid loss-1.8167, acc-0.4968, test loss-1.8176, acc-0.4910
Iter-58220, train loss-1.7417, acc-0.5400, valid loss-1.8166, acc-0.4968, test loss-1.8175, acc-0.4910
Iter-58230, train loss-1.8526, acc-0.5200, valid loss-1.8165, acc-0.4968, test loss-1.8175, acc-0.4910
Iter-58240, train loss-1.9125, acc-0.4400, valid loss-1.8165, acc-0.4968, test loss-1.8174, acc-0.4910
Iter-58250, train loss-1.8386, acc-0.5200, valid loss-1.8165, acc-0.4968, test loss-1.8174, acc-0.4910
Iter-58260, train loss-1.8710, acc-0.4200, valid loss-1.8164, acc-0.4970, test loss-1.8173, acc-0.4911
Iter-58270, train loss-1.9034, acc-0.4000, valid loss-1.8163, acc-0.4970, test loss-1.8173, acc-0.4911
Iter-58280, train loss-1.8099, acc-0.5000, valid loss-1.8163, acc-0.4968, test loss-1.8172, acc-0.4911
Iter-58290, train loss-1.8540, acc-0.4000, valid loss-1.8163, acc-0.4970, test loss-1.8172, acc-0.4910
Iter-58300, train loss-1.8712, acc-0.4600, valid loss-1.8162, acc-0.4970, test loss-1.8171, acc-0.4909
Iter-58310, train loss-1.7919, acc-0.4800, valid loss-1.8162, acc-0.4968, test loss-1.8171, acc-0.4911
Iter-58320, train loss-1.7811, acc-0.5800, valid loss-1.8161, acc-0.4968, test loss-1.8170, acc-0.4909
Iter-58330, train loss-1.7523, acc-0.5000, valid loss-1.8161, acc-0.4966, test loss-1.8170, acc-0.4911
Iter-58340, train loss-1.8622, acc-0.2800, valid loss-1.8160, acc-0.4964, test loss-1.8169, acc-0.4910
Iter-58350, train loss-1.8788, acc-0.4800, valid loss-1.8160, acc-0.4966, test loss-1.8169, acc-0.4913
Iter-58360, train loss-1.8276, acc-0.4800, valid loss-1.8159, acc-0.4966, test loss-1.8168, acc-0.4912
Iter-58370, train loss-1.9489, acc-0.3400, valid loss-1.8159, acc-0.4966, test loss-1.8168, acc-0.4914
Iter-58380, train loss-1.9055, acc-0.5000, valid loss-1.8158, acc-0.4966, test loss-1.8167, acc-0.4913
Iter-58390, train loss-1.9525, acc-0.3600, valid loss-1.8158, acc-0.4964, test loss-1.8167, acc-0.4913
Iter-58400, train loss-1.7428, acc-0.5600, valid loss-1.8157, acc-0.4964, test loss-1.8166, acc-0.4913
Iter-58410, train loss-1.9015, acc-0.4200, valid loss-1.8157, acc-0.4966, test loss-1.8166, acc-0.4913
Iter-58420, train loss-1.8750, acc-0.3800, valid loss-1.8156, acc-0.4966, test loss-1.8165, acc-0.4914
Iter-58430, train loss-1.8407, acc-0.4400, valid loss-1.8156, acc-0.4964, test loss-1.8165, acc-0.4913
Iter-58440, train loss-1.7887, acc-0.5800, valid loss-1.8155, acc-0.4964, test loss-1.8164, acc-0.4914
Iter-58450, train loss-1.8731, acc-0.4400, valid loss-1.8155, acc-0.4964, test loss-1.8164, acc-0.4914
Iter-58460, train loss-1.7845, acc-0.5400, valid loss-1.8154, acc-0.4968, test loss-1.8163, acc-0.4914
Iter-58470, train loss-1.8424, acc-0.3800, valid loss-1.8154, acc-0.4968, test loss-1.8163, acc-0.4913
Iter-58480, train loss-1.8443, acc-0.3600, valid loss-1.8153, acc-0.4968, test loss-1.8162, acc-0.4912
Iter-58490, train loss-1.8688, acc-0.4600, valid loss-1.8152, acc-0.4968, test loss-1.8162, acc-0.4912
Iter-58500, train loss-1.7877, acc-0.4800, valid loss-1.8152, acc-0.4970, test loss-1.8161, acc-0.4912
Iter-58510, train loss-1.7984, acc-0.4800, valid loss-1.8151, acc-0.4970, test loss-1.8161, acc-0.4913
Iter-58520, train loss-1.8535, acc-0.4800, valid loss-1.8151, acc-0.4970, test loss-1.8160, acc-0.4915
Iter-58530, train loss-1.9022, acc-0.3600, valid loss-1.8150, acc-0.4972, test loss-1.8160, acc-0.4916
Iter-58540, train loss-1.7658, acc-0.5400, valid loss-1.8150, acc-0.4972, test loss-1.8159, acc-0.4915
Iter-58550, train loss-1.8862, acc-0.4000, valid loss-1.8149, acc-0.4974, test loss-1.8159, acc-0.4914
Iter-58560, train loss-1.8126, acc-0.4400, valid loss-1.8149, acc-0.4972, test loss-1.8158, acc-0.4914
Iter-58570, train loss-1.7531, acc-0.5000, valid loss-1.8148, acc-0.4972, test loss-1.8158, acc-0.4914
Iter-58580, train loss-1.8659, acc-0.5200, valid loss-1.8148, acc-0.4972, test loss-1.8157, acc-0.4915
Iter-58590, train loss-1.8516, acc-0.5200, valid loss-1.8147, acc-0.4974, test loss-1.8157, acc-0.4915
Iter-58600, train loss-1.7925, acc-0.5600, valid loss-1.8147, acc-0.4972, test loss-1.8156, acc-0.4916
Iter-58610, train loss-1.7680, acc-0.5800, valid loss-1.8146, acc-0.4974, test loss-1.8156, acc-0.4916
Iter-58620, train loss-1.7842, acc-0.5000, valid loss-1.8146, acc-0.4972, test loss-1.8155, acc-0.4915
Iter-58630, train loss-1.8324, acc-0.4600, valid loss-1.8145, acc-0.4972, test loss-1.8155, acc-0.4914
Iter-58640, train loss-1.8171, acc-0.5800, valid loss-1.8145, acc-0.4972, test loss-1.8154, acc-0.4914
Iter-58650, train loss-1.9018, acc-0.5000, valid loss-1.8144, acc-0.4972, test loss-1.8154, acc-0.4914
Iter-58660, train loss-1.9078, acc-0.3800, valid loss-1.8144, acc-0.4974, test loss-1.8153, acc-0.4915
Iter-58670, train loss-1.7317, acc-0.5000, valid loss-1.8143, acc-0.4972, test loss-1.8153, acc-0.4917
Iter-58680, train loss-1.8000, acc-0.5400, valid loss-1.8143, acc-0.4972, test loss-1.8152, acc-0.4918
Iter-58690, train loss-1.8185, acc-0.5200, valid loss-1.8142, acc-0.4972, test loss-1.8152, acc-0.4918
Iter-58700, train loss-1.8309, acc-0.3600, valid loss-1.8142, acc-0.4972, test loss-1.8151, acc-0.4918
Iter-58710, train loss-1.8406, acc-0.4800, valid loss-1.8141, acc-0.4972, test loss-1.8151, acc-0.4916
Iter-58720, train loss-1.7415, acc-0.6600, valid loss-1.8141, acc-0.4972, test loss-1.8150, acc-0.4916
Iter-58730, train loss-1.8268, acc-0.5000, valid loss-1.8140, acc-0.4974, test loss-1.8150, acc-0.4916
Iter-58740, train loss-1.7220, acc-0.5200, valid loss-1.8140, acc-0.4974, test loss-1.8149, acc-0.4916
Iter-58750, train loss-1.8988, acc-0.4000, valid loss-1.8139, acc-0.4972, test loss-1.8149, acc-0.4916
Iter-58760, train loss-1.8334, acc-0.5200, valid loss-1.8139, acc-0.4972, test loss-1.8148, acc-0.4916
Iter-58770, train loss-1.7724, acc-0.5400, valid loss-1.8138, acc-0.4972, test loss-1.8148, acc-0.4916
Iter-58780, train loss-1.8476, acc-0.4600, valid loss-1.8138, acc-0.4970, test loss-1.8147, acc-0.4915
Iter-58790, train loss-1.7745, acc-0.5000, valid loss-1.8137, acc-0.4972, test loss-1.8147, acc-0.4914
Iter-58800, train loss-1.8824, acc-0.4400, valid loss-1.8137, acc-0.4972, test loss-1.8146, acc-0.4916
Iter-58810, train loss-1.8233, acc-0.4800, valid loss-1.8136, acc-0.4972, test loss-1.8146, acc-0.4916
Iter-58820, train loss-1.7836, acc-0.5600, valid loss-1.8136, acc-0.4972, test loss-1.8145, acc-0.4917
Iter-58830, train loss-1.8357, acc-0.5000, valid loss-1.8135, acc-0.4972, test loss-1.8145, acc-0.4917
Iter-58840, train loss-1.7712, acc-0.5400, valid loss-1.8135, acc-0.4972, test loss-1.8144, acc-0.4919
Iter-58850, train loss-1.7771, acc-0.5200, valid loss-1.8134, acc-0.4970, test loss-1.8144, acc-0.4916
Iter-58860, train loss-1.8465, acc-0.4000, valid loss-1.8134, acc-0.4970, test loss-1.8143, acc-0.4918
Iter-58870, train loss-1.8112, acc-0.5400, valid loss-1.8133, acc-0.4970, test loss-1.8142, acc-0.4919
Iter-58880, train loss-1.7677, acc-0.5000, valid loss-1.8133, acc-0.4970, test loss-1.8142, acc-0.4918
Iter-58890, train loss-1.8056, acc-0.5200, valid loss-1.8132, acc-0.4972, test loss-1.8141, acc-0.4918
Iter-58900, train loss-1.8495, acc-0.4200, valid loss-1.8132, acc-0.4972, test loss-1.8141, acc-0.4920
Iter-58910, train loss-1.8483, acc-0.4200, valid loss-1.8131, acc-0.4972, test loss-1.8140, acc-0.4919
Iter-58920, train loss-1.8775, acc-0.5000, valid loss-1.8131, acc-0.4970, test loss-1.8140, acc-0.4921
Iter-58930, train loss-1.8326, acc-0.5600, valid loss-1.8130, acc-0.4970, test loss-1.8139, acc-0.4922
Iter-58940, train loss-1.8909, acc-0.4200, valid loss-1.8130, acc-0.4970, test loss-1.8139, acc-0.4921
Iter-58950, train loss-1.7611, acc-0.5800, valid loss-1.8129, acc-0.4970, test loss-1.8138, acc-0.4922
Iter-58960, train loss-1.9202, acc-0.4200, valid loss-1.8129, acc-0.4970, test loss-1.8138, acc-0.4921
Iter-58970, train loss-1.8053, acc-0.5800, valid loss-1.8128, acc-0.4972, test loss-1.8137, acc-0.4921
Iter-58980, train loss-1.8643, acc-0.3600, valid loss-1.8128, acc-0.4972, test loss-1.8137, acc-0.4921
Iter-58990, train loss-1.8929, acc-0.4600, valid loss-1.8127, acc-0.4972, test loss-1.8136, acc-0.4922
Iter-59000, train loss-1.7922, acc-0.5600, valid loss-1.8127, acc-0.4970, test loss-1.8136, acc-0.4923
Iter-59010, train loss-1.8001, acc-0.5200, valid loss-1.8126, acc-0.4970, test loss-1.8135, acc-0.4924
Iter-59020, train loss-1.8071, acc-0.5200, valid loss-1.8126, acc-0.4970, test loss-1.8135, acc-0.4924
Iter-59030, train loss-1.8551, acc-0.3600, valid loss-1.8125, acc-0.4972, test loss-1.8134, acc-0.4924
Iter-59040, train loss-1.9956, acc-0.3400, valid loss-1.8125, acc-0.4974, test loss-1.8134, acc-0.4925
Iter-59050, train loss-1.8731, acc-0.4200, valid loss-1.8124, acc-0.4972, test loss-1.8133, acc-0.4927
Iter-59060, train loss-1.8241, acc-0.5600, valid loss-1.8124, acc-0.4974, test loss-1.8133, acc-0.4926
Iter-59070, train loss-1.7736, acc-0.4800, valid loss-1.8123, acc-0.4974, test loss-1.8132, acc-0.4927
Iter-59080, train loss-1.8543, acc-0.4200, valid loss-1.8123, acc-0.4976, test loss-1.8132, acc-0.4927
Iter-59090, train loss-1.8248, acc-0.4400, valid loss-1.8122, acc-0.4976, test loss-1.8131, acc-0.4926
Iter-59100, train loss-1.7533, acc-0.6400, valid loss-1.8122, acc-0.4980, test loss-1.8131, acc-0.4927
Iter-59110, train loss-1.8600, acc-0.5000, valid loss-1.8121, acc-0.4980, test loss-1.8130, acc-0.4927
Iter-59120, train loss-1.8276, acc-0.5400, valid loss-1.8121, acc-0.4980, test loss-1.8130, acc-0.4929
Iter-59130, train loss-1.8725, acc-0.4400, valid loss-1.8120, acc-0.4980, test loss-1.8129, acc-0.4929
Iter-59140, train loss-1.8694, acc-0.3800, valid loss-1.8120, acc-0.4982, test loss-1.8129, acc-0.4932
Iter-59150, train loss-1.8805, acc-0.4600, valid loss-1.8119, acc-0.4980, test loss-1.8128, acc-0.4933
Iter-59160, train loss-1.9006, acc-0.4800, valid loss-1.8119, acc-0.4980, test loss-1.8128, acc-0.4930
Iter-59170, train loss-1.8398, acc-0.5200, valid loss-1.8118, acc-0.4980, test loss-1.8127, acc-0.4930
Iter-59180, train loss-1.9517, acc-0.4800, valid loss-1.8118, acc-0.4980, test loss-1.8127, acc-0.4931
Iter-59190, train loss-1.7177, acc-0.6400, valid loss-1.8117, acc-0.4980, test loss-1.8126, acc-0.4928
Iter-59200, train loss-1.7634, acc-0.5800, valid loss-1.8117, acc-0.4980, test loss-1.8126, acc-0.4928
Iter-59210, train loss-1.6942, acc-0.6000, valid loss-1.8116, acc-0.4980, test loss-1.8125, acc-0.4929
Iter-59220, train loss-1.7539, acc-0.6000, valid loss-1.8116, acc-0.4982, test loss-1.8125, acc-0.4928
Iter-59230, train loss-1.7688, acc-0.5200, valid loss-1.8115, acc-0.4982, test loss-1.8124, acc-0.4927
Iter-59240, train loss-1.8455, acc-0.4800, valid loss-1.8115, acc-0.4982, test loss-1.8124, acc-0.4930
Iter-59250, train loss-1.8135, acc-0.4400, valid loss-1.8114, acc-0.4982, test loss-1.8124, acc-0.4930
Iter-59260, train loss-1.8743, acc-0.4200, valid loss-1.8114, acc-0.4980, test loss-1.8123, acc-0.4929
Iter-59270, train loss-1.8683, acc-0.4200, valid loss-1.8113, acc-0.4982, test loss-1.8123, acc-0.4928
Iter-59280, train loss-1.9077, acc-0.4800, valid loss-1.8113, acc-0.4984, test loss-1.8122, acc-0.4930
Iter-59290, train loss-1.7979, acc-0.5200, valid loss-1.8112, acc-0.4986, test loss-1.8121, acc-0.4929
Iter-59300, train loss-1.8074, acc-0.5000, valid loss-1.8112, acc-0.4982, test loss-1.8121, acc-0.4929
Iter-59310, train loss-1.8011, acc-0.4400, valid loss-1.8111, acc-0.4982, test loss-1.8120, acc-0.4927
Iter-59320, train loss-1.7643, acc-0.5200, valid loss-1.8111, acc-0.4984, test loss-1.8120, acc-0.4927
Iter-59330, train loss-1.8541, acc-0.5200, valid loss-1.8110, acc-0.4986, test loss-1.8119, acc-0.4927
Iter-59340, train loss-1.7987, acc-0.4200, valid loss-1.8110, acc-0.4986, test loss-1.8119, acc-0.4928
Iter-59350, train loss-1.7445, acc-0.6400, valid loss-1.8109, acc-0.4982, test loss-1.8118, acc-0.4929
Iter-59360, train loss-1.9643, acc-0.4400, valid loss-1.8109, acc-0.4982, test loss-1.8118, acc-0.4929
Iter-59370, train loss-1.8991, acc-0.4200, valid loss-1.8108, acc-0.4982, test loss-1.8118, acc-0.4928
Iter-59380, train loss-1.7397, acc-0.5800, valid loss-1.8108, acc-0.4982, test loss-1.8117, acc-0.4928
Iter-59390, train loss-1.8799, acc-0.4200, valid loss-1.8107, acc-0.4982, test loss-1.8117, acc-0.4928
Iter-59400, train loss-1.8287, acc-0.5200, valid loss-1.8107, acc-0.4982, test loss-1.8116, acc-0.4927
Iter-59410, train loss-1.8844, acc-0.5000, valid loss-1.8106, acc-0.4984, test loss-1.8116, acc-0.4928
Iter-59420, train loss-1.8362, acc-0.4600, valid loss-1.8106, acc-0.4984, test loss-1.8115, acc-0.4927
Iter-59430, train loss-1.8473, acc-0.5200, valid loss-1.8105, acc-0.4982, test loss-1.8115, acc-0.4927
Iter-59440, train loss-1.7865, acc-0.4800, valid loss-1.8105, acc-0.4982, test loss-1.8114, acc-0.4929
Iter-59450, train loss-1.8809, acc-0.4200, valid loss-1.8104, acc-0.4982, test loss-1.8114, acc-0.4928
Iter-59460, train loss-1.8653, acc-0.4000, valid loss-1.8104, acc-0.4982, test loss-1.8113, acc-0.4928
Iter-59470, train loss-1.8548, acc-0.5600, valid loss-1.8103, acc-0.4986, test loss-1.8113, acc-0.4929
Iter-59480, train loss-1.8422, acc-0.5000, valid loss-1.8103, acc-0.4986, test loss-1.8112, acc-0.4929
Iter-59490, train loss-1.8690, acc-0.4400, valid loss-1.8102, acc-0.4984, test loss-1.8112, acc-0.4928
Iter-59500, train loss-1.7909, acc-0.4800, valid loss-1.8102, acc-0.4986, test loss-1.8111, acc-0.4929
Iter-59510, train loss-1.7621, acc-0.5600, valid loss-1.8101, acc-0.4986, test loss-1.8111, acc-0.4929
Iter-59520, train loss-1.8861, acc-0.4200, valid loss-1.8101, acc-0.4986, test loss-1.8110, acc-0.4931
Iter-59530, train loss-1.8555, acc-0.4200, valid loss-1.8100, acc-0.4984, test loss-1.8110, acc-0.4932
Iter-59540, train loss-1.9133, acc-0.3800, valid loss-1.8100, acc-0.4984, test loss-1.8109, acc-0.4932
Iter-59550, train loss-1.8313, acc-0.5000, valid loss-1.8099, acc-0.4984, test loss-1.8109, acc-0.4931
Iter-59560, train loss-1.8856, acc-0.3800, valid loss-1.8099, acc-0.4988, test loss-1.8108, acc-0.4928
Iter-59570, train loss-1.8479, acc-0.4400, valid loss-1.8098, acc-0.4986, test loss-1.8108, acc-0.4930
Iter-59580, train loss-1.8047, acc-0.4600, valid loss-1.8098, acc-0.4984, test loss-1.8107, acc-0.4928
Iter-59590, train loss-1.8723, acc-0.4600, valid loss-1.8097, acc-0.4984, test loss-1.8107, acc-0.4930
Iter-59600, train loss-1.8064, acc-0.5600, valid loss-1.8097, acc-0.4984, test loss-1.8106, acc-0.4930
Iter-59610, train loss-1.7722, acc-0.5200, valid loss-1.8096, acc-0.4986, test loss-1.8106, acc-0.4928
Iter-59620, train loss-1.7303, acc-0.6000, valid loss-1.8096, acc-0.4986, test loss-1.8105, acc-0.4930
Iter-59630, train loss-1.8174, acc-0.4400, valid loss-1.8095, acc-0.4986, test loss-1.8105, acc-0.4930
Iter-59640, train loss-1.8508, acc-0.4400, valid loss-1.8095, acc-0.4986, test loss-1.8104, acc-0.4929
Iter-59650, train loss-1.8522, acc-0.4600, valid loss-1.8094, acc-0.4988, test loss-1.8104, acc-0.4929
Iter-59660, train loss-1.8596, acc-0.4600, valid loss-1.8094, acc-0.4990, test loss-1.8103, acc-0.4929
Iter-59670, train loss-1.7998, acc-0.5000, valid loss-1.8093, acc-0.4986, test loss-1.8103, acc-0.4929
Iter-59680, train loss-1.8328, acc-0.4800, valid loss-1.8093, acc-0.4990, test loss-1.8102, acc-0.4930
Iter-59690, train loss-1.9846, acc-0.3400, valid loss-1.8092, acc-0.4986, test loss-1.8102, acc-0.4931
Iter-59700, train loss-1.8017, acc-0.6000, valid loss-1.8092, acc-0.4986, test loss-1.8101, acc-0.4931
Iter-59710, train loss-1.7832, acc-0.5400, valid loss-1.8091, acc-0.4986, test loss-1.8101, acc-0.4930
Iter-59720, train loss-1.8616, acc-0.4200, valid loss-1.8091, acc-0.4990, test loss-1.8100, acc-0.4931
Iter-59730, train loss-1.7926, acc-0.5800, valid loss-1.8090, acc-0.4986, test loss-1.8100, acc-0.4932
Iter-59740, train loss-1.7868, acc-0.5400, valid loss-1.8090, acc-0.4988, test loss-1.8099, acc-0.4930
Iter-59750, train loss-1.8266, acc-0.5400, valid loss-1.8089, acc-0.4988, test loss-1.8099, acc-0.4930
Iter-59760, train loss-1.8342, acc-0.4000, valid loss-1.8089, acc-0.4986, test loss-1.8098, acc-0.4931
Iter-59770, train loss-1.7820, acc-0.5600, valid loss-1.8088, acc-0.4986, test loss-1.8098, acc-0.4931
Iter-59780, train loss-1.7827, acc-0.6000, valid loss-1.8088, acc-0.4990, test loss-1.8097, acc-0.4932
Iter-59790, train loss-1.7811, acc-0.4200, valid loss-1.8088, acc-0.4990, test loss-1.8097, acc-0.4931
Iter-59800, train loss-1.8688, acc-0.5000, valid loss-1.8087, acc-0.4990, test loss-1.8096, acc-0.4931
Iter-59810, train loss-1.8840, acc-0.4200, valid loss-1.8087, acc-0.4990, test loss-1.8096, acc-0.4930
Iter-59820, train loss-1.8167, acc-0.4400, valid loss-1.8086, acc-0.4990, test loss-1.8095, acc-0.4930
Iter-59830, train loss-1.7817, acc-0.4400, valid loss-1.8086, acc-0.4988, test loss-1.8095, acc-0.4930
Iter-59840, train loss-1.7537, acc-0.5800, valid loss-1.8085, acc-0.4988, test loss-1.8094, acc-0.4930
Iter-59850, train loss-1.8923, acc-0.4200, valid loss-1.8085, acc-0.4990, test loss-1.8094, acc-0.4930
Iter-59860, train loss-1.8408, acc-0.4800, valid loss-1.8084, acc-0.4990, test loss-1.8093, acc-0.4929
Iter-59870, train loss-1.7731, acc-0.6000, valid loss-1.8084, acc-0.4990, test loss-1.8093, acc-0.4928
Iter-59880, train loss-1.9210, acc-0.4200, valid loss-1.8083, acc-0.4990, test loss-1.8092, acc-0.4928
Iter-59890, train loss-1.8786, acc-0.4800, valid loss-1.8083, acc-0.4990, test loss-1.8092, acc-0.4929
Iter-59900, train loss-1.9008, acc-0.3600, valid loss-1.8082, acc-0.4990, test loss-1.8091, acc-0.4929
Iter-59910, train loss-1.7631, acc-0.6400, valid loss-1.8082, acc-0.4988, test loss-1.8091, acc-0.4929
Iter-59920, train loss-1.7749, acc-0.6200, valid loss-1.8081, acc-0.4990, test loss-1.8090, acc-0.4927
Iter-59930, train loss-1.7544, acc-0.5600, valid loss-1.8081, acc-0.4988, test loss-1.8090, acc-0.4929
Iter-59940, train loss-1.8080, acc-0.5000, valid loss-1.8080, acc-0.4986, test loss-1.8089, acc-0.4929
Iter-59950, train loss-1.6949, acc-0.5400, valid loss-1.8080, acc-0.4984, test loss-1.8089, acc-0.4929
Iter-59960, train loss-1.9224, acc-0.5200, valid loss-1.8079, acc-0.4986, test loss-1.8088, acc-0.4930
Iter-59970, train loss-1.9220, acc-0.3600, valid loss-1.8079, acc-0.4988, test loss-1.8088, acc-0.4927
Iter-59980, train loss-1.7210, acc-0.5400, valid loss-1.8078, acc-0.4992, test loss-1.8087, acc-0.4927
Iter-59990, train loss-1.7906, acc-0.5200, valid loss-1.8078, acc-0.4990, test loss-1.8087, acc-0.4928
Iter-60000, train loss-1.9421, acc-0.3800, valid loss-1.8077, acc-0.4990, test loss-1.8086, acc-0.4927
Iter-60010, train loss-1.8129, acc-0.5200, valid loss-1.8077, acc-0.4990, test loss-1.8086, acc-0.4927
Iter-60020, train loss-1.7465, acc-0.5800, valid loss-1.8076, acc-0.4988, test loss-1.8085, acc-0.4928
Iter-60030, train loss-1.8166, acc-0.5000, valid loss-1.8076, acc-0.4988, test loss-1.8085, acc-0.4928
Iter-60040, train loss-1.7760, acc-0.5800, valid loss-1.8075, acc-0.4988, test loss-1.8084, acc-0.4928
Iter-60050, train loss-1.9247, acc-0.3800, valid loss-1.8075, acc-0.4988, test loss-1.8084, acc-0.4929
Iter-60060, train loss-1.8724, acc-0.4800, valid loss-1.8074, acc-0.4990, test loss-1.8083, acc-0.4929
Iter-60070, train loss-1.7607, acc-0.6000, valid loss-1.8074, acc-0.4988, test loss-1.8083, acc-0.4929
Iter-60080, train loss-1.8235, acc-0.5000, valid loss-1.8073, acc-0.4988, test loss-1.8082, acc-0.4929
Iter-60090, train loss-1.8475, acc-0.4200, valid loss-1.8073, acc-0.4988, test loss-1.8082, acc-0.4931
Iter-60100, train loss-1.7678, acc-0.5200, valid loss-1.8072, acc-0.4988, test loss-1.8081, acc-0.4930
Iter-60110, train loss-1.7341, acc-0.5600, valid loss-1.8072, acc-0.4988, test loss-1.8081, acc-0.4930
Iter-60120, train loss-1.7375, acc-0.5600, valid loss-1.8071, acc-0.4990, test loss-1.8080, acc-0.4930
Iter-60130, train loss-1.8554, acc-0.5000, valid loss-1.8071, acc-0.4990, test loss-1.8080, acc-0.4930
Iter-60140, train loss-1.8266, acc-0.5000, valid loss-1.8070, acc-0.4990, test loss-1.8079, acc-0.4930
Iter-60150, train loss-1.8505, acc-0.4800, valid loss-1.8070, acc-0.4992, test loss-1.8079, acc-0.4929
Iter-60160, train loss-1.7538, acc-0.6000, valid loss-1.8069, acc-0.4996, test loss-1.8078, acc-0.4931
Iter-60170, train loss-1.9024, acc-0.4800, valid loss-1.8069, acc-0.4992, test loss-1.8078, acc-0.4931
Iter-60180, train loss-1.7978, acc-0.5400, valid loss-1.8068, acc-0.4994, test loss-1.8077, acc-0.4930
Iter-60190, train loss-1.8476, acc-0.4600, valid loss-1.8068, acc-0.4994, test loss-1.8077, acc-0.4929
Iter-60200, train loss-1.7611, acc-0.5200, valid loss-1.8067, acc-0.4994, test loss-1.8076, acc-0.4929
Iter-60210, train loss-1.7770, acc-0.6000, valid loss-1.8067, acc-0.4994, test loss-1.8076, acc-0.4930
Iter-60220, train loss-1.8962, acc-0.4200, valid loss-1.8066, acc-0.4994, test loss-1.8076, acc-0.4928
Iter-60230, train loss-1.8860, acc-0.4200, valid loss-1.8066, acc-0.4994, test loss-1.8075, acc-0.4928
Iter-60240, train loss-1.8157, acc-0.4600, valid loss-1.8065, acc-0.4992, test loss-1.8074, acc-0.4929
Iter-60250, train loss-1.7933, acc-0.5600, valid loss-1.8065, acc-0.4996, test loss-1.8074, acc-0.4931
Iter-60260, train loss-1.8698, acc-0.4200, valid loss-1.8064, acc-0.4996, test loss-1.8074, acc-0.4932
Iter-60270, train loss-1.7861, acc-0.4400, valid loss-1.8064, acc-0.4996, test loss-1.8073, acc-0.4931
Iter-60280, train loss-1.7949, acc-0.4800, valid loss-1.8064, acc-0.4998, test loss-1.8073, acc-0.4932
Iter-60290, train loss-1.7714, acc-0.5200, valid loss-1.8063, acc-0.4996, test loss-1.8072, acc-0.4932
Iter-60300, train loss-1.8749, acc-0.4000, valid loss-1.8063, acc-0.4996, test loss-1.8072, acc-0.4933
Iter-60310, train loss-1.8566, acc-0.5000, valid loss-1.8062, acc-0.4994, test loss-1.8071, acc-0.4933
Iter-60320, train loss-1.8188, acc-0.5800, valid loss-1.8062, acc-0.4996, test loss-1.8071, acc-0.4934
Iter-60330, train loss-1.8268, acc-0.5200, valid loss-1.8061, acc-0.4998, test loss-1.8070, acc-0.4935
Iter-60340, train loss-1.7791, acc-0.5600, valid loss-1.8061, acc-0.4998, test loss-1.8070, acc-0.4933
Iter-60350, train loss-1.7706, acc-0.6200, valid loss-1.8060, acc-0.4996, test loss-1.8069, acc-0.4932
Iter-60360, train loss-1.8062, acc-0.5200, valid loss-1.8060, acc-0.4998, test loss-1.8069, acc-0.4933
Iter-60370, train loss-1.8202, acc-0.4600, valid loss-1.8059, acc-0.4998, test loss-1.8068, acc-0.4931
Iter-60380, train loss-1.8401, acc-0.4800, valid loss-1.8059, acc-0.4996, test loss-1.8068, acc-0.4930
Iter-60390, train loss-1.8404, acc-0.5000, valid loss-1.8058, acc-0.4998, test loss-1.8067, acc-0.4934
Iter-60400, train loss-1.8102, acc-0.5000, valid loss-1.8058, acc-0.5000, test loss-1.8067, acc-0.4932
Iter-60410, train loss-1.8788, acc-0.4200, valid loss-1.8057, acc-0.4998, test loss-1.8066, acc-0.4933
Iter-60420, train loss-1.8647, acc-0.3600, valid loss-1.8057, acc-0.5000, test loss-1.8066, acc-0.4933
Iter-60430, train loss-1.8248, acc-0.5600, valid loss-1.8056, acc-0.4998, test loss-1.8065, acc-0.4933
Iter-60440, train loss-1.7960, acc-0.4800, valid loss-1.8056, acc-0.4998, test loss-1.8065, acc-0.4933
Iter-60450, train loss-1.8127, acc-0.5400, valid loss-1.8055, acc-0.4998, test loss-1.8064, acc-0.4934
Iter-60460, train loss-1.9557, acc-0.4200, valid loss-1.8055, acc-0.4998, test loss-1.8064, acc-0.4934
Iter-60470, train loss-1.7162, acc-0.5400, valid loss-1.8054, acc-0.4998, test loss-1.8063, acc-0.4934
Iter-60480, train loss-1.8552, acc-0.4600, valid loss-1.8054, acc-0.4998, test loss-1.8063, acc-0.4934
Iter-60490, train loss-1.9282, acc-0.3800, valid loss-1.8053, acc-0.4998, test loss-1.8062, acc-0.4932
Iter-60500, train loss-1.9069, acc-0.4000, valid loss-1.8053, acc-0.4998, test loss-1.8062, acc-0.4931
Iter-60510, train loss-1.7826, acc-0.5400, valid loss-1.8052, acc-0.4996, test loss-1.8061, acc-0.4932
Iter-60520, train loss-1.7825, acc-0.4800, valid loss-1.8052, acc-0.4998, test loss-1.8061, acc-0.4930
Iter-60530, train loss-1.8649, acc-0.3800, valid loss-1.8051, acc-0.4996, test loss-1.8060, acc-0.4932
Iter-60540, train loss-1.7049, acc-0.5400, valid loss-1.8051, acc-0.4998, test loss-1.8060, acc-0.4936
Iter-60550, train loss-1.8379, acc-0.4800, valid loss-1.8050, acc-0.4998, test loss-1.8059, acc-0.4938
Iter-60560, train loss-1.7892, acc-0.5400, valid loss-1.8050, acc-0.5000, test loss-1.8059, acc-0.4935
Iter-60570, train loss-1.8219, acc-0.5400, valid loss-1.8049, acc-0.4998, test loss-1.8058, acc-0.4936
Iter-60580, train loss-1.8283, acc-0.5600, valid loss-1.8049, acc-0.4998, test loss-1.8058, acc-0.4937
Iter-60590, train loss-1.7926, acc-0.4600, valid loss-1.8048, acc-0.4998, test loss-1.8057, acc-0.4937
Iter-60600, train loss-1.8882, acc-0.3600, valid loss-1.8048, acc-0.4998, test loss-1.8057, acc-0.4939
Iter-60610, train loss-1.7457, acc-0.5400, valid loss-1.8047, acc-0.4996, test loss-1.8056, acc-0.4939
Iter-60620, train loss-1.8519, acc-0.4200, valid loss-1.8047, acc-0.4998, test loss-1.8056, acc-0.4937
Iter-60630, train loss-1.8533, acc-0.4600, valid loss-1.8046, acc-0.4996, test loss-1.8055, acc-0.4937
Iter-60640, train loss-1.8054, acc-0.5200, valid loss-1.8046, acc-0.4998, test loss-1.8055, acc-0.4938
Iter-60650, train loss-1.8528, acc-0.4000, valid loss-1.8045, acc-0.5000, test loss-1.8054, acc-0.4938
Iter-60660, train loss-1.8164, acc-0.4600, valid loss-1.8045, acc-0.5000, test loss-1.8054, acc-0.4936
Iter-60670, train loss-1.7351, acc-0.5800, valid loss-1.8044, acc-0.5000, test loss-1.8053, acc-0.4938
Iter-60680, train loss-1.8075, acc-0.5000, valid loss-1.8044, acc-0.5000, test loss-1.8053, acc-0.4939
Iter-60690, train loss-1.8005, acc-0.4600, valid loss-1.8043, acc-0.5000, test loss-1.8052, acc-0.4939
Iter-60700, train loss-1.7905, acc-0.5600, valid loss-1.8043, acc-0.5000, test loss-1.8052, acc-0.4938
Iter-60710, train loss-1.8883, acc-0.4400, valid loss-1.8042, acc-0.5000, test loss-1.8051, acc-0.4939
Iter-60720, train loss-1.8320, acc-0.4800, valid loss-1.8042, acc-0.5000, test loss-1.8051, acc-0.4940
Iter-60730, train loss-1.8491, acc-0.5200, valid loss-1.8041, acc-0.5000, test loss-1.8050, acc-0.4939
Iter-60740, train loss-1.7808, acc-0.4800, valid loss-1.8041, acc-0.4998, test loss-1.8050, acc-0.4941
Iter-60750, train loss-1.8681, acc-0.3400, valid loss-1.8040, acc-0.4998, test loss-1.8049, acc-0.4941
Iter-60760, train loss-1.7895, acc-0.5800, valid loss-1.8040, acc-0.4998, test loss-1.8049, acc-0.4941
Iter-60770, train loss-1.8394, acc-0.5000, valid loss-1.8039, acc-0.5000, test loss-1.8049, acc-0.4939
Iter-60780, train loss-1.8994, acc-0.4600, valid loss-1.8039, acc-0.5000, test loss-1.8048, acc-0.4941
Iter-60790, train loss-1.6738, acc-0.6000, valid loss-1.8038, acc-0.5000, test loss-1.8048, acc-0.4940
Iter-60800, train loss-1.8087, acc-0.5400, valid loss-1.8038, acc-0.5002, test loss-1.8047, acc-0.4941
Iter-60810, train loss-1.8181, acc-0.5000, valid loss-1.8038, acc-0.5002, test loss-1.8047, acc-0.4943
Iter-60820, train loss-1.7969, acc-0.5000, valid loss-1.8037, acc-0.5000, test loss-1.8046, acc-0.4943
Iter-60830, train loss-1.7386, acc-0.5600, valid loss-1.8037, acc-0.5000, test loss-1.8046, acc-0.4944
Iter-60840, train loss-1.8146, acc-0.4400, valid loss-1.8036, acc-0.5000, test loss-1.8045, acc-0.4944
Iter-60850, train loss-1.8473, acc-0.4200, valid loss-1.8036, acc-0.5000, test loss-1.8045, acc-0.4945
Iter-60860, train loss-1.8135, acc-0.5400, valid loss-1.8035, acc-0.5000, test loss-1.8044, acc-0.4946
Iter-60870, train loss-1.7464, acc-0.5800, valid loss-1.8035, acc-0.5000, test loss-1.8044, acc-0.4945
Iter-60880, train loss-1.9134, acc-0.4000, valid loss-1.8034, acc-0.5000, test loss-1.8043, acc-0.4946
Iter-60890, train loss-1.8005, acc-0.5800, valid loss-1.8034, acc-0.5000, test loss-1.8043, acc-0.4946
Iter-60900, train loss-1.7269, acc-0.4800, valid loss-1.8033, acc-0.5000, test loss-1.8042, acc-0.4944
Iter-60910, train loss-1.8192, acc-0.5000, valid loss-1.8033, acc-0.5000, test loss-1.8042, acc-0.4946
Iter-60920, train loss-1.8911, acc-0.4800, valid loss-1.8032, acc-0.5000, test loss-1.8041, acc-0.4946
Iter-60930, train loss-1.7872, acc-0.4000, valid loss-1.8032, acc-0.5000, test loss-1.8041, acc-0.4946
Iter-60940, train loss-1.8190, acc-0.5600, valid loss-1.8031, acc-0.5000, test loss-1.8040, acc-0.4947
Iter-60950, train loss-1.8646, acc-0.4200, valid loss-1.8031, acc-0.5000, test loss-1.8040, acc-0.4947
Iter-60960, train loss-1.8338, acc-0.4400, valid loss-1.8030, acc-0.5000, test loss-1.8039, acc-0.4947
Iter-60970, train loss-1.8673, acc-0.3600, valid loss-1.8030, acc-0.4996, test loss-1.8039, acc-0.4948
Iter-60980, train loss-1.8441, acc-0.4400, valid loss-1.8030, acc-0.5000, test loss-1.8038, acc-0.4947
Iter-60990, train loss-1.8682, acc-0.4800, valid loss-1.8029, acc-0.4998, test loss-1.8038, acc-0.4948
Iter-61000, train loss-1.7980, acc-0.5200, valid loss-1.8029, acc-0.5000, test loss-1.8037, acc-0.4947
Iter-61010, train loss-1.7365, acc-0.6400, valid loss-1.8028, acc-0.4998, test loss-1.8037, acc-0.4947
Iter-61020, train loss-1.8626, acc-0.5200, valid loss-1.8028, acc-0.4996, test loss-1.8036, acc-0.4948
Iter-61030, train loss-1.9121, acc-0.4200, valid loss-1.8027, acc-0.4996, test loss-1.8036, acc-0.4948
Iter-61040, train loss-1.8902, acc-0.4600, valid loss-1.8026, acc-0.4996, test loss-1.8035, acc-0.4948
Iter-61050, train loss-1.8188, acc-0.4800, valid loss-1.8026, acc-0.4998, test loss-1.8035, acc-0.4948
Iter-61060, train loss-1.8374, acc-0.5200, valid loss-1.8026, acc-0.5000, test loss-1.8035, acc-0.4948
Iter-61070, train loss-1.7451, acc-0.6200, valid loss-1.8025, acc-0.5000, test loss-1.8034, acc-0.4949
Iter-61080, train loss-1.6720, acc-0.5600, valid loss-1.8025, acc-0.5000, test loss-1.8034, acc-0.4951
Iter-61090, train loss-1.8563, acc-0.4400, valid loss-1.8024, acc-0.4998, test loss-1.8033, acc-0.4950
Iter-61100, train loss-1.7298, acc-0.5000, valid loss-1.8024, acc-0.4998, test loss-1.8033, acc-0.4950
Iter-61110, train loss-1.7330, acc-0.6400, valid loss-1.8023, acc-0.4996, test loss-1.8032, acc-0.4949
Iter-61120, train loss-1.6692, acc-0.6800, valid loss-1.8023, acc-0.4998, test loss-1.8031, acc-0.4950
Iter-61130, train loss-1.8013, acc-0.5200, valid loss-1.8022, acc-0.4998, test loss-1.8031, acc-0.4949
Iter-61140, train loss-1.7364, acc-0.4600, valid loss-1.8022, acc-0.4998, test loss-1.8031, acc-0.4949
Iter-61150, train loss-1.8852, acc-0.4800, valid loss-1.8021, acc-0.4998, test loss-1.8030, acc-0.4951
Iter-61160, train loss-1.9751, acc-0.3200, valid loss-1.8021, acc-0.4996, test loss-1.8030, acc-0.4951
Iter-61170, train loss-1.8303, acc-0.5200, valid loss-1.8020, acc-0.4998, test loss-1.8029, acc-0.4951
Iter-61180, train loss-1.7484, acc-0.4400, valid loss-1.8020, acc-0.4996, test loss-1.8029, acc-0.4951
Iter-61190, train loss-1.7462, acc-0.6000, valid loss-1.8019, acc-0.4998, test loss-1.8028, acc-0.4952
Iter-61200, train loss-1.8508, acc-0.4600, valid loss-1.8019, acc-0.4998, test loss-1.8028, acc-0.4952
Iter-61210, train loss-1.8488, acc-0.5200, valid loss-1.8018, acc-0.4996, test loss-1.8027, acc-0.4952
Iter-61220, train loss-1.8008, acc-0.5400, valid loss-1.8018, acc-0.4994, test loss-1.8027, acc-0.4951
Iter-61230, train loss-1.8285, acc-0.5200, valid loss-1.8017, acc-0.4996, test loss-1.8026, acc-0.4951
Iter-61240, train loss-1.8036, acc-0.5400, valid loss-1.8017, acc-0.4994, test loss-1.8026, acc-0.4951
Iter-61250, train loss-1.8292, acc-0.4800, valid loss-1.8016, acc-0.4994, test loss-1.8025, acc-0.4949
Iter-61260, train loss-1.8941, acc-0.3200, valid loss-1.8016, acc-0.4992, test loss-1.8025, acc-0.4947
Iter-61270, train loss-1.7528, acc-0.5000, valid loss-1.8015, acc-0.4990, test loss-1.8024, acc-0.4948
Iter-61280, train loss-1.8714, acc-0.3600, valid loss-1.8015, acc-0.4992, test loss-1.8024, acc-0.4949
Iter-61290, train loss-1.7937, acc-0.4800, valid loss-1.8014, acc-0.4990, test loss-1.8023, acc-0.4948
Iter-61300, train loss-1.7343, acc-0.5200, valid loss-1.8014, acc-0.4992, test loss-1.8023, acc-0.4948
Iter-61310, train loss-1.8579, acc-0.4800, valid loss-1.8013, acc-0.4994, test loss-1.8022, acc-0.4951
Iter-61320, train loss-1.8302, acc-0.4000, valid loss-1.8013, acc-0.4994, test loss-1.8022, acc-0.4950
Iter-61330, train loss-1.8279, acc-0.4400, valid loss-1.8012, acc-0.4994, test loss-1.8021, acc-0.4949
Iter-61340, train loss-1.8851, acc-0.4400, valid loss-1.8012, acc-0.4992, test loss-1.8021, acc-0.4948
Iter-61350, train loss-1.7613, acc-0.6000, valid loss-1.8011, acc-0.4992, test loss-1.8020, acc-0.4950
Iter-61360, train loss-1.8813, acc-0.3800, valid loss-1.8011, acc-0.4992, test loss-1.8020, acc-0.4950
Iter-61370, train loss-1.7233, acc-0.6600, valid loss-1.8010, acc-0.4992, test loss-1.8019, acc-0.4953
Iter-61380, train loss-1.6633, acc-0.6800, valid loss-1.8010, acc-0.4992, test loss-1.8019, acc-0.4952
Iter-61390, train loss-1.7450, acc-0.5400, valid loss-1.8009, acc-0.4992, test loss-1.8018, acc-0.4949
Iter-61400, train loss-1.7534, acc-0.5800, valid loss-1.8009, acc-0.4992, test loss-1.8018, acc-0.4949
Iter-61410, train loss-1.8448, acc-0.5200, valid loss-1.8008, acc-0.4992, test loss-1.8017, acc-0.4950
Iter-61420, train loss-1.7778, acc-0.5200, valid loss-1.8008, acc-0.4992, test loss-1.8017, acc-0.4949
Iter-61430, train loss-1.7136, acc-0.5000, valid loss-1.8007, acc-0.4992, test loss-1.8016, acc-0.4949
Iter-61440, train loss-1.8543, acc-0.4000, valid loss-1.8007, acc-0.4990, test loss-1.8016, acc-0.4949
Iter-61450, train loss-1.7317, acc-0.5200, valid loss-1.8006, acc-0.4990, test loss-1.8015, acc-0.4949
Iter-61460, train loss-1.8002, acc-0.5000, valid loss-1.8006, acc-0.4994, test loss-1.8015, acc-0.4949
Iter-61470, train loss-1.7858, acc-0.4800, valid loss-1.8005, acc-0.5000, test loss-1.8014, acc-0.4946
Iter-61480, train loss-1.9002, acc-0.4000, valid loss-1.8005, acc-0.4998, test loss-1.8014, acc-0.4946
Iter-61490, train loss-1.8628, acc-0.5800, valid loss-1.8004, acc-0.4996, test loss-1.8013, acc-0.4947
Iter-61500, train loss-1.8693, acc-0.5600, valid loss-1.8004, acc-0.5000, test loss-1.8013, acc-0.4948
Iter-61510, train loss-1.8045, acc-0.4800, valid loss-1.8003, acc-0.5002, test loss-1.8012, acc-0.4947
Iter-61520, train loss-1.8426, acc-0.5000, valid loss-1.8003, acc-0.5002, test loss-1.8012, acc-0.4947
Iter-61530, train loss-1.6947, acc-0.6000, valid loss-1.8002, acc-0.5000, test loss-1.8011, acc-0.4947
Iter-61540, train loss-1.8804, acc-0.5000, valid loss-1.8002, acc-0.4998, test loss-1.8011, acc-0.4947
Iter-61550, train loss-1.8025, acc-0.4800, valid loss-1.8001, acc-0.4998, test loss-1.8010, acc-0.4947
Iter-61560, train loss-1.8290, acc-0.5200, valid loss-1.8001, acc-0.4998, test loss-1.8010, acc-0.4947
Iter-61570, train loss-1.7974, acc-0.4400, valid loss-1.8000, acc-0.5000, test loss-1.8009, acc-0.4946
Iter-61580, train loss-1.7912, acc-0.5200, valid loss-1.8000, acc-0.5004, test loss-1.8009, acc-0.4946
Iter-61590, train loss-1.7806, acc-0.5200, valid loss-1.7999, acc-0.5004, test loss-1.8008, acc-0.4946
Iter-61600, train loss-1.8636, acc-0.4200, valid loss-1.7999, acc-0.5004, test loss-1.8008, acc-0.4947
Iter-61610, train loss-1.8516, acc-0.4400, valid loss-1.7998, acc-0.5002, test loss-1.8007, acc-0.4949
Iter-61620, train loss-1.7695, acc-0.4800, valid loss-1.7998, acc-0.5002, test loss-1.8007, acc-0.4950
Iter-61630, train loss-1.8505, acc-0.4400, valid loss-1.7997, acc-0.5004, test loss-1.8007, acc-0.4951
Iter-61640, train loss-1.7892, acc-0.4200, valid loss-1.7997, acc-0.5004, test loss-1.8006, acc-0.4952
Iter-61650, train loss-1.7358, acc-0.5200, valid loss-1.7996, acc-0.5004, test loss-1.8006, acc-0.4953
Iter-61660, train loss-1.7034, acc-0.6600, valid loss-1.7996, acc-0.5006, test loss-1.8005, acc-0.4953
Iter-61670, train loss-1.8974, acc-0.4200, valid loss-1.7995, acc-0.5004, test loss-1.8005, acc-0.4954
Iter-61680, train loss-1.8057, acc-0.5600, valid loss-1.7995, acc-0.5006, test loss-1.8004, acc-0.4955
Iter-61690, train loss-1.7866, acc-0.5000, valid loss-1.7994, acc-0.5006, test loss-1.8004, acc-0.4955
Iter-61700, train loss-1.8503, acc-0.4200, valid loss-1.7994, acc-0.5006, test loss-1.8003, acc-0.4953
Iter-61710, train loss-1.9013, acc-0.4000, valid loss-1.7994, acc-0.5006, test loss-1.8003, acc-0.4954
Iter-61720, train loss-1.7941, acc-0.5200, valid loss-1.7993, acc-0.5008, test loss-1.8002, acc-0.4953
Iter-61730, train loss-1.9169, acc-0.3400, valid loss-1.7993, acc-0.5008, test loss-1.8002, acc-0.4953
Iter-61740, train loss-1.7298, acc-0.5200, valid loss-1.7992, acc-0.5008, test loss-1.8001, acc-0.4952
Iter-61750, train loss-1.7655, acc-0.5400, valid loss-1.7992, acc-0.5010, test loss-1.8001, acc-0.4952
Iter-61760, train loss-1.7717, acc-0.5200, valid loss-1.7991, acc-0.5010, test loss-1.8000, acc-0.4952
Iter-61770, train loss-1.8310, acc-0.4400, valid loss-1.7991, acc-0.5010, test loss-1.8000, acc-0.4953
Iter-61780, train loss-1.8022, acc-0.5600, valid loss-1.7990, acc-0.5010, test loss-1.7999, acc-0.4953
Iter-61790, train loss-1.8723, acc-0.5000, valid loss-1.7990, acc-0.5010, test loss-1.7999, acc-0.4956
Iter-61800, train loss-1.7844, acc-0.4800, valid loss-1.7989, acc-0.5010, test loss-1.7998, acc-0.4955
Iter-61810, train loss-1.7525, acc-0.4400, valid loss-1.7989, acc-0.5010, test loss-1.7998, acc-0.4954
Iter-61820, train loss-1.7788, acc-0.4400, valid loss-1.7988, acc-0.5010, test loss-1.7997, acc-0.4956
Iter-61830, train loss-1.8369, acc-0.4600, valid loss-1.7988, acc-0.5010, test loss-1.7997, acc-0.4955
Iter-61840, train loss-1.8909, acc-0.4400, valid loss-1.7987, acc-0.5010, test loss-1.7996, acc-0.4955
Iter-61850, train loss-1.8470, acc-0.4600, valid loss-1.7987, acc-0.5010, test loss-1.7996, acc-0.4955
Iter-61860, train loss-1.7604, acc-0.5200, valid loss-1.7986, acc-0.5010, test loss-1.7996, acc-0.4954
Iter-61870, train loss-1.6950, acc-0.5200, valid loss-1.7986, acc-0.5010, test loss-1.7995, acc-0.4955
Iter-61880, train loss-1.6863, acc-0.5800, valid loss-1.7985, acc-0.5008, test loss-1.7995, acc-0.4955
Iter-61890, train loss-1.8623, acc-0.4800, valid loss-1.7985, acc-0.5008, test loss-1.7994, acc-0.4955
Iter-61900, train loss-1.8353, acc-0.5600, valid loss-1.7985, acc-0.5008, test loss-1.7994, acc-0.4956
Iter-61910, train loss-1.8665, acc-0.4400, valid loss-1.7984, acc-0.5012, test loss-1.7993, acc-0.4957
Iter-61920, train loss-1.8248, acc-0.4400, valid loss-1.7984, acc-0.5012, test loss-1.7993, acc-0.4959
Iter-61930, train loss-1.7978, acc-0.5800, valid loss-1.7983, acc-0.5012, test loss-1.7992, acc-0.4960
Iter-61940, train loss-1.7328, acc-0.5200, valid loss-1.7983, acc-0.5012, test loss-1.7992, acc-0.4959
Iter-61950, train loss-1.8964, acc-0.4200, valid loss-1.7982, acc-0.5014, test loss-1.7991, acc-0.4960
Iter-61960, train loss-1.7948, acc-0.5600, valid loss-1.7982, acc-0.5012, test loss-1.7991, acc-0.4957
Iter-61970, train loss-1.7676, acc-0.6400, valid loss-1.7981, acc-0.5012, test loss-1.7990, acc-0.4958
Iter-61980, train loss-1.7386, acc-0.4200, valid loss-1.7981, acc-0.5012, test loss-1.7990, acc-0.4958
Iter-61990, train loss-1.8846, acc-0.5000, valid loss-1.7980, acc-0.5012, test loss-1.7989, acc-0.4958
Iter-62000, train loss-1.8057, acc-0.5000, valid loss-1.7980, acc-0.5012, test loss-1.7989, acc-0.4959
Iter-62010, train loss-1.7821, acc-0.5200, valid loss-1.7979, acc-0.5012, test loss-1.7988, acc-0.4960
Iter-62020, train loss-1.8302, acc-0.4800, valid loss-1.7979, acc-0.5012, test loss-1.7988, acc-0.4963
Iter-62030, train loss-1.8761, acc-0.5200, valid loss-1.7978, acc-0.5016, test loss-1.7987, acc-0.4960
Iter-62040, train loss-1.8204, acc-0.5200, valid loss-1.7978, acc-0.5016, test loss-1.7987, acc-0.4960
Iter-62050, train loss-1.8788, acc-0.3600, valid loss-1.7977, acc-0.5016, test loss-1.7986, acc-0.4962
Iter-62060, train loss-1.7750, acc-0.5000, valid loss-1.7977, acc-0.5016, test loss-1.7986, acc-0.4963
Iter-62070, train loss-1.7741, acc-0.5000, valid loss-1.7976, acc-0.5016, test loss-1.7986, acc-0.4963
Iter-62080, train loss-1.8448, acc-0.4600, valid loss-1.7976, acc-0.5014, test loss-1.7985, acc-0.4963
Iter-62090, train loss-1.8373, acc-0.5200, valid loss-1.7976, acc-0.5014, test loss-1.7985, acc-0.4960
Iter-62100, train loss-1.8041, acc-0.4800, valid loss-1.7975, acc-0.5014, test loss-1.7984, acc-0.4962
Iter-62110, train loss-1.8959, acc-0.3800, valid loss-1.7975, acc-0.5012, test loss-1.7984, acc-0.4960
Iter-62120, train loss-1.7512, acc-0.5000, valid loss-1.7974, acc-0.5014, test loss-1.7983, acc-0.4959
Iter-62130, train loss-1.8409, acc-0.4600, valid loss-1.7974, acc-0.5016, test loss-1.7983, acc-0.4961
Iter-62140, train loss-1.8517, acc-0.3800, valid loss-1.7973, acc-0.5016, test loss-1.7982, acc-0.4960
Iter-62150, train loss-1.8252, acc-0.4800, valid loss-1.7973, acc-0.5022, test loss-1.7982, acc-0.4960
Iter-62160, train loss-1.7983, acc-0.5400, valid loss-1.7972, acc-0.5018, test loss-1.7981, acc-0.4959
Iter-62170, train loss-1.7486, acc-0.5200, valid loss-1.7972, acc-0.5018, test loss-1.7981, acc-0.4960
Iter-62180, train loss-1.9020, acc-0.4800, valid loss-1.7971, acc-0.5016, test loss-1.7980, acc-0.4960
Iter-62190, train loss-1.7118, acc-0.4400, valid loss-1.7971, acc-0.5018, test loss-1.7980, acc-0.4963
Iter-62200, train loss-1.8081, acc-0.5000, valid loss-1.7970, acc-0.5016, test loss-1.7979, acc-0.4963
Iter-62210, train loss-1.8415, acc-0.4400, valid loss-1.7970, acc-0.5014, test loss-1.7979, acc-0.4962
Iter-62220, train loss-1.7320, acc-0.5200, valid loss-1.7969, acc-0.5018, test loss-1.7978, acc-0.4963
Iter-62230, train loss-1.8494, acc-0.4200, valid loss-1.7969, acc-0.5020, test loss-1.7978, acc-0.4963
Iter-62240, train loss-1.8603, acc-0.4800, valid loss-1.7968, acc-0.5016, test loss-1.7977, acc-0.4964
Iter-62250, train loss-1.7841, acc-0.5200, valid loss-1.7968, acc-0.5016, test loss-1.7977, acc-0.4964
Iter-62260, train loss-1.7642, acc-0.3800, valid loss-1.7967, acc-0.5016, test loss-1.7976, acc-0.4963
Iter-62270, train loss-1.8518, acc-0.4800, valid loss-1.7967, acc-0.5016, test loss-1.7976, acc-0.4964
Iter-62280, train loss-1.9136, acc-0.3200, valid loss-1.7966, acc-0.5018, test loss-1.7975, acc-0.4965
Iter-62290, train loss-1.8144, acc-0.5600, valid loss-1.7966, acc-0.5018, test loss-1.7975, acc-0.4964
Iter-62300, train loss-1.8480, acc-0.4200, valid loss-1.7965, acc-0.5020, test loss-1.7974, acc-0.4963
Iter-62310, train loss-1.8692, acc-0.4000, valid loss-1.7965, acc-0.5020, test loss-1.7974, acc-0.4962
Iter-62320, train loss-1.8777, acc-0.4400, valid loss-1.7964, acc-0.5016, test loss-1.7973, acc-0.4963
Iter-62330, train loss-1.8231, acc-0.4400, valid loss-1.7964, acc-0.5016, test loss-1.7973, acc-0.4963
Iter-62340, train loss-1.8093, acc-0.5400, valid loss-1.7964, acc-0.5016, test loss-1.7973, acc-0.4966
Iter-62350, train loss-1.9246, acc-0.3800, valid loss-1.7963, acc-0.5018, test loss-1.7972, acc-0.4965
Iter-62360, train loss-1.8544, acc-0.4200, valid loss-1.7963, acc-0.5018, test loss-1.7972, acc-0.4964
Iter-62370, train loss-1.8545, acc-0.4600, valid loss-1.7962, acc-0.5020, test loss-1.7971, acc-0.4964
Iter-62380, train loss-1.8252, acc-0.5000, valid loss-1.7962, acc-0.5022, test loss-1.7971, acc-0.4965
Iter-62390, train loss-1.8380, acc-0.5000, valid loss-1.7961, acc-0.5020, test loss-1.7970, acc-0.4964
Iter-62400, train loss-1.8287, acc-0.4800, valid loss-1.7961, acc-0.5018, test loss-1.7970, acc-0.4964
Iter-62410, train loss-1.9543, acc-0.3600, valid loss-1.7960, acc-0.5020, test loss-1.7969, acc-0.4965
Iter-62420, train loss-1.8270, acc-0.4400, valid loss-1.7960, acc-0.5022, test loss-1.7969, acc-0.4965
Iter-62430, train loss-1.6487, acc-0.6800, valid loss-1.7959, acc-0.5022, test loss-1.7968, acc-0.4964
Iter-62440, train loss-1.9055, acc-0.3400, valid loss-1.7959, acc-0.5022, test loss-1.7968, acc-0.4964
Iter-62450, train loss-1.7783, acc-0.5400, valid loss-1.7958, acc-0.5022, test loss-1.7967, acc-0.4965
Iter-62460, train loss-1.7878, acc-0.5400, valid loss-1.7958, acc-0.5022, test loss-1.7967, acc-0.4964
Iter-62470, train loss-1.8221, acc-0.5600, valid loss-1.7957, acc-0.5022, test loss-1.7966, acc-0.4965
Iter-62480, train loss-1.8589, acc-0.4000, valid loss-1.7957, acc-0.5020, test loss-1.7966, acc-0.4963
Iter-62490, train loss-1.8097, acc-0.4600, valid loss-1.7956, acc-0.5020, test loss-1.7965, acc-0.4963
Iter-62500, train loss-1.6886, acc-0.5400, valid loss-1.7956, acc-0.5020, test loss-1.7965, acc-0.4963
Iter-62510, train loss-1.8388, acc-0.4200, valid loss-1.7955, acc-0.5020, test loss-1.7964, acc-0.4963
Iter-62520, train loss-1.8450, acc-0.5400, valid loss-1.7955, acc-0.5022, test loss-1.7964, acc-0.4964
Iter-62530, train loss-1.8230, acc-0.3800, valid loss-1.7954, acc-0.5020, test loss-1.7963, acc-0.4963
Iter-62540, train loss-1.7904, acc-0.5200, valid loss-1.7954, acc-0.5022, test loss-1.7963, acc-0.4962
Iter-62550, train loss-1.8005, acc-0.5400, valid loss-1.7953, acc-0.5018, test loss-1.7963, acc-0.4961
Iter-62560, train loss-1.8124, acc-0.4800, valid loss-1.7953, acc-0.5020, test loss-1.7962, acc-0.4961
Iter-62570, train loss-1.7872, acc-0.4600, valid loss-1.7952, acc-0.5022, test loss-1.7962, acc-0.4965
Iter-62580, train loss-1.8004, acc-0.4400, valid loss-1.7952, acc-0.5024, test loss-1.7961, acc-0.4965
Iter-62590, train loss-1.8005, acc-0.4400, valid loss-1.7951, acc-0.5022, test loss-1.7961, acc-0.4965
Iter-62600, train loss-1.7696, acc-0.5800, valid loss-1.7951, acc-0.5024, test loss-1.7960, acc-0.4965
Iter-62610, train loss-1.8626, acc-0.4600, valid loss-1.7950, acc-0.5030, test loss-1.7960, acc-0.4964
Iter-62620, train loss-1.7198, acc-0.5600, valid loss-1.7950, acc-0.5026, test loss-1.7959, acc-0.4965
Iter-62630, train loss-1.8081, acc-0.5200, valid loss-1.7949, acc-0.5024, test loss-1.7959, acc-0.4964
Iter-62640, train loss-1.8828, acc-0.4000, valid loss-1.7949, acc-0.5024, test loss-1.7958, acc-0.4966
Iter-62650, train loss-1.7837, acc-0.4600, valid loss-1.7948, acc-0.5022, test loss-1.7958, acc-0.4964
Iter-62660, train loss-1.7721, acc-0.4800, valid loss-1.7948, acc-0.5024, test loss-1.7957, acc-0.4965
Iter-62670, train loss-1.9293, acc-0.4800, valid loss-1.7948, acc-0.5026, test loss-1.7957, acc-0.4965
Iter-62680, train loss-1.8501, acc-0.4600, valid loss-1.7947, acc-0.5026, test loss-1.7956, acc-0.4967
Iter-62690, train loss-1.8329, acc-0.4800, valid loss-1.7947, acc-0.5028, test loss-1.7956, acc-0.4965
Iter-62700, train loss-1.7768, acc-0.5400, valid loss-1.7946, acc-0.5026, test loss-1.7955, acc-0.4967
Iter-62710, train loss-1.8872, acc-0.4200, valid loss-1.7946, acc-0.5028, test loss-1.7955, acc-0.4968
Iter-62720, train loss-1.9108, acc-0.4000, valid loss-1.7945, acc-0.5030, test loss-1.7954, acc-0.4968
Iter-62730, train loss-1.7973, acc-0.5200, valid loss-1.7945, acc-0.5032, test loss-1.7954, acc-0.4968
Iter-62740, train loss-1.7988, acc-0.5400, valid loss-1.7944, acc-0.5032, test loss-1.7953, acc-0.4968
Iter-62750, train loss-1.7663, acc-0.5000, valid loss-1.7944, acc-0.5030, test loss-1.7953, acc-0.4968
Iter-62760, train loss-1.8175, acc-0.5600, valid loss-1.7943, acc-0.5030, test loss-1.7952, acc-0.4968
Iter-62770, train loss-1.6774, acc-0.5800, valid loss-1.7943, acc-0.5028, test loss-1.7952, acc-0.4968
Iter-62780, train loss-1.7355, acc-0.5600, valid loss-1.7942, acc-0.5030, test loss-1.7952, acc-0.4969
Iter-62790, train loss-1.8210, acc-0.5200, valid loss-1.7942, acc-0.5028, test loss-1.7951, acc-0.4968
Iter-62800, train loss-1.8367, acc-0.4800, valid loss-1.7941, acc-0.5030, test loss-1.7951, acc-0.4967
Iter-62810, train loss-1.8912, acc-0.3800, valid loss-1.7941, acc-0.5030, test loss-1.7950, acc-0.4968
Iter-62820, train loss-1.7537, acc-0.5800, valid loss-1.7940, acc-0.5030, test loss-1.7950, acc-0.4967
Iter-62830, train loss-1.7581, acc-0.5000, valid loss-1.7940, acc-0.5028, test loss-1.7949, acc-0.4967
Iter-62840, train loss-1.9693, acc-0.3600, valid loss-1.7940, acc-0.5030, test loss-1.7949, acc-0.4968
Iter-62850, train loss-1.7715, acc-0.5400, valid loss-1.7939, acc-0.5032, test loss-1.7948, acc-0.4969
Iter-62860, train loss-1.7814, acc-0.5000, valid loss-1.7939, acc-0.5034, test loss-1.7948, acc-0.4969
Iter-62870, train loss-1.6635, acc-0.6000, valid loss-1.7938, acc-0.5032, test loss-1.7947, acc-0.4969
Iter-62880, train loss-1.8293, acc-0.5800, valid loss-1.7938, acc-0.5034, test loss-1.7947, acc-0.4968
Iter-62890, train loss-1.8352, acc-0.4200, valid loss-1.7937, acc-0.5034, test loss-1.7946, acc-0.4967
Iter-62900, train loss-1.7910, acc-0.4800, valid loss-1.7937, acc-0.5034, test loss-1.7946, acc-0.4969
Iter-62910, train loss-1.8448, acc-0.4600, valid loss-1.7936, acc-0.5034, test loss-1.7945, acc-0.4968
Iter-62920, train loss-1.7712, acc-0.4200, valid loss-1.7936, acc-0.5032, test loss-1.7945, acc-0.4968
Iter-62930, train loss-1.8556, acc-0.4600, valid loss-1.7935, acc-0.5032, test loss-1.7944, acc-0.4969
Iter-62940, train loss-1.8439, acc-0.4600, valid loss-1.7935, acc-0.5032, test loss-1.7944, acc-0.4970
Iter-62950, train loss-1.7904, acc-0.5400, valid loss-1.7934, acc-0.5030, test loss-1.7943, acc-0.4970
Iter-62960, train loss-1.7610, acc-0.4400, valid loss-1.7934, acc-0.5030, test loss-1.7943, acc-0.4971
Iter-62970, train loss-1.7964, acc-0.4800, valid loss-1.7933, acc-0.5032, test loss-1.7942, acc-0.4970
Iter-62980, train loss-1.7541, acc-0.5800, valid loss-1.7933, acc-0.5030, test loss-1.7942, acc-0.4972
Iter-62990, train loss-1.7742, acc-0.5000, valid loss-1.7932, acc-0.5030, test loss-1.7941, acc-0.4969
Iter-63000, train loss-1.7735, acc-0.5600, valid loss-1.7932, acc-0.5032, test loss-1.7941, acc-0.4967
Iter-63010, train loss-1.7554, acc-0.5400, valid loss-1.7931, acc-0.5032, test loss-1.7940, acc-0.4969
Iter-63020, train loss-1.7125, acc-0.5200, valid loss-1.7931, acc-0.5032, test loss-1.7940, acc-0.4970
Iter-63030, train loss-1.6883, acc-0.5600, valid loss-1.7931, acc-0.5032, test loss-1.7939, acc-0.4972
Iter-63040, train loss-1.6571, acc-0.6600, valid loss-1.7930, acc-0.5030, test loss-1.7939, acc-0.4972
Iter-63050, train loss-1.8039, acc-0.4800, valid loss-1.7930, acc-0.5030, test loss-1.7939, acc-0.4973
Iter-63060, train loss-1.8175, acc-0.5600, valid loss-1.7929, acc-0.5030, test loss-1.7938, acc-0.4974
Iter-63070, train loss-1.8599, acc-0.3800, valid loss-1.7929, acc-0.5032, test loss-1.7938, acc-0.4975
Iter-63080, train loss-1.9520, acc-0.4000, valid loss-1.7928, acc-0.5034, test loss-1.7937, acc-0.4976
Iter-63090, train loss-1.7598, acc-0.5400, valid loss-1.7928, acc-0.5034, test loss-1.7937, acc-0.4977
Iter-63100, train loss-1.8393, acc-0.4800, valid loss-1.7927, acc-0.5038, test loss-1.7936, acc-0.4976
Iter-63110, train loss-1.8207, acc-0.5200, valid loss-1.7927, acc-0.5040, test loss-1.7936, acc-0.4976
Iter-63120, train loss-1.8809, acc-0.4200, valid loss-1.7926, acc-0.5038, test loss-1.7935, acc-0.4978
Iter-63130, train loss-1.9126, acc-0.3400, valid loss-1.7926, acc-0.5038, test loss-1.7935, acc-0.4976
Iter-63140, train loss-1.8049, acc-0.4600, valid loss-1.7925, acc-0.5038, test loss-1.7934, acc-0.4976
Iter-63150, train loss-1.7048, acc-0.5200, valid loss-1.7925, acc-0.5038, test loss-1.7934, acc-0.4975
Iter-63160, train loss-1.7224, acc-0.6600, valid loss-1.7924, acc-0.5038, test loss-1.7933, acc-0.4976
Iter-63170, train loss-1.8037, acc-0.5000, valid loss-1.7924, acc-0.5040, test loss-1.7933, acc-0.4976
Iter-63180, train loss-1.9315, acc-0.4000, valid loss-1.7923, acc-0.5038, test loss-1.7932, acc-0.4976
Iter-63190, train loss-1.7690, acc-0.4800, valid loss-1.7923, acc-0.5040, test loss-1.7932, acc-0.4977
Iter-63200, train loss-1.8409, acc-0.4800, valid loss-1.7922, acc-0.5040, test loss-1.7931, acc-0.4977
Iter-63210, train loss-1.8969, acc-0.4800, valid loss-1.7922, acc-0.5042, test loss-1.7931, acc-0.4976
Iter-63220, train loss-1.7450, acc-0.5600, valid loss-1.7922, acc-0.5042, test loss-1.7931, acc-0.4975
Iter-63230, train loss-1.8009, acc-0.5400, valid loss-1.7921, acc-0.5042, test loss-1.7930, acc-0.4975
Iter-63240, train loss-1.7858, acc-0.4800, valid loss-1.7921, acc-0.5042, test loss-1.7930, acc-0.4976
Iter-63250, train loss-1.8617, acc-0.4400, valid loss-1.7920, acc-0.5044, test loss-1.7929, acc-0.4976
Iter-63260, train loss-1.8012, acc-0.4800, valid loss-1.7920, acc-0.5044, test loss-1.7929, acc-0.4975
Iter-63270, train loss-1.6852, acc-0.6000, valid loss-1.7919, acc-0.5042, test loss-1.7928, acc-0.4975
Iter-63280, train loss-1.9196, acc-0.3200, valid loss-1.7919, acc-0.5042, test loss-1.7928, acc-0.4977
Iter-63290, train loss-1.7948, acc-0.5400, valid loss-1.7918, acc-0.5042, test loss-1.7927, acc-0.4974
Iter-63300, train loss-1.8330, acc-0.4600, valid loss-1.7918, acc-0.5042, test loss-1.7927, acc-0.4978
Iter-63310, train loss-1.7730, acc-0.4600, valid loss-1.7917, acc-0.5042, test loss-1.7926, acc-0.4980
Iter-63320, train loss-1.7421, acc-0.5600, valid loss-1.7917, acc-0.5042, test loss-1.7926, acc-0.4979
Iter-63330, train loss-1.7634, acc-0.5800, valid loss-1.7916, acc-0.5042, test loss-1.7925, acc-0.4981
Iter-63340, train loss-1.8305, acc-0.4600, valid loss-1.7916, acc-0.5042, test loss-1.7925, acc-0.4980
Iter-63350, train loss-1.7498, acc-0.5400, valid loss-1.7915, acc-0.5040, test loss-1.7924, acc-0.4983
Iter-63360, train loss-1.7737, acc-0.4400, valid loss-1.7915, acc-0.5040, test loss-1.7924, acc-0.4984
Iter-63370, train loss-1.8283, acc-0.3800, valid loss-1.7914, acc-0.5040, test loss-1.7923, acc-0.4983
Iter-63380, train loss-1.8491, acc-0.5000, valid loss-1.7914, acc-0.5040, test loss-1.7923, acc-0.4984
Iter-63390, train loss-1.8022, acc-0.5000, valid loss-1.7913, acc-0.5040, test loss-1.7923, acc-0.4983
Iter-63400, train loss-1.8335, acc-0.4400, valid loss-1.7913, acc-0.5040, test loss-1.7922, acc-0.4983
Iter-63410, train loss-1.7938, acc-0.5200, valid loss-1.7912, acc-0.5040, test loss-1.7922, acc-0.4982
Iter-63420, train loss-1.8061, acc-0.5400, valid loss-1.7912, acc-0.5042, test loss-1.7921, acc-0.4982
Iter-63430, train loss-1.7742, acc-0.6000, valid loss-1.7912, acc-0.5042, test loss-1.7921, acc-0.4981
Iter-63440, train loss-1.7115, acc-0.4600, valid loss-1.7911, acc-0.5042, test loss-1.7920, acc-0.4980
Iter-63450, train loss-1.8037, acc-0.4800, valid loss-1.7911, acc-0.5042, test loss-1.7920, acc-0.4980
Iter-63460, train loss-1.7672, acc-0.5600, valid loss-1.7910, acc-0.5042, test loss-1.7919, acc-0.4980
Iter-63470, train loss-1.8218, acc-0.4400, valid loss-1.7910, acc-0.5040, test loss-1.7919, acc-0.4980
Iter-63480, train loss-1.8427, acc-0.4200, valid loss-1.7909, acc-0.5042, test loss-1.7918, acc-0.4980
Iter-63490, train loss-1.7682, acc-0.5200, valid loss-1.7909, acc-0.5042, test loss-1.7918, acc-0.4979
Iter-63500, train loss-1.8567, acc-0.4400, valid loss-1.7908, acc-0.5044, test loss-1.7917, acc-0.4980
Iter-63510, train loss-1.8389, acc-0.4000, valid loss-1.7908, acc-0.5042, test loss-1.7917, acc-0.4981
Iter-63520, train loss-1.8685, acc-0.3800, valid loss-1.7907, acc-0.5042, test loss-1.7917, acc-0.4980
Iter-63530, train loss-1.7762, acc-0.5000, valid loss-1.7907, acc-0.5044, test loss-1.7916, acc-0.4981
Iter-63540, train loss-1.8586, acc-0.4600, valid loss-1.7906, acc-0.5046, test loss-1.7916, acc-0.4979
Iter-63550, train loss-1.8922, acc-0.5000, valid loss-1.7906, acc-0.5044, test loss-1.7915, acc-0.4981
Iter-63560, train loss-1.8257, acc-0.5000, valid loss-1.7905, acc-0.5042, test loss-1.7915, acc-0.4981
Iter-63570, train loss-1.8539, acc-0.5200, valid loss-1.7905, acc-0.5044, test loss-1.7914, acc-0.4983
Iter-63580, train loss-1.7174, acc-0.6400, valid loss-1.7905, acc-0.5042, test loss-1.7914, acc-0.4983
Iter-63590, train loss-1.7451, acc-0.6200, valid loss-1.7904, acc-0.5046, test loss-1.7913, acc-0.4984
Iter-63600, train loss-1.7711, acc-0.5800, valid loss-1.7904, acc-0.5046, test loss-1.7913, acc-0.4984
Iter-63610, train loss-1.7963, acc-0.4800, valid loss-1.7903, acc-0.5046, test loss-1.7912, acc-0.4984
Iter-63620, train loss-1.7491, acc-0.6000, valid loss-1.7903, acc-0.5042, test loss-1.7912, acc-0.4983
Iter-63630, train loss-1.8378, acc-0.4000, valid loss-1.7902, acc-0.5042, test loss-1.7911, acc-0.4984
Iter-63640, train loss-1.7989, acc-0.5000, valid loss-1.7902, acc-0.5040, test loss-1.7911, acc-0.4985
Iter-63650, train loss-1.7983, acc-0.4400, valid loss-1.7901, acc-0.5040, test loss-1.7910, acc-0.4982
Iter-63660, train loss-1.8694, acc-0.4600, valid loss-1.7901, acc-0.5040, test loss-1.7910, acc-0.4981
Iter-63670, train loss-1.8508, acc-0.4800, valid loss-1.7900, acc-0.5040, test loss-1.7910, acc-0.4982
Iter-63680, train loss-1.7602, acc-0.5800, valid loss-1.7900, acc-0.5040, test loss-1.7909, acc-0.4981
Iter-63690, train loss-1.8085, acc-0.4400, valid loss-1.7900, acc-0.5040, test loss-1.7909, acc-0.4981
Iter-63700, train loss-1.7917, acc-0.5000, valid loss-1.7899, acc-0.5044, test loss-1.7908, acc-0.4982
Iter-63710, train loss-1.7896, acc-0.5000, valid loss-1.7899, acc-0.5042, test loss-1.7908, acc-0.4983
Iter-63720, train loss-1.7289, acc-0.4600, valid loss-1.7898, acc-0.5044, test loss-1.7907, acc-0.4983
Iter-63730, train loss-1.7317, acc-0.5200, valid loss-1.7898, acc-0.5040, test loss-1.7907, acc-0.4983
Iter-63740, train loss-1.8325, acc-0.4600, valid loss-1.7897, acc-0.5044, test loss-1.7906, acc-0.4985
Iter-63750, train loss-1.8345, acc-0.4200, valid loss-1.7897, acc-0.5042, test loss-1.7906, acc-0.4984
Iter-63760, train loss-1.7801, acc-0.4600, valid loss-1.7896, acc-0.5042, test loss-1.7905, acc-0.4984
Iter-63770, train loss-1.8069, acc-0.5000, valid loss-1.7896, acc-0.5044, test loss-1.7905, acc-0.4984
Iter-63780, train loss-1.6966, acc-0.5400, valid loss-1.7895, acc-0.5044, test loss-1.7904, acc-0.4985
Iter-63790, train loss-1.7374, acc-0.5200, valid loss-1.7895, acc-0.5046, test loss-1.7904, acc-0.4986
Iter-63800, train loss-1.8407, acc-0.4800, valid loss-1.7894, acc-0.5046, test loss-1.7903, acc-0.4985
Iter-63810, train loss-1.8080, acc-0.5000, valid loss-1.7894, acc-0.5046, test loss-1.7903, acc-0.4988
Iter-63820, train loss-1.8023, acc-0.4400, valid loss-1.7893, acc-0.5046, test loss-1.7902, acc-0.4987
Iter-63830, train loss-1.8655, acc-0.4600, valid loss-1.7893, acc-0.5046, test loss-1.7902, acc-0.4987
Iter-63840, train loss-1.7483, acc-0.5800, valid loss-1.7893, acc-0.5048, test loss-1.7901, acc-0.4985
Iter-63850, train loss-1.8931, acc-0.3800, valid loss-1.7892, acc-0.5048, test loss-1.7901, acc-0.4987
Iter-63860, train loss-1.7588, acc-0.5400, valid loss-1.7892, acc-0.5048, test loss-1.7901, acc-0.4986
Iter-63870, train loss-1.8250, acc-0.4800, valid loss-1.7891, acc-0.5048, test loss-1.7900, acc-0.4983
Iter-63880, train loss-1.7660, acc-0.5000, valid loss-1.7891, acc-0.5046, test loss-1.7900, acc-0.4981
Iter-63890, train loss-1.8367, acc-0.4600, valid loss-1.7890, acc-0.5046, test loss-1.7899, acc-0.4985
Iter-63900, train loss-1.7419, acc-0.5600, valid loss-1.7890, acc-0.5044, test loss-1.7899, acc-0.4984
Iter-63910, train loss-1.7507, acc-0.5600, valid loss-1.7889, acc-0.5044, test loss-1.7898, acc-0.4986
Iter-63920, train loss-1.8257, acc-0.4400, valid loss-1.7889, acc-0.5044, test loss-1.7898, acc-0.4985
Iter-63930, train loss-1.7342, acc-0.5200, valid loss-1.7889, acc-0.5044, test loss-1.7897, acc-0.4985
Iter-63940, train loss-1.9309, acc-0.4000, valid loss-1.7888, acc-0.5046, test loss-1.7897, acc-0.4984
Iter-63950, train loss-1.9297, acc-0.3800, valid loss-1.7888, acc-0.5046, test loss-1.7896, acc-0.4985
Iter-63960, train loss-1.7574, acc-0.4800, valid loss-1.7887, acc-0.5046, test loss-1.7896, acc-0.4986
Iter-63970, train loss-1.8631, acc-0.4000, valid loss-1.7887, acc-0.5046, test loss-1.7895, acc-0.4985
Iter-63980, train loss-1.8580, acc-0.4000, valid loss-1.7886, acc-0.5046, test loss-1.7895, acc-0.4984
Iter-63990, train loss-1.8851, acc-0.3600, valid loss-1.7886, acc-0.5046, test loss-1.7895, acc-0.4985
Iter-64000, train loss-1.8029, acc-0.6000, valid loss-1.7885, acc-0.5046, test loss-1.7894, acc-0.4984
Iter-64010, train loss-1.9035, acc-0.4600, valid loss-1.7885, acc-0.5046, test loss-1.7894, acc-0.4984
Iter-64020, train loss-1.8071, acc-0.5200, valid loss-1.7884, acc-0.5044, test loss-1.7893, acc-0.4985
Iter-64030, train loss-1.7146, acc-0.5800, valid loss-1.7884, acc-0.5046, test loss-1.7893, acc-0.4985
Iter-64040, train loss-1.8435, acc-0.5400, valid loss-1.7884, acc-0.5046, test loss-1.7892, acc-0.4986
Iter-64050, train loss-1.7218, acc-0.5000, valid loss-1.7883, acc-0.5046, test loss-1.7892, acc-0.4987
Iter-64060, train loss-1.6887, acc-0.5600, valid loss-1.7883, acc-0.5048, test loss-1.7891, acc-0.4987
Iter-64070, train loss-1.7779, acc-0.4600, valid loss-1.7882, acc-0.5048, test loss-1.7891, acc-0.4987
Iter-64080, train loss-1.8802, acc-0.4400, valid loss-1.7882, acc-0.5048, test loss-1.7890, acc-0.4986
Iter-64090, train loss-1.7492, acc-0.5200, valid loss-1.7881, acc-0.5048, test loss-1.7890, acc-0.4986
Iter-64100, train loss-1.9264, acc-0.3400, valid loss-1.7881, acc-0.5046, test loss-1.7889, acc-0.4987
Iter-64110, train loss-1.7288, acc-0.5400, valid loss-1.7880, acc-0.5046, test loss-1.7889, acc-0.4985
Iter-64120, train loss-1.8235, acc-0.5200, valid loss-1.7880, acc-0.5046, test loss-1.7888, acc-0.4988
Iter-64130, train loss-1.8386, acc-0.4800, valid loss-1.7879, acc-0.5046, test loss-1.7888, acc-0.4989
Iter-64140, train loss-1.8146, acc-0.5600, valid loss-1.7879, acc-0.5044, test loss-1.7887, acc-0.4989
Iter-64150, train loss-1.8691, acc-0.4200, valid loss-1.7878, acc-0.5044, test loss-1.7887, acc-0.4989
Iter-64160, train loss-1.7857, acc-0.5400, valid loss-1.7878, acc-0.5048, test loss-1.7887, acc-0.4991
Iter-64170, train loss-1.6930, acc-0.5600, valid loss-1.7877, acc-0.5046, test loss-1.7886, acc-0.4989
Iter-64180, train loss-1.8683, acc-0.3400, valid loss-1.7877, acc-0.5048, test loss-1.7886, acc-0.4989
Iter-64190, train loss-1.7537, acc-0.4800, valid loss-1.7876, acc-0.5048, test loss-1.7885, acc-0.4989
Iter-64200, train loss-1.8284, acc-0.5200, valid loss-1.7876, acc-0.5048, test loss-1.7885, acc-0.4989
Iter-64210, train loss-1.8188, acc-0.5400, valid loss-1.7876, acc-0.5048, test loss-1.7884, acc-0.4989
Iter-64220, train loss-1.7780, acc-0.5000, valid loss-1.7875, acc-0.5044, test loss-1.7884, acc-0.4987
Iter-64230, train loss-1.8297, acc-0.4400, valid loss-1.7875, acc-0.5046, test loss-1.7883, acc-0.4988
Iter-64240, train loss-1.8217, acc-0.4400, valid loss-1.7874, acc-0.5046, test loss-1.7883, acc-0.4987
Iter-64250, train loss-1.7664, acc-0.5600, valid loss-1.7874, acc-0.5046, test loss-1.7882, acc-0.4987
Iter-64260, train loss-1.7686, acc-0.5400, valid loss-1.7873, acc-0.5046, test loss-1.7882, acc-0.4990
Iter-64270, train loss-1.8186, acc-0.5600, valid loss-1.7873, acc-0.5046, test loss-1.7881, acc-0.4988
Iter-64280, train loss-1.6710, acc-0.6400, valid loss-1.7872, acc-0.5046, test loss-1.7881, acc-0.4988
Iter-64290, train loss-1.8629, acc-0.4600, valid loss-1.7872, acc-0.5046, test loss-1.7881, acc-0.4988
Iter-64300, train loss-1.7423, acc-0.5000, valid loss-1.7871, acc-0.5046, test loss-1.7880, acc-0.4989
Iter-64310, train loss-1.8817, acc-0.4200, valid loss-1.7871, acc-0.5046, test loss-1.7880, acc-0.4991
Iter-64320, train loss-1.7624, acc-0.4800, valid loss-1.7870, acc-0.5046, test loss-1.7879, acc-0.4988
Iter-64330, train loss-1.8554, acc-0.4400, valid loss-1.7870, acc-0.5048, test loss-1.7879, acc-0.4990
Iter-64340, train loss-1.7797, acc-0.4800, valid loss-1.7870, acc-0.5050, test loss-1.7878, acc-0.4992
Iter-64350, train loss-1.7959, acc-0.5000, valid loss-1.7869, acc-0.5052, test loss-1.7878, acc-0.4992
Iter-64360, train loss-1.9275, acc-0.3800, valid loss-1.7869, acc-0.5052, test loss-1.7877, acc-0.4990
Iter-64370, train loss-1.7101, acc-0.5600, valid loss-1.7868, acc-0.5052, test loss-1.7877, acc-0.4990
Iter-64380, train loss-1.6939, acc-0.6400, valid loss-1.7868, acc-0.5052, test loss-1.7876, acc-0.4991
Iter-64390, train loss-1.8422, acc-0.4600, valid loss-1.7867, acc-0.5050, test loss-1.7876, acc-0.4990
Iter-64400, train loss-1.7264, acc-0.4800, valid loss-1.7867, acc-0.5054, test loss-1.7875, acc-0.4991
Iter-64410, train loss-1.7963, acc-0.4400, valid loss-1.7866, acc-0.5052, test loss-1.7875, acc-0.4989
Iter-64420, train loss-1.8030, acc-0.4800, valid loss-1.7866, acc-0.5054, test loss-1.7875, acc-0.4992
Iter-64430, train loss-1.7913, acc-0.4600, valid loss-1.7865, acc-0.5056, test loss-1.7874, acc-0.4991
Iter-64440, train loss-1.8720, acc-0.4200, valid loss-1.7865, acc-0.5056, test loss-1.7874, acc-0.4991
Iter-64450, train loss-1.8362, acc-0.4400, valid loss-1.7865, acc-0.5056, test loss-1.7873, acc-0.4991
Iter-64460, train loss-1.7722, acc-0.5400, valid loss-1.7864, acc-0.5056, test loss-1.7873, acc-0.4990
Iter-64470, train loss-1.8147, acc-0.5400, valid loss-1.7864, acc-0.5054, test loss-1.7872, acc-0.4991
Iter-64480, train loss-1.8209, acc-0.5000, valid loss-1.7863, acc-0.5052, test loss-1.7872, acc-0.4990
Iter-64490, train loss-1.7855, acc-0.5000, valid loss-1.7863, acc-0.5052, test loss-1.7871, acc-0.4990
Iter-64500, train loss-1.7506, acc-0.6200, valid loss-1.7862, acc-0.5050, test loss-1.7871, acc-0.4991
Iter-64510, train loss-1.7645, acc-0.6000, valid loss-1.7862, acc-0.5050, test loss-1.7870, acc-0.4990
Iter-64520, train loss-1.8068, acc-0.4400, valid loss-1.7861, acc-0.5052, test loss-1.7870, acc-0.4989
Iter-64530, train loss-1.8008, acc-0.4800, valid loss-1.7861, acc-0.5050, test loss-1.7869, acc-0.4990
Iter-64540, train loss-1.7325, acc-0.6200, valid loss-1.7860, acc-0.5050, test loss-1.7869, acc-0.4991
Iter-64550, train loss-1.8365, acc-0.4200, valid loss-1.7860, acc-0.5052, test loss-1.7869, acc-0.4990
Iter-64560, train loss-1.7703, acc-0.4800, valid loss-1.7859, acc-0.5050, test loss-1.7868, acc-0.4990
Iter-64570, train loss-1.8288, acc-0.5000, valid loss-1.7859, acc-0.5050, test loss-1.7868, acc-0.4991
Iter-64580, train loss-1.7377, acc-0.5400, valid loss-1.7858, acc-0.5050, test loss-1.7867, acc-0.4992
Iter-64590, train loss-1.7004, acc-0.6000, valid loss-1.7858, acc-0.5050, test loss-1.7867, acc-0.4990
Iter-64600, train loss-1.7884, acc-0.4200, valid loss-1.7858, acc-0.5050, test loss-1.7866, acc-0.4991
Iter-64610, train loss-1.8060, acc-0.5000, valid loss-1.7857, acc-0.5050, test loss-1.7866, acc-0.4991
Iter-64620, train loss-1.7002, acc-0.4800, valid loss-1.7857, acc-0.5050, test loss-1.7865, acc-0.4992
Iter-64630, train loss-1.8582, acc-0.4800, valid loss-1.7856, acc-0.5050, test loss-1.7865, acc-0.4991
Iter-64640, train loss-1.6928, acc-0.6600, valid loss-1.7856, acc-0.5048, test loss-1.7864, acc-0.4991
Iter-64650, train loss-1.7823, acc-0.5200, valid loss-1.7855, acc-0.5046, test loss-1.7864, acc-0.4992
Iter-64660, train loss-1.8074, acc-0.5200, valid loss-1.7855, acc-0.5048, test loss-1.7863, acc-0.4992
Iter-64670, train loss-1.7315, acc-0.6400, valid loss-1.7854, acc-0.5048, test loss-1.7863, acc-0.4992
Iter-64680, train loss-1.8446, acc-0.4800, valid loss-1.7854, acc-0.5048, test loss-1.7862, acc-0.4992
Iter-64690, train loss-1.7660, acc-0.6000, valid loss-1.7853, acc-0.5048, test loss-1.7862, acc-0.4992
Iter-64700, train loss-1.8238, acc-0.4800, valid loss-1.7853, acc-0.5050, test loss-1.7862, acc-0.4992
Iter-64710, train loss-1.8354, acc-0.4200, valid loss-1.7853, acc-0.5050, test loss-1.7861, acc-0.4992
Iter-64720, train loss-1.8080, acc-0.5000, valid loss-1.7852, acc-0.5050, test loss-1.7861, acc-0.4990
Iter-64730, train loss-1.7207, acc-0.5800, valid loss-1.7852, acc-0.5050, test loss-1.7860, acc-0.4989
Iter-64740, train loss-1.9128, acc-0.4200, valid loss-1.7851, acc-0.5056, test loss-1.7860, acc-0.4989
Iter-64750, train loss-1.7947, acc-0.4800, valid loss-1.7851, acc-0.5054, test loss-1.7859, acc-0.4989
Iter-64760, train loss-1.7906, acc-0.5000, valid loss-1.7850, acc-0.5060, test loss-1.7859, acc-0.4991
Iter-64770, train loss-1.8344, acc-0.4400, valid loss-1.7850, acc-0.5058, test loss-1.7858, acc-0.4992
Iter-64780, train loss-1.7024, acc-0.4200, valid loss-1.7849, acc-0.5058, test loss-1.7858, acc-0.4992
Iter-64790, train loss-1.7038, acc-0.5200, valid loss-1.7849, acc-0.5058, test loss-1.7857, acc-0.4993
Iter-64800, train loss-1.7745, acc-0.5000, valid loss-1.7848, acc-0.5058, test loss-1.7857, acc-0.4993
Iter-64810, train loss-1.7053, acc-0.7200, valid loss-1.7848, acc-0.5060, test loss-1.7856, acc-0.4994
Iter-64820, train loss-1.8136, acc-0.4800, valid loss-1.7848, acc-0.5062, test loss-1.7856, acc-0.4993
Iter-64830, train loss-1.7757, acc-0.5400, valid loss-1.7847, acc-0.5060, test loss-1.7856, acc-0.4992
Iter-64840, train loss-1.8926, acc-0.3800, valid loss-1.7847, acc-0.5058, test loss-1.7855, acc-0.4992
Iter-64850, train loss-1.7252, acc-0.4400, valid loss-1.7846, acc-0.5060, test loss-1.7855, acc-0.4993
Iter-64860, train loss-1.8167, acc-0.4400, valid loss-1.7846, acc-0.5062, test loss-1.7854, acc-0.4994
Iter-64870, train loss-1.8129, acc-0.4200, valid loss-1.7845, acc-0.5062, test loss-1.7854, acc-0.4992
Iter-64880, train loss-1.7878, acc-0.5000, valid loss-1.7845, acc-0.5062, test loss-1.7853, acc-0.4991
Iter-64890, train loss-1.8738, acc-0.4600, valid loss-1.7844, acc-0.5064, test loss-1.7853, acc-0.4993
Iter-64900, train loss-1.8629, acc-0.3600, valid loss-1.7844, acc-0.5064, test loss-1.7852, acc-0.4992
Iter-64910, train loss-1.8094, acc-0.5200, valid loss-1.7843, acc-0.5060, test loss-1.7852, acc-0.4992
Iter-64920, train loss-1.8601, acc-0.4800, valid loss-1.7843, acc-0.5062, test loss-1.7851, acc-0.4992
Iter-64930, train loss-1.9109, acc-0.3200, valid loss-1.7843, acc-0.5064, test loss-1.7851, acc-0.4992
Iter-64940, train loss-1.8677, acc-0.5200, valid loss-1.7842, acc-0.5068, test loss-1.7850, acc-0.4993
Iter-64950, train loss-1.8604, acc-0.4800, valid loss-1.7842, acc-0.5068, test loss-1.7850, acc-0.4992
Iter-64960, train loss-1.7571, acc-0.7000, valid loss-1.7841, acc-0.5062, test loss-1.7849, acc-0.4994
Iter-64970, train loss-1.8029, acc-0.4600, valid loss-1.7841, acc-0.5064, test loss-1.7849, acc-0.4991
Iter-64980, train loss-1.7643, acc-0.4600, valid loss-1.7840, acc-0.5064, test loss-1.7849, acc-0.4992
Iter-64990, train loss-1.7920, acc-0.4000, valid loss-1.7840, acc-0.5066, test loss-1.7848, acc-0.4992
Iter-65000, train loss-1.7467, acc-0.5600, valid loss-1.7839, acc-0.5064, test loss-1.7848, acc-0.4991
Iter-65010, train loss-1.8465, acc-0.5000, valid loss-1.7839, acc-0.5064, test loss-1.7847, acc-0.4993
Iter-65020, train loss-1.7882, acc-0.4200, valid loss-1.7838, acc-0.5066, test loss-1.7847, acc-0.4992
Iter-65030, train loss-1.7192, acc-0.5400, valid loss-1.7838, acc-0.5066, test loss-1.7846, acc-0.4992
Iter-65040, train loss-1.7669, acc-0.5200, valid loss-1.7838, acc-0.5068, test loss-1.7846, acc-0.4993
Iter-65050, train loss-1.8696, acc-0.4400, valid loss-1.7837, acc-0.5068, test loss-1.7845, acc-0.4992
Iter-65060, train loss-1.8145, acc-0.4000, valid loss-1.7837, acc-0.5068, test loss-1.7845, acc-0.4991
Iter-65070, train loss-1.7867, acc-0.5600, valid loss-1.7836, acc-0.5066, test loss-1.7844, acc-0.4994
Iter-65080, train loss-1.7957, acc-0.5000, valid loss-1.7836, acc-0.5070, test loss-1.7844, acc-0.4993
Iter-65090, train loss-1.7930, acc-0.4800, valid loss-1.7835, acc-0.5072, test loss-1.7843, acc-0.4994
Iter-65100, train loss-1.8851, acc-0.3800, valid loss-1.7835, acc-0.5072, test loss-1.7843, acc-0.4993
Iter-65110, train loss-1.7712, acc-0.5600, valid loss-1.7834, acc-0.5072, test loss-1.7843, acc-0.4992
Iter-65120, train loss-1.6888, acc-0.6000, valid loss-1.7834, acc-0.5068, test loss-1.7842, acc-0.4991
Iter-65130, train loss-1.7757, acc-0.4800, valid loss-1.7833, acc-0.5068, test loss-1.7842, acc-0.4991
Iter-65140, train loss-1.8536, acc-0.4600, valid loss-1.7833, acc-0.5072, test loss-1.7841, acc-0.4993
Iter-65150, train loss-1.8120, acc-0.4800, valid loss-1.7832, acc-0.5070, test loss-1.7841, acc-0.4992
Iter-65160, train loss-1.7575, acc-0.5600, valid loss-1.7832, acc-0.5072, test loss-1.7840, acc-0.4993
Iter-65170, train loss-1.8449, acc-0.5000, valid loss-1.7832, acc-0.5072, test loss-1.7840, acc-0.4993
Iter-65180, train loss-1.7553, acc-0.5800, valid loss-1.7831, acc-0.5072, test loss-1.7839, acc-0.4994
Iter-65190, train loss-1.7894, acc-0.4800, valid loss-1.7831, acc-0.5072, test loss-1.7839, acc-0.4993
Iter-65200, train loss-1.7553, acc-0.5800, valid loss-1.7830, acc-0.5072, test loss-1.7838, acc-0.4993
Iter-65210, train loss-1.6890, acc-0.5200, valid loss-1.7830, acc-0.5072, test loss-1.7838, acc-0.4993
Iter-65220, train loss-1.6939, acc-0.5600, valid loss-1.7829, acc-0.5072, test loss-1.7838, acc-0.4994
Iter-65230, train loss-1.8340, acc-0.4600, valid loss-1.7829, acc-0.5072, test loss-1.7837, acc-0.4993
Iter-65240, train loss-1.8227, acc-0.5200, valid loss-1.7828, acc-0.5072, test loss-1.7837, acc-0.4993
Iter-65250, train loss-1.6959, acc-0.5600, valid loss-1.7828, acc-0.5072, test loss-1.7836, acc-0.4993
Iter-65260, train loss-1.7476, acc-0.5200, valid loss-1.7827, acc-0.5072, test loss-1.7836, acc-0.4992
Iter-65270, train loss-1.8517, acc-0.4400, valid loss-1.7827, acc-0.5072, test loss-1.7835, acc-0.4992
Iter-65280, train loss-1.7874, acc-0.5600, valid loss-1.7826, acc-0.5072, test loss-1.7835, acc-0.4993
Iter-65290, train loss-1.7809, acc-0.4200, valid loss-1.7826, acc-0.5072, test loss-1.7834, acc-0.4993
Iter-65300, train loss-1.7775, acc-0.4600, valid loss-1.7826, acc-0.5072, test loss-1.7834, acc-0.4994
Iter-65310, train loss-1.8178, acc-0.5600, valid loss-1.7825, acc-0.5072, test loss-1.7833, acc-0.4994
Iter-65320, train loss-1.8152, acc-0.4200, valid loss-1.7825, acc-0.5072, test loss-1.7833, acc-0.4993
Iter-65330, train loss-1.7975, acc-0.5000, valid loss-1.7824, acc-0.5070, test loss-1.7833, acc-0.4994
Iter-65340, train loss-1.8282, acc-0.5200, valid loss-1.7824, acc-0.5070, test loss-1.7832, acc-0.4995
Iter-65350, train loss-1.7510, acc-0.5000, valid loss-1.7823, acc-0.5072, test loss-1.7832, acc-0.4995
Iter-65360, train loss-1.7759, acc-0.6000, valid loss-1.7823, acc-0.5072, test loss-1.7831, acc-0.4995
Iter-65370, train loss-1.7880, acc-0.4200, valid loss-1.7822, acc-0.5072, test loss-1.7831, acc-0.4993
Iter-65380, train loss-1.8527, acc-0.4800, valid loss-1.7822, acc-0.5072, test loss-1.7830, acc-0.4993
Iter-65390, train loss-1.8219, acc-0.6200, valid loss-1.7821, acc-0.5070, test loss-1.7830, acc-0.4994
Iter-65400, train loss-1.8197, acc-0.4200, valid loss-1.7821, acc-0.5072, test loss-1.7829, acc-0.4994
Iter-65410, train loss-1.7201, acc-0.5400, valid loss-1.7821, acc-0.5068, test loss-1.7829, acc-0.4994
Iter-65420, train loss-1.7616, acc-0.5400, valid loss-1.7820, acc-0.5068, test loss-1.7828, acc-0.4995
Iter-65430, train loss-1.6881, acc-0.5600, valid loss-1.7820, acc-0.5072, test loss-1.7828, acc-0.4995
Iter-65440, train loss-1.6826, acc-0.6000, valid loss-1.7819, acc-0.5068, test loss-1.7827, acc-0.4996
Iter-65450, train loss-1.7470, acc-0.5600, valid loss-1.7819, acc-0.5068, test loss-1.7827, acc-0.4993
Iter-65460, train loss-1.7455, acc-0.5600, valid loss-1.7818, acc-0.5074, test loss-1.7827, acc-0.4994
Iter-65470, train loss-1.8298, acc-0.4600, valid loss-1.7818, acc-0.5072, test loss-1.7826, acc-0.4992
Iter-65480, train loss-1.8957, acc-0.4000, valid loss-1.7817, acc-0.5072, test loss-1.7826, acc-0.4992
Iter-65490, train loss-1.7982, acc-0.5200, valid loss-1.7817, acc-0.5072, test loss-1.7825, acc-0.4995
Iter-65500, train loss-1.7583, acc-0.5600, valid loss-1.7816, acc-0.5072, test loss-1.7825, acc-0.4995
Iter-65510, train loss-1.8248, acc-0.4400, valid loss-1.7816, acc-0.5072, test loss-1.7824, acc-0.4996
Iter-65520, train loss-1.8444, acc-0.4200, valid loss-1.7816, acc-0.5072, test loss-1.7824, acc-0.4996
Iter-65530, train loss-1.7154, acc-0.5200, valid loss-1.7815, acc-0.5074, test loss-1.7823, acc-0.4996
Iter-65540, train loss-1.8110, acc-0.4600, valid loss-1.7815, acc-0.5074, test loss-1.7823, acc-0.4997
Iter-65550, train loss-1.7231, acc-0.5000, valid loss-1.7814, acc-0.5072, test loss-1.7822, acc-0.4994
Iter-65560, train loss-1.7820, acc-0.5200, valid loss-1.7814, acc-0.5072, test loss-1.7822, acc-0.4993
Iter-65570, train loss-1.8761, acc-0.3400, valid loss-1.7813, acc-0.5072, test loss-1.7821, acc-0.4994
Iter-65580, train loss-1.8457, acc-0.5600, valid loss-1.7813, acc-0.5074, test loss-1.7821, acc-0.4994
Iter-65590, train loss-1.7402, acc-0.5800, valid loss-1.7812, acc-0.5074, test loss-1.7821, acc-0.4994
Iter-65600, train loss-1.7004, acc-0.5600, valid loss-1.7812, acc-0.5074, test loss-1.7820, acc-0.4994
Iter-65610, train loss-1.8114, acc-0.6000, valid loss-1.7812, acc-0.5076, test loss-1.7820, acc-0.4996
Iter-65620, train loss-1.9572, acc-0.3000, valid loss-1.7811, acc-0.5074, test loss-1.7819, acc-0.4995
Iter-65630, train loss-1.7661, acc-0.4600, valid loss-1.7811, acc-0.5074, test loss-1.7819, acc-0.4995
Iter-65640, train loss-1.8221, acc-0.4800, valid loss-1.7810, acc-0.5074, test loss-1.7818, acc-0.4994
Iter-65650, train loss-1.8865, acc-0.4000, valid loss-1.7810, acc-0.5074, test loss-1.7818, acc-0.4994
Iter-65660, train loss-1.9474, acc-0.3600, valid loss-1.7809, acc-0.5074, test loss-1.7817, acc-0.4994
Iter-65670, train loss-1.6968, acc-0.6400, valid loss-1.7809, acc-0.5074, test loss-1.7817, acc-0.4993
Iter-65680, train loss-1.8817, acc-0.4200, valid loss-1.7808, acc-0.5076, test loss-1.7816, acc-0.4993
Iter-65690, train loss-1.8739, acc-0.5400, valid loss-1.7808, acc-0.5074, test loss-1.7816, acc-0.4993
Iter-65700, train loss-1.7619, acc-0.5800, valid loss-1.7807, acc-0.5074, test loss-1.7816, acc-0.4994
Iter-65710, train loss-1.8674, acc-0.4800, valid loss-1.7807, acc-0.5074, test loss-1.7815, acc-0.4995
Iter-65720, train loss-1.8449, acc-0.5000, valid loss-1.7806, acc-0.5074, test loss-1.7815, acc-0.4994
Iter-65730, train loss-1.8127, acc-0.6000, valid loss-1.7806, acc-0.5074, test loss-1.7814, acc-0.4994
Iter-65740, train loss-1.7570, acc-0.5800, valid loss-1.7805, acc-0.5074, test loss-1.7814, acc-0.4994
Iter-65750, train loss-1.8024, acc-0.4800, valid loss-1.7805, acc-0.5074, test loss-1.7813, acc-0.4996
Iter-65760, train loss-1.7528, acc-0.5200, valid loss-1.7805, acc-0.5074, test loss-1.7813, acc-0.4994
Iter-65770, train loss-1.8655, acc-0.3600, valid loss-1.7804, acc-0.5074, test loss-1.7812, acc-0.4995
Iter-65780, train loss-1.8003, acc-0.5200, valid loss-1.7804, acc-0.5074, test loss-1.7812, acc-0.4994
Iter-65790, train loss-1.7965, acc-0.5200, valid loss-1.7803, acc-0.5074, test loss-1.7811, acc-0.4995
Iter-65800, train loss-1.8231, acc-0.4600, valid loss-1.7803, acc-0.5074, test loss-1.7811, acc-0.4994
Iter-65810, train loss-1.7894, acc-0.4800, valid loss-1.7802, acc-0.5074, test loss-1.7811, acc-0.4994
Iter-65820, train loss-1.8029, acc-0.5000, valid loss-1.7802, acc-0.5074, test loss-1.7810, acc-0.4995
Iter-65830, train loss-1.8678, acc-0.3800, valid loss-1.7801, acc-0.5076, test loss-1.7810, acc-0.4996
Iter-65840, train loss-1.8204, acc-0.5000, valid loss-1.7801, acc-0.5076, test loss-1.7809, acc-0.4996
Iter-65850, train loss-1.7922, acc-0.5200, valid loss-1.7800, acc-0.5076, test loss-1.7809, acc-0.4997
Iter-65860, train loss-1.7645, acc-0.5400, valid loss-1.7800, acc-0.5076, test loss-1.7808, acc-0.4997
Iter-65870, train loss-1.7588, acc-0.4800, valid loss-1.7800, acc-0.5076, test loss-1.7808, acc-0.4997
Iter-65880, train loss-1.8024, acc-0.5000, valid loss-1.7799, acc-0.5076, test loss-1.7807, acc-0.4998
Iter-65890, train loss-1.8340, acc-0.4800, valid loss-1.7799, acc-0.5076, test loss-1.7807, acc-0.4997
Iter-65900, train loss-1.7060, acc-0.5400, valid loss-1.7798, acc-0.5076, test loss-1.7806, acc-0.4997
Iter-65910, train loss-1.8996, acc-0.4400, valid loss-1.7798, acc-0.5076, test loss-1.7806, acc-0.4996
Iter-65920, train loss-1.7632, acc-0.5000, valid loss-1.7797, acc-0.5076, test loss-1.7806, acc-0.4999
Iter-65930, train loss-1.7431, acc-0.4200, valid loss-1.7797, acc-0.5076, test loss-1.7805, acc-0.5004
Iter-65940, train loss-1.7864, acc-0.4800, valid loss-1.7796, acc-0.5076, test loss-1.7805, acc-0.5000
Iter-65950, train loss-1.7880, acc-0.6200, valid loss-1.7796, acc-0.5082, test loss-1.7804, acc-0.5001
Iter-65960, train loss-1.7275, acc-0.5600, valid loss-1.7795, acc-0.5082, test loss-1.7804, acc-0.5000
Iter-65970, train loss-1.8220, acc-0.5200, valid loss-1.7795, acc-0.5084, test loss-1.7803, acc-0.5003
Iter-65980, train loss-1.7690, acc-0.6200, valid loss-1.7795, acc-0.5080, test loss-1.7803, acc-0.5001
Iter-65990, train loss-1.8755, acc-0.4600, valid loss-1.7794, acc-0.5080, test loss-1.7802, acc-0.5004
Iter-66000, train loss-1.8217, acc-0.4600, valid loss-1.7794, acc-0.5080, test loss-1.7802, acc-0.5003
Iter-66010, train loss-1.8711, acc-0.4600, valid loss-1.7793, acc-0.5080, test loss-1.7802, acc-0.5003
Iter-66020, train loss-1.8058, acc-0.5200, valid loss-1.7793, acc-0.5080, test loss-1.7801, acc-0.5001
Iter-66030, train loss-1.8280, acc-0.4400, valid loss-1.7792, acc-0.5078, test loss-1.7801, acc-0.5001
Iter-66040, train loss-1.7609, acc-0.5400, valid loss-1.7792, acc-0.5080, test loss-1.7800, acc-0.5003
Iter-66050, train loss-1.8877, acc-0.4400, valid loss-1.7792, acc-0.5076, test loss-1.7800, acc-0.5003
Iter-66060, train loss-1.8433, acc-0.4800, valid loss-1.7791, acc-0.5076, test loss-1.7799, acc-0.5003
Iter-66070, train loss-1.7573, acc-0.5000, valid loss-1.7791, acc-0.5076, test loss-1.7799, acc-0.5002
Iter-66080, train loss-1.7875, acc-0.5000, valid loss-1.7790, acc-0.5076, test loss-1.7798, acc-0.5004
Iter-66090, train loss-1.8797, acc-0.4600, valid loss-1.7790, acc-0.5078, test loss-1.7798, acc-0.5004
Iter-66100, train loss-1.9041, acc-0.4000, valid loss-1.7789, acc-0.5078, test loss-1.7797, acc-0.5001
Iter-66110, train loss-1.7861, acc-0.4600, valid loss-1.7789, acc-0.5080, test loss-1.7797, acc-0.5004
Iter-66120, train loss-1.6639, acc-0.5800, valid loss-1.7788, acc-0.5080, test loss-1.7797, acc-0.5003
Iter-66130, train loss-1.7040, acc-0.4400, valid loss-1.7788, acc-0.5078, test loss-1.7796, acc-0.5005
Iter-66140, train loss-1.7372, acc-0.5400, valid loss-1.7788, acc-0.5074, test loss-1.7796, acc-0.5005
Iter-66150, train loss-1.7761, acc-0.4400, valid loss-1.7787, acc-0.5074, test loss-1.7795, acc-0.5004
Iter-66160, train loss-1.8027, acc-0.4600, valid loss-1.7787, acc-0.5076, test loss-1.7795, acc-0.5008
Iter-66170, train loss-1.7765, acc-0.4800, valid loss-1.7786, acc-0.5076, test loss-1.7794, acc-0.5004
Iter-66180, train loss-1.8311, acc-0.4800, valid loss-1.7786, acc-0.5076, test loss-1.7794, acc-0.5001
Iter-66190, train loss-1.7672, acc-0.4600, valid loss-1.7785, acc-0.5076, test loss-1.7793, acc-0.5001
Iter-66200, train loss-1.8170, acc-0.4600, valid loss-1.7785, acc-0.5076, test loss-1.7793, acc-0.5003
Iter-66210, train loss-1.8138, acc-0.3600, valid loss-1.7784, acc-0.5076, test loss-1.7792, acc-0.5004
Iter-66220, train loss-1.7808, acc-0.4000, valid loss-1.7784, acc-0.5076, test loss-1.7792, acc-0.5003
Iter-66230, train loss-1.7611, acc-0.5600, valid loss-1.7784, acc-0.5076, test loss-1.7792, acc-0.5004
Iter-66240, train loss-1.7559, acc-0.5000, valid loss-1.7783, acc-0.5076, test loss-1.7791, acc-0.5004
Iter-66250, train loss-1.8722, acc-0.3600, valid loss-1.7783, acc-0.5074, test loss-1.7791, acc-0.5006
Iter-66260, train loss-1.5365, acc-0.7200, valid loss-1.7782, acc-0.5074, test loss-1.7790, acc-0.5006
Iter-66270, train loss-1.7543, acc-0.5600, valid loss-1.7782, acc-0.5076, test loss-1.7790, acc-0.5006
Iter-66280, train loss-1.8253, acc-0.3800, valid loss-1.7781, acc-0.5074, test loss-1.7789, acc-0.5005
Iter-66290, train loss-1.8047, acc-0.6200, valid loss-1.7781, acc-0.5074, test loss-1.7789, acc-0.5006
Iter-66300, train loss-1.7196, acc-0.6400, valid loss-1.7780, acc-0.5074, test loss-1.7788, acc-0.5005
Iter-66310, train loss-1.7488, acc-0.5200, valid loss-1.7780, acc-0.5078, test loss-1.7788, acc-0.5003
Iter-66320, train loss-1.7908, acc-0.4400, valid loss-1.7779, acc-0.5076, test loss-1.7787, acc-0.5004
Iter-66330, train loss-1.7862, acc-0.5000, valid loss-1.7779, acc-0.5076, test loss-1.7787, acc-0.5004
Iter-66340, train loss-1.7580, acc-0.5600, valid loss-1.7779, acc-0.5076, test loss-1.7787, acc-0.5003
Iter-66350, train loss-1.7554, acc-0.4800, valid loss-1.7778, acc-0.5076, test loss-1.7786, acc-0.5003
Iter-66360, train loss-1.8038, acc-0.4400, valid loss-1.7778, acc-0.5076, test loss-1.7786, acc-0.5003
Iter-66370, train loss-1.7318, acc-0.5600, valid loss-1.7777, acc-0.5078, test loss-1.7785, acc-0.5003
Iter-66380, train loss-1.9733, acc-0.4200, valid loss-1.7777, acc-0.5078, test loss-1.7785, acc-0.5002
Iter-66390, train loss-1.8304, acc-0.5400, valid loss-1.7776, acc-0.5076, test loss-1.7784, acc-0.5005
Iter-66400, train loss-1.8419, acc-0.3800, valid loss-1.7776, acc-0.5078, test loss-1.7784, acc-0.5005
Iter-66410, train loss-1.7928, acc-0.4400, valid loss-1.7775, acc-0.5076, test loss-1.7783, acc-0.5005
Iter-66420, train loss-1.7512, acc-0.5000, valid loss-1.7775, acc-0.5074, test loss-1.7783, acc-0.5005
Iter-66430, train loss-1.7468, acc-0.5200, valid loss-1.7775, acc-0.5078, test loss-1.7782, acc-0.5002
Iter-66440, train loss-1.8322, acc-0.5200, valid loss-1.7774, acc-0.5078, test loss-1.7782, acc-0.5002
Iter-66450, train loss-1.7341, acc-0.5600, valid loss-1.7774, acc-0.5078, test loss-1.7782, acc-0.5002
Iter-66460, train loss-1.7486, acc-0.5000, valid loss-1.7773, acc-0.5078, test loss-1.7781, acc-0.5002
Iter-66470, train loss-1.7781, acc-0.5000, valid loss-1.7773, acc-0.5078, test loss-1.7781, acc-0.5006
Iter-66480, train loss-1.7057, acc-0.5400, valid loss-1.7772, acc-0.5078, test loss-1.7780, acc-0.5005
Iter-66490, train loss-1.8042, acc-0.4600, valid loss-1.7772, acc-0.5076, test loss-1.7780, acc-0.5007
Iter-66500, train loss-1.7727, acc-0.4600, valid loss-1.7771, acc-0.5080, test loss-1.7779, acc-0.5007
Iter-66510, train loss-1.8049, acc-0.4600, valid loss-1.7771, acc-0.5080, test loss-1.7779, acc-0.5005
Iter-66520, train loss-1.8553, acc-0.5000, valid loss-1.7770, acc-0.5078, test loss-1.7778, acc-0.5007
Iter-66530, train loss-1.7436, acc-0.5600, valid loss-1.7770, acc-0.5078, test loss-1.7778, acc-0.5007
Iter-66540, train loss-1.7816, acc-0.5200, valid loss-1.7770, acc-0.5076, test loss-1.7778, acc-0.5007
Iter-66550, train loss-1.7307, acc-0.5400, valid loss-1.7769, acc-0.5074, test loss-1.7777, acc-0.5006
Iter-66560, train loss-1.8409, acc-0.3800, valid loss-1.7769, acc-0.5076, test loss-1.7777, acc-0.5004
Iter-66570, train loss-1.7942, acc-0.4200, valid loss-1.7768, acc-0.5076, test loss-1.7776, acc-0.5005
Iter-66580, train loss-1.7736, acc-0.4600, valid loss-1.7768, acc-0.5076, test loss-1.7776, acc-0.5004
Iter-66590, train loss-1.6598, acc-0.5800, valid loss-1.7767, acc-0.5078, test loss-1.7775, acc-0.5006
Iter-66600, train loss-1.8183, acc-0.4800, valid loss-1.7767, acc-0.5076, test loss-1.7775, acc-0.5005
Iter-66610, train loss-1.7965, acc-0.4000, valid loss-1.7766, acc-0.5076, test loss-1.7774, acc-0.5005
Iter-66620, train loss-1.7414, acc-0.5600, valid loss-1.7766, acc-0.5076, test loss-1.7774, acc-0.5006
Iter-66630, train loss-1.7950, acc-0.4600, valid loss-1.7766, acc-0.5076, test loss-1.7774, acc-0.5005
Iter-66640, train loss-1.7824, acc-0.4600, valid loss-1.7765, acc-0.5078, test loss-1.7773, acc-0.5007
Iter-66650, train loss-1.7489, acc-0.5600, valid loss-1.7765, acc-0.5078, test loss-1.7773, acc-0.5007
Iter-66660, train loss-1.8144, acc-0.4200, valid loss-1.7764, acc-0.5076, test loss-1.7772, acc-0.5008
Iter-66670, train loss-1.8922, acc-0.4800, valid loss-1.7764, acc-0.5080, test loss-1.7772, acc-0.5007
Iter-66680, train loss-1.7119, acc-0.5800, valid loss-1.7763, acc-0.5078, test loss-1.7771, acc-0.5008
Iter-66690, train loss-1.8971, acc-0.3800, valid loss-1.7763, acc-0.5080, test loss-1.7771, acc-0.5011
Iter-66700, train loss-1.8613, acc-0.4200, valid loss-1.7762, acc-0.5080, test loss-1.7770, acc-0.5012
Iter-66710, train loss-1.7503, acc-0.5600, valid loss-1.7762, acc-0.5080, test loss-1.7770, acc-0.5010
Iter-66720, train loss-1.8625, acc-0.4400, valid loss-1.7762, acc-0.5080, test loss-1.7770, acc-0.5010
Iter-66730, train loss-1.6984, acc-0.6000, valid loss-1.7761, acc-0.5080, test loss-1.7769, acc-0.5011
Iter-66740, train loss-1.8689, acc-0.3600, valid loss-1.7761, acc-0.5080, test loss-1.7769, acc-0.5010
Iter-66750, train loss-1.7910, acc-0.4400, valid loss-1.7760, acc-0.5080, test loss-1.7768, acc-0.5011
Iter-66760, train loss-1.7550, acc-0.5200, valid loss-1.7760, acc-0.5080, test loss-1.7768, acc-0.5010
Iter-66770, train loss-1.8938, acc-0.4400, valid loss-1.7759, acc-0.5080, test loss-1.7767, acc-0.5010
Iter-66780, train loss-1.8499, acc-0.4400, valid loss-1.7759, acc-0.5078, test loss-1.7767, acc-0.5010
Iter-66790, train loss-1.7389, acc-0.6600, valid loss-1.7758, acc-0.5080, test loss-1.7766, acc-0.5009
Iter-66800, train loss-1.6867, acc-0.5600, valid loss-1.7758, acc-0.5080, test loss-1.7766, acc-0.5009
Iter-66810, train loss-1.8012, acc-0.4800, valid loss-1.7757, acc-0.5080, test loss-1.7765, acc-0.5009
Iter-66820, train loss-1.7759, acc-0.5600, valid loss-1.7757, acc-0.5080, test loss-1.7765, acc-0.5010
Iter-66830, train loss-1.7978, acc-0.4200, valid loss-1.7757, acc-0.5080, test loss-1.7765, acc-0.5011
Iter-66840, train loss-1.6913, acc-0.5400, valid loss-1.7756, acc-0.5080, test loss-1.7764, acc-0.5010
Iter-66850, train loss-1.8525, acc-0.4800, valid loss-1.7756, acc-0.5078, test loss-1.7764, acc-0.5010
Iter-66860, train loss-1.7534, acc-0.5000, valid loss-1.7755, acc-0.5078, test loss-1.7763, acc-0.5010
Iter-66870, train loss-1.7791, acc-0.5600, valid loss-1.7755, acc-0.5078, test loss-1.7763, acc-0.5009
Iter-66880, train loss-1.8235, acc-0.4400, valid loss-1.7754, acc-0.5078, test loss-1.7762, acc-0.5009
Iter-66890, train loss-1.8356, acc-0.5400, valid loss-1.7754, acc-0.5078, test loss-1.7762, acc-0.5010
Iter-66900, train loss-1.7669, acc-0.5000, valid loss-1.7754, acc-0.5076, test loss-1.7761, acc-0.5010
Iter-66910, train loss-1.7509, acc-0.5800, valid loss-1.7753, acc-0.5078, test loss-1.7761, acc-0.5010
Iter-66920, train loss-1.7654, acc-0.5400, valid loss-1.7753, acc-0.5076, test loss-1.7761, acc-0.5010
Iter-66930, train loss-1.8734, acc-0.4000, valid loss-1.7752, acc-0.5076, test loss-1.7760, acc-0.5011
Iter-66940, train loss-1.8160, acc-0.5000, valid loss-1.7752, acc-0.5076, test loss-1.7760, acc-0.5009
Iter-66950, train loss-1.7339, acc-0.5000, valid loss-1.7751, acc-0.5076, test loss-1.7759, acc-0.5010
Iter-66960, train loss-1.7810, acc-0.4200, valid loss-1.7751, acc-0.5076, test loss-1.7759, acc-0.5010
Iter-66970, train loss-1.7413, acc-0.5800, valid loss-1.7750, acc-0.5076, test loss-1.7758, acc-0.5010
Iter-66980, train loss-1.7388, acc-0.4800, valid loss-1.7750, acc-0.5078, test loss-1.7758, acc-0.5010
Iter-66990, train loss-1.8142, acc-0.4600, valid loss-1.7749, acc-0.5076, test loss-1.7757, acc-0.5008
Iter-67000, train loss-1.9059, acc-0.3600, valid loss-1.7749, acc-0.5076, test loss-1.7757, acc-0.5008
Iter-67010, train loss-1.8066, acc-0.5000, valid loss-1.7748, acc-0.5076, test loss-1.7756, acc-0.5010
Iter-67020, train loss-1.8801, acc-0.4600, valid loss-1.7748, acc-0.5076, test loss-1.7756, acc-0.5008
Iter-67030, train loss-1.8746, acc-0.3800, valid loss-1.7748, acc-0.5076, test loss-1.7756, acc-0.5008
Iter-67040, train loss-1.8854, acc-0.4600, valid loss-1.7747, acc-0.5078, test loss-1.7755, acc-0.5009
Iter-67050, train loss-1.6846, acc-0.6000, valid loss-1.7747, acc-0.5078, test loss-1.7755, acc-0.5009
Iter-67060, train loss-1.7670, acc-0.5000, valid loss-1.7746, acc-0.5076, test loss-1.7754, acc-0.5009
Iter-67070, train loss-1.6789, acc-0.5200, valid loss-1.7746, acc-0.5078, test loss-1.7754, acc-0.5008
Iter-67080, train loss-1.8486, acc-0.3800, valid loss-1.7745, acc-0.5080, test loss-1.7753, acc-0.5008
Iter-67090, train loss-1.8309, acc-0.5200, valid loss-1.7745, acc-0.5080, test loss-1.7753, acc-0.5008
Iter-67100, train loss-1.7896, acc-0.5400, valid loss-1.7744, acc-0.5082, test loss-1.7752, acc-0.5010
Iter-67110, train loss-1.8952, acc-0.4000, valid loss-1.7744, acc-0.5080, test loss-1.7752, acc-0.5011
Iter-67120, train loss-1.8198, acc-0.4000, valid loss-1.7744, acc-0.5082, test loss-1.7752, acc-0.5010
Iter-67130, train loss-1.7604, acc-0.5600, valid loss-1.7743, acc-0.5080, test loss-1.7751, acc-0.5011
Iter-67140, train loss-1.7417, acc-0.5800, valid loss-1.7743, acc-0.5082, test loss-1.7751, acc-0.5011
Iter-67150, train loss-1.8594, acc-0.3800, valid loss-1.7742, acc-0.5082, test loss-1.7750, acc-0.5013
Iter-67160, train loss-1.8145, acc-0.5200, valid loss-1.7742, acc-0.5082, test loss-1.7750, acc-0.5012
Iter-67170, train loss-1.6688, acc-0.6200, valid loss-1.7741, acc-0.5082, test loss-1.7749, acc-0.5011
Iter-67180, train loss-1.8507, acc-0.3600, valid loss-1.7741, acc-0.5082, test loss-1.7749, acc-0.5011
Iter-67190, train loss-1.7402, acc-0.5200, valid loss-1.7740, acc-0.5082, test loss-1.7748, acc-0.5011
Iter-67200, train loss-1.7867, acc-0.5000, valid loss-1.7740, acc-0.5080, test loss-1.7748, acc-0.5012
Iter-67210, train loss-1.8411, acc-0.4400, valid loss-1.7739, acc-0.5082, test loss-1.7748, acc-0.5011
Iter-67220, train loss-1.7130, acc-0.6000, valid loss-1.7739, acc-0.5082, test loss-1.7747, acc-0.5012
Iter-67230, train loss-1.8096, acc-0.5000, valid loss-1.7739, acc-0.5080, test loss-1.7747, acc-0.5011
Iter-67240, train loss-1.8315, acc-0.4000, valid loss-1.7738, acc-0.5084, test loss-1.7746, acc-0.5012
Iter-67250, train loss-1.7636, acc-0.5200, valid loss-1.7738, acc-0.5084, test loss-1.7746, acc-0.5012
Iter-67260, train loss-1.8108, acc-0.5400, valid loss-1.7737, acc-0.5084, test loss-1.7745, acc-0.5012
Iter-67270, train loss-1.8390, acc-0.4800, valid loss-1.7737, acc-0.5084, test loss-1.7745, acc-0.5014
Iter-67280, train loss-1.7935, acc-0.5000, valid loss-1.7736, acc-0.5084, test loss-1.7744, acc-0.5013
Iter-67290, train loss-1.8535, acc-0.3400, valid loss-1.7736, acc-0.5084, test loss-1.7744, acc-0.5010
Iter-67300, train loss-1.8611, acc-0.4600, valid loss-1.7736, acc-0.5084, test loss-1.7744, acc-0.5013
Iter-67310, train loss-1.8514, acc-0.3800, valid loss-1.7735, acc-0.5084, test loss-1.7743, acc-0.5012
Iter-67320, train loss-1.8622, acc-0.3600, valid loss-1.7735, acc-0.5084, test loss-1.7743, acc-0.5009
Iter-67330, train loss-1.8016, acc-0.4600, valid loss-1.7734, acc-0.5084, test loss-1.7742, acc-0.5010
Iter-67340, train loss-1.8011, acc-0.5600, valid loss-1.7734, acc-0.5084, test loss-1.7742, acc-0.5011
Iter-67350, train loss-1.8166, acc-0.4800, valid loss-1.7733, acc-0.5084, test loss-1.7741, acc-0.5011
Iter-67360, train loss-1.7391, acc-0.5600, valid loss-1.7733, acc-0.5084, test loss-1.7741, acc-0.5010
Iter-67370, train loss-1.8328, acc-0.4600, valid loss-1.7733, acc-0.5082, test loss-1.7741, acc-0.5013
Iter-67380, train loss-1.7439, acc-0.5400, valid loss-1.7732, acc-0.5084, test loss-1.7740, acc-0.5012
Iter-67390, train loss-1.7508, acc-0.5000, valid loss-1.7732, acc-0.5084, test loss-1.7740, acc-0.5012
Iter-67400, train loss-1.7348, acc-0.5600, valid loss-1.7731, acc-0.5084, test loss-1.7739, acc-0.5014
Iter-67410, train loss-1.7851, acc-0.4600, valid loss-1.7731, acc-0.5084, test loss-1.7739, acc-0.5014
Iter-67420, train loss-1.8473, acc-0.4600, valid loss-1.7730, acc-0.5086, test loss-1.7738, acc-0.5012
Iter-67430, train loss-1.7489, acc-0.5200, valid loss-1.7730, acc-0.5086, test loss-1.7738, acc-0.5012
Iter-67440, train loss-1.8489, acc-0.3800, valid loss-1.7730, acc-0.5088, test loss-1.7737, acc-0.5014
Iter-67450, train loss-1.8292, acc-0.4200, valid loss-1.7729, acc-0.5090, test loss-1.7737, acc-0.5012
Iter-67460, train loss-1.7820, acc-0.5200, valid loss-1.7729, acc-0.5088, test loss-1.7737, acc-0.5012
Iter-67470, train loss-1.7699, acc-0.5600, valid loss-1.7728, acc-0.5088, test loss-1.7736, acc-0.5016
Iter-67480, train loss-1.7110, acc-0.5400, valid loss-1.7728, acc-0.5088, test loss-1.7736, acc-0.5015
Iter-67490, train loss-1.7761, acc-0.5400, valid loss-1.7727, acc-0.5088, test loss-1.7735, acc-0.5016
Iter-67500, train loss-1.7579, acc-0.4800, valid loss-1.7727, acc-0.5090, test loss-1.7735, acc-0.5017
Iter-67510, train loss-1.7638, acc-0.5400, valid loss-1.7727, acc-0.5092, test loss-1.7734, acc-0.5017
Iter-67520, train loss-1.7583, acc-0.5600, valid loss-1.7726, acc-0.5090, test loss-1.7734, acc-0.5016
Iter-67530, train loss-1.8359, acc-0.4200, valid loss-1.7726, acc-0.5090, test loss-1.7734, acc-0.5017
Iter-67540, train loss-1.7117, acc-0.5400, valid loss-1.7725, acc-0.5090, test loss-1.7733, acc-0.5019
Iter-67550, train loss-1.7763, acc-0.5200, valid loss-1.7725, acc-0.5090, test loss-1.7733, acc-0.5017
Iter-67560, train loss-1.7420, acc-0.5600, valid loss-1.7724, acc-0.5090, test loss-1.7732, acc-0.5019
Iter-67570, train loss-1.7635, acc-0.6000, valid loss-1.7724, acc-0.5090, test loss-1.7732, acc-0.5018
Iter-67580, train loss-1.8479, acc-0.4000, valid loss-1.7724, acc-0.5090, test loss-1.7731, acc-0.5018
Iter-67590, train loss-1.7970, acc-0.4000, valid loss-1.7723, acc-0.5092, test loss-1.7731, acc-0.5018
Iter-67600, train loss-1.8208, acc-0.5000, valid loss-1.7723, acc-0.5090, test loss-1.7730, acc-0.5019
Iter-67610, train loss-1.7451, acc-0.5600, valid loss-1.7722, acc-0.5090, test loss-1.7730, acc-0.5019
Iter-67620, train loss-1.7666, acc-0.5600, valid loss-1.7722, acc-0.5090, test loss-1.7730, acc-0.5020
Iter-67630, train loss-1.8673, acc-0.4200, valid loss-1.7721, acc-0.5090, test loss-1.7729, acc-0.5018
Iter-67640, train loss-1.8655, acc-0.4400, valid loss-1.7721, acc-0.5092, test loss-1.7729, acc-0.5019
Iter-67650, train loss-1.7523, acc-0.5600, valid loss-1.7720, acc-0.5092, test loss-1.7728, acc-0.5020
Iter-67660, train loss-1.8560, acc-0.5200, valid loss-1.7720, acc-0.5094, test loss-1.7728, acc-0.5022
Iter-67670, train loss-1.7615, acc-0.5600, valid loss-1.7720, acc-0.5092, test loss-1.7727, acc-0.5022
Iter-67680, train loss-1.8350, acc-0.5200, valid loss-1.7719, acc-0.5090, test loss-1.7727, acc-0.5022
Iter-67690, train loss-1.7702, acc-0.5000, valid loss-1.7719, acc-0.5090, test loss-1.7726, acc-0.5023
Iter-67700, train loss-1.7643, acc-0.5000, valid loss-1.7718, acc-0.5090, test loss-1.7726, acc-0.5022
Iter-67710, train loss-1.7535, acc-0.4400, valid loss-1.7718, acc-0.5092, test loss-1.7726, acc-0.5022
Iter-67720, train loss-1.8040, acc-0.4600, valid loss-1.7717, acc-0.5088, test loss-1.7725, acc-0.5021
Iter-67730, train loss-1.8104, acc-0.4800, valid loss-1.7717, acc-0.5092, test loss-1.7725, acc-0.5022
Iter-67740, train loss-1.7761, acc-0.5800, valid loss-1.7716, acc-0.5092, test loss-1.7724, acc-0.5021
Iter-67750, train loss-1.8073, acc-0.5400, valid loss-1.7716, acc-0.5092, test loss-1.7724, acc-0.5021
Iter-67760, train loss-1.9010, acc-0.4000, valid loss-1.7715, acc-0.5092, test loss-1.7723, acc-0.5021
Iter-67770, train loss-1.7838, acc-0.4000, valid loss-1.7715, acc-0.5092, test loss-1.7723, acc-0.5021
Iter-67780, train loss-1.9032, acc-0.3600, valid loss-1.7715, acc-0.5092, test loss-1.7723, acc-0.5021
Iter-67790, train loss-1.8418, acc-0.5200, valid loss-1.7714, acc-0.5094, test loss-1.7722, acc-0.5023
Iter-67800, train loss-1.7488, acc-0.5000, valid loss-1.7714, acc-0.5094, test loss-1.7722, acc-0.5022
Iter-67810, train loss-1.7481, acc-0.6200, valid loss-1.7713, acc-0.5096, test loss-1.7721, acc-0.5022
Iter-67820, train loss-1.7532, acc-0.5400, valid loss-1.7713, acc-0.5096, test loss-1.7721, acc-0.5021
Iter-67830, train loss-1.7605, acc-0.4800, valid loss-1.7712, acc-0.5096, test loss-1.7720, acc-0.5022
Iter-67840, train loss-1.8339, acc-0.4200, valid loss-1.7712, acc-0.5094, test loss-1.7720, acc-0.5021
Iter-67850, train loss-1.7043, acc-0.5600, valid loss-1.7711, acc-0.5092, test loss-1.7719, acc-0.5021
Iter-67860, train loss-1.7903, acc-0.4600, valid loss-1.7711, acc-0.5090, test loss-1.7719, acc-0.5020
Iter-67870, train loss-1.7676, acc-0.5200, valid loss-1.7710, acc-0.5090, test loss-1.7718, acc-0.5020
Iter-67880, train loss-1.8451, acc-0.4800, valid loss-1.7710, acc-0.5092, test loss-1.7718, acc-0.5020
Iter-67890, train loss-1.7633, acc-0.4800, valid loss-1.7710, acc-0.5092, test loss-1.7718, acc-0.5019
Iter-67900, train loss-1.8312, acc-0.5400, valid loss-1.7709, acc-0.5094, test loss-1.7717, acc-0.5020
Iter-67910, train loss-1.7337, acc-0.5800, valid loss-1.7709, acc-0.5092, test loss-1.7717, acc-0.5019
Iter-67920, train loss-1.7468, acc-0.5200, valid loss-1.7708, acc-0.5094, test loss-1.7716, acc-0.5020
Iter-67930, train loss-1.7754, acc-0.4000, valid loss-1.7708, acc-0.5092, test loss-1.7716, acc-0.5020
Iter-67940, train loss-1.8466, acc-0.4600, valid loss-1.7707, acc-0.5092, test loss-1.7715, acc-0.5019
Iter-67950, train loss-1.7466, acc-0.5200, valid loss-1.7707, acc-0.5092, test loss-1.7715, acc-0.5018
Iter-67960, train loss-1.7973, acc-0.4600, valid loss-1.7706, acc-0.5094, test loss-1.7714, acc-0.5020
Iter-67970, train loss-1.8443, acc-0.5000, valid loss-1.7706, acc-0.5094, test loss-1.7714, acc-0.5021
Iter-67980, train loss-1.7367, acc-0.5400, valid loss-1.7706, acc-0.5094, test loss-1.7713, acc-0.5022
Iter-67990, train loss-1.7629, acc-0.5000, valid loss-1.7705, acc-0.5094, test loss-1.7713, acc-0.5021
Iter-68000, train loss-1.8311, acc-0.4600, valid loss-1.7705, acc-0.5096, test loss-1.7713, acc-0.5018
Iter-68010, train loss-1.7787, acc-0.4800, valid loss-1.7704, acc-0.5096, test loss-1.7712, acc-0.5020
Iter-68020, train loss-1.8262, acc-0.4400, valid loss-1.7704, acc-0.5094, test loss-1.7712, acc-0.5020
Iter-68030, train loss-1.8773, acc-0.4000, valid loss-1.7703, acc-0.5096, test loss-1.7711, acc-0.5020
Iter-68040, train loss-1.7802, acc-0.5600, valid loss-1.7703, acc-0.5094, test loss-1.7711, acc-0.5020
Iter-68050, train loss-1.7385, acc-0.5600, valid loss-1.7702, acc-0.5096, test loss-1.7710, acc-0.5020
Iter-68060, train loss-1.8823, acc-0.4000, valid loss-1.7702, acc-0.5094, test loss-1.7710, acc-0.5022
Iter-68070, train loss-1.9230, acc-0.5000, valid loss-1.7702, acc-0.5092, test loss-1.7710, acc-0.5020
Iter-68080, train loss-1.7784, acc-0.5400, valid loss-1.7701, acc-0.5092, test loss-1.7709, acc-0.5020
Iter-68090, train loss-1.8544, acc-0.4600, valid loss-1.7701, acc-0.5092, test loss-1.7709, acc-0.5020
Iter-68100, train loss-1.9006, acc-0.4400, valid loss-1.7700, acc-0.5096, test loss-1.7708, acc-0.5021
Iter-68110, train loss-1.9091, acc-0.3200, valid loss-1.7700, acc-0.5094, test loss-1.7708, acc-0.5019
Iter-68120, train loss-1.7894, acc-0.5600, valid loss-1.7699, acc-0.5092, test loss-1.7707, acc-0.5021
Iter-68130, train loss-1.7638, acc-0.5200, valid loss-1.7699, acc-0.5094, test loss-1.7707, acc-0.5022
Iter-68140, train loss-1.7243, acc-0.5200, valid loss-1.7699, acc-0.5094, test loss-1.7706, acc-0.5021
Iter-68150, train loss-1.7285, acc-0.4800, valid loss-1.7698, acc-0.5094, test loss-1.7706, acc-0.5021
Iter-68160, train loss-1.8069, acc-0.4800, valid loss-1.7698, acc-0.5094, test loss-1.7706, acc-0.5022
Iter-68170, train loss-1.7565, acc-0.5600, valid loss-1.7697, acc-0.5094, test loss-1.7705, acc-0.5022
Iter-68180, train loss-1.7495, acc-0.5200, valid loss-1.7697, acc-0.5092, test loss-1.7705, acc-0.5021
Iter-68190, train loss-1.8479, acc-0.5200, valid loss-1.7696, acc-0.5090, test loss-1.7704, acc-0.5020
Iter-68200, train loss-1.7474, acc-0.5200, valid loss-1.7696, acc-0.5092, test loss-1.7704, acc-0.5021
Iter-68210, train loss-1.9250, acc-0.3600, valid loss-1.7696, acc-0.5090, test loss-1.7703, acc-0.5021
Iter-68220, train loss-1.6880, acc-0.6200, valid loss-1.7695, acc-0.5090, test loss-1.7703, acc-0.5021
Iter-68230, train loss-1.7642, acc-0.5000, valid loss-1.7695, acc-0.5092, test loss-1.7702, acc-0.5023
Iter-68240, train loss-1.6813, acc-0.6200, valid loss-1.7694, acc-0.5088, test loss-1.7702, acc-0.5020
Iter-68250, train loss-1.7700, acc-0.5000, valid loss-1.7694, acc-0.5092, test loss-1.7701, acc-0.5023
Iter-68260, train loss-1.8034, acc-0.4800, valid loss-1.7693, acc-0.5090, test loss-1.7701, acc-0.5024
Iter-68270, train loss-1.7522, acc-0.6400, valid loss-1.7693, acc-0.5090, test loss-1.7701, acc-0.5025
Iter-68280, train loss-1.7954, acc-0.5600, valid loss-1.7692, acc-0.5090, test loss-1.7700, acc-0.5025
Iter-68290, train loss-1.6848, acc-0.5800, valid loss-1.7692, acc-0.5090, test loss-1.7700, acc-0.5026
Iter-68300, train loss-1.8358, acc-0.4200, valid loss-1.7692, acc-0.5090, test loss-1.7699, acc-0.5025
Iter-68310, train loss-1.8730, acc-0.3600, valid loss-1.7691, acc-0.5090, test loss-1.7699, acc-0.5025
Iter-68320, train loss-1.7651, acc-0.5000, valid loss-1.7691, acc-0.5090, test loss-1.7698, acc-0.5025
Iter-68330, train loss-1.7065, acc-0.5600, valid loss-1.7690, acc-0.5090, test loss-1.7698, acc-0.5025
Iter-68340, train loss-1.8067, acc-0.6000, valid loss-1.7690, acc-0.5090, test loss-1.7698, acc-0.5021
Iter-68350, train loss-1.8153, acc-0.4400, valid loss-1.7689, acc-0.5090, test loss-1.7697, acc-0.5023
Iter-68360, train loss-1.8230, acc-0.5600, valid loss-1.7689, acc-0.5090, test loss-1.7697, acc-0.5025
Iter-68370, train loss-1.8754, acc-0.4800, valid loss-1.7688, acc-0.5090, test loss-1.7696, acc-0.5025
Iter-68380, train loss-1.7663, acc-0.4400, valid loss-1.7688, acc-0.5092, test loss-1.7696, acc-0.5025
Iter-68390, train loss-1.6881, acc-0.5000, valid loss-1.7688, acc-0.5092, test loss-1.7695, acc-0.5025
Iter-68400, train loss-1.8757, acc-0.3600, valid loss-1.7687, acc-0.5090, test loss-1.7695, acc-0.5023
Iter-68410, train loss-1.7879, acc-0.4600, valid loss-1.7687, acc-0.5092, test loss-1.7694, acc-0.5024
Iter-68420, train loss-1.8221, acc-0.3600, valid loss-1.7686, acc-0.5088, test loss-1.7694, acc-0.5025
Iter-68430, train loss-1.8113, acc-0.5000, valid loss-1.7686, acc-0.5092, test loss-1.7694, acc-0.5027
Iter-68440, train loss-1.8596, acc-0.4400, valid loss-1.7685, acc-0.5092, test loss-1.7693, acc-0.5026
Iter-68450, train loss-1.9167, acc-0.4600, valid loss-1.7685, acc-0.5090, test loss-1.7693, acc-0.5027
Iter-68460, train loss-1.7551, acc-0.5400, valid loss-1.7685, acc-0.5090, test loss-1.7692, acc-0.5028
Iter-68470, train loss-1.7389, acc-0.5200, valid loss-1.7684, acc-0.5092, test loss-1.7692, acc-0.5027
Iter-68480, train loss-1.8368, acc-0.4400, valid loss-1.7684, acc-0.5092, test loss-1.7691, acc-0.5027
Iter-68490, train loss-1.8701, acc-0.5000, valid loss-1.7683, acc-0.5094, test loss-1.7691, acc-0.5028
Iter-68500, train loss-1.7562, acc-0.4800, valid loss-1.7683, acc-0.5092, test loss-1.7690, acc-0.5027
Iter-68510, train loss-1.8632, acc-0.4600, valid loss-1.7682, acc-0.5094, test loss-1.7690, acc-0.5028
Iter-68520, train loss-1.8250, acc-0.4600, valid loss-1.7682, acc-0.5094, test loss-1.7690, acc-0.5028
Iter-68530, train loss-1.6665, acc-0.6400, valid loss-1.7681, acc-0.5094, test loss-1.7689, acc-0.5027
Iter-68540, train loss-1.7788, acc-0.5400, valid loss-1.7681, acc-0.5094, test loss-1.7689, acc-0.5027
Iter-68550, train loss-1.7245, acc-0.4800, valid loss-1.7680, acc-0.5092, test loss-1.7688, acc-0.5029
Iter-68560, train loss-1.7724, acc-0.5600, valid loss-1.7680, acc-0.5090, test loss-1.7688, acc-0.5029
Iter-68570, train loss-1.8138, acc-0.4800, valid loss-1.7680, acc-0.5090, test loss-1.7687, acc-0.5027
Iter-68580, train loss-1.7284, acc-0.5600, valid loss-1.7679, acc-0.5096, test loss-1.7687, acc-0.5028
Iter-68590, train loss-1.7477, acc-0.4800, valid loss-1.7679, acc-0.5094, test loss-1.7686, acc-0.5027
Iter-68600, train loss-1.7778, acc-0.5200, valid loss-1.7678, acc-0.5094, test loss-1.7686, acc-0.5027
Iter-68610, train loss-1.8396, acc-0.4200, valid loss-1.7678, acc-0.5094, test loss-1.7685, acc-0.5027
Iter-68620, train loss-1.7957, acc-0.5600, valid loss-1.7677, acc-0.5092, test loss-1.7685, acc-0.5027
Iter-68630, train loss-1.7640, acc-0.5400, valid loss-1.7677, acc-0.5092, test loss-1.7685, acc-0.5027
Iter-68640, train loss-1.7602, acc-0.5400, valid loss-1.7676, acc-0.5092, test loss-1.7684, acc-0.5026
Iter-68650, train loss-1.7618, acc-0.5400, valid loss-1.7676, acc-0.5092, test loss-1.7684, acc-0.5026
Iter-68660, train loss-1.7875, acc-0.5000, valid loss-1.7675, acc-0.5092, test loss-1.7683, acc-0.5026
Iter-68670, train loss-1.8607, acc-0.4800, valid loss-1.7675, acc-0.5094, test loss-1.7683, acc-0.5025
Iter-68680, train loss-1.6911, acc-0.5800, valid loss-1.7675, acc-0.5094, test loss-1.7682, acc-0.5025
Iter-68690, train loss-1.7311, acc-0.5200, valid loss-1.7674, acc-0.5094, test loss-1.7682, acc-0.5025
Iter-68700, train loss-1.7465, acc-0.4600, valid loss-1.7674, acc-0.5094, test loss-1.7682, acc-0.5026
Iter-68710, train loss-1.7105, acc-0.4600, valid loss-1.7673, acc-0.5094, test loss-1.7681, acc-0.5026
Iter-68720, train loss-1.7964, acc-0.5200, valid loss-1.7673, acc-0.5096, test loss-1.7681, acc-0.5027
Iter-68730, train loss-1.8418, acc-0.4200, valid loss-1.7672, acc-0.5096, test loss-1.7680, acc-0.5028
Iter-68740, train loss-1.7801, acc-0.5600, valid loss-1.7672, acc-0.5096, test loss-1.7680, acc-0.5028
Iter-68750, train loss-1.8377, acc-0.4000, valid loss-1.7672, acc-0.5094, test loss-1.7679, acc-0.5031
Iter-68760, train loss-1.8564, acc-0.4400, valid loss-1.7671, acc-0.5094, test loss-1.7679, acc-0.5029
Iter-68770, train loss-1.7273, acc-0.5400, valid loss-1.7671, acc-0.5094, test loss-1.7678, acc-0.5028
Iter-68780, train loss-1.9206, acc-0.3800, valid loss-1.7670, acc-0.5094, test loss-1.7678, acc-0.5027
Iter-68790, train loss-1.8084, acc-0.4200, valid loss-1.7670, acc-0.5094, test loss-1.7678, acc-0.5028
Iter-68800, train loss-1.8685, acc-0.4200, valid loss-1.7669, acc-0.5094, test loss-1.7677, acc-0.5027
Iter-68810, train loss-1.8125, acc-0.4400, valid loss-1.7669, acc-0.5094, test loss-1.7677, acc-0.5026
Iter-68820, train loss-1.7389, acc-0.5400, valid loss-1.7668, acc-0.5096, test loss-1.7676, acc-0.5028
Iter-68830, train loss-1.7689, acc-0.5000, valid loss-1.7668, acc-0.5098, test loss-1.7676, acc-0.5027
Iter-68840, train loss-1.6974, acc-0.5200, valid loss-1.7668, acc-0.5096, test loss-1.7675, acc-0.5028
Iter-68850, train loss-1.8797, acc-0.3800, valid loss-1.7667, acc-0.5100, test loss-1.7675, acc-0.5027
Iter-68860, train loss-1.8099, acc-0.5200, valid loss-1.7667, acc-0.5100, test loss-1.7675, acc-0.5026
Iter-68870, train loss-1.8243, acc-0.4800, valid loss-1.7666, acc-0.5100, test loss-1.7674, acc-0.5026
Iter-68880, train loss-1.7407, acc-0.5800, valid loss-1.7666, acc-0.5098, test loss-1.7674, acc-0.5026
Iter-68890, train loss-1.7754, acc-0.4800, valid loss-1.7665, acc-0.5098, test loss-1.7673, acc-0.5027
Iter-68900, train loss-1.8340, acc-0.3800, valid loss-1.7665, acc-0.5098, test loss-1.7673, acc-0.5027
Iter-68910, train loss-1.7740, acc-0.5200, valid loss-1.7665, acc-0.5098, test loss-1.7672, acc-0.5027
Iter-68920, train loss-1.7855, acc-0.5600, valid loss-1.7664, acc-0.5100, test loss-1.7672, acc-0.5027
Iter-68930, train loss-1.9088, acc-0.3600, valid loss-1.7664, acc-0.5104, test loss-1.7671, acc-0.5029
Iter-68940, train loss-1.7824, acc-0.5800, valid loss-1.7663, acc-0.5100, test loss-1.7671, acc-0.5029
Iter-68950, train loss-1.6930, acc-0.5000, valid loss-1.7663, acc-0.5106, test loss-1.7671, acc-0.5029
Iter-68960, train loss-1.8283, acc-0.5200, valid loss-1.7662, acc-0.5106, test loss-1.7670, acc-0.5029
Iter-68970, train loss-1.7428, acc-0.5000, valid loss-1.7662, acc-0.5106, test loss-1.7670, acc-0.5029
Iter-68980, train loss-1.8305, acc-0.4400, valid loss-1.7662, acc-0.5106, test loss-1.7669, acc-0.5028
Iter-68990, train loss-1.7235, acc-0.5600, valid loss-1.7661, acc-0.5102, test loss-1.7669, acc-0.5029
Iter-69000, train loss-1.7496, acc-0.6000, valid loss-1.7661, acc-0.5104, test loss-1.7668, acc-0.5030
Iter-69010, train loss-1.7119, acc-0.6400, valid loss-1.7660, acc-0.5098, test loss-1.7668, acc-0.5029
Iter-69020, train loss-1.8201, acc-0.4600, valid loss-1.7660, acc-0.5100, test loss-1.7667, acc-0.5029
Iter-69030, train loss-1.8737, acc-0.4000, valid loss-1.7659, acc-0.5100, test loss-1.7667, acc-0.5032
Iter-69040, train loss-1.7533, acc-0.4800, valid loss-1.7659, acc-0.5100, test loss-1.7667, acc-0.5030
Iter-69050, train loss-1.7908, acc-0.5600, valid loss-1.7658, acc-0.5100, test loss-1.7666, acc-0.5033
Iter-69060, train loss-1.7245, acc-0.5600, valid loss-1.7658, acc-0.5104, test loss-1.7666, acc-0.5030
Iter-69070, train loss-1.7217, acc-0.6200, valid loss-1.7658, acc-0.5104, test loss-1.7665, acc-0.5031
Iter-69080, train loss-1.7627, acc-0.5600, valid loss-1.7657, acc-0.5104, test loss-1.7665, acc-0.5031
Iter-69090, train loss-1.7869, acc-0.5000, valid loss-1.7657, acc-0.5106, test loss-1.7664, acc-0.5031
Iter-69100, train loss-1.8180, acc-0.5600, valid loss-1.7656, acc-0.5106, test loss-1.7664, acc-0.5031
Iter-69110, train loss-1.7564, acc-0.4400, valid loss-1.7656, acc-0.5106, test loss-1.7664, acc-0.5031
Iter-69120, train loss-1.8598, acc-0.3600, valid loss-1.7655, acc-0.5106, test loss-1.7663, acc-0.5031
Iter-69130, train loss-1.6999, acc-0.5600, valid loss-1.7655, acc-0.5104, test loss-1.7663, acc-0.5031
Iter-69140, train loss-1.8546, acc-0.4600, valid loss-1.7654, acc-0.5104, test loss-1.7662, acc-0.5031
Iter-69150, train loss-1.7454, acc-0.5800, valid loss-1.7654, acc-0.5104, test loss-1.7662, acc-0.5031
Iter-69160, train loss-1.7739, acc-0.5400, valid loss-1.7654, acc-0.5106, test loss-1.7661, acc-0.5031
Iter-69170, train loss-1.8526, acc-0.4800, valid loss-1.7653, acc-0.5106, test loss-1.7661, acc-0.5031
Iter-69180, train loss-1.7215, acc-0.5000, valid loss-1.7653, acc-0.5108, test loss-1.7660, acc-0.5031
Iter-69190, train loss-1.7648, acc-0.5800, valid loss-1.7652, acc-0.5108, test loss-1.7660, acc-0.5031
Iter-69200, train loss-1.8483, acc-0.4000, valid loss-1.7652, acc-0.5108, test loss-1.7660, acc-0.5031
Iter-69210, train loss-1.8181, acc-0.4400, valid loss-1.7651, acc-0.5108, test loss-1.7659, acc-0.5032
Iter-69220, train loss-1.6872, acc-0.6200, valid loss-1.7651, acc-0.5108, test loss-1.7659, acc-0.5032
Iter-69230, train loss-1.8356, acc-0.4600, valid loss-1.7650, acc-0.5108, test loss-1.7658, acc-0.5032
Iter-69240, train loss-1.7649, acc-0.5000, valid loss-1.7650, acc-0.5108, test loss-1.7658, acc-0.5033
Iter-69250, train loss-1.7747, acc-0.5400, valid loss-1.7650, acc-0.5106, test loss-1.7657, acc-0.5033
Iter-69260, train loss-1.7175, acc-0.4200, valid loss-1.7649, acc-0.5106, test loss-1.7657, acc-0.5033
Iter-69270, train loss-1.7557, acc-0.5600, valid loss-1.7649, acc-0.5104, test loss-1.7657, acc-0.5033
Iter-69280, train loss-1.7296, acc-0.5600, valid loss-1.7648, acc-0.5108, test loss-1.7656, acc-0.5033
Iter-69290, train loss-1.7371, acc-0.4600, valid loss-1.7648, acc-0.5106, test loss-1.7656, acc-0.5035
Iter-69300, train loss-1.8426, acc-0.4800, valid loss-1.7647, acc-0.5108, test loss-1.7655, acc-0.5033
Iter-69310, train loss-1.8412, acc-0.4800, valid loss-1.7647, acc-0.5108, test loss-1.7655, acc-0.5034
Iter-69320, train loss-1.8040, acc-0.5000, valid loss-1.7646, acc-0.5110, test loss-1.7654, acc-0.5033
Iter-69330, train loss-1.7592, acc-0.4800, valid loss-1.7646, acc-0.5110, test loss-1.7654, acc-0.5033
Iter-69340, train loss-1.7387, acc-0.5200, valid loss-1.7646, acc-0.5110, test loss-1.7653, acc-0.5033
Iter-69350, train loss-1.8344, acc-0.4400, valid loss-1.7645, acc-0.5112, test loss-1.7653, acc-0.5032
Iter-69360, train loss-1.7983, acc-0.4600, valid loss-1.7645, acc-0.5112, test loss-1.7653, acc-0.5032
Iter-69370, train loss-1.7533, acc-0.4600, valid loss-1.7644, acc-0.5112, test loss-1.7652, acc-0.5032
Iter-69380, train loss-1.7958, acc-0.5400, valid loss-1.7644, acc-0.5110, test loss-1.7652, acc-0.5032
Iter-69390, train loss-1.7203, acc-0.5600, valid loss-1.7643, acc-0.5110, test loss-1.7651, acc-0.5031
Iter-69400, train loss-1.7900, acc-0.5800, valid loss-1.7643, acc-0.5112, test loss-1.7651, acc-0.5033
Iter-69410, train loss-1.8349, acc-0.4600, valid loss-1.7643, acc-0.5112, test loss-1.7650, acc-0.5033
Iter-69420, train loss-1.7682, acc-0.4600, valid loss-1.7642, acc-0.5112, test loss-1.7650, acc-0.5033
Iter-69430, train loss-1.7287, acc-0.4800, valid loss-1.7642, acc-0.5112, test loss-1.7650, acc-0.5034
Iter-69440, train loss-1.7041, acc-0.6400, valid loss-1.7641, acc-0.5112, test loss-1.7649, acc-0.5033
Iter-69450, train loss-1.8304, acc-0.4800, valid loss-1.7641, acc-0.5114, test loss-1.7649, acc-0.5033
Iter-69460, train loss-1.7265, acc-0.5800, valid loss-1.7640, acc-0.5110, test loss-1.7648, acc-0.5033
Iter-69470, train loss-1.7701, acc-0.5200, valid loss-1.7640, acc-0.5112, test loss-1.7648, acc-0.5032
Iter-69480, train loss-1.6818, acc-0.6600, valid loss-1.7640, acc-0.5114, test loss-1.7647, acc-0.5032
Iter-69490, train loss-1.6708, acc-0.5800, valid loss-1.7639, acc-0.5114, test loss-1.7647, acc-0.5032
Iter-69500, train loss-1.7583, acc-0.5000, valid loss-1.7639, acc-0.5116, test loss-1.7647, acc-0.5033
Iter-69510, train loss-1.8591, acc-0.3800, valid loss-1.7638, acc-0.5114, test loss-1.7646, acc-0.5031
Iter-69520, train loss-1.7024, acc-0.4800, valid loss-1.7638, acc-0.5114, test loss-1.7646, acc-0.5032
Iter-69530, train loss-1.8655, acc-0.4200, valid loss-1.7637, acc-0.5114, test loss-1.7645, acc-0.5030
Iter-69540, train loss-1.7445, acc-0.4600, valid loss-1.7637, acc-0.5114, test loss-1.7645, acc-0.5030
Iter-69550, train loss-1.7658, acc-0.5200, valid loss-1.7636, acc-0.5112, test loss-1.7644, acc-0.5032
Iter-69560, train loss-1.7493, acc-0.5000, valid loss-1.7636, acc-0.5114, test loss-1.7644, acc-0.5031
Iter-69570, train loss-1.8379, acc-0.5000, valid loss-1.7636, acc-0.5114, test loss-1.7643, acc-0.5031
Iter-69580, train loss-1.8073, acc-0.5600, valid loss-1.7635, acc-0.5116, test loss-1.7643, acc-0.5031
Iter-69590, train loss-1.7789, acc-0.5400, valid loss-1.7635, acc-0.5116, test loss-1.7643, acc-0.5031
Iter-69600, train loss-1.7219, acc-0.5200, valid loss-1.7634, acc-0.5116, test loss-1.7642, acc-0.5030
Iter-69610, train loss-1.5905, acc-0.5400, valid loss-1.7634, acc-0.5118, test loss-1.7642, acc-0.5030
Iter-69620, train loss-1.7963, acc-0.5000, valid loss-1.7633, acc-0.5118, test loss-1.7641, acc-0.5031
Iter-69630, train loss-1.7903, acc-0.4800, valid loss-1.7633, acc-0.5120, test loss-1.7641, acc-0.5032
Iter-69640, train loss-1.7697, acc-0.4600, valid loss-1.7633, acc-0.5120, test loss-1.7640, acc-0.5031
Iter-69650, train loss-1.8156, acc-0.4400, valid loss-1.7632, acc-0.5116, test loss-1.7640, acc-0.5031
Iter-69660, train loss-1.6891, acc-0.5400, valid loss-1.7632, acc-0.5122, test loss-1.7640, acc-0.5033
Iter-69670, train loss-1.7200, acc-0.5200, valid loss-1.7631, acc-0.5122, test loss-1.7639, acc-0.5032
Iter-69680, train loss-1.8266, acc-0.5200, valid loss-1.7631, acc-0.5122, test loss-1.7639, acc-0.5033
Iter-69690, train loss-1.6909, acc-0.6000, valid loss-1.7630, acc-0.5122, test loss-1.7638, acc-0.5033
Iter-69700, train loss-1.7544, acc-0.5000, valid loss-1.7630, acc-0.5122, test loss-1.7638, acc-0.5034
Iter-69710, train loss-1.7542, acc-0.4800, valid loss-1.7630, acc-0.5122, test loss-1.7637, acc-0.5033
Iter-69720, train loss-1.6889, acc-0.5400, valid loss-1.7629, acc-0.5118, test loss-1.7637, acc-0.5033
Iter-69730, train loss-1.6356, acc-0.5600, valid loss-1.7629, acc-0.5120, test loss-1.7636, acc-0.5033
Iter-69740, train loss-1.6328, acc-0.6400, valid loss-1.7628, acc-0.5120, test loss-1.7636, acc-0.5034
Iter-69750, train loss-1.9194, acc-0.4800, valid loss-1.7628, acc-0.5120, test loss-1.7636, acc-0.5034
Iter-69760, train loss-1.7912, acc-0.4800, valid loss-1.7627, acc-0.5118, test loss-1.7635, acc-0.5034
Iter-69770, train loss-1.8478, acc-0.4600, valid loss-1.7627, acc-0.5120, test loss-1.7635, acc-0.5033
Iter-69780, train loss-1.8039, acc-0.5800, valid loss-1.7627, acc-0.5118, test loss-1.7634, acc-0.5035
Iter-69790, train loss-1.6151, acc-0.5000, valid loss-1.7626, acc-0.5116, test loss-1.7634, acc-0.5036
Iter-69800, train loss-1.7578, acc-0.4000, valid loss-1.7626, acc-0.5118, test loss-1.7633, acc-0.5035
Iter-69810, train loss-1.7546, acc-0.4400, valid loss-1.7625, acc-0.5118, test loss-1.7633, acc-0.5035
Iter-69820, train loss-1.6926, acc-0.5600, valid loss-1.7625, acc-0.5118, test loss-1.7633, acc-0.5035
Iter-69830, train loss-1.7239, acc-0.5400, valid loss-1.7624, acc-0.5116, test loss-1.7632, acc-0.5035
Iter-69840, train loss-1.7165, acc-0.5400, valid loss-1.7624, acc-0.5118, test loss-1.7632, acc-0.5035
Iter-69850, train loss-1.7631, acc-0.6000, valid loss-1.7623, acc-0.5120, test loss-1.7631, acc-0.5034
Iter-69860, train loss-1.8204, acc-0.5200, valid loss-1.7623, acc-0.5122, test loss-1.7631, acc-0.5036
Iter-69870, train loss-1.7133, acc-0.5800, valid loss-1.7623, acc-0.5124, test loss-1.7630, acc-0.5036
Iter-69880, train loss-1.8033, acc-0.5000, valid loss-1.7622, acc-0.5126, test loss-1.7630, acc-0.5036
Iter-69890, train loss-1.8255, acc-0.4600, valid loss-1.7622, acc-0.5126, test loss-1.7630, acc-0.5036
Iter-69900, train loss-1.7312, acc-0.5200, valid loss-1.7621, acc-0.5124, test loss-1.7629, acc-0.5036
Iter-69910, train loss-1.8617, acc-0.4400, valid loss-1.7621, acc-0.5126, test loss-1.7629, acc-0.5036
Iter-69920, train loss-1.7294, acc-0.4200, valid loss-1.7621, acc-0.5128, test loss-1.7628, acc-0.5036
Iter-69930, train loss-1.7126, acc-0.6000, valid loss-1.7620, acc-0.5128, test loss-1.7628, acc-0.5035
Iter-69940, train loss-1.8653, acc-0.5400, valid loss-1.7620, acc-0.5126, test loss-1.7628, acc-0.5035
Iter-69950, train loss-1.7591, acc-0.5400, valid loss-1.7619, acc-0.5130, test loss-1.7627, acc-0.5035
Iter-69960, train loss-1.7020, acc-0.5400, valid loss-1.7619, acc-0.5130, test loss-1.7627, acc-0.5035
Iter-69970, train loss-1.7553, acc-0.4600, valid loss-1.7618, acc-0.5126, test loss-1.7626, acc-0.5035
Iter-69980, train loss-1.6914, acc-0.5600, valid loss-1.7618, acc-0.5128, test loss-1.7626, acc-0.5036
Iter-69990, train loss-1.7669, acc-0.4200, valid loss-1.7618, acc-0.5128, test loss-1.7625, acc-0.5035
Iter-70000, train loss-1.6910, acc-0.6200, valid loss-1.7617, acc-0.5128, test loss-1.7625, acc-0.5036
Iter-70010, train loss-1.7279, acc-0.4200, valid loss-1.7617, acc-0.5128, test loss-1.7624, acc-0.5036
Iter-70020, train loss-1.7661, acc-0.4800, valid loss-1.7616, acc-0.5128, test loss-1.7624, acc-0.5036
Iter-70030, train loss-1.7300, acc-0.5200, valid loss-1.7616, acc-0.5130, test loss-1.7624, acc-0.5036
Iter-70040, train loss-1.7551, acc-0.5000, valid loss-1.7615, acc-0.5130, test loss-1.7623, acc-0.5036
Iter-70050, train loss-1.7412, acc-0.6000, valid loss-1.7615, acc-0.5130, test loss-1.7623, acc-0.5035
Iter-70060, train loss-1.6961, acc-0.5600, valid loss-1.7614, acc-0.5130, test loss-1.7622, acc-0.5035
Iter-70070, train loss-1.8288, acc-0.5400, valid loss-1.7614, acc-0.5130, test loss-1.7622, acc-0.5036
Iter-70080, train loss-1.7144, acc-0.6600, valid loss-1.7614, acc-0.5132, test loss-1.7621, acc-0.5036
Iter-70090, train loss-1.8520, acc-0.4400, valid loss-1.7613, acc-0.5132, test loss-1.7621, acc-0.5035
Iter-70100, train loss-1.7968, acc-0.5000, valid loss-1.7613, acc-0.5130, test loss-1.7621, acc-0.5036
Iter-70110, train loss-1.8799, acc-0.3200, valid loss-1.7612, acc-0.5130, test loss-1.7620, acc-0.5035
Iter-70120, train loss-1.7870, acc-0.4800, valid loss-1.7612, acc-0.5130, test loss-1.7620, acc-0.5035
Iter-70130, train loss-1.6359, acc-0.5800, valid loss-1.7611, acc-0.5130, test loss-1.7619, acc-0.5034
Iter-70140, train loss-1.8457, acc-0.4400, valid loss-1.7611, acc-0.5126, test loss-1.7619, acc-0.5036
Iter-70150, train loss-1.7497, acc-0.5600, valid loss-1.7610, acc-0.5128, test loss-1.7618, acc-0.5036
Iter-70160, train loss-1.7532, acc-0.5800, valid loss-1.7610, acc-0.5130, test loss-1.7618, acc-0.5035
Iter-70170, train loss-1.6570, acc-0.5600, valid loss-1.7610, acc-0.5128, test loss-1.7617, acc-0.5035
Iter-70180, train loss-1.8453, acc-0.4600, valid loss-1.7609, acc-0.5128, test loss-1.7617, acc-0.5035
Iter-70190, train loss-1.7869, acc-0.4400, valid loss-1.7609, acc-0.5124, test loss-1.7617, acc-0.5036
Iter-70200, train loss-1.6979, acc-0.5400, valid loss-1.7608, acc-0.5124, test loss-1.7616, acc-0.5037
Iter-70210, train loss-1.8188, acc-0.5000, valid loss-1.7608, acc-0.5124, test loss-1.7616, acc-0.5036
Iter-70220, train loss-2.0315, acc-0.3400, valid loss-1.7607, acc-0.5126, test loss-1.7615, acc-0.5037
Iter-70230, train loss-1.8549, acc-0.3400, valid loss-1.7607, acc-0.5128, test loss-1.7615, acc-0.5038
Iter-70240, train loss-1.8194, acc-0.4400, valid loss-1.7607, acc-0.5128, test loss-1.7614, acc-0.5038
Iter-70250, train loss-1.8221, acc-0.5000, valid loss-1.7606, acc-0.5130, test loss-1.7614, acc-0.5037
Iter-70260, train loss-1.7377, acc-0.5000, valid loss-1.7606, acc-0.5132, test loss-1.7614, acc-0.5037
Iter-70270, train loss-1.8114, acc-0.4600, valid loss-1.7605, acc-0.5126, test loss-1.7613, acc-0.5036
Iter-70280, train loss-1.7417, acc-0.5400, valid loss-1.7605, acc-0.5126, test loss-1.7613, acc-0.5036
Iter-70290, train loss-1.8722, acc-0.4400, valid loss-1.7605, acc-0.5126, test loss-1.7612, acc-0.5037
Iter-70300, train loss-1.8143, acc-0.4400, valid loss-1.7604, acc-0.5128, test loss-1.7612, acc-0.5037
Iter-70310, train loss-1.8269, acc-0.4200, valid loss-1.7604, acc-0.5132, test loss-1.7612, acc-0.5037
Iter-70320, train loss-1.6889, acc-0.5600, valid loss-1.7603, acc-0.5132, test loss-1.7611, acc-0.5038
Iter-70330, train loss-1.7915, acc-0.4400, valid loss-1.7603, acc-0.5132, test loss-1.7611, acc-0.5038
Iter-70340, train loss-1.8749, acc-0.3800, valid loss-1.7602, acc-0.5132, test loss-1.7610, acc-0.5038
Iter-70350, train loss-1.7149, acc-0.5400, valid loss-1.7602, acc-0.5132, test loss-1.7610, acc-0.5039
Iter-70360, train loss-1.8393, acc-0.4600, valid loss-1.7601, acc-0.5132, test loss-1.7609, acc-0.5038
Iter-70370, train loss-1.8410, acc-0.4800, valid loss-1.7601, acc-0.5134, test loss-1.7609, acc-0.5040
Iter-70380, train loss-1.7470, acc-0.5200, valid loss-1.7601, acc-0.5134, test loss-1.7609, acc-0.5038
Iter-70390, train loss-1.7172, acc-0.6200, valid loss-1.7600, acc-0.5134, test loss-1.7608, acc-0.5041
Iter-70400, train loss-1.7805, acc-0.4600, valid loss-1.7600, acc-0.5134, test loss-1.7608, acc-0.5040
Iter-70410, train loss-1.7749, acc-0.4400, valid loss-1.7599, acc-0.5134, test loss-1.7607, acc-0.5039
Iter-70420, train loss-1.7658, acc-0.5800, valid loss-1.7599, acc-0.5134, test loss-1.7607, acc-0.5041
Iter-70430, train loss-1.8065, acc-0.5200, valid loss-1.7598, acc-0.5134, test loss-1.7606, acc-0.5043
Iter-70440, train loss-1.7591, acc-0.5400, valid loss-1.7598, acc-0.5136, test loss-1.7606, acc-0.5041
Iter-70450, train loss-1.8213, acc-0.4600, valid loss-1.7598, acc-0.5134, test loss-1.7606, acc-0.5039
Iter-70460, train loss-1.7774, acc-0.4000, valid loss-1.7597, acc-0.5136, test loss-1.7605, acc-0.5040
Iter-70470, train loss-1.7246, acc-0.4400, valid loss-1.7597, acc-0.5136, test loss-1.7605, acc-0.5039
Iter-70480, train loss-1.6821, acc-0.5000, valid loss-1.7596, acc-0.5134, test loss-1.7604, acc-0.5040
Iter-70490, train loss-1.7077, acc-0.5600, valid loss-1.7596, acc-0.5134, test loss-1.7604, acc-0.5038
Iter-70500, train loss-1.7270, acc-0.5400, valid loss-1.7595, acc-0.5134, test loss-1.7603, acc-0.5039
Iter-70510, train loss-1.7913, acc-0.6000, valid loss-1.7595, acc-0.5134, test loss-1.7603, acc-0.5040
Iter-70520, train loss-1.7506, acc-0.5800, valid loss-1.7594, acc-0.5134, test loss-1.7602, acc-0.5041
Iter-70530, train loss-1.6798, acc-0.5200, valid loss-1.7594, acc-0.5134, test loss-1.7602, acc-0.5040
Iter-70540, train loss-1.7049, acc-0.6000, valid loss-1.7594, acc-0.5134, test loss-1.7602, acc-0.5040
Iter-70550, train loss-1.8180, acc-0.4800, valid loss-1.7593, acc-0.5136, test loss-1.7601, acc-0.5039
Iter-70560, train loss-1.8387, acc-0.3800, valid loss-1.7593, acc-0.5134, test loss-1.7601, acc-0.5039
Iter-70570, train loss-1.7023, acc-0.5400, valid loss-1.7592, acc-0.5134, test loss-1.7600, acc-0.5039
Iter-70580, train loss-1.7232, acc-0.5800, valid loss-1.7592, acc-0.5134, test loss-1.7600, acc-0.5040
Iter-70590, train loss-1.7835, acc-0.5000, valid loss-1.7591, acc-0.5132, test loss-1.7599, acc-0.5042
Iter-70600, train loss-1.7370, acc-0.5200, valid loss-1.7591, acc-0.5134, test loss-1.7599, acc-0.5040
Iter-70610, train loss-1.7518, acc-0.4600, valid loss-1.7591, acc-0.5132, test loss-1.7598, acc-0.5041
Iter-70620, train loss-1.8387, acc-0.4200, valid loss-1.7590, acc-0.5132, test loss-1.7598, acc-0.5045
Iter-70630, train loss-1.7946, acc-0.5200, valid loss-1.7590, acc-0.5134, test loss-1.7598, acc-0.5042
Iter-70640, train loss-1.8345, acc-0.5000, valid loss-1.7589, acc-0.5132, test loss-1.7597, acc-0.5041
Iter-70650, train loss-1.8925, acc-0.4800, valid loss-1.7589, acc-0.5136, test loss-1.7597, acc-0.5042
Iter-70660, train loss-1.7555, acc-0.4800, valid loss-1.7588, acc-0.5134, test loss-1.7596, acc-0.5041
Iter-70670, train loss-1.7833, acc-0.5600, valid loss-1.7588, acc-0.5132, test loss-1.7596, acc-0.5041
Iter-70680, train loss-1.7540, acc-0.5600, valid loss-1.7588, acc-0.5134, test loss-1.7596, acc-0.5040
Iter-70690, train loss-1.6917, acc-0.6000, valid loss-1.7587, acc-0.5134, test loss-1.7595, acc-0.5041
Iter-70700, train loss-1.6990, acc-0.5800, valid loss-1.7587, acc-0.5134, test loss-1.7595, acc-0.5040
Iter-70710, train loss-1.7819, acc-0.5000, valid loss-1.7586, acc-0.5136, test loss-1.7594, acc-0.5044
Iter-70720, train loss-1.7580, acc-0.4600, valid loss-1.7586, acc-0.5134, test loss-1.7594, acc-0.5043
Iter-70730, train loss-1.8327, acc-0.4600, valid loss-1.7585, acc-0.5134, test loss-1.7593, acc-0.5043
Iter-70740, train loss-1.8228, acc-0.4200, valid loss-1.7585, acc-0.5134, test loss-1.7593, acc-0.5043
Iter-70750, train loss-1.6480, acc-0.6000, valid loss-1.7585, acc-0.5134, test loss-1.7593, acc-0.5042
Iter-70760, train loss-1.8567, acc-0.4200, valid loss-1.7584, acc-0.5134, test loss-1.7592, acc-0.5042
Iter-70770, train loss-1.7301, acc-0.6200, valid loss-1.7584, acc-0.5134, test loss-1.7592, acc-0.5040
Iter-70780, train loss-1.6923, acc-0.5000, valid loss-1.7583, acc-0.5134, test loss-1.7591, acc-0.5040
Iter-70790, train loss-1.8565, acc-0.4200, valid loss-1.7583, acc-0.5134, test loss-1.7591, acc-0.5040
Iter-70800, train loss-1.6074, acc-0.6600, valid loss-1.7582, acc-0.5134, test loss-1.7590, acc-0.5040
Iter-70810, train loss-1.7590, acc-0.4600, valid loss-1.7582, acc-0.5134, test loss-1.7590, acc-0.5041
Iter-70820, train loss-1.7868, acc-0.5400, valid loss-1.7582, acc-0.5134, test loss-1.7590, acc-0.5041
Iter-70830, train loss-1.8280, acc-0.4600, valid loss-1.7581, acc-0.5134, test loss-1.7589, acc-0.5042
Iter-70840, train loss-1.7427, acc-0.5200, valid loss-1.7581, acc-0.5136, test loss-1.7589, acc-0.5045
Iter-70850, train loss-1.7818, acc-0.5800, valid loss-1.7580, acc-0.5136, test loss-1.7588, acc-0.5044
Iter-70860, train loss-1.8350, acc-0.4000, valid loss-1.7580, acc-0.5134, test loss-1.7588, acc-0.5042
Iter-70870, train loss-1.7700, acc-0.4600, valid loss-1.7580, acc-0.5136, test loss-1.7587, acc-0.5044
Iter-70880, train loss-1.8252, acc-0.4800, valid loss-1.7579, acc-0.5136, test loss-1.7587, acc-0.5043
Iter-70890, train loss-1.7971, acc-0.5000, valid loss-1.7579, acc-0.5138, test loss-1.7587, acc-0.5043
Iter-70900, train loss-1.8027, acc-0.5400, valid loss-1.7578, acc-0.5136, test loss-1.7586, acc-0.5042
Iter-70910, train loss-1.7533, acc-0.5000, valid loss-1.7578, acc-0.5138, test loss-1.7586, acc-0.5042
Iter-70920, train loss-1.8482, acc-0.5000, valid loss-1.7578, acc-0.5138, test loss-1.7585, acc-0.5045
Iter-70930, train loss-1.7753, acc-0.6200, valid loss-1.7577, acc-0.5138, test loss-1.7585, acc-0.5046
Iter-70940, train loss-1.8375, acc-0.4200, valid loss-1.7577, acc-0.5138, test loss-1.7585, acc-0.5045
Iter-70950, train loss-1.7901, acc-0.5200, valid loss-1.7576, acc-0.5140, test loss-1.7584, acc-0.5045
Iter-70960, train loss-1.7748, acc-0.5600, valid loss-1.7576, acc-0.5138, test loss-1.7584, acc-0.5046
Iter-70970, train loss-1.7722, acc-0.4800, valid loss-1.7575, acc-0.5138, test loss-1.7583, acc-0.5045
Iter-70980, train loss-1.6643, acc-0.4600, valid loss-1.7575, acc-0.5138, test loss-1.7583, acc-0.5046
Iter-70990, train loss-1.7367, acc-0.5600, valid loss-1.7575, acc-0.5136, test loss-1.7582, acc-0.5048
Iter-71000, train loss-1.8111, acc-0.4400, valid loss-1.7574, acc-0.5138, test loss-1.7582, acc-0.5047
Iter-71010, train loss-1.7799, acc-0.4200, valid loss-1.7574, acc-0.5134, test loss-1.7582, acc-0.5046
Iter-71020, train loss-1.6971, acc-0.5400, valid loss-1.7573, acc-0.5134, test loss-1.7581, acc-0.5047
Iter-71030, train loss-1.6615, acc-0.6000, valid loss-1.7573, acc-0.5136, test loss-1.7581, acc-0.5046
Iter-71040, train loss-1.6679, acc-0.6000, valid loss-1.7572, acc-0.5138, test loss-1.7580, acc-0.5045
Iter-71050, train loss-1.7462, acc-0.4800, valid loss-1.7572, acc-0.5138, test loss-1.7580, acc-0.5047
Iter-71060, train loss-1.8056, acc-0.4000, valid loss-1.7572, acc-0.5136, test loss-1.7579, acc-0.5045
Iter-71070, train loss-1.7419, acc-0.5400, valid loss-1.7571, acc-0.5136, test loss-1.7579, acc-0.5045
Iter-71080, train loss-1.8273, acc-0.3800, valid loss-1.7571, acc-0.5138, test loss-1.7579, acc-0.5046
Iter-71090, train loss-1.9560, acc-0.3800, valid loss-1.7570, acc-0.5138, test loss-1.7578, acc-0.5047
Iter-71100, train loss-1.8080, acc-0.4800, valid loss-1.7570, acc-0.5140, test loss-1.7578, acc-0.5046
Iter-71110, train loss-1.7341, acc-0.5200, valid loss-1.7569, acc-0.5140, test loss-1.7577, acc-0.5048
Iter-71120, train loss-1.7244, acc-0.5800, valid loss-1.7569, acc-0.5140, test loss-1.7577, acc-0.5048
Iter-71130, train loss-1.7627, acc-0.4800, valid loss-1.7569, acc-0.5140, test loss-1.7576, acc-0.5048
Iter-71140, train loss-1.7820, acc-0.5000, valid loss-1.7568, acc-0.5140, test loss-1.7576, acc-0.5049
Iter-71150, train loss-1.7122, acc-0.5200, valid loss-1.7568, acc-0.5138, test loss-1.7576, acc-0.5047
Iter-71160, train loss-1.6952, acc-0.5400, valid loss-1.7567, acc-0.5134, test loss-1.7575, acc-0.5047
Iter-71170, train loss-1.7553, acc-0.4800, valid loss-1.7567, acc-0.5138, test loss-1.7575, acc-0.5047
Iter-71180, train loss-1.6600, acc-0.5600, valid loss-1.7566, acc-0.5138, test loss-1.7574, acc-0.5047
Iter-71190, train loss-1.6916, acc-0.4800, valid loss-1.7566, acc-0.5136, test loss-1.7574, acc-0.5050
Iter-71200, train loss-1.7743, acc-0.4600, valid loss-1.7566, acc-0.5138, test loss-1.7573, acc-0.5045
Iter-71210, train loss-1.7577, acc-0.5200, valid loss-1.7565, acc-0.5138, test loss-1.7573, acc-0.5046
Iter-71220, train loss-1.8107, acc-0.5600, valid loss-1.7565, acc-0.5136, test loss-1.7573, acc-0.5047
Iter-71230, train loss-1.8714, acc-0.5000, valid loss-1.7564, acc-0.5136, test loss-1.7572, acc-0.5047
Iter-71240, train loss-1.8144, acc-0.5400, valid loss-1.7564, acc-0.5140, test loss-1.7572, acc-0.5047
Iter-71250, train loss-1.7536, acc-0.4000, valid loss-1.7564, acc-0.5142, test loss-1.7571, acc-0.5045
Iter-71260, train loss-1.6955, acc-0.6400, valid loss-1.7563, acc-0.5142, test loss-1.7571, acc-0.5044
Iter-71270, train loss-1.7749, acc-0.5000, valid loss-1.7563, acc-0.5142, test loss-1.7571, acc-0.5047
Iter-71280, train loss-1.7353, acc-0.5000, valid loss-1.7562, acc-0.5142, test loss-1.7570, acc-0.5047
Iter-71290, train loss-1.7484, acc-0.5200, valid loss-1.7562, acc-0.5142, test loss-1.7570, acc-0.5048
Iter-71300, train loss-1.8413, acc-0.4000, valid loss-1.7561, acc-0.5144, test loss-1.7569, acc-0.5052
Iter-71310, train loss-1.7974, acc-0.4600, valid loss-1.7561, acc-0.5140, test loss-1.7569, acc-0.5051
Iter-71320, train loss-1.7920, acc-0.5000, valid loss-1.7561, acc-0.5140, test loss-1.7568, acc-0.5050
Iter-71330, train loss-1.8817, acc-0.4200, valid loss-1.7560, acc-0.5140, test loss-1.7568, acc-0.5047
Iter-71340, train loss-1.8271, acc-0.4400, valid loss-1.7560, acc-0.5138, test loss-1.7568, acc-0.5051
Iter-71350, train loss-1.7244, acc-0.6000, valid loss-1.7559, acc-0.5140, test loss-1.7567, acc-0.5050
Iter-71360, train loss-1.7995, acc-0.5400, valid loss-1.7559, acc-0.5140, test loss-1.7567, acc-0.5052
Iter-71370, train loss-1.6425, acc-0.5600, valid loss-1.7559, acc-0.5140, test loss-1.7566, acc-0.5052
Iter-71380, train loss-1.8270, acc-0.4800, valid loss-1.7558, acc-0.5138, test loss-1.7566, acc-0.5052
Iter-71390, train loss-1.7437, acc-0.4800, valid loss-1.7558, acc-0.5138, test loss-1.7566, acc-0.5051
Iter-71400, train loss-1.7522, acc-0.5600, valid loss-1.7557, acc-0.5140, test loss-1.7565, acc-0.5050
Iter-71410, train loss-1.7646, acc-0.5400, valid loss-1.7557, acc-0.5140, test loss-1.7565, acc-0.5049
Iter-71420, train loss-1.7653, acc-0.5600, valid loss-1.7556, acc-0.5140, test loss-1.7564, acc-0.5049
Iter-71430, train loss-1.8429, acc-0.3400, valid loss-1.7556, acc-0.5140, test loss-1.7564, acc-0.5051
Iter-71440, train loss-1.8908, acc-0.4800, valid loss-1.7556, acc-0.5142, test loss-1.7563, acc-0.5050
Iter-71450, train loss-1.7674, acc-0.5800, valid loss-1.7555, acc-0.5142, test loss-1.7563, acc-0.5048
Iter-71460, train loss-1.8194, acc-0.4400, valid loss-1.7555, acc-0.5142, test loss-1.7563, acc-0.5048
Iter-71470, train loss-1.6657, acc-0.5600, valid loss-1.7554, acc-0.5142, test loss-1.7562, acc-0.5049
Iter-71480, train loss-1.7544, acc-0.4800, valid loss-1.7554, acc-0.5142, test loss-1.7562, acc-0.5049
Iter-71490, train loss-1.7780, acc-0.5400, valid loss-1.7553, acc-0.5142, test loss-1.7561, acc-0.5050
Iter-71500, train loss-1.7857, acc-0.5000, valid loss-1.7553, acc-0.5142, test loss-1.7561, acc-0.5049
Iter-71510, train loss-1.6816, acc-0.6000, valid loss-1.7552, acc-0.5142, test loss-1.7560, acc-0.5048
Iter-71520, train loss-1.7289, acc-0.5800, valid loss-1.7552, acc-0.5142, test loss-1.7560, acc-0.5046
Iter-71530, train loss-1.6862, acc-0.5200, valid loss-1.7552, acc-0.5144, test loss-1.7560, acc-0.5048
Iter-71540, train loss-1.7916, acc-0.4400, valid loss-1.7551, acc-0.5142, test loss-1.7559, acc-0.5049
Iter-71550, train loss-1.8867, acc-0.3000, valid loss-1.7551, acc-0.5144, test loss-1.7559, acc-0.5049
Iter-71560, train loss-1.6515, acc-0.6000, valid loss-1.7550, acc-0.5146, test loss-1.7558, acc-0.5051
Iter-71570, train loss-1.7960, acc-0.5000, valid loss-1.7550, acc-0.5146, test loss-1.7558, acc-0.5049
Iter-71580, train loss-1.7800, acc-0.4600, valid loss-1.7550, acc-0.5144, test loss-1.7557, acc-0.5048
Iter-71590, train loss-1.6890, acc-0.6200, valid loss-1.7549, acc-0.5146, test loss-1.7557, acc-0.5048
Iter-71600, train loss-1.7835, acc-0.4400, valid loss-1.7549, acc-0.5144, test loss-1.7557, acc-0.5048
Iter-71610, train loss-1.7370, acc-0.5800, valid loss-1.7548, acc-0.5144, test loss-1.7556, acc-0.5049
Iter-71620, train loss-1.8639, acc-0.4200, valid loss-1.7548, acc-0.5144, test loss-1.7556, acc-0.5047
Iter-71630, train loss-1.7508, acc-0.4600, valid loss-1.7547, acc-0.5144, test loss-1.7555, acc-0.5047
Iter-71640, train loss-1.7806, acc-0.4800, valid loss-1.7547, acc-0.5146, test loss-1.7555, acc-0.5046
Iter-71650, train loss-1.8066, acc-0.4800, valid loss-1.7547, acc-0.5146, test loss-1.7555, acc-0.5047
Iter-71660, train loss-1.6843, acc-0.5400, valid loss-1.7546, acc-0.5148, test loss-1.7554, acc-0.5043
Iter-71670, train loss-1.7649, acc-0.5200, valid loss-1.7546, acc-0.5148, test loss-1.7554, acc-0.5044
Iter-71680, train loss-1.7437, acc-0.5600, valid loss-1.7545, acc-0.5148, test loss-1.7553, acc-0.5044
Iter-71690, train loss-1.8393, acc-0.5000, valid loss-1.7545, acc-0.5146, test loss-1.7553, acc-0.5045
Iter-71700, train loss-1.6971, acc-0.5000, valid loss-1.7544, acc-0.5146, test loss-1.7552, acc-0.5044
Iter-71710, train loss-1.7832, acc-0.4800, valid loss-1.7544, acc-0.5144, test loss-1.7552, acc-0.5043
Iter-71720, train loss-1.6846, acc-0.6400, valid loss-1.7544, acc-0.5142, test loss-1.7552, acc-0.5044
Iter-71730, train loss-1.8113, acc-0.4000, valid loss-1.7543, acc-0.5148, test loss-1.7551, acc-0.5043
Iter-71740, train loss-1.7881, acc-0.5200, valid loss-1.7543, acc-0.5148, test loss-1.7551, acc-0.5042
Iter-71750, train loss-1.7631, acc-0.5800, valid loss-1.7542, acc-0.5150, test loss-1.7550, acc-0.5043
Iter-71760, train loss-1.7192, acc-0.5600, valid loss-1.7542, acc-0.5148, test loss-1.7550, acc-0.5043
Iter-71770, train loss-1.7513, acc-0.5200, valid loss-1.7542, acc-0.5146, test loss-1.7550, acc-0.5042
Iter-71780, train loss-1.6252, acc-0.6000, valid loss-1.7541, acc-0.5144, test loss-1.7549, acc-0.5044
Iter-71790, train loss-1.7418, acc-0.5200, valid loss-1.7541, acc-0.5146, test loss-1.7549, acc-0.5044
Iter-71800, train loss-1.7225, acc-0.5800, valid loss-1.7540, acc-0.5148, test loss-1.7548, acc-0.5043
Iter-71810, train loss-1.7191, acc-0.5800, valid loss-1.7540, acc-0.5148, test loss-1.7548, acc-0.5044
Iter-71820, train loss-1.8131, acc-0.5600, valid loss-1.7539, acc-0.5148, test loss-1.7547, acc-0.5045
Iter-71830, train loss-1.7924, acc-0.5000, valid loss-1.7539, acc-0.5146, test loss-1.7547, acc-0.5044
Iter-71840, train loss-1.8156, acc-0.4600, valid loss-1.7538, acc-0.5146, test loss-1.7547, acc-0.5044
Iter-71850, train loss-1.8531, acc-0.4200, valid loss-1.7538, acc-0.5146, test loss-1.7546, acc-0.5044
Iter-71860, train loss-1.6659, acc-0.5400, valid loss-1.7538, acc-0.5148, test loss-1.7546, acc-0.5044
Iter-71870, train loss-1.6382, acc-0.6200, valid loss-1.7537, acc-0.5146, test loss-1.7545, acc-0.5043
Iter-71880, train loss-1.7192, acc-0.6000, valid loss-1.7537, acc-0.5146, test loss-1.7545, acc-0.5044
Iter-71890, train loss-1.6484, acc-0.6000, valid loss-1.7536, acc-0.5146, test loss-1.7544, acc-0.5044
Iter-71900, train loss-1.7468, acc-0.4600, valid loss-1.7536, acc-0.5148, test loss-1.7544, acc-0.5044
Iter-71910, train loss-1.6688, acc-0.7200, valid loss-1.7536, acc-0.5146, test loss-1.7544, acc-0.5045
Iter-71920, train loss-1.7550, acc-0.5800, valid loss-1.7535, acc-0.5148, test loss-1.7543, acc-0.5044
Iter-71930, train loss-1.8240, acc-0.5200, valid loss-1.7535, acc-0.5150, test loss-1.7543, acc-0.5044
Iter-71940, train loss-1.7488, acc-0.6200, valid loss-1.7534, acc-0.5146, test loss-1.7542, acc-0.5044
Iter-71950, train loss-1.7415, acc-0.5200, valid loss-1.7534, acc-0.5148, test loss-1.7542, acc-0.5043
Iter-71960, train loss-1.7368, acc-0.5400, valid loss-1.7534, acc-0.5146, test loss-1.7542, acc-0.5043
Iter-71970, train loss-1.7476, acc-0.5000, valid loss-1.7533, acc-0.5144, test loss-1.7541, acc-0.5043
Iter-71980, train loss-1.7353, acc-0.4800, valid loss-1.7533, acc-0.5148, test loss-1.7541, acc-0.5044
Iter-71990, train loss-1.6992, acc-0.5800, valid loss-1.7532, acc-0.5146, test loss-1.7540, acc-0.5045
Iter-72000, train loss-1.7028, acc-0.5800, valid loss-1.7532, acc-0.5148, test loss-1.7540, acc-0.5044
Iter-72010, train loss-1.8379, acc-0.3600, valid loss-1.7531, acc-0.5150, test loss-1.7539, acc-0.5045
Iter-72020, train loss-1.7913, acc-0.5200, valid loss-1.7531, acc-0.5150, test loss-1.7539, acc-0.5045
Iter-72030, train loss-1.7748, acc-0.4400, valid loss-1.7531, acc-0.5150, test loss-1.7539, acc-0.5046
Iter-72040, train loss-1.8354, acc-0.5200, valid loss-1.7530, acc-0.5148, test loss-1.7538, acc-0.5044
Iter-72050, train loss-1.7596, acc-0.5600, valid loss-1.7530, acc-0.5150, test loss-1.7538, acc-0.5044
Iter-72060, train loss-1.7256, acc-0.5400, valid loss-1.7529, acc-0.5150, test loss-1.7537, acc-0.5047
Iter-72070, train loss-1.7829, acc-0.4400, valid loss-1.7529, acc-0.5150, test loss-1.7537, acc-0.5048
Iter-72080, train loss-1.7795, acc-0.5200, valid loss-1.7529, acc-0.5148, test loss-1.7536, acc-0.5048
Iter-72090, train loss-1.8146, acc-0.4200, valid loss-1.7528, acc-0.5148, test loss-1.7536, acc-0.5048
Iter-72100, train loss-1.7773, acc-0.5200, valid loss-1.7528, acc-0.5148, test loss-1.7536, acc-0.5047
Iter-72110, train loss-1.7919, acc-0.5000, valid loss-1.7527, acc-0.5150, test loss-1.7535, acc-0.5047
Iter-72120, train loss-1.7403, acc-0.5600, valid loss-1.7527, acc-0.5148, test loss-1.7535, acc-0.5048
Iter-72130, train loss-1.8106, acc-0.4000, valid loss-1.7527, acc-0.5148, test loss-1.7534, acc-0.5046
Iter-72140, train loss-1.8294, acc-0.4800, valid loss-1.7526, acc-0.5148, test loss-1.7534, acc-0.5046
Iter-72150, train loss-1.6764, acc-0.5000, valid loss-1.7526, acc-0.5148, test loss-1.7534, acc-0.5047
Iter-72160, train loss-1.7610, acc-0.5600, valid loss-1.7525, acc-0.5150, test loss-1.7533, acc-0.5044
Iter-72170, train loss-1.6840, acc-0.6000, valid loss-1.7525, acc-0.5152, test loss-1.7533, acc-0.5047
Iter-72180, train loss-1.7457, acc-0.5000, valid loss-1.7524, acc-0.5152, test loss-1.7532, acc-0.5047
Iter-72190, train loss-1.7183, acc-0.5200, valid loss-1.7524, acc-0.5150, test loss-1.7532, acc-0.5048
Iter-72200, train loss-1.7820, acc-0.4600, valid loss-1.7524, acc-0.5150, test loss-1.7532, acc-0.5047
Iter-72210, train loss-1.6421, acc-0.5600, valid loss-1.7523, acc-0.5148, test loss-1.7531, acc-0.5046
Iter-72220, train loss-1.8420, acc-0.3400, valid loss-1.7523, acc-0.5150, test loss-1.7531, acc-0.5046
Iter-72230, train loss-1.8075, acc-0.4800, valid loss-1.7522, acc-0.5150, test loss-1.7530, acc-0.5047
Iter-72240, train loss-1.7703, acc-0.4800, valid loss-1.7522, acc-0.5150, test loss-1.7530, acc-0.5048
Iter-72250, train loss-1.7153, acc-0.5000, valid loss-1.7521, acc-0.5150, test loss-1.7529, acc-0.5047
Iter-72260, train loss-1.7618, acc-0.4000, valid loss-1.7521, acc-0.5150, test loss-1.7529, acc-0.5048
Iter-72270, train loss-1.7197, acc-0.5400, valid loss-1.7521, acc-0.5150, test loss-1.7528, acc-0.5047
Iter-72280, train loss-1.7644, acc-0.5200, valid loss-1.7520, acc-0.5146, test loss-1.7528, acc-0.5047
Iter-72290, train loss-1.7638, acc-0.4200, valid loss-1.7520, acc-0.5148, test loss-1.7528, acc-0.5048
Iter-72300, train loss-1.6790, acc-0.5400, valid loss-1.7519, acc-0.5150, test loss-1.7527, acc-0.5048
Iter-72310, train loss-1.8114, acc-0.5400, valid loss-1.7519, acc-0.5150, test loss-1.7527, acc-0.5047
Iter-72320, train loss-1.6888, acc-0.6400, valid loss-1.7518, acc-0.5150, test loss-1.7526, acc-0.5045
Iter-72330, train loss-1.7864, acc-0.5600, valid loss-1.7518, acc-0.5152, test loss-1.7526, acc-0.5046
Iter-72340, train loss-1.8542, acc-0.5000, valid loss-1.7518, acc-0.5154, test loss-1.7526, acc-0.5044
Iter-72350, train loss-1.8399, acc-0.5000, valid loss-1.7517, acc-0.5154, test loss-1.7525, acc-0.5044
Iter-72360, train loss-1.7469, acc-0.5200, valid loss-1.7517, acc-0.5152, test loss-1.7525, acc-0.5044
Iter-72370, train loss-1.7659, acc-0.4800, valid loss-1.7516, acc-0.5150, test loss-1.7524, acc-0.5045
Iter-72380, train loss-1.6117, acc-0.5800, valid loss-1.7516, acc-0.5148, test loss-1.7524, acc-0.5047
Iter-72390, train loss-1.7662, acc-0.4400, valid loss-1.7515, acc-0.5148, test loss-1.7524, acc-0.5047
Iter-72400, train loss-1.6929, acc-0.5200, valid loss-1.7515, acc-0.5150, test loss-1.7523, acc-0.5047
Iter-72410, train loss-1.8414, acc-0.5400, valid loss-1.7515, acc-0.5150, test loss-1.7523, acc-0.5047
Iter-72420, train loss-1.7994, acc-0.4400, valid loss-1.7514, acc-0.5150, test loss-1.7522, acc-0.5047
Iter-72430, train loss-1.7052, acc-0.5600, valid loss-1.7514, acc-0.5152, test loss-1.7522, acc-0.5046
Iter-72440, train loss-1.7727, acc-0.5200, valid loss-1.7513, acc-0.5152, test loss-1.7521, acc-0.5046
Iter-72450, train loss-1.7643, acc-0.5800, valid loss-1.7513, acc-0.5154, test loss-1.7521, acc-0.5044
Iter-72460, train loss-1.7676, acc-0.5200, valid loss-1.7513, acc-0.5156, test loss-1.7521, acc-0.5045
Iter-72470, train loss-1.7937, acc-0.5800, valid loss-1.7512, acc-0.5156, test loss-1.7520, acc-0.5046
Iter-72480, train loss-1.7391, acc-0.5400, valid loss-1.7512, acc-0.5156, test loss-1.7520, acc-0.5045
Iter-72490, train loss-1.7530, acc-0.5200, valid loss-1.7511, acc-0.5156, test loss-1.7519, acc-0.5048
Iter-72500, train loss-1.7139, acc-0.5800, valid loss-1.7511, acc-0.5158, test loss-1.7519, acc-0.5048
Iter-72510, train loss-1.7884, acc-0.4600, valid loss-1.7510, acc-0.5160, test loss-1.7519, acc-0.5047
Iter-72520, train loss-1.9083, acc-0.4000, valid loss-1.7510, acc-0.5162, test loss-1.7518, acc-0.5047
Iter-72530, train loss-1.7520, acc-0.5000, valid loss-1.7510, acc-0.5158, test loss-1.7518, acc-0.5048
Iter-72540, train loss-1.7081, acc-0.5600, valid loss-1.7509, acc-0.5158, test loss-1.7517, acc-0.5047
Iter-72550, train loss-1.7091, acc-0.6400, valid loss-1.7509, acc-0.5158, test loss-1.7517, acc-0.5047
Iter-72560, train loss-1.6148, acc-0.5800, valid loss-1.7508, acc-0.5158, test loss-1.7516, acc-0.5049
Iter-72570, train loss-1.7937, acc-0.5000, valid loss-1.7508, acc-0.5160, test loss-1.7516, acc-0.5046
Iter-72580, train loss-1.7514, acc-0.5200, valid loss-1.7508, acc-0.5158, test loss-1.7516, acc-0.5049
Iter-72590, train loss-1.7397, acc-0.5000, valid loss-1.7507, acc-0.5158, test loss-1.7515, acc-0.5048
Iter-72600, train loss-1.7404, acc-0.4800, valid loss-1.7507, acc-0.5158, test loss-1.7515, acc-0.5049
Iter-72610, train loss-1.7339, acc-0.6000, valid loss-1.7506, acc-0.5154, test loss-1.7515, acc-0.5048
Iter-72620, train loss-1.8062, acc-0.4200, valid loss-1.7506, acc-0.5156, test loss-1.7514, acc-0.5048
Iter-72630, train loss-1.8430, acc-0.4200, valid loss-1.7506, acc-0.5158, test loss-1.7514, acc-0.5048
Iter-72640, train loss-1.7613, acc-0.5000, valid loss-1.7505, acc-0.5158, test loss-1.7513, acc-0.5049
Iter-72650, train loss-1.7854, acc-0.4800, valid loss-1.7505, acc-0.5158, test loss-1.7513, acc-0.5049
Iter-72660, train loss-1.7859, acc-0.5000, valid loss-1.7504, acc-0.5158, test loss-1.7512, acc-0.5048
Iter-72670, train loss-1.7326, acc-0.4600, valid loss-1.7504, acc-0.5158, test loss-1.7512, acc-0.5048
Iter-72680, train loss-1.7679, acc-0.4800, valid loss-1.7503, acc-0.5160, test loss-1.7512, acc-0.5046
Iter-72690, train loss-1.7154, acc-0.5800, valid loss-1.7503, acc-0.5160, test loss-1.7511, acc-0.5045
Iter-72700, train loss-1.7998, acc-0.3000, valid loss-1.7503, acc-0.5160, test loss-1.7511, acc-0.5046
Iter-72710, train loss-1.8627, acc-0.4800, valid loss-1.7502, acc-0.5160, test loss-1.7510, acc-0.5047
Iter-72720, train loss-1.6957, acc-0.5800, valid loss-1.7502, acc-0.5160, test loss-1.7510, acc-0.5047
Iter-72730, train loss-1.7251, acc-0.5200, valid loss-1.7501, acc-0.5160, test loss-1.7510, acc-0.5045
Iter-72740, train loss-1.7773, acc-0.5200, valid loss-1.7501, acc-0.5162, test loss-1.7509, acc-0.5046
Iter-72750, train loss-1.7896, acc-0.6000, valid loss-1.7501, acc-0.5160, test loss-1.7509, acc-0.5046
Iter-72760, train loss-1.7019, acc-0.6200, valid loss-1.7500, acc-0.5160, test loss-1.7508, acc-0.5046
Iter-72770, train loss-1.7758, acc-0.4800, valid loss-1.7500, acc-0.5160, test loss-1.7508, acc-0.5046
Iter-72780, train loss-1.7676, acc-0.4600, valid loss-1.7499, acc-0.5160, test loss-1.7507, acc-0.5044
Iter-72790, train loss-1.7335, acc-0.5400, valid loss-1.7499, acc-0.5160, test loss-1.7507, acc-0.5046
Iter-72800, train loss-1.8790, acc-0.4000, valid loss-1.7499, acc-0.5160, test loss-1.7507, acc-0.5047
Iter-72810, train loss-1.8850, acc-0.3800, valid loss-1.7498, acc-0.5158, test loss-1.7506, acc-0.5045
Iter-72820, train loss-1.7656, acc-0.5600, valid loss-1.7498, acc-0.5160, test loss-1.7506, acc-0.5045
Iter-72830, train loss-1.8102, acc-0.5000, valid loss-1.7497, acc-0.5158, test loss-1.7505, acc-0.5045
Iter-72840, train loss-1.8228, acc-0.4400, valid loss-1.7497, acc-0.5156, test loss-1.7505, acc-0.5045
Iter-72850, train loss-1.7714, acc-0.5600, valid loss-1.7496, acc-0.5156, test loss-1.7505, acc-0.5045
Iter-72860, train loss-1.7655, acc-0.4600, valid loss-1.7496, acc-0.5156, test loss-1.7504, acc-0.5046
Iter-72870, train loss-1.8221, acc-0.4000, valid loss-1.7496, acc-0.5156, test loss-1.7504, acc-0.5044
Iter-72880, train loss-1.6083, acc-0.6000, valid loss-1.7495, acc-0.5156, test loss-1.7503, acc-0.5045
Iter-72890, train loss-1.8773, acc-0.3600, valid loss-1.7495, acc-0.5158, test loss-1.7503, acc-0.5047
Iter-72900, train loss-1.8545, acc-0.3600, valid loss-1.7494, acc-0.5158, test loss-1.7503, acc-0.5046
Iter-72910, train loss-1.7468, acc-0.6000, valid loss-1.7494, acc-0.5158, test loss-1.7502, acc-0.5046
Iter-72920, train loss-1.6963, acc-0.4600, valid loss-1.7494, acc-0.5158, test loss-1.7502, acc-0.5049
Iter-72930, train loss-1.8271, acc-0.4600, valid loss-1.7493, acc-0.5158, test loss-1.7501, acc-0.5048
Iter-72940, train loss-1.8071, acc-0.5600, valid loss-1.7493, acc-0.5158, test loss-1.7501, acc-0.5047
Iter-72950, train loss-1.8125, acc-0.4200, valid loss-1.7492, acc-0.5158, test loss-1.7501, acc-0.5047
Iter-72960, train loss-1.7425, acc-0.5800, valid loss-1.7492, acc-0.5158, test loss-1.7500, acc-0.5046
Iter-72970, train loss-1.8692, acc-0.4200, valid loss-1.7492, acc-0.5158, test loss-1.7500, acc-0.5047
Iter-72980, train loss-1.7341, acc-0.5200, valid loss-1.7491, acc-0.5158, test loss-1.7499, acc-0.5047
Iter-72990, train loss-1.7852, acc-0.4400, valid loss-1.7491, acc-0.5156, test loss-1.7499, acc-0.5047
Iter-73000, train loss-1.8893, acc-0.3800, valid loss-1.7490, acc-0.5156, test loss-1.7499, acc-0.5047
Iter-73010, train loss-1.7698, acc-0.4600, valid loss-1.7490, acc-0.5158, test loss-1.7498, acc-0.5047
Iter-73020, train loss-1.7585, acc-0.5200, valid loss-1.7490, acc-0.5156, test loss-1.7498, acc-0.5047
Iter-73030, train loss-1.7810, acc-0.5200, valid loss-1.7489, acc-0.5156, test loss-1.7497, acc-0.5047
Iter-73040, train loss-1.6721, acc-0.4800, valid loss-1.7489, acc-0.5156, test loss-1.7497, acc-0.5046
Iter-73050, train loss-1.7551, acc-0.4800, valid loss-1.7488, acc-0.5156, test loss-1.7497, acc-0.5048
Iter-73060, train loss-1.6643, acc-0.6200, valid loss-1.7488, acc-0.5156, test loss-1.7496, acc-0.5046
Iter-73070, train loss-1.7069, acc-0.5400, valid loss-1.7488, acc-0.5156, test loss-1.7496, acc-0.5047
Iter-73080, train loss-1.8645, acc-0.4200, valid loss-1.7487, acc-0.5156, test loss-1.7495, acc-0.5048
Iter-73090, train loss-1.8070, acc-0.4800, valid loss-1.7487, acc-0.5158, test loss-1.7495, acc-0.5049
Iter-73100, train loss-1.7190, acc-0.4800, valid loss-1.7486, acc-0.5158, test loss-1.7495, acc-0.5048
Iter-73110, train loss-1.7926, acc-0.4400, valid loss-1.7486, acc-0.5158, test loss-1.7494, acc-0.5049
Iter-73120, train loss-1.7217, acc-0.5000, valid loss-1.7485, acc-0.5158, test loss-1.7494, acc-0.5047
Iter-73130, train loss-1.8024, acc-0.5200, valid loss-1.7485, acc-0.5158, test loss-1.7493, acc-0.5047
Iter-73140, train loss-1.8451, acc-0.3800, valid loss-1.7485, acc-0.5158, test loss-1.7493, acc-0.5047
Iter-73150, train loss-1.7952, acc-0.4200, valid loss-1.7484, acc-0.5160, test loss-1.7492, acc-0.5048
Iter-73160, train loss-1.8203, acc-0.5000, valid loss-1.7484, acc-0.5158, test loss-1.7492, acc-0.5050
Iter-73170, train loss-1.7131, acc-0.5400, valid loss-1.7483, acc-0.5158, test loss-1.7492, acc-0.5050
Iter-73180, train loss-1.7112, acc-0.5400, valid loss-1.7483, acc-0.5160, test loss-1.7491, acc-0.5048
Iter-73190, train loss-1.6855, acc-0.6400, valid loss-1.7483, acc-0.5158, test loss-1.7491, acc-0.5050
Iter-73200, train loss-1.8060, acc-0.4800, valid loss-1.7482, acc-0.5158, test loss-1.7490, acc-0.5050
Iter-73210, train loss-1.8487, acc-0.4200, valid loss-1.7482, acc-0.5158, test loss-1.7490, acc-0.5049
Iter-73220, train loss-1.6943, acc-0.5600, valid loss-1.7481, acc-0.5158, test loss-1.7490, acc-0.5050
Iter-73230, train loss-1.6457, acc-0.5600, valid loss-1.7481, acc-0.5158, test loss-1.7489, acc-0.5049
Iter-73240, train loss-1.7656, acc-0.5000, valid loss-1.7481, acc-0.5156, test loss-1.7489, acc-0.5050
Iter-73250, train loss-1.8095, acc-0.5000, valid loss-1.7480, acc-0.5156, test loss-1.7488, acc-0.5051
Iter-73260, train loss-1.7269, acc-0.4800, valid loss-1.7480, acc-0.5156, test loss-1.7488, acc-0.5051
Iter-73270, train loss-1.7285, acc-0.4800, valid loss-1.7479, acc-0.5156, test loss-1.7488, acc-0.5050
Iter-73280, train loss-1.8154, acc-0.4200, valid loss-1.7479, acc-0.5156, test loss-1.7487, acc-0.5051
Iter-73290, train loss-1.7220, acc-0.4800, valid loss-1.7479, acc-0.5156, test loss-1.7487, acc-0.5051
Iter-73300, train loss-1.7716, acc-0.4200, valid loss-1.7478, acc-0.5156, test loss-1.7486, acc-0.5051
Iter-73310, train loss-1.8006, acc-0.4000, valid loss-1.7478, acc-0.5156, test loss-1.7486, acc-0.5052
Iter-73320, train loss-1.8772, acc-0.4000, valid loss-1.7477, acc-0.5156, test loss-1.7486, acc-0.5052
Iter-73330, train loss-1.6139, acc-0.6000, valid loss-1.7477, acc-0.5156, test loss-1.7485, acc-0.5053
Iter-73340, train loss-1.7920, acc-0.4600, valid loss-1.7477, acc-0.5156, test loss-1.7485, acc-0.5052
Iter-73350, train loss-1.6321, acc-0.5800, valid loss-1.7476, acc-0.5156, test loss-1.7484, acc-0.5052
Iter-73360, train loss-1.8287, acc-0.5000, valid loss-1.7476, acc-0.5156, test loss-1.7484, acc-0.5053
Iter-73370, train loss-1.7797, acc-0.4400, valid loss-1.7475, acc-0.5156, test loss-1.7483, acc-0.5055
Iter-73380, train loss-1.7723, acc-0.4600, valid loss-1.7475, acc-0.5156, test loss-1.7483, acc-0.5055
Iter-73390, train loss-1.6912, acc-0.4600, valid loss-1.7475, acc-0.5156, test loss-1.7483, acc-0.5055
Iter-73400, train loss-1.6937, acc-0.5800, valid loss-1.7474, acc-0.5156, test loss-1.7482, acc-0.5056
Iter-73410, train loss-1.7695, acc-0.4600, valid loss-1.7474, acc-0.5158, test loss-1.7482, acc-0.5054
Iter-73420, train loss-1.7402, acc-0.4200, valid loss-1.7473, acc-0.5160, test loss-1.7481, acc-0.5054
Iter-73430, train loss-1.7942, acc-0.4800, valid loss-1.7473, acc-0.5160, test loss-1.7481, acc-0.5055
Iter-73440, train loss-1.7668, acc-0.5000, valid loss-1.7473, acc-0.5160, test loss-1.7481, acc-0.5056
Iter-73450, train loss-1.7489, acc-0.5600, valid loss-1.7472, acc-0.5160, test loss-1.7480, acc-0.5058
Iter-73460, train loss-1.8615, acc-0.3800, valid loss-1.7472, acc-0.5158, test loss-1.7480, acc-0.5055
Iter-73470, train loss-1.8180, acc-0.5000, valid loss-1.7471, acc-0.5160, test loss-1.7479, acc-0.5055
Iter-73480, train loss-1.7992, acc-0.5400, valid loss-1.7471, acc-0.5160, test loss-1.7479, acc-0.5056
Iter-73490, train loss-1.8011, acc-0.4600, valid loss-1.7470, acc-0.5160, test loss-1.7479, acc-0.5056
Iter-73500, train loss-1.7938, acc-0.5000, valid loss-1.7470, acc-0.5160, test loss-1.7478, acc-0.5057
Iter-73510, train loss-1.6886, acc-0.5400, valid loss-1.7470, acc-0.5158, test loss-1.7478, acc-0.5056
Iter-73520, train loss-1.8411, acc-0.4600, valid loss-1.7469, acc-0.5158, test loss-1.7477, acc-0.5054
Iter-73530, train loss-1.7467, acc-0.5200, valid loss-1.7469, acc-0.5156, test loss-1.7477, acc-0.5056
Iter-73540, train loss-1.6872, acc-0.5600, valid loss-1.7468, acc-0.5156, test loss-1.7476, acc-0.5059
Iter-73550, train loss-1.8011, acc-0.5400, valid loss-1.7468, acc-0.5156, test loss-1.7476, acc-0.5057
Iter-73560, train loss-1.6898, acc-0.5600, valid loss-1.7468, acc-0.5156, test loss-1.7476, acc-0.5059
Iter-73570, train loss-1.7826, acc-0.6000, valid loss-1.7467, acc-0.5156, test loss-1.7475, acc-0.5058
Iter-73580, train loss-1.7059, acc-0.6000, valid loss-1.7467, acc-0.5156, test loss-1.7475, acc-0.5057
Iter-73590, train loss-1.5884, acc-0.5800, valid loss-1.7466, acc-0.5160, test loss-1.7474, acc-0.5056
Iter-73600, train loss-1.7789, acc-0.5400, valid loss-1.7466, acc-0.5160, test loss-1.7474, acc-0.5060
Iter-73610, train loss-1.7924, acc-0.4800, valid loss-1.7466, acc-0.5158, test loss-1.7473, acc-0.5058
Iter-73620, train loss-1.7857, acc-0.5200, valid loss-1.7465, acc-0.5158, test loss-1.7473, acc-0.5058
Iter-73630, train loss-1.7929, acc-0.4800, valid loss-1.7465, acc-0.5160, test loss-1.7473, acc-0.5060
Iter-73640, train loss-1.8688, acc-0.4000, valid loss-1.7464, acc-0.5160, test loss-1.7472, acc-0.5060
Iter-73650, train loss-1.8256, acc-0.4400, valid loss-1.7464, acc-0.5160, test loss-1.7472, acc-0.5059
Iter-73660, train loss-1.6801, acc-0.5800, valid loss-1.7463, acc-0.5160, test loss-1.7471, acc-0.5059
Iter-73670, train loss-1.6685, acc-0.5200, valid loss-1.7463, acc-0.5160, test loss-1.7471, acc-0.5059
Iter-73680, train loss-1.7991, acc-0.5400, valid loss-1.7463, acc-0.5160, test loss-1.7471, acc-0.5062
Iter-73690, train loss-1.8064, acc-0.4200, valid loss-1.7462, acc-0.5160, test loss-1.7470, acc-0.5062
Iter-73700, train loss-1.8857, acc-0.4400, valid loss-1.7462, acc-0.5160, test loss-1.7470, acc-0.5060
Iter-73710, train loss-1.7491, acc-0.5400, valid loss-1.7461, acc-0.5160, test loss-1.7469, acc-0.5061
Iter-73720, train loss-1.7045, acc-0.5400, valid loss-1.7461, acc-0.5160, test loss-1.7469, acc-0.5061
Iter-73730, train loss-1.7785, acc-0.5200, valid loss-1.7461, acc-0.5160, test loss-1.7468, acc-0.5059
Iter-73740, train loss-1.6651, acc-0.6200, valid loss-1.7460, acc-0.5160, test loss-1.7468, acc-0.5059
Iter-73750, train loss-1.6856, acc-0.5600, valid loss-1.7460, acc-0.5160, test loss-1.7468, acc-0.5059
Iter-73760, train loss-1.7902, acc-0.4600, valid loss-1.7459, acc-0.5160, test loss-1.7467, acc-0.5059
Iter-73770, train loss-1.6599, acc-0.5600, valid loss-1.7459, acc-0.5160, test loss-1.7467, acc-0.5059
Iter-73780, train loss-1.7683, acc-0.5200, valid loss-1.7459, acc-0.5160, test loss-1.7466, acc-0.5061
Iter-73790, train loss-1.7982, acc-0.4600, valid loss-1.7458, acc-0.5160, test loss-1.7466, acc-0.5061
Iter-73800, train loss-1.6660, acc-0.5400, valid loss-1.7458, acc-0.5160, test loss-1.7466, acc-0.5061
Iter-73810, train loss-1.6883, acc-0.6000, valid loss-1.7457, acc-0.5160, test loss-1.7465, acc-0.5062
Iter-73820, train loss-1.6857, acc-0.6600, valid loss-1.7457, acc-0.5160, test loss-1.7465, acc-0.5058
Iter-73830, train loss-1.7408, acc-0.4800, valid loss-1.7456, acc-0.5160, test loss-1.7464, acc-0.5062
Iter-73840, train loss-1.6880, acc-0.5000, valid loss-1.7456, acc-0.5158, test loss-1.7464, acc-0.5060
Iter-73850, train loss-1.6980, acc-0.6200, valid loss-1.7455, acc-0.5158, test loss-1.7464, acc-0.5059
Iter-73860, train loss-1.7744, acc-0.4200, valid loss-1.7455, acc-0.5160, test loss-1.7463, acc-0.5059
Iter-73870, train loss-1.7620, acc-0.4800, valid loss-1.7455, acc-0.5162, test loss-1.7463, acc-0.5061
Iter-73880, train loss-1.7815, acc-0.4600, valid loss-1.7454, acc-0.5162, test loss-1.7462, acc-0.5061
Iter-73890, train loss-1.6911, acc-0.5800, valid loss-1.7454, acc-0.5162, test loss-1.7462, acc-0.5060
Iter-73900, train loss-1.7620, acc-0.4800, valid loss-1.7453, acc-0.5162, test loss-1.7461, acc-0.5059
Iter-73910, train loss-1.8141, acc-0.5200, valid loss-1.7453, acc-0.5162, test loss-1.7461, acc-0.5059
Iter-73920, train loss-1.6753, acc-0.5400, valid loss-1.7453, acc-0.5162, test loss-1.7460, acc-0.5065
Iter-73930, train loss-1.8091, acc-0.4200, valid loss-1.7452, acc-0.5162, test loss-1.7460, acc-0.5064
Iter-73940, train loss-1.6303, acc-0.6000, valid loss-1.7452, acc-0.5162, test loss-1.7460, acc-0.5062
Iter-73950, train loss-1.7248, acc-0.5000, valid loss-1.7451, acc-0.5162, test loss-1.7459, acc-0.5062
Iter-73960, train loss-1.8585, acc-0.3600, valid loss-1.7451, acc-0.5162, test loss-1.7459, acc-0.5058
Iter-73970, train loss-1.7011, acc-0.5000, valid loss-1.7450, acc-0.5162, test loss-1.7458, acc-0.5059
Iter-73980, train loss-1.7065, acc-0.5800, valid loss-1.7450, acc-0.5162, test loss-1.7458, acc-0.5059
Iter-73990, train loss-1.7841, acc-0.4800, valid loss-1.7450, acc-0.5162, test loss-1.7458, acc-0.5059
Iter-74000, train loss-1.9294, acc-0.3800, valid loss-1.7449, acc-0.5162, test loss-1.7457, acc-0.5059
Iter-74010, train loss-1.7688, acc-0.5400, valid loss-1.7449, acc-0.5162, test loss-1.7457, acc-0.5059
Iter-74020, train loss-1.8276, acc-0.5200, valid loss-1.7448, acc-0.5162, test loss-1.7456, acc-0.5060
Iter-74030, train loss-1.8235, acc-0.3800, valid loss-1.7448, acc-0.5160, test loss-1.7456, acc-0.5061
Iter-74040, train loss-1.7121, acc-0.5600, valid loss-1.7448, acc-0.5160, test loss-1.7456, acc-0.5059
Iter-74050, train loss-1.6503, acc-0.6000, valid loss-1.7447, acc-0.5160, test loss-1.7455, acc-0.5061
Iter-74060, train loss-1.7852, acc-0.4800, valid loss-1.7447, acc-0.5158, test loss-1.7455, acc-0.5060
Iter-74070, train loss-1.6586, acc-0.5800, valid loss-1.7446, acc-0.5160, test loss-1.7454, acc-0.5062
Iter-74080, train loss-1.7664, acc-0.5000, valid loss-1.7446, acc-0.5158, test loss-1.7454, acc-0.5062
Iter-74090, train loss-1.6490, acc-0.6200, valid loss-1.7446, acc-0.5158, test loss-1.7454, acc-0.5062
Iter-74100, train loss-1.7898, acc-0.4800, valid loss-1.7445, acc-0.5158, test loss-1.7453, acc-0.5063
Iter-74110, train loss-1.6535, acc-0.5000, valid loss-1.7445, acc-0.5160, test loss-1.7453, acc-0.5063
Iter-74120, train loss-1.8013, acc-0.5200, valid loss-1.7444, acc-0.5160, test loss-1.7452, acc-0.5064
Iter-74130, train loss-1.8402, acc-0.4000, valid loss-1.7444, acc-0.5160, test loss-1.7452, acc-0.5064
Iter-74140, train loss-1.8415, acc-0.5200, valid loss-1.7444, acc-0.5160, test loss-1.7452, acc-0.5063
Iter-74150, train loss-1.6940, acc-0.5200, valid loss-1.7443, acc-0.5160, test loss-1.7451, acc-0.5062
Iter-74160, train loss-1.8741, acc-0.4400, valid loss-1.7443, acc-0.5160, test loss-1.7451, acc-0.5062
Iter-74170, train loss-1.7325, acc-0.5800, valid loss-1.7442, acc-0.5160, test loss-1.7450, acc-0.5062
Iter-74180, train loss-1.6610, acc-0.6400, valid loss-1.7442, acc-0.5164, test loss-1.7450, acc-0.5063
Iter-74190, train loss-1.7960, acc-0.5600, valid loss-1.7442, acc-0.5162, test loss-1.7450, acc-0.5063
Iter-74200, train loss-1.7227, acc-0.5600, valid loss-1.7441, acc-0.5164, test loss-1.7449, acc-0.5065
Iter-74210, train loss-1.6429, acc-0.6000, valid loss-1.7441, acc-0.5164, test loss-1.7449, acc-0.5066
Iter-74220, train loss-1.8174, acc-0.4800, valid loss-1.7440, acc-0.5164, test loss-1.7448, acc-0.5065
Iter-74230, train loss-1.7809, acc-0.5000, valid loss-1.7440, acc-0.5164, test loss-1.7448, acc-0.5067
Iter-74240, train loss-1.6940, acc-0.6200, valid loss-1.7440, acc-0.5164, test loss-1.7447, acc-0.5063
Iter-74250, train loss-1.7991, acc-0.3800, valid loss-1.7439, acc-0.5164, test loss-1.7447, acc-0.5064
Iter-74260, train loss-1.6283, acc-0.6400, valid loss-1.7439, acc-0.5162, test loss-1.7447, acc-0.5064
Iter-74270, train loss-1.7753, acc-0.4600, valid loss-1.7438, acc-0.5164, test loss-1.7446, acc-0.5064
Iter-74280, train loss-1.8209, acc-0.5000, valid loss-1.7438, acc-0.5162, test loss-1.7446, acc-0.5064
Iter-74290, train loss-1.8044, acc-0.4000, valid loss-1.7438, acc-0.5162, test loss-1.7445, acc-0.5066
Iter-74300, train loss-1.7318, acc-0.4800, valid loss-1.7437, acc-0.5162, test loss-1.7445, acc-0.5066
Iter-74310, train loss-1.8045, acc-0.5200, valid loss-1.7437, acc-0.5164, test loss-1.7445, acc-0.5065
Iter-74320, train loss-1.7838, acc-0.4800, valid loss-1.7436, acc-0.5164, test loss-1.7444, acc-0.5064
Iter-74330, train loss-1.7708, acc-0.6000, valid loss-1.7436, acc-0.5166, test loss-1.7444, acc-0.5064
Iter-74340, train loss-1.6558, acc-0.5600, valid loss-1.7436, acc-0.5166, test loss-1.7443, acc-0.5064
Iter-74350, train loss-1.6285, acc-0.5800, valid loss-1.7435, acc-0.5166, test loss-1.7443, acc-0.5064
Iter-74360, train loss-1.7422, acc-0.4800, valid loss-1.7435, acc-0.5166, test loss-1.7443, acc-0.5065
Iter-74370, train loss-1.7711, acc-0.4600, valid loss-1.7434, acc-0.5166, test loss-1.7442, acc-0.5064
Iter-74380, train loss-1.7468, acc-0.6400, valid loss-1.7434, acc-0.5166, test loss-1.7442, acc-0.5064
Iter-74390, train loss-1.8227, acc-0.4200, valid loss-1.7433, acc-0.5166, test loss-1.7441, acc-0.5064
Iter-74400, train loss-1.6603, acc-0.5400, valid loss-1.7433, acc-0.5166, test loss-1.7441, acc-0.5062
Iter-74410, train loss-1.7970, acc-0.4800, valid loss-1.7433, acc-0.5166, test loss-1.7441, acc-0.5063
Iter-74420, train loss-1.8346, acc-0.4800, valid loss-1.7432, acc-0.5166, test loss-1.7440, acc-0.5063
Iter-74430, train loss-1.7876, acc-0.4400, valid loss-1.7432, acc-0.5166, test loss-1.7440, acc-0.5063
Iter-74440, train loss-1.7112, acc-0.4800, valid loss-1.7431, acc-0.5166, test loss-1.7439, acc-0.5063
Iter-74450, train loss-1.8479, acc-0.4400, valid loss-1.7431, acc-0.5166, test loss-1.7439, acc-0.5063
Iter-74460, train loss-1.8448, acc-0.4600, valid loss-1.7431, acc-0.5164, test loss-1.7439, acc-0.5063
Iter-74470, train loss-1.8388, acc-0.3800, valid loss-1.7430, acc-0.5166, test loss-1.7438, acc-0.5065
Iter-74480, train loss-1.7012, acc-0.5000, valid loss-1.7430, acc-0.5166, test loss-1.7438, acc-0.5066
Iter-74490, train loss-1.6153, acc-0.5600, valid loss-1.7429, acc-0.5166, test loss-1.7437, acc-0.5066
Iter-74500, train loss-1.6778, acc-0.6200, valid loss-1.7429, acc-0.5164, test loss-1.7437, acc-0.5066
Iter-74510, train loss-1.7711, acc-0.4400, valid loss-1.7428, acc-0.5164, test loss-1.7437, acc-0.5065
Iter-74520, train loss-1.8427, acc-0.4200, valid loss-1.7428, acc-0.5164, test loss-1.7436, acc-0.5066
Iter-74530, train loss-1.8367, acc-0.4000, valid loss-1.7428, acc-0.5166, test loss-1.7436, acc-0.5064
Iter-74540, train loss-1.6431, acc-0.6800, valid loss-1.7427, acc-0.5166, test loss-1.7435, acc-0.5065
Iter-74550, train loss-1.7926, acc-0.4600, valid loss-1.7427, acc-0.5168, test loss-1.7435, acc-0.5064
Iter-74560, train loss-1.7312, acc-0.5600, valid loss-1.7426, acc-0.5166, test loss-1.7435, acc-0.5063
Iter-74570, train loss-1.6901, acc-0.5200, valid loss-1.7426, acc-0.5166, test loss-1.7434, acc-0.5065
Iter-74580, train loss-1.7161, acc-0.5600, valid loss-1.7425, acc-0.5166, test loss-1.7434, acc-0.5062
Iter-74590, train loss-1.6330, acc-0.6800, valid loss-1.7425, acc-0.5166, test loss-1.7433, acc-0.5065
Iter-74600, train loss-1.7794, acc-0.4000, valid loss-1.7425, acc-0.5166, test loss-1.7433, acc-0.5065
Iter-74610, train loss-1.7905, acc-0.4200, valid loss-1.7424, acc-0.5168, test loss-1.7433, acc-0.5065
Iter-74620, train loss-1.7648, acc-0.5600, valid loss-1.7424, acc-0.5170, test loss-1.7432, acc-0.5065
Iter-74630, train loss-1.7103, acc-0.5800, valid loss-1.7423, acc-0.5166, test loss-1.7432, acc-0.5066
Iter-74640, train loss-1.7096, acc-0.5200, valid loss-1.7423, acc-0.5170, test loss-1.7431, acc-0.5065
Iter-74650, train loss-1.8106, acc-0.3000, valid loss-1.7423, acc-0.5170, test loss-1.7431, acc-0.5064
Iter-74660, train loss-1.8325, acc-0.3800, valid loss-1.7422, acc-0.5168, test loss-1.7430, acc-0.5066
Iter-74670, train loss-1.7518, acc-0.5400, valid loss-1.7422, acc-0.5166, test loss-1.7430, acc-0.5066
Iter-74680, train loss-1.8227, acc-0.5200, valid loss-1.7421, acc-0.5166, test loss-1.7430, acc-0.5065
Iter-74690, train loss-1.7249, acc-0.5400, valid loss-1.7421, acc-0.5166, test loss-1.7429, acc-0.5068
Iter-74700, train loss-1.8488, acc-0.4400, valid loss-1.7421, acc-0.5166, test loss-1.7429, acc-0.5070
Iter-74710, train loss-1.6497, acc-0.5600, valid loss-1.7420, acc-0.5166, test loss-1.7428, acc-0.5068
Iter-74720, train loss-1.7695, acc-0.4800, valid loss-1.7420, acc-0.5166, test loss-1.7428, acc-0.5068
Iter-74730, train loss-1.8161, acc-0.4400, valid loss-1.7419, acc-0.5166, test loss-1.7428, acc-0.5072
Iter-74740, train loss-1.8682, acc-0.4800, valid loss-1.7419, acc-0.5166, test loss-1.7427, acc-0.5071
Iter-74750, train loss-1.6890, acc-0.5600, valid loss-1.7419, acc-0.5166, test loss-1.7427, acc-0.5071
Iter-74760, train loss-1.8275, acc-0.4200, valid loss-1.7418, acc-0.5166, test loss-1.7426, acc-0.5070
Iter-74770, train loss-1.8335, acc-0.3600, valid loss-1.7418, acc-0.5166, test loss-1.7426, acc-0.5069
Iter-74780, train loss-1.7819, acc-0.3800, valid loss-1.7417, acc-0.5166, test loss-1.7426, acc-0.5071
Iter-74790, train loss-1.8008, acc-0.3800, valid loss-1.7417, acc-0.5166, test loss-1.7425, acc-0.5071
Iter-74800, train loss-1.7561, acc-0.5000, valid loss-1.7417, acc-0.5166, test loss-1.7425, acc-0.5073
Iter-74810, train loss-1.8620, acc-0.5200, valid loss-1.7416, acc-0.5166, test loss-1.7424, acc-0.5073
Iter-74820, train loss-1.8809, acc-0.5000, valid loss-1.7416, acc-0.5164, test loss-1.7424, acc-0.5072
Iter-74830, train loss-1.7859, acc-0.5200, valid loss-1.7415, acc-0.5164, test loss-1.7423, acc-0.5072
Iter-74840, train loss-1.7599, acc-0.5000, valid loss-1.7415, acc-0.5164, test loss-1.7423, acc-0.5071
Iter-74850, train loss-1.7055, acc-0.5600, valid loss-1.7415, acc-0.5164, test loss-1.7423, acc-0.5071
Iter-74860, train loss-1.8920, acc-0.4600, valid loss-1.7414, acc-0.5162, test loss-1.7422, acc-0.5073
Iter-74870, train loss-1.8080, acc-0.4000, valid loss-1.7414, acc-0.5162, test loss-1.7422, acc-0.5073
Iter-74880, train loss-1.7054, acc-0.6000, valid loss-1.7413, acc-0.5162, test loss-1.7421, acc-0.5074
Iter-74890, train loss-1.8249, acc-0.5400, valid loss-1.7413, acc-0.5164, test loss-1.7421, acc-0.5077
Iter-74900, train loss-1.5936, acc-0.6400, valid loss-1.7413, acc-0.5162, test loss-1.7421, acc-0.5076
Iter-74910, train loss-1.7401, acc-0.5200, valid loss-1.7412, acc-0.5162, test loss-1.7420, acc-0.5075
Iter-74920, train loss-1.8842, acc-0.3400, valid loss-1.7412, acc-0.5164, test loss-1.7420, acc-0.5075
Iter-74930, train loss-1.7411, acc-0.5200, valid loss-1.7411, acc-0.5162, test loss-1.7419, acc-0.5074
Iter-74940, train loss-1.7394, acc-0.5800, valid loss-1.7411, acc-0.5164, test loss-1.7419, acc-0.5075
Iter-74950, train loss-1.7855, acc-0.5200, valid loss-1.7410, acc-0.5162, test loss-1.7419, acc-0.5076
Iter-74960, train loss-1.8288, acc-0.5000, valid loss-1.7410, acc-0.5162, test loss-1.7418, acc-0.5075
Iter-74970, train loss-1.7735, acc-0.5200, valid loss-1.7410, acc-0.5162, test loss-1.7418, acc-0.5077
Iter-74980, train loss-1.8020, acc-0.5000, valid loss-1.7409, acc-0.5164, test loss-1.7417, acc-0.5078
Iter-74990, train loss-1.6965, acc-0.5400, valid loss-1.7409, acc-0.5166, test loss-1.7417, acc-0.5075
Iter-75000, train loss-1.7548, acc-0.5400, valid loss-1.7409, acc-0.5166, test loss-1.7417, acc-0.5075
Iter-75010, train loss-1.7131, acc-0.5400, valid loss-1.7408, acc-0.5166, test loss-1.7416, acc-0.5076
Iter-75020, train loss-1.7298, acc-0.5800, valid loss-1.7408, acc-0.5166, test loss-1.7416, acc-0.5077
Iter-75030, train loss-1.7002, acc-0.5400, valid loss-1.7407, acc-0.5166, test loss-1.7415, acc-0.5075
Iter-75040, train loss-1.8307, acc-0.4800, valid loss-1.7407, acc-0.5166, test loss-1.7415, acc-0.5076
Iter-75050, train loss-1.7484, acc-0.5600, valid loss-1.7406, acc-0.5166, test loss-1.7415, acc-0.5077
Iter-75060, train loss-1.8320, acc-0.4600, valid loss-1.7406, acc-0.5166, test loss-1.7414, acc-0.5077
Iter-75070, train loss-1.7903, acc-0.4400, valid loss-1.7406, acc-0.5166, test loss-1.7414, acc-0.5078
Iter-75080, train loss-1.6792, acc-0.5400, valid loss-1.7405, acc-0.5166, test loss-1.7413, acc-0.5078
Iter-75090, train loss-1.7325, acc-0.6400, valid loss-1.7405, acc-0.5166, test loss-1.7413, acc-0.5079
Iter-75100, train loss-1.6749, acc-0.5800, valid loss-1.7404, acc-0.5166, test loss-1.7413, acc-0.5080
Iter-75110, train loss-1.6763, acc-0.6600, valid loss-1.7404, acc-0.5168, test loss-1.7412, acc-0.5078
Iter-75120, train loss-1.8105, acc-0.4000, valid loss-1.7404, acc-0.5168, test loss-1.7412, acc-0.5080
Iter-75130, train loss-1.8216, acc-0.4200, valid loss-1.7403, acc-0.5168, test loss-1.7411, acc-0.5079
Iter-75140, train loss-1.8167, acc-0.4800, valid loss-1.7403, acc-0.5168, test loss-1.7411, acc-0.5079
Iter-75150, train loss-1.7584, acc-0.4600, valid loss-1.7402, acc-0.5168, test loss-1.7411, acc-0.5079
Iter-75160, train loss-1.7258, acc-0.6200, valid loss-1.7402, acc-0.5168, test loss-1.7410, acc-0.5078
Iter-75170, train loss-1.6837, acc-0.6200, valid loss-1.7401, acc-0.5168, test loss-1.7410, acc-0.5079
Iter-75180, train loss-1.6545, acc-0.5800, valid loss-1.7401, acc-0.5168, test loss-1.7409, acc-0.5079
Iter-75190, train loss-1.8919, acc-0.4000, valid loss-1.7401, acc-0.5168, test loss-1.7409, acc-0.5079
Iter-75200, train loss-1.6271, acc-0.6200, valid loss-1.7400, acc-0.5166, test loss-1.7408, acc-0.5079
Iter-75210, train loss-1.8225, acc-0.5000, valid loss-1.7400, acc-0.5166, test loss-1.7408, acc-0.5080
Iter-75220, train loss-1.7098, acc-0.5200, valid loss-1.7400, acc-0.5164, test loss-1.7408, acc-0.5080
Iter-75230, train loss-1.8248, acc-0.4600, valid loss-1.7399, acc-0.5166, test loss-1.7407, acc-0.5079
Iter-75240, train loss-1.8591, acc-0.3200, valid loss-1.7399, acc-0.5166, test loss-1.7407, acc-0.5079
Iter-75250, train loss-1.7537, acc-0.4600, valid loss-1.7398, acc-0.5164, test loss-1.7407, acc-0.5080
Iter-75260, train loss-1.6640, acc-0.6000, valid loss-1.7398, acc-0.5166, test loss-1.7406, acc-0.5078
Iter-75270, train loss-1.7683, acc-0.5000, valid loss-1.7398, acc-0.5166, test loss-1.7406, acc-0.5078
Iter-75280, train loss-1.7517, acc-0.5000, valid loss-1.7397, acc-0.5166, test loss-1.7405, acc-0.5075
Iter-75290, train loss-1.7079, acc-0.5600, valid loss-1.7397, acc-0.5166, test loss-1.7405, acc-0.5076
Iter-75300, train loss-1.8599, acc-0.4400, valid loss-1.7396, acc-0.5166, test loss-1.7405, acc-0.5075
Iter-75310, train loss-1.7924, acc-0.4600, valid loss-1.7396, acc-0.5168, test loss-1.7404, acc-0.5075
Iter-75320, train loss-1.7320, acc-0.5000, valid loss-1.7396, acc-0.5168, test loss-1.7404, acc-0.5073
Iter-75330, train loss-1.7980, acc-0.5000, valid loss-1.7395, acc-0.5170, test loss-1.7403, acc-0.5075
Iter-75340, train loss-1.7938, acc-0.4800, valid loss-1.7395, acc-0.5172, test loss-1.7403, acc-0.5076
Iter-75350, train loss-1.6655, acc-0.6200, valid loss-1.7394, acc-0.5168, test loss-1.7403, acc-0.5075
Iter-75360, train loss-1.7649, acc-0.5200, valid loss-1.7394, acc-0.5168, test loss-1.7402, acc-0.5075
Iter-75370, train loss-1.8046, acc-0.4200, valid loss-1.7394, acc-0.5170, test loss-1.7402, acc-0.5077
Iter-75380, train loss-1.7349, acc-0.5000, valid loss-1.7393, acc-0.5174, test loss-1.7401, acc-0.5077
Iter-75390, train loss-1.7808, acc-0.4600, valid loss-1.7393, acc-0.5168, test loss-1.7401, acc-0.5076
Iter-75400, train loss-1.7011, acc-0.6000, valid loss-1.7392, acc-0.5168, test loss-1.7400, acc-0.5076
Iter-75410, train loss-1.7756, acc-0.4400, valid loss-1.7392, acc-0.5170, test loss-1.7400, acc-0.5075
Iter-75420, train loss-1.7690, acc-0.5200, valid loss-1.7392, acc-0.5168, test loss-1.7400, acc-0.5076
Iter-75430, train loss-1.8583, acc-0.4400, valid loss-1.7391, acc-0.5168, test loss-1.7399, acc-0.5076
Iter-75440, train loss-1.7682, acc-0.4800, valid loss-1.7391, acc-0.5168, test loss-1.7399, acc-0.5076
Iter-75450, train loss-1.6882, acc-0.5200, valid loss-1.7390, acc-0.5170, test loss-1.7399, acc-0.5077
Iter-75460, train loss-1.7435, acc-0.5200, valid loss-1.7390, acc-0.5168, test loss-1.7398, acc-0.5078
Iter-75470, train loss-1.7394, acc-0.4600, valid loss-1.7390, acc-0.5168, test loss-1.7398, acc-0.5078
Iter-75480, train loss-1.7966, acc-0.4600, valid loss-1.7389, acc-0.5172, test loss-1.7397, acc-0.5078
Iter-75490, train loss-1.7415, acc-0.5400, valid loss-1.7389, acc-0.5172, test loss-1.7397, acc-0.5079
Iter-75500, train loss-1.8018, acc-0.5000, valid loss-1.7388, acc-0.5174, test loss-1.7397, acc-0.5078
Iter-75510, train loss-1.7136, acc-0.6000, valid loss-1.7388, acc-0.5176, test loss-1.7396, acc-0.5077
Iter-75520, train loss-1.7266, acc-0.6000, valid loss-1.7388, acc-0.5176, test loss-1.7396, acc-0.5078
Iter-75530, train loss-1.7029, acc-0.5200, valid loss-1.7387, acc-0.5172, test loss-1.7395, acc-0.5079
Iter-75540, train loss-1.7606, acc-0.4400, valid loss-1.7387, acc-0.5170, test loss-1.7395, acc-0.5080
Iter-75550, train loss-1.8175, acc-0.4200, valid loss-1.7386, acc-0.5170, test loss-1.7395, acc-0.5079
Iter-75560, train loss-1.5971, acc-0.5600, valid loss-1.7386, acc-0.5172, test loss-1.7394, acc-0.5078
Iter-75570, train loss-1.7259, acc-0.5000, valid loss-1.7386, acc-0.5174, test loss-1.7394, acc-0.5078
Iter-75580, train loss-1.7728, acc-0.5600, valid loss-1.7385, acc-0.5172, test loss-1.7393, acc-0.5078
Iter-75590, train loss-1.8184, acc-0.4000, valid loss-1.7385, acc-0.5172, test loss-1.7393, acc-0.5079
Iter-75600, train loss-1.6911, acc-0.5600, valid loss-1.7384, acc-0.5176, test loss-1.7392, acc-0.5079
Iter-75610, train loss-1.6856, acc-0.4800, valid loss-1.7384, acc-0.5176, test loss-1.7392, acc-0.5079
Iter-75620, train loss-1.8199, acc-0.4000, valid loss-1.7383, acc-0.5176, test loss-1.7392, acc-0.5079
Iter-75630, train loss-1.6734, acc-0.5400, valid loss-1.7383, acc-0.5176, test loss-1.7391, acc-0.5079
Iter-75640, train loss-1.7724, acc-0.4800, valid loss-1.7383, acc-0.5176, test loss-1.7391, acc-0.5080
Iter-75650, train loss-1.7671, acc-0.5400, valid loss-1.7382, acc-0.5174, test loss-1.7390, acc-0.5081
Iter-75660, train loss-1.7178, acc-0.5400, valid loss-1.7382, acc-0.5174, test loss-1.7390, acc-0.5080
Iter-75670, train loss-1.8045, acc-0.4200, valid loss-1.7381, acc-0.5172, test loss-1.7390, acc-0.5080
Iter-75680, train loss-1.6102, acc-0.5200, valid loss-1.7381, acc-0.5170, test loss-1.7389, acc-0.5080
Iter-75690, train loss-1.7416, acc-0.4800, valid loss-1.7381, acc-0.5174, test loss-1.7389, acc-0.5080
Iter-75700, train loss-1.7383, acc-0.5200, valid loss-1.7380, acc-0.5172, test loss-1.7388, acc-0.5079
Iter-75710, train loss-1.7683, acc-0.3800, valid loss-1.7380, acc-0.5172, test loss-1.7388, acc-0.5079
Iter-75720, train loss-1.6542, acc-0.5600, valid loss-1.7379, acc-0.5172, test loss-1.7388, acc-0.5079
Iter-75730, train loss-1.6954, acc-0.5600, valid loss-1.7379, acc-0.5170, test loss-1.7387, acc-0.5079
Iter-75740, train loss-1.7470, acc-0.4800, valid loss-1.7379, acc-0.5172, test loss-1.7387, acc-0.5078
Iter-75750, train loss-1.7547, acc-0.5000, valid loss-1.7378, acc-0.5172, test loss-1.7386, acc-0.5078
Iter-75760, train loss-1.7553, acc-0.5400, valid loss-1.7378, acc-0.5172, test loss-1.7386, acc-0.5078
Iter-75770, train loss-1.7533, acc-0.5400, valid loss-1.7377, acc-0.5172, test loss-1.7386, acc-0.5079
Iter-75780, train loss-1.7256, acc-0.5000, valid loss-1.7377, acc-0.5172, test loss-1.7385, acc-0.5080
Iter-75790, train loss-1.7229, acc-0.4800, valid loss-1.7377, acc-0.5172, test loss-1.7385, acc-0.5080
Iter-75800, train loss-1.8299, acc-0.3400, valid loss-1.7376, acc-0.5174, test loss-1.7384, acc-0.5080
Iter-75810, train loss-1.7956, acc-0.5000, valid loss-1.7376, acc-0.5172, test loss-1.7384, acc-0.5080
Iter-75820, train loss-1.8054, acc-0.4600, valid loss-1.7375, acc-0.5174, test loss-1.7384, acc-0.5080
Iter-75830, train loss-1.5789, acc-0.6600, valid loss-1.7375, acc-0.5174, test loss-1.7383, acc-0.5081
Iter-75840, train loss-1.8577, acc-0.4000, valid loss-1.7375, acc-0.5174, test loss-1.7383, acc-0.5081
Iter-75850, train loss-1.7548, acc-0.5000, valid loss-1.7374, acc-0.5170, test loss-1.7382, acc-0.5079
Iter-75860, train loss-1.7537, acc-0.5000, valid loss-1.7374, acc-0.5174, test loss-1.7382, acc-0.5080
Iter-75870, train loss-1.8467, acc-0.3800, valid loss-1.7373, acc-0.5172, test loss-1.7382, acc-0.5080
Iter-75880, train loss-1.7399, acc-0.4400, valid loss-1.7373, acc-0.5172, test loss-1.7381, acc-0.5081
Iter-75890, train loss-1.7048, acc-0.5600, valid loss-1.7372, acc-0.5172, test loss-1.7381, acc-0.5080
Iter-75900, train loss-1.7622, acc-0.5200, valid loss-1.7372, acc-0.5172, test loss-1.7380, acc-0.5082
Iter-75910, train loss-1.6883, acc-0.6000, valid loss-1.7372, acc-0.5170, test loss-1.7380, acc-0.5081
Iter-75920, train loss-1.6342, acc-0.5600, valid loss-1.7371, acc-0.5170, test loss-1.7379, acc-0.5081
Iter-75930, train loss-1.7414, acc-0.5400, valid loss-1.7371, acc-0.5170, test loss-1.7379, acc-0.5083
Iter-75940, train loss-1.7584, acc-0.3600, valid loss-1.7370, acc-0.5170, test loss-1.7379, acc-0.5083
Iter-75950, train loss-1.8472, acc-0.4800, valid loss-1.7370, acc-0.5170, test loss-1.7378, acc-0.5081
Iter-75960, train loss-1.7770, acc-0.5400, valid loss-1.7370, acc-0.5172, test loss-1.7378, acc-0.5081
Iter-75970, train loss-1.8360, acc-0.4800, valid loss-1.7369, acc-0.5172, test loss-1.7377, acc-0.5082
Iter-75980, train loss-1.6277, acc-0.6400, valid loss-1.7369, acc-0.5170, test loss-1.7377, acc-0.5082
Iter-75990, train loss-1.6866, acc-0.5000, valid loss-1.7369, acc-0.5172, test loss-1.7377, acc-0.5082
Iter-76000, train loss-1.7307, acc-0.5200, valid loss-1.7368, acc-0.5174, test loss-1.7376, acc-0.5082
Iter-76010, train loss-1.7737, acc-0.5400, valid loss-1.7368, acc-0.5172, test loss-1.7376, acc-0.5084
Iter-76020, train loss-1.7727, acc-0.5000, valid loss-1.7367, acc-0.5170, test loss-1.7376, acc-0.5084
Iter-76030, train loss-1.7729, acc-0.5400, valid loss-1.7367, acc-0.5172, test loss-1.7375, acc-0.5082
Iter-76040, train loss-1.7095, acc-0.5800, valid loss-1.7367, acc-0.5174, test loss-1.7375, acc-0.5082
Iter-76050, train loss-1.6557, acc-0.5800, valid loss-1.7366, acc-0.5176, test loss-1.7374, acc-0.5081
Iter-76060, train loss-1.9250, acc-0.3800, valid loss-1.7366, acc-0.5176, test loss-1.7374, acc-0.5082
Iter-76070, train loss-1.6965, acc-0.5400, valid loss-1.7365, acc-0.5182, test loss-1.7374, acc-0.5081
Iter-76080, train loss-1.7568, acc-0.5400, valid loss-1.7365, acc-0.5180, test loss-1.7373, acc-0.5080
Iter-76090, train loss-1.5517, acc-0.6600, valid loss-1.7365, acc-0.5180, test loss-1.7373, acc-0.5082
Iter-76100, train loss-1.7816, acc-0.4000, valid loss-1.7364, acc-0.5180, test loss-1.7372, acc-0.5081
Iter-76110, train loss-1.7622, acc-0.4000, valid loss-1.7364, acc-0.5180, test loss-1.7372, acc-0.5081
Iter-76120, train loss-1.7242, acc-0.5200, valid loss-1.7363, acc-0.5182, test loss-1.7372, acc-0.5083
Iter-76130, train loss-1.6505, acc-0.6200, valid loss-1.7363, acc-0.5182, test loss-1.7371, acc-0.5082
Iter-76140, train loss-1.8102, acc-0.4800, valid loss-1.7363, acc-0.5184, test loss-1.7371, acc-0.5084
Iter-76150, train loss-1.6875, acc-0.5800, valid loss-1.7362, acc-0.5182, test loss-1.7370, acc-0.5082
Iter-76160, train loss-1.6788, acc-0.6200, valid loss-1.7362, acc-0.5182, test loss-1.7370, acc-0.5081
Iter-76170, train loss-1.7333, acc-0.4400, valid loss-1.7361, acc-0.5182, test loss-1.7370, acc-0.5082
Iter-76180, train loss-1.8499, acc-0.4800, valid loss-1.7361, acc-0.5184, test loss-1.7369, acc-0.5081
Iter-76190, train loss-1.8464, acc-0.4000, valid loss-1.7361, acc-0.5182, test loss-1.7369, acc-0.5082
Iter-76200, train loss-1.6406, acc-0.6200, valid loss-1.7360, acc-0.5182, test loss-1.7368, acc-0.5082
Iter-76210, train loss-1.7174, acc-0.5200, valid loss-1.7360, acc-0.5182, test loss-1.7368, acc-0.5081
Iter-76220, train loss-1.7151, acc-0.5600, valid loss-1.7359, acc-0.5182, test loss-1.7368, acc-0.5081
Iter-76230, train loss-1.7162, acc-0.5600, valid loss-1.7359, acc-0.5184, test loss-1.7367, acc-0.5082
Iter-76240, train loss-1.6723, acc-0.5000, valid loss-1.7358, acc-0.5184, test loss-1.7367, acc-0.5082
Iter-76250, train loss-1.6008, acc-0.6800, valid loss-1.7358, acc-0.5184, test loss-1.7366, acc-0.5083
Iter-76260, train loss-1.7139, acc-0.5600, valid loss-1.7358, acc-0.5184, test loss-1.7366, acc-0.5085
Iter-76270, train loss-1.8238, acc-0.4400, valid loss-1.7357, acc-0.5184, test loss-1.7366, acc-0.5082
Iter-76280, train loss-1.7405, acc-0.5400, valid loss-1.7357, acc-0.5184, test loss-1.7365, acc-0.5083
Iter-76290, train loss-1.7739, acc-0.4600, valid loss-1.7356, acc-0.5184, test loss-1.7365, acc-0.5081
Iter-76300, train loss-1.8266, acc-0.5000, valid loss-1.7356, acc-0.5184, test loss-1.7364, acc-0.5082
Iter-76310, train loss-1.7805, acc-0.4200, valid loss-1.7356, acc-0.5186, test loss-1.7364, acc-0.5081
Iter-76320, train loss-1.7539, acc-0.4600, valid loss-1.7355, acc-0.5184, test loss-1.7364, acc-0.5083
Iter-76330, train loss-1.6854, acc-0.5800, valid loss-1.7355, acc-0.5184, test loss-1.7363, acc-0.5081
Iter-76340, train loss-1.7530, acc-0.6000, valid loss-1.7355, acc-0.5182, test loss-1.7363, acc-0.5081
Iter-76350, train loss-1.6821, acc-0.5800, valid loss-1.7354, acc-0.5182, test loss-1.7362, acc-0.5082
Iter-76360, train loss-1.7683, acc-0.4000, valid loss-1.7354, acc-0.5182, test loss-1.7362, acc-0.5081
Iter-76370, train loss-1.7682, acc-0.5400, valid loss-1.7353, acc-0.5182, test loss-1.7362, acc-0.5081
Iter-76380, train loss-1.7713, acc-0.5400, valid loss-1.7353, acc-0.5184, test loss-1.7361, acc-0.5083
Iter-76390, train loss-1.7167, acc-0.5600, valid loss-1.7353, acc-0.5184, test loss-1.7361, acc-0.5085
Iter-76400, train loss-1.8583, acc-0.6000, valid loss-1.7352, acc-0.5186, test loss-1.7360, acc-0.5084
Iter-76410, train loss-1.6942, acc-0.5400, valid loss-1.7352, acc-0.5184, test loss-1.7360, acc-0.5084
Iter-76420, train loss-1.6354, acc-0.7000, valid loss-1.7351, acc-0.5184, test loss-1.7360, acc-0.5085
Iter-76430, train loss-1.7524, acc-0.5000, valid loss-1.7351, acc-0.5182, test loss-1.7359, acc-0.5086
Iter-76440, train loss-1.8210, acc-0.4600, valid loss-1.7350, acc-0.5182, test loss-1.7359, acc-0.5085
Iter-76450, train loss-1.8156, acc-0.4600, valid loss-1.7350, acc-0.5182, test loss-1.7358, acc-0.5084
Iter-76460, train loss-1.7599, acc-0.5400, valid loss-1.7350, acc-0.5186, test loss-1.7358, acc-0.5084
Iter-76470, train loss-1.7819, acc-0.5200, valid loss-1.7349, acc-0.5186, test loss-1.7357, acc-0.5084
Iter-76480, train loss-1.7490, acc-0.5400, valid loss-1.7349, acc-0.5188, test loss-1.7357, acc-0.5084
Iter-76490, train loss-1.8419, acc-0.4600, valid loss-1.7349, acc-0.5186, test loss-1.7357, acc-0.5084
Iter-76500, train loss-1.6415, acc-0.5400, valid loss-1.7348, acc-0.5186, test loss-1.7356, acc-0.5085
Iter-76510, train loss-1.8157, acc-0.5800, valid loss-1.7348, acc-0.5186, test loss-1.7356, acc-0.5084
Iter-76520, train loss-1.7209, acc-0.5600, valid loss-1.7347, acc-0.5186, test loss-1.7356, acc-0.5084
Iter-76530, train loss-1.7621, acc-0.5200, valid loss-1.7347, acc-0.5186, test loss-1.7355, acc-0.5083
Iter-76540, train loss-1.8878, acc-0.4000, valid loss-1.7347, acc-0.5188, test loss-1.7355, acc-0.5083
Iter-76550, train loss-1.6891, acc-0.5600, valid loss-1.7346, acc-0.5190, test loss-1.7354, acc-0.5083
Iter-76560, train loss-1.6921, acc-0.6000, valid loss-1.7346, acc-0.5190, test loss-1.7354, acc-0.5084
Iter-76570, train loss-1.7764, acc-0.4000, valid loss-1.7345, acc-0.5192, test loss-1.7354, acc-0.5085
Iter-76580, train loss-1.8532, acc-0.5000, valid loss-1.7345, acc-0.5190, test loss-1.7353, acc-0.5085
Iter-76590, train loss-1.8380, acc-0.3600, valid loss-1.7345, acc-0.5190, test loss-1.7353, acc-0.5085
Iter-76600, train loss-1.7397, acc-0.4600, valid loss-1.7344, acc-0.5190, test loss-1.7352, acc-0.5085
Iter-76610, train loss-1.8222, acc-0.4800, valid loss-1.7344, acc-0.5194, test loss-1.7352, acc-0.5084
Iter-76620, train loss-1.7799, acc-0.4600, valid loss-1.7343, acc-0.5192, test loss-1.7352, acc-0.5085
Iter-76630, train loss-1.7354, acc-0.4800, valid loss-1.7343, acc-0.5192, test loss-1.7351, acc-0.5087
Iter-76640, train loss-1.7943, acc-0.5200, valid loss-1.7343, acc-0.5190, test loss-1.7351, acc-0.5087
Iter-76650, train loss-1.6611, acc-0.5600, valid loss-1.7342, acc-0.5192, test loss-1.7350, acc-0.5087
Iter-76660, train loss-1.7557, acc-0.4600, valid loss-1.7342, acc-0.5190, test loss-1.7350, acc-0.5087
Iter-76670, train loss-1.8410, acc-0.4600, valid loss-1.7341, acc-0.5192, test loss-1.7350, acc-0.5087
Iter-76680, train loss-1.8028, acc-0.4200, valid loss-1.7341, acc-0.5196, test loss-1.7349, acc-0.5086
Iter-76690, train loss-1.6711, acc-0.6400, valid loss-1.7341, acc-0.5196, test loss-1.7349, acc-0.5086
Iter-76700, train loss-1.7272, acc-0.5000, valid loss-1.7340, acc-0.5196, test loss-1.7348, acc-0.5086
Iter-76710, train loss-1.7809, acc-0.4200, valid loss-1.7340, acc-0.5196, test loss-1.7348, acc-0.5086
Iter-76720, train loss-1.7022, acc-0.5600, valid loss-1.7339, acc-0.5196, test loss-1.7348, acc-0.5086
Iter-76730, train loss-1.7551, acc-0.5600, valid loss-1.7339, acc-0.5196, test loss-1.7347, acc-0.5087
Iter-76740, train loss-1.8085, acc-0.4800, valid loss-1.7339, acc-0.5194, test loss-1.7347, acc-0.5087
Iter-76750, train loss-1.7769, acc-0.4600, valid loss-1.7338, acc-0.5194, test loss-1.7346, acc-0.5087
Iter-76760, train loss-1.6682, acc-0.6200, valid loss-1.7338, acc-0.5192, test loss-1.7346, acc-0.5088
Iter-76770, train loss-1.6789, acc-0.5400, valid loss-1.7338, acc-0.5192, test loss-1.7346, acc-0.5087
Iter-76780, train loss-1.7685, acc-0.5200, valid loss-1.7337, acc-0.5194, test loss-1.7345, acc-0.5089
Iter-76790, train loss-1.8699, acc-0.3400, valid loss-1.7337, acc-0.5192, test loss-1.7345, acc-0.5089
Iter-76800, train loss-1.8333, acc-0.3800, valid loss-1.7336, acc-0.5196, test loss-1.7345, acc-0.5089
Iter-76810, train loss-1.8246, acc-0.4400, valid loss-1.7336, acc-0.5194, test loss-1.7344, acc-0.5088
Iter-76820, train loss-1.8314, acc-0.4200, valid loss-1.7336, acc-0.5194, test loss-1.7344, acc-0.5088
Iter-76830, train loss-1.8158, acc-0.3800, valid loss-1.7335, acc-0.5194, test loss-1.7343, acc-0.5088
Iter-76840, train loss-1.7421, acc-0.5600, valid loss-1.7335, acc-0.5194, test loss-1.7343, acc-0.5089
Iter-76850, train loss-1.7316, acc-0.6000, valid loss-1.7334, acc-0.5198, test loss-1.7343, acc-0.5089
Iter-76860, train loss-1.7273, acc-0.5800, valid loss-1.7334, acc-0.5198, test loss-1.7342, acc-0.5089
Iter-76870, train loss-1.7557, acc-0.4000, valid loss-1.7334, acc-0.5198, test loss-1.7342, acc-0.5089
Iter-76880, train loss-1.8673, acc-0.4200, valid loss-1.7333, acc-0.5192, test loss-1.7341, acc-0.5089
Iter-76890, train loss-1.7515, acc-0.5400, valid loss-1.7333, acc-0.5198, test loss-1.7341, acc-0.5088
Iter-76900, train loss-1.8295, acc-0.5000, valid loss-1.7333, acc-0.5196, test loss-1.7341, acc-0.5088
Iter-76910, train loss-1.7711, acc-0.4400, valid loss-1.7332, acc-0.5196, test loss-1.7340, acc-0.5088
Iter-76920, train loss-1.7056, acc-0.5600, valid loss-1.7332, acc-0.5194, test loss-1.7340, acc-0.5087
Iter-76930, train loss-1.6438, acc-0.6000, valid loss-1.7331, acc-0.5196, test loss-1.7339, acc-0.5087
Iter-76940, train loss-1.8599, acc-0.4600, valid loss-1.7331, acc-0.5198, test loss-1.7339, acc-0.5087
Iter-76950, train loss-1.6996, acc-0.5400, valid loss-1.7331, acc-0.5198, test loss-1.7339, acc-0.5087
Iter-76960, train loss-1.6813, acc-0.5200, valid loss-1.7330, acc-0.5196, test loss-1.7338, acc-0.5087
Iter-76970, train loss-1.6577, acc-0.5600, valid loss-1.7330, acc-0.5200, test loss-1.7338, acc-0.5087
Iter-76980, train loss-1.7694, acc-0.5200, valid loss-1.7329, acc-0.5198, test loss-1.7337, acc-0.5086
Iter-76990, train loss-1.6906, acc-0.5400, valid loss-1.7329, acc-0.5198, test loss-1.7337, acc-0.5086
Iter-77000, train loss-1.6355, acc-0.7600, valid loss-1.7329, acc-0.5198, test loss-1.7337, acc-0.5085
Iter-77010, train loss-1.7201, acc-0.7000, valid loss-1.7328, acc-0.5198, test loss-1.7336, acc-0.5086
Iter-77020, train loss-1.8139, acc-0.4800, valid loss-1.7328, acc-0.5198, test loss-1.7336, acc-0.5086
Iter-77030, train loss-1.6898, acc-0.6200, valid loss-1.7327, acc-0.5198, test loss-1.7335, acc-0.5088
Iter-77040, train loss-1.6783, acc-0.5400, valid loss-1.7327, acc-0.5196, test loss-1.7335, acc-0.5087
Iter-77050, train loss-1.6909, acc-0.5400, valid loss-1.7327, acc-0.5196, test loss-1.7335, acc-0.5086
Iter-77060, train loss-1.6465, acc-0.6200, valid loss-1.7326, acc-0.5196, test loss-1.7334, acc-0.5086
Iter-77070, train loss-1.6175, acc-0.6400, valid loss-1.7326, acc-0.5200, test loss-1.7334, acc-0.5086
Iter-77080, train loss-1.7814, acc-0.4600, valid loss-1.7325, acc-0.5194, test loss-1.7333, acc-0.5086
Iter-77090, train loss-1.7948, acc-0.4000, valid loss-1.7325, acc-0.5200, test loss-1.7333, acc-0.5087
Iter-77100, train loss-1.8141, acc-0.4800, valid loss-1.7325, acc-0.5198, test loss-1.7333, acc-0.5087
Iter-77110, train loss-1.7326, acc-0.4800, valid loss-1.7324, acc-0.5198, test loss-1.7332, acc-0.5088
Iter-77120, train loss-1.6697, acc-0.6600, valid loss-1.7324, acc-0.5200, test loss-1.7332, acc-0.5086
Iter-77130, train loss-1.8320, acc-0.4000, valid loss-1.7323, acc-0.5198, test loss-1.7332, acc-0.5087
Iter-77140, train loss-1.7013, acc-0.5000, valid loss-1.7323, acc-0.5196, test loss-1.7331, acc-0.5088
Iter-77150, train loss-1.8235, acc-0.4200, valid loss-1.7323, acc-0.5202, test loss-1.7331, acc-0.5088
Iter-77160, train loss-1.8136, acc-0.4000, valid loss-1.7322, acc-0.5202, test loss-1.7330, acc-0.5088
Iter-77170, train loss-1.8214, acc-0.5400, valid loss-1.7322, acc-0.5202, test loss-1.7330, acc-0.5088
Iter-77180, train loss-1.7607, acc-0.4600, valid loss-1.7321, acc-0.5202, test loss-1.7330, acc-0.5087
Iter-77190, train loss-1.7056, acc-0.4800, valid loss-1.7321, acc-0.5202, test loss-1.7329, acc-0.5088
Iter-77200, train loss-1.7743, acc-0.5200, valid loss-1.7321, acc-0.5202, test loss-1.7329, acc-0.5087
Iter-77210, train loss-1.7191, acc-0.6400, valid loss-1.7320, acc-0.5202, test loss-1.7328, acc-0.5089
Iter-77220, train loss-1.7131, acc-0.6200, valid loss-1.7320, acc-0.5202, test loss-1.7328, acc-0.5090
Iter-77230, train loss-1.7465, acc-0.5200, valid loss-1.7319, acc-0.5208, test loss-1.7328, acc-0.5088
Iter-77240, train loss-1.8483, acc-0.3600, valid loss-1.7319, acc-0.5204, test loss-1.7327, acc-0.5088
Iter-77250, train loss-1.8975, acc-0.3400, valid loss-1.7319, acc-0.5202, test loss-1.7327, acc-0.5088
Iter-77260, train loss-1.6200, acc-0.5800, valid loss-1.7318, acc-0.5202, test loss-1.7326, acc-0.5085
Iter-77270, train loss-1.7562, acc-0.5400, valid loss-1.7318, acc-0.5200, test loss-1.7326, acc-0.5085
Iter-77280, train loss-1.7156, acc-0.5400, valid loss-1.7317, acc-0.5202, test loss-1.7326, acc-0.5086
Iter-77290, train loss-1.6584, acc-0.5800, valid loss-1.7317, acc-0.5200, test loss-1.7325, acc-0.5084
Iter-77300, train loss-1.7697, acc-0.5400, valid loss-1.7317, acc-0.5206, test loss-1.7325, acc-0.5083
Iter-77310, train loss-1.7028, acc-0.5400, valid loss-1.7316, acc-0.5204, test loss-1.7324, acc-0.5082
Iter-77320, train loss-1.8023, acc-0.4600, valid loss-1.7316, acc-0.5202, test loss-1.7324, acc-0.5085
Iter-77330, train loss-1.7592, acc-0.5000, valid loss-1.7315, acc-0.5202, test loss-1.7324, acc-0.5085
Iter-77340, train loss-1.7506, acc-0.5400, valid loss-1.7315, acc-0.5204, test loss-1.7323, acc-0.5084
Iter-77350, train loss-1.6927, acc-0.6200, valid loss-1.7315, acc-0.5200, test loss-1.7323, acc-0.5085
Iter-77360, train loss-1.6818, acc-0.5000, valid loss-1.7314, acc-0.5202, test loss-1.7322, acc-0.5085
Iter-77370, train loss-1.6393, acc-0.5800, valid loss-1.7314, acc-0.5200, test loss-1.7322, acc-0.5087
Iter-77380, train loss-1.6502, acc-0.5000, valid loss-1.7313, acc-0.5206, test loss-1.7322, acc-0.5085
Iter-77390, train loss-1.9275, acc-0.3800, valid loss-1.7313, acc-0.5206, test loss-1.7321, acc-0.5086
Iter-77400, train loss-1.8339, acc-0.4400, valid loss-1.7313, acc-0.5202, test loss-1.7321, acc-0.5087
Iter-77410, train loss-1.7651, acc-0.5400, valid loss-1.7312, acc-0.5202, test loss-1.7320, acc-0.5087
Iter-77420, train loss-1.7543, acc-0.4600, valid loss-1.7312, acc-0.5202, test loss-1.7320, acc-0.5085
Iter-77430, train loss-1.6965, acc-0.5600, valid loss-1.7312, acc-0.5204, test loss-1.7320, acc-0.5087
Iter-77440, train loss-1.7164, acc-0.5600, valid loss-1.7311, acc-0.5204, test loss-1.7319, acc-0.5088
Iter-77450, train loss-1.7195, acc-0.4600, valid loss-1.7311, acc-0.5202, test loss-1.7319, acc-0.5087
Iter-77460, train loss-1.7445, acc-0.4800, valid loss-1.7310, acc-0.5204, test loss-1.7318, acc-0.5089
Iter-77470, train loss-1.6951, acc-0.5400, valid loss-1.7310, acc-0.5204, test loss-1.7318, acc-0.5088
Iter-77480, train loss-1.8110, acc-0.4000, valid loss-1.7310, acc-0.5202, test loss-1.7318, acc-0.5088
Iter-77490, train loss-1.6364, acc-0.5200, valid loss-1.7309, acc-0.5200, test loss-1.7317, acc-0.5087
Iter-77500, train loss-1.7005, acc-0.6600, valid loss-1.7309, acc-0.5200, test loss-1.7317, acc-0.5087
Iter-77510, train loss-1.6974, acc-0.5800, valid loss-1.7308, acc-0.5202, test loss-1.7317, acc-0.5088
Iter-77520, train loss-1.7021, acc-0.4400, valid loss-1.7308, acc-0.5202, test loss-1.7316, acc-0.5086
Iter-77530, train loss-1.5903, acc-0.5400, valid loss-1.7308, acc-0.5202, test loss-1.7316, acc-0.5089
Iter-77540, train loss-1.6986, acc-0.6200, valid loss-1.7307, acc-0.5202, test loss-1.7315, acc-0.5088
Iter-77550, train loss-1.7396, acc-0.5200, valid loss-1.7307, acc-0.5202, test loss-1.7315, acc-0.5088
Iter-77560, train loss-1.7834, acc-0.5000, valid loss-1.7306, acc-0.5202, test loss-1.7314, acc-0.5087
Iter-77570, train loss-1.7190, acc-0.4800, valid loss-1.7306, acc-0.5206, test loss-1.7314, acc-0.5089
Iter-77580, train loss-1.7056, acc-0.6000, valid loss-1.7306, acc-0.5204, test loss-1.7314, acc-0.5089
Iter-77590, train loss-1.6741, acc-0.5600, valid loss-1.7305, acc-0.5208, test loss-1.7313, acc-0.5088
Iter-77600, train loss-1.7630, acc-0.4400, valid loss-1.7305, acc-0.5206, test loss-1.7313, acc-0.5089
Iter-77610, train loss-1.7975, acc-0.4400, valid loss-1.7304, acc-0.5202, test loss-1.7313, acc-0.5087
Iter-77620, train loss-1.7627, acc-0.4400, valid loss-1.7304, acc-0.5202, test loss-1.7312, acc-0.5087
Iter-77630, train loss-1.8966, acc-0.3600, valid loss-1.7304, acc-0.5204, test loss-1.7312, acc-0.5087
Iter-77640, train loss-1.8473, acc-0.4200, valid loss-1.7303, acc-0.5206, test loss-1.7311, acc-0.5088
Iter-77650, train loss-1.7359, acc-0.4600, valid loss-1.7303, acc-0.5206, test loss-1.7311, acc-0.5086
Iter-77660, train loss-1.7878, acc-0.4200, valid loss-1.7302, acc-0.5204, test loss-1.7311, acc-0.5088
Iter-77670, train loss-1.7849, acc-0.5400, valid loss-1.7302, acc-0.5208, test loss-1.7310, acc-0.5087
Iter-77680, train loss-1.7541, acc-0.4600, valid loss-1.7302, acc-0.5208, test loss-1.7310, acc-0.5087
Iter-77690, train loss-1.7527, acc-0.5000, valid loss-1.7301, acc-0.5206, test loss-1.7309, acc-0.5086
Iter-77700, train loss-1.7240, acc-0.4800, valid loss-1.7301, acc-0.5208, test loss-1.7309, acc-0.5086
Iter-77710, train loss-1.8581, acc-0.4600, valid loss-1.7301, acc-0.5208, test loss-1.7309, acc-0.5086
Iter-77720, train loss-1.6928, acc-0.5600, valid loss-1.7300, acc-0.5206, test loss-1.7308, acc-0.5086
Iter-77730, train loss-1.8543, acc-0.4200, valid loss-1.7300, acc-0.5208, test loss-1.7308, acc-0.5087
Iter-77740, train loss-1.6369, acc-0.5200, valid loss-1.7299, acc-0.5204, test loss-1.7307, acc-0.5086
Iter-77750, train loss-1.6589, acc-0.6200, valid loss-1.7299, acc-0.5204, test loss-1.7307, acc-0.5088
Iter-77760, train loss-1.7030, acc-0.5200, valid loss-1.7299, acc-0.5206, test loss-1.7307, acc-0.5088
Iter-77770, train loss-1.8559, acc-0.3000, valid loss-1.7298, acc-0.5204, test loss-1.7306, acc-0.5087
Iter-77780, train loss-1.8651, acc-0.5000, valid loss-1.7298, acc-0.5204, test loss-1.7306, acc-0.5087
Iter-77790, train loss-1.7180, acc-0.5200, valid loss-1.7297, acc-0.5204, test loss-1.7306, acc-0.5088
Iter-77800, train loss-1.7793, acc-0.4000, valid loss-1.7297, acc-0.5206, test loss-1.7305, acc-0.5087
Iter-77810, train loss-1.5943, acc-0.6000, valid loss-1.7297, acc-0.5206, test loss-1.7305, acc-0.5086
Iter-77820, train loss-1.8861, acc-0.4200, valid loss-1.7296, acc-0.5206, test loss-1.7304, acc-0.5086
Iter-77830, train loss-1.8762, acc-0.4200, valid loss-1.7296, acc-0.5208, test loss-1.7304, acc-0.5087
Iter-77840, train loss-1.6857, acc-0.4800, valid loss-1.7295, acc-0.5208, test loss-1.7304, acc-0.5088
Iter-77850, train loss-1.7979, acc-0.4800, valid loss-1.7295, acc-0.5208, test loss-1.7303, acc-0.5087
Iter-77860, train loss-1.7979, acc-0.5000, valid loss-1.7295, acc-0.5206, test loss-1.7303, acc-0.5087
Iter-77870, train loss-1.6637, acc-0.6400, valid loss-1.7294, acc-0.5208, test loss-1.7303, acc-0.5086
Iter-77880, train loss-1.8086, acc-0.4200, valid loss-1.7294, acc-0.5208, test loss-1.7302, acc-0.5087
Iter-77890, train loss-1.7133, acc-0.5800, valid loss-1.7293, acc-0.5206, test loss-1.7302, acc-0.5085
Iter-77900, train loss-1.6940, acc-0.5000, valid loss-1.7293, acc-0.5206, test loss-1.7301, acc-0.5085
Iter-77910, train loss-1.8063, acc-0.4800, valid loss-1.7293, acc-0.5206, test loss-1.7301, acc-0.5087
Iter-77920, train loss-1.7811, acc-0.5200, valid loss-1.7292, acc-0.5208, test loss-1.7301, acc-0.5085
Iter-77930, train loss-1.7117, acc-0.4800, valid loss-1.7292, acc-0.5206, test loss-1.7300, acc-0.5084
Iter-77940, train loss-1.7491, acc-0.4400, valid loss-1.7291, acc-0.5208, test loss-1.7300, acc-0.5085
Iter-77950, train loss-1.7145, acc-0.5200, valid loss-1.7291, acc-0.5208, test loss-1.7299, acc-0.5085
Iter-77960, train loss-1.7311, acc-0.5000, valid loss-1.7291, acc-0.5208, test loss-1.7299, acc-0.5085
Iter-77970, train loss-1.7299, acc-0.5400, valid loss-1.7290, acc-0.5208, test loss-1.7299, acc-0.5085
Iter-77980, train loss-1.6936, acc-0.5600, valid loss-1.7290, acc-0.5208, test loss-1.7298, acc-0.5085
Iter-77990, train loss-1.6513, acc-0.6600, valid loss-1.7290, acc-0.5208, test loss-1.7298, acc-0.5084
Iter-78000, train loss-1.8011, acc-0.5600, valid loss-1.7289, acc-0.5210, test loss-1.7297, acc-0.5084
Iter-78010, train loss-1.8433, acc-0.3800, valid loss-1.7289, acc-0.5210, test loss-1.7297, acc-0.5085
Iter-78020, train loss-1.6217, acc-0.6600, valid loss-1.7288, acc-0.5210, test loss-1.7297, acc-0.5084
Iter-78030, train loss-1.7975, acc-0.4200, valid loss-1.7288, acc-0.5208, test loss-1.7296, acc-0.5084
Iter-78040, train loss-1.6593, acc-0.5600, valid loss-1.7287, acc-0.5212, test loss-1.7296, acc-0.5084
Iter-78050, train loss-1.8052, acc-0.4200, valid loss-1.7287, acc-0.5212, test loss-1.7295, acc-0.5084
Iter-78060, train loss-1.8102, acc-0.5000, valid loss-1.7287, acc-0.5216, test loss-1.7295, acc-0.5083
Iter-78070, train loss-1.6940, acc-0.5600, valid loss-1.7286, acc-0.5216, test loss-1.7295, acc-0.5083
Iter-78080, train loss-1.7316, acc-0.5600, valid loss-1.7286, acc-0.5214, test loss-1.7294, acc-0.5083
Iter-78090, train loss-1.7653, acc-0.5000, valid loss-1.7285, acc-0.5214, test loss-1.7294, acc-0.5084
Iter-78100, train loss-1.8602, acc-0.4200, valid loss-1.7285, acc-0.5212, test loss-1.7293, acc-0.5085
Iter-78110, train loss-1.7939, acc-0.4600, valid loss-1.7285, acc-0.5212, test loss-1.7293, acc-0.5085
Iter-78120, train loss-1.7289, acc-0.4400, valid loss-1.7284, acc-0.5212, test loss-1.7293, acc-0.5086
Iter-78130, train loss-1.7549, acc-0.4800, valid loss-1.7284, acc-0.5212, test loss-1.7292, acc-0.5087
Iter-78140, train loss-1.7555, acc-0.5600, valid loss-1.7284, acc-0.5212, test loss-1.7292, acc-0.5088
Iter-78150, train loss-1.7837, acc-0.4600, valid loss-1.7283, acc-0.5212, test loss-1.7292, acc-0.5088
Iter-78160, train loss-1.7507, acc-0.5600, valid loss-1.7283, acc-0.5212, test loss-1.7291, acc-0.5087
Iter-78170, train loss-1.8589, acc-0.3200, valid loss-1.7282, acc-0.5214, test loss-1.7291, acc-0.5088
Iter-78180, train loss-1.8600, acc-0.3600, valid loss-1.7282, acc-0.5214, test loss-1.7290, acc-0.5087
Iter-78190, train loss-1.7076, acc-0.5800, valid loss-1.7282, acc-0.5216, test loss-1.7290, acc-0.5087
Iter-78200, train loss-1.7209, acc-0.5400, valid loss-1.7281, acc-0.5216, test loss-1.7290, acc-0.5089
Iter-78210, train loss-1.8135, acc-0.4800, valid loss-1.7281, acc-0.5216, test loss-1.7289, acc-0.5089
Iter-78220, train loss-1.8437, acc-0.4200, valid loss-1.7281, acc-0.5216, test loss-1.7289, acc-0.5089
Iter-78230, train loss-1.7726, acc-0.5200, valid loss-1.7280, acc-0.5214, test loss-1.7288, acc-0.5089
Iter-78240, train loss-1.6937, acc-0.4600, valid loss-1.7280, acc-0.5216, test loss-1.7288, acc-0.5090
Iter-78250, train loss-1.8034, acc-0.4600, valid loss-1.7279, acc-0.5216, test loss-1.7288, acc-0.5090
Iter-78260, train loss-1.8797, acc-0.3600, valid loss-1.7279, acc-0.5216, test loss-1.7287, acc-0.5088
Iter-78270, train loss-1.8224, acc-0.4200, valid loss-1.7279, acc-0.5216, test loss-1.7287, acc-0.5088
Iter-78280, train loss-1.7264, acc-0.5200, valid loss-1.7278, acc-0.5216, test loss-1.7286, acc-0.5087
Iter-78290, train loss-1.6950, acc-0.5400, valid loss-1.7278, acc-0.5216, test loss-1.7286, acc-0.5086
Iter-78300, train loss-1.7794, acc-0.5000, valid loss-1.7277, acc-0.5214, test loss-1.7286, acc-0.5086
Iter-78310, train loss-1.5400, acc-0.7200, valid loss-1.7277, acc-0.5214, test loss-1.7285, acc-0.5087
Iter-78320, train loss-1.6982, acc-0.5400, valid loss-1.7277, acc-0.5214, test loss-1.7285, acc-0.5087
Iter-78330, train loss-1.6765, acc-0.5600, valid loss-1.7276, acc-0.5214, test loss-1.7285, acc-0.5086
Iter-78340, train loss-1.7520, acc-0.4600, valid loss-1.7276, acc-0.5214, test loss-1.7284, acc-0.5087
Iter-78350, train loss-1.6079, acc-0.6400, valid loss-1.7275, acc-0.5214, test loss-1.7284, acc-0.5086
Iter-78360, train loss-1.6368, acc-0.6400, valid loss-1.7275, acc-0.5212, test loss-1.7283, acc-0.5087
Iter-78370, train loss-1.6153, acc-0.5400, valid loss-1.7275, acc-0.5212, test loss-1.7283, acc-0.5088
Iter-78380, train loss-1.7719, acc-0.5000, valid loss-1.7274, acc-0.5212, test loss-1.7283, acc-0.5087
Iter-78390, train loss-1.7662, acc-0.5600, valid loss-1.7274, acc-0.5214, test loss-1.7282, acc-0.5087
Iter-78400, train loss-1.6939, acc-0.5200, valid loss-1.7273, acc-0.5212, test loss-1.7282, acc-0.5086
Iter-78410, train loss-1.8118, acc-0.4600, valid loss-1.7273, acc-0.5214, test loss-1.7281, acc-0.5086
Iter-78420, train loss-1.6821, acc-0.5000, valid loss-1.7273, acc-0.5212, test loss-1.7281, acc-0.5087
Iter-78430, train loss-1.5974, acc-0.5400, valid loss-1.7272, acc-0.5210, test loss-1.7281, acc-0.5086
Iter-78440, train loss-1.6919, acc-0.5000, valid loss-1.7272, acc-0.5212, test loss-1.7280, acc-0.5088
Iter-78450, train loss-1.8095, acc-0.4400, valid loss-1.7271, acc-0.5210, test loss-1.7280, acc-0.5088
Iter-78460, train loss-1.8361, acc-0.4200, valid loss-1.7271, acc-0.5212, test loss-1.7279, acc-0.5088
Iter-78470, train loss-1.8126, acc-0.4000, valid loss-1.7271, acc-0.5212, test loss-1.7279, acc-0.5088
Iter-78480, train loss-1.7591, acc-0.5200, valid loss-1.7270, acc-0.5212, test loss-1.7279, acc-0.5090
Iter-78490, train loss-1.6939, acc-0.4600, valid loss-1.7270, acc-0.5212, test loss-1.7278, acc-0.5090
Iter-78500, train loss-1.7582, acc-0.5200, valid loss-1.7270, acc-0.5216, test loss-1.7278, acc-0.5090
Iter-78510, train loss-1.9019, acc-0.3800, valid loss-1.7269, acc-0.5216, test loss-1.7277, acc-0.5091
Iter-78520, train loss-1.6960, acc-0.4800, valid loss-1.7269, acc-0.5216, test loss-1.7277, acc-0.5091
Iter-78530, train loss-1.7856, acc-0.4000, valid loss-1.7268, acc-0.5216, test loss-1.7277, acc-0.5091
Iter-78540, train loss-1.7559, acc-0.4800, valid loss-1.7268, acc-0.5218, test loss-1.7276, acc-0.5091
Iter-78550, train loss-1.7474, acc-0.5400, valid loss-1.7268, acc-0.5216, test loss-1.7276, acc-0.5092
Iter-78560, train loss-1.7833, acc-0.5200, valid loss-1.7267, acc-0.5216, test loss-1.7276, acc-0.5092
Iter-78570, train loss-1.7609, acc-0.4000, valid loss-1.7267, acc-0.5216, test loss-1.7275, acc-0.5091
Iter-78580, train loss-1.7384, acc-0.4800, valid loss-1.7267, acc-0.5216, test loss-1.7275, acc-0.5092
Iter-78590, train loss-1.7001, acc-0.5000, valid loss-1.7266, acc-0.5218, test loss-1.7274, acc-0.5091
Iter-78600, train loss-1.7430, acc-0.6000, valid loss-1.7266, acc-0.5216, test loss-1.7274, acc-0.5091
Iter-78610, train loss-1.7229, acc-0.5200, valid loss-1.7265, acc-0.5214, test loss-1.7274, acc-0.5092
Iter-78620, train loss-1.7636, acc-0.4600, valid loss-1.7265, acc-0.5216, test loss-1.7273, acc-0.5091
Iter-78630, train loss-1.7461, acc-0.5200, valid loss-1.7265, acc-0.5216, test loss-1.7273, acc-0.5091
Iter-78640, train loss-1.7308, acc-0.5600, valid loss-1.7264, acc-0.5218, test loss-1.7272, acc-0.5090
Iter-78650, train loss-1.7529, acc-0.5400, valid loss-1.7264, acc-0.5218, test loss-1.7272, acc-0.5090
Iter-78660, train loss-1.7539, acc-0.5000, valid loss-1.7263, acc-0.5218, test loss-1.7272, acc-0.5089
Iter-78670, train loss-1.7343, acc-0.5600, valid loss-1.7263, acc-0.5220, test loss-1.7271, acc-0.5089
Iter-78680, train loss-1.7624, acc-0.4800, valid loss-1.7263, acc-0.5220, test loss-1.7271, acc-0.5089
Iter-78690, train loss-1.7763, acc-0.5600, valid loss-1.7262, acc-0.5220, test loss-1.7270, acc-0.5089
Iter-78700, train loss-1.6445, acc-0.6200, valid loss-1.7262, acc-0.5220, test loss-1.7270, acc-0.5089
Iter-78710, train loss-1.7507, acc-0.5000, valid loss-1.7261, acc-0.5220, test loss-1.7270, acc-0.5090
Iter-78720, train loss-1.7724, acc-0.4000, valid loss-1.7261, acc-0.5216, test loss-1.7269, acc-0.5090
Iter-78730, train loss-1.6342, acc-0.5600, valid loss-1.7261, acc-0.5216, test loss-1.7269, acc-0.5091
Iter-78740, train loss-1.7044, acc-0.5200, valid loss-1.7260, acc-0.5218, test loss-1.7269, acc-0.5090
Iter-78750, train loss-1.7381, acc-0.4800, valid loss-1.7260, acc-0.5218, test loss-1.7268, acc-0.5091
Iter-78760, train loss-1.7418, acc-0.5600, valid loss-1.7260, acc-0.5218, test loss-1.7268, acc-0.5091
Iter-78770, train loss-1.7302, acc-0.4400, valid loss-1.7259, acc-0.5220, test loss-1.7267, acc-0.5091
Iter-78780, train loss-1.7504, acc-0.5200, valid loss-1.7259, acc-0.5216, test loss-1.7267, acc-0.5091
Iter-78790, train loss-1.5395, acc-0.7200, valid loss-1.7258, acc-0.5216, test loss-1.7267, acc-0.5091
Iter-78800, train loss-1.7159, acc-0.4800, valid loss-1.7258, acc-0.5216, test loss-1.7266, acc-0.5090
Iter-78810, train loss-1.6687, acc-0.5600, valid loss-1.7258, acc-0.5216, test loss-1.7266, acc-0.5091
Iter-78820, train loss-1.7122, acc-0.5600, valid loss-1.7257, acc-0.5216, test loss-1.7265, acc-0.5089
Iter-78830, train loss-1.7187, acc-0.4800, valid loss-1.7257, acc-0.5214, test loss-1.7265, acc-0.5090
Iter-78840, train loss-1.7741, acc-0.5400, valid loss-1.7256, acc-0.5214, test loss-1.7265, acc-0.5091
Iter-78850, train loss-1.6814, acc-0.5000, valid loss-1.7256, acc-0.5214, test loss-1.7264, acc-0.5091
Iter-78860, train loss-1.7316, acc-0.5200, valid loss-1.7256, acc-0.5216, test loss-1.7264, acc-0.5090
Iter-78870, train loss-1.7689, acc-0.4000, valid loss-1.7255, acc-0.5216, test loss-1.7263, acc-0.5091
Iter-78880, train loss-1.7155, acc-0.5200, valid loss-1.7255, acc-0.5214, test loss-1.7263, acc-0.5091
Iter-78890, train loss-1.7103, acc-0.5200, valid loss-1.7254, acc-0.5214, test loss-1.7263, acc-0.5091
Iter-78900, train loss-1.7572, acc-0.5000, valid loss-1.7254, acc-0.5214, test loss-1.7262, acc-0.5091
Iter-78910, train loss-1.9353, acc-0.3600, valid loss-1.7254, acc-0.5218, test loss-1.7262, acc-0.5091
Iter-78920, train loss-1.8693, acc-0.4400, valid loss-1.7253, acc-0.5216, test loss-1.7262, acc-0.5092
Iter-78930, train loss-1.6660, acc-0.5800, valid loss-1.7253, acc-0.5214, test loss-1.7261, acc-0.5091
Iter-78940, train loss-1.6952, acc-0.5200, valid loss-1.7253, acc-0.5216, test loss-1.7261, acc-0.5092
Iter-78950, train loss-1.7960, acc-0.4800, valid loss-1.7252, acc-0.5218, test loss-1.7260, acc-0.5091
Iter-78960, train loss-1.8325, acc-0.3800, valid loss-1.7252, acc-0.5218, test loss-1.7260, acc-0.5091
Iter-78970, train loss-1.7562, acc-0.4800, valid loss-1.7251, acc-0.5218, test loss-1.7260, acc-0.5091
Iter-78980, train loss-1.7441, acc-0.5600, valid loss-1.7251, acc-0.5216, test loss-1.7259, acc-0.5094
Iter-78990, train loss-1.6778, acc-0.6400, valid loss-1.7251, acc-0.5214, test loss-1.7259, acc-0.5094
Iter-79000, train loss-1.7751, acc-0.5400, valid loss-1.7250, acc-0.5216, test loss-1.7259, acc-0.5094
Iter-79010, train loss-1.7033, acc-0.6000, valid loss-1.7250, acc-0.5218, test loss-1.7258, acc-0.5094
Iter-79020, train loss-1.6501, acc-0.5200, valid loss-1.7250, acc-0.5218, test loss-1.7258, acc-0.5092
Iter-79030, train loss-1.8023, acc-0.4200, valid loss-1.7249, acc-0.5218, test loss-1.7257, acc-0.5093
Iter-79040, train loss-1.6673, acc-0.5400, valid loss-1.7249, acc-0.5214, test loss-1.7257, acc-0.5092
Iter-79050, train loss-1.7060, acc-0.5600, valid loss-1.7248, acc-0.5214, test loss-1.7257, acc-0.5092
Iter-79060, train loss-1.6702, acc-0.5400, valid loss-1.7248, acc-0.5214, test loss-1.7256, acc-0.5092
Iter-79070, train loss-1.7517, acc-0.4200, valid loss-1.7248, acc-0.5216, test loss-1.7256, acc-0.5091
Iter-79080, train loss-1.7019, acc-0.5400, valid loss-1.7247, acc-0.5218, test loss-1.7255, acc-0.5092
Iter-79090, train loss-1.6425, acc-0.5000, valid loss-1.7247, acc-0.5220, test loss-1.7255, acc-0.5092
Iter-79100, train loss-1.7675, acc-0.5000, valid loss-1.7246, acc-0.5220, test loss-1.7255, acc-0.5091
Iter-79110, train loss-1.6897, acc-0.5200, valid loss-1.7246, acc-0.5218, test loss-1.7254, acc-0.5092
Iter-79120, train loss-1.8904, acc-0.3400, valid loss-1.7246, acc-0.5216, test loss-1.7254, acc-0.5093
Iter-79130, train loss-1.6556, acc-0.5400, valid loss-1.7245, acc-0.5218, test loss-1.7254, acc-0.5093
Iter-79140, train loss-1.5991, acc-0.6600, valid loss-1.7245, acc-0.5220, test loss-1.7253, acc-0.5093
Iter-79150, train loss-1.6529, acc-0.6000, valid loss-1.7245, acc-0.5218, test loss-1.7253, acc-0.5093
Iter-79160, train loss-1.7679, acc-0.5000, valid loss-1.7244, acc-0.5218, test loss-1.7252, acc-0.5094
Iter-79170, train loss-1.7601, acc-0.5000, valid loss-1.7244, acc-0.5218, test loss-1.7252, acc-0.5093
Iter-79180, train loss-1.7646, acc-0.4800, valid loss-1.7243, acc-0.5218, test loss-1.7252, acc-0.5094
Iter-79190, train loss-1.7766, acc-0.4800, valid loss-1.7243, acc-0.5220, test loss-1.7251, acc-0.5094
Iter-79200, train loss-1.7728, acc-0.4400, valid loss-1.7243, acc-0.5220, test loss-1.7251, acc-0.5096
Iter-79210, train loss-1.9072, acc-0.3400, valid loss-1.7242, acc-0.5220, test loss-1.7250, acc-0.5096
Iter-79220, train loss-1.8367, acc-0.4200, valid loss-1.7242, acc-0.5220, test loss-1.7250, acc-0.5096
Iter-79230, train loss-1.7476, acc-0.5200, valid loss-1.7241, acc-0.5220, test loss-1.7250, acc-0.5096
Iter-79240, train loss-1.7375, acc-0.4200, valid loss-1.7241, acc-0.5220, test loss-1.7249, acc-0.5096
Iter-79250, train loss-1.6989, acc-0.5000, valid loss-1.7241, acc-0.5220, test loss-1.7249, acc-0.5096
Iter-79260, train loss-1.7992, acc-0.5200, valid loss-1.7240, acc-0.5220, test loss-1.7249, acc-0.5095
Iter-79270, train loss-1.7875, acc-0.4800, valid loss-1.7240, acc-0.5220, test loss-1.7248, acc-0.5096
Iter-79280, train loss-1.7144, acc-0.5800, valid loss-1.7239, acc-0.5220, test loss-1.7248, acc-0.5096
Iter-79290, train loss-1.7909, acc-0.4600, valid loss-1.7239, acc-0.5220, test loss-1.7247, acc-0.5096
Iter-79300, train loss-1.8069, acc-0.4000, valid loss-1.7239, acc-0.5218, test loss-1.7247, acc-0.5096
Iter-79310, train loss-1.7687, acc-0.4800, valid loss-1.7238, acc-0.5220, test loss-1.7247, acc-0.5096
Iter-79320, train loss-1.7180, acc-0.5600, valid loss-1.7238, acc-0.5220, test loss-1.7246, acc-0.5096
Iter-79330, train loss-1.7237, acc-0.4800, valid loss-1.7237, acc-0.5222, test loss-1.7246, acc-0.5097
Iter-79340, train loss-1.8016, acc-0.4600, valid loss-1.7237, acc-0.5222, test loss-1.7246, acc-0.5097
Iter-79350, train loss-1.7867, acc-0.5400, valid loss-1.7237, acc-0.5222, test loss-1.7245, acc-0.5097
Iter-79360, train loss-1.7963, acc-0.4600, valid loss-1.7236, acc-0.5222, test loss-1.7245, acc-0.5097
Iter-79370, train loss-1.8430, acc-0.4600, valid loss-1.7236, acc-0.5222, test loss-1.7244, acc-0.5097
Iter-79380, train loss-1.7398, acc-0.5800, valid loss-1.7236, acc-0.5222, test loss-1.7244, acc-0.5096
Iter-79390, train loss-1.7959, acc-0.4400, valid loss-1.7235, acc-0.5222, test loss-1.7244, acc-0.5096
Iter-79400, train loss-1.7775, acc-0.5000, valid loss-1.7235, acc-0.5222, test loss-1.7243, acc-0.5096
Iter-79410, train loss-1.7186, acc-0.5000, valid loss-1.7234, acc-0.5222, test loss-1.7243, acc-0.5097
Iter-79420, train loss-1.8079, acc-0.4800, valid loss-1.7234, acc-0.5224, test loss-1.7242, acc-0.5097
Iter-79430, train loss-1.7324, acc-0.5000, valid loss-1.7234, acc-0.5224, test loss-1.7242, acc-0.5097
Iter-79440, train loss-1.7956, acc-0.4400, valid loss-1.7233, acc-0.5224, test loss-1.7242, acc-0.5097
Iter-79450, train loss-1.7520, acc-0.4600, valid loss-1.7233, acc-0.5224, test loss-1.7241, acc-0.5097
Iter-79460, train loss-1.7942, acc-0.5200, valid loss-1.7233, acc-0.5222, test loss-1.7241, acc-0.5097
Iter-79470, train loss-1.8896, acc-0.3600, valid loss-1.7232, acc-0.5226, test loss-1.7241, acc-0.5097
Iter-79480, train loss-1.8293, acc-0.3400, valid loss-1.7232, acc-0.5226, test loss-1.7240, acc-0.5097
Iter-79490, train loss-1.7924, acc-0.4400, valid loss-1.7231, acc-0.5226, test loss-1.7240, acc-0.5097
Iter-79500, train loss-1.8480, acc-0.3600, valid loss-1.7231, acc-0.5224, test loss-1.7239, acc-0.5098
Iter-79510, train loss-1.7261, acc-0.4800, valid loss-1.7231, acc-0.5226, test loss-1.7239, acc-0.5097
Iter-79520, train loss-1.7938, acc-0.4600, valid loss-1.7230, acc-0.5222, test loss-1.7239, acc-0.5097
Iter-79530, train loss-1.7147, acc-0.5600, valid loss-1.7230, acc-0.5222, test loss-1.7238, acc-0.5097
Iter-79540, train loss-1.6659, acc-0.5000, valid loss-1.7230, acc-0.5222, test loss-1.7238, acc-0.5097
Iter-79550, train loss-1.7315, acc-0.4800, valid loss-1.7229, acc-0.5222, test loss-1.7238, acc-0.5096
Iter-79560, train loss-1.7136, acc-0.5200, valid loss-1.7229, acc-0.5222, test loss-1.7237, acc-0.5095
Iter-79570, train loss-1.6543, acc-0.5800, valid loss-1.7228, acc-0.5220, test loss-1.7237, acc-0.5096
Iter-79580, train loss-1.8138, acc-0.4200, valid loss-1.7228, acc-0.5222, test loss-1.7236, acc-0.5096
Iter-79590, train loss-1.6864, acc-0.4600, valid loss-1.7228, acc-0.5218, test loss-1.7236, acc-0.5096
Iter-79600, train loss-1.6792, acc-0.5400, valid loss-1.7227, acc-0.5220, test loss-1.7236, acc-0.5096
Iter-79610, train loss-1.6544, acc-0.5800, valid loss-1.7227, acc-0.5218, test loss-1.7235, acc-0.5096
Iter-79620, train loss-1.6033, acc-0.5200, valid loss-1.7227, acc-0.5222, test loss-1.7235, acc-0.5096
Iter-79630, train loss-1.7705, acc-0.5000, valid loss-1.7226, acc-0.5216, test loss-1.7235, acc-0.5097
Iter-79640, train loss-1.7397, acc-0.5000, valid loss-1.7226, acc-0.5216, test loss-1.7234, acc-0.5098
Iter-79650, train loss-1.7850, acc-0.5200, valid loss-1.7226, acc-0.5216, test loss-1.7234, acc-0.5099
Iter-79660, train loss-1.7049, acc-0.6200, valid loss-1.7225, acc-0.5216, test loss-1.7233, acc-0.5099
Iter-79670, train loss-1.8145, acc-0.4600, valid loss-1.7225, acc-0.5216, test loss-1.7233, acc-0.5099
Iter-79680, train loss-1.6873, acc-0.5000, valid loss-1.7224, acc-0.5218, test loss-1.7233, acc-0.5100
Iter-79690, train loss-1.8185, acc-0.4800, valid loss-1.7224, acc-0.5218, test loss-1.7232, acc-0.5100
Iter-79700, train loss-1.7750, acc-0.4600, valid loss-1.7224, acc-0.5222, test loss-1.7232, acc-0.5100
Iter-79710, train loss-1.8094, acc-0.4200, valid loss-1.7223, acc-0.5220, test loss-1.7231, acc-0.5100
Iter-79720, train loss-1.7119, acc-0.5800, valid loss-1.7223, acc-0.5220, test loss-1.7231, acc-0.5100
Iter-79730, train loss-1.7276, acc-0.4600, valid loss-1.7223, acc-0.5220, test loss-1.7231, acc-0.5100
Iter-79740, train loss-1.7012, acc-0.4400, valid loss-1.7222, acc-0.5218, test loss-1.7230, acc-0.5100
Iter-79750, train loss-1.6607, acc-0.5400, valid loss-1.7222, acc-0.5220, test loss-1.7230, acc-0.5100
Iter-79760, train loss-1.7730, acc-0.5800, valid loss-1.7222, acc-0.5218, test loss-1.7230, acc-0.5099
Iter-79770, train loss-1.7521, acc-0.5600, valid loss-1.7221, acc-0.5224, test loss-1.7229, acc-0.5099
Iter-79780, train loss-1.7731, acc-0.4600, valid loss-1.7221, acc-0.5220, test loss-1.7229, acc-0.5099
Iter-79790, train loss-1.7164, acc-0.5400, valid loss-1.7220, acc-0.5226, test loss-1.7228, acc-0.5100
Iter-79800, train loss-1.7019, acc-0.5200, valid loss-1.7220, acc-0.5226, test loss-1.7228, acc-0.5100
Iter-79810, train loss-1.8170, acc-0.4800, valid loss-1.7220, acc-0.5224, test loss-1.7228, acc-0.5100
Iter-79820, train loss-1.7999, acc-0.4800, valid loss-1.7219, acc-0.5228, test loss-1.7227, acc-0.5098
Iter-79830, train loss-1.7716, acc-0.3200, valid loss-1.7219, acc-0.5228, test loss-1.7227, acc-0.5098
Iter-79840, train loss-1.8230, acc-0.4200, valid loss-1.7219, acc-0.5226, test loss-1.7227, acc-0.5100
Iter-79850, train loss-1.7481, acc-0.5400, valid loss-1.7218, acc-0.5228, test loss-1.7226, acc-0.5101
Iter-79860, train loss-1.6551, acc-0.6200, valid loss-1.7218, acc-0.5230, test loss-1.7226, acc-0.5099
Iter-79870, train loss-1.6839, acc-0.5200, valid loss-1.7217, acc-0.5230, test loss-1.7225, acc-0.5099
Iter-79880, train loss-1.7312, acc-0.5000, valid loss-1.7217, acc-0.5232, test loss-1.7225, acc-0.5098
Iter-79890, train loss-1.8303, acc-0.4000, valid loss-1.7217, acc-0.5232, test loss-1.7225, acc-0.5099
Iter-79900, train loss-1.7121, acc-0.5600, valid loss-1.7216, acc-0.5232, test loss-1.7224, acc-0.5099
Iter-79910, train loss-1.7480, acc-0.4600, valid loss-1.7216, acc-0.5228, test loss-1.7224, acc-0.5099
Iter-79920, train loss-1.6304, acc-0.5400, valid loss-1.7215, acc-0.5228, test loss-1.7224, acc-0.5099
Iter-79930, train loss-1.6559, acc-0.5800, valid loss-1.7215, acc-0.5228, test loss-1.7223, acc-0.5100
Iter-79940, train loss-1.7388, acc-0.4600, valid loss-1.7215, acc-0.5230, test loss-1.7223, acc-0.5099
Iter-79950, train loss-1.7344, acc-0.5200, valid loss-1.7214, acc-0.5230, test loss-1.7223, acc-0.5097
Iter-79960, train loss-1.7342, acc-0.5600, valid loss-1.7214, acc-0.5228, test loss-1.7222, acc-0.5097
Iter-79970, train loss-1.7488, acc-0.4800, valid loss-1.7214, acc-0.5226, test loss-1.7222, acc-0.5098
Iter-79980, train loss-1.7733, acc-0.6000, valid loss-1.7213, acc-0.5224, test loss-1.7221, acc-0.5097
Iter-79990, train loss-1.6889, acc-0.5200, valid loss-1.7213, acc-0.5222, test loss-1.7221, acc-0.5100
Iter-80000, train loss-1.7298, acc-0.4600, valid loss-1.7213, acc-0.5220, test loss-1.7221, acc-0.5101
Iter-80010, train loss-1.7873, acc-0.4400, valid loss-1.7212, acc-0.5222, test loss-1.7220, acc-0.5100
Iter-80020, train loss-1.8108, acc-0.4200, valid loss-1.7212, acc-0.5224, test loss-1.7220, acc-0.5101
Iter-80030, train loss-1.7102, acc-0.5400, valid loss-1.7211, acc-0.5224, test loss-1.7219, acc-0.5101
Iter-80040, train loss-1.6609, acc-0.5600, valid loss-1.7211, acc-0.5224, test loss-1.7219, acc-0.5100
Iter-80050, train loss-1.7915, acc-0.4600, valid loss-1.7211, acc-0.5224, test loss-1.7219, acc-0.5102
Iter-80060, train loss-1.7204, acc-0.5400, valid loss-1.7210, acc-0.5222, test loss-1.7218, acc-0.5102
Iter-80070, train loss-1.9063, acc-0.3400, valid loss-1.7210, acc-0.5222, test loss-1.7218, acc-0.5102
Iter-80080, train loss-1.8246, acc-0.4600, valid loss-1.7209, acc-0.5224, test loss-1.7218, acc-0.5102
Iter-80090, train loss-1.6795, acc-0.6000, valid loss-1.7209, acc-0.5222, test loss-1.7217, acc-0.5103
Iter-80100, train loss-1.6923, acc-0.5800, valid loss-1.7209, acc-0.5226, test loss-1.7217, acc-0.5103
Iter-80110, train loss-1.6674, acc-0.5400, valid loss-1.7208, acc-0.5226, test loss-1.7216, acc-0.5103
Iter-80120, train loss-1.6701, acc-0.6200, valid loss-1.7208, acc-0.5222, test loss-1.7216, acc-0.5103
Iter-80130, train loss-1.7679, acc-0.4400, valid loss-1.7208, acc-0.5222, test loss-1.7216, acc-0.5103
Iter-80140, train loss-1.7190, acc-0.5400, valid loss-1.7207, acc-0.5226, test loss-1.7215, acc-0.5103
Iter-80150, train loss-1.7116, acc-0.5400, valid loss-1.7207, acc-0.5222, test loss-1.7215, acc-0.5102
Iter-80160, train loss-1.8050, acc-0.4600, valid loss-1.7206, acc-0.5222, test loss-1.7214, acc-0.5102
Iter-80170, train loss-1.7879, acc-0.4000, valid loss-1.7206, acc-0.5226, test loss-1.7214, acc-0.5103
Iter-80180, train loss-1.7204, acc-0.5600, valid loss-1.7206, acc-0.5226, test loss-1.7214, acc-0.5103
Iter-80190, train loss-1.5875, acc-0.7000, valid loss-1.7205, acc-0.5226, test loss-1.7213, acc-0.5103
Iter-80200, train loss-1.8045, acc-0.5000, valid loss-1.7205, acc-0.5226, test loss-1.7213, acc-0.5103
Iter-80210, train loss-1.7636, acc-0.4600, valid loss-1.7205, acc-0.5228, test loss-1.7213, acc-0.5104
Iter-80220, train loss-1.8036, acc-0.4400, valid loss-1.7204, acc-0.5224, test loss-1.7212, acc-0.5104
Iter-80230, train loss-1.8229, acc-0.4000, valid loss-1.7204, acc-0.5224, test loss-1.7212, acc-0.5104
Iter-80240, train loss-1.8013, acc-0.4800, valid loss-1.7203, acc-0.5226, test loss-1.7211, acc-0.5104
Iter-80250, train loss-1.7207, acc-0.5400, valid loss-1.7203, acc-0.5226, test loss-1.7211, acc-0.5104
Iter-80260, train loss-1.7251, acc-0.4400, valid loss-1.7203, acc-0.5226, test loss-1.7211, acc-0.5103
Iter-80270, train loss-1.8794, acc-0.4400, valid loss-1.7202, acc-0.5226, test loss-1.7210, acc-0.5103
Iter-80280, train loss-1.7147, acc-0.5400, valid loss-1.7202, acc-0.5226, test loss-1.7210, acc-0.5102
Iter-80290, train loss-1.6783, acc-0.5800, valid loss-1.7202, acc-0.5226, test loss-1.7210, acc-0.5102
Iter-80300, train loss-1.8266, acc-0.4600, valid loss-1.7201, acc-0.5230, test loss-1.7209, acc-0.5102
Iter-80310, train loss-1.6150, acc-0.5800, valid loss-1.7201, acc-0.5228, test loss-1.7209, acc-0.5102
Iter-80320, train loss-1.8424, acc-0.4400, valid loss-1.7200, acc-0.5228, test loss-1.7209, acc-0.5102
Iter-80330, train loss-1.6092, acc-0.5800, valid loss-1.7200, acc-0.5230, test loss-1.7208, acc-0.5103
Iter-80340, train loss-1.6830, acc-0.5200, valid loss-1.7200, acc-0.5230, test loss-1.7208, acc-0.5104
Iter-80350, train loss-1.6317, acc-0.6200, valid loss-1.7199, acc-0.5228, test loss-1.7207, acc-0.5105
Iter-80360, train loss-1.7504, acc-0.4800, valid loss-1.7199, acc-0.5230, test loss-1.7207, acc-0.5106
Iter-80370, train loss-1.7507, acc-0.5000, valid loss-1.7199, acc-0.5230, test loss-1.7207, acc-0.5105
Iter-80380, train loss-1.6204, acc-0.6200, valid loss-1.7198, acc-0.5230, test loss-1.7206, acc-0.5105
Iter-80390, train loss-1.7729, acc-0.4600, valid loss-1.7198, acc-0.5228, test loss-1.7206, acc-0.5104
Iter-80400, train loss-1.6581, acc-0.5800, valid loss-1.7197, acc-0.5228, test loss-1.7206, acc-0.5104
Iter-80410, train loss-1.6403, acc-0.6600, valid loss-1.7197, acc-0.5228, test loss-1.7205, acc-0.5105
Iter-80420, train loss-1.7802, acc-0.4400, valid loss-1.7197, acc-0.5228, test loss-1.7205, acc-0.5105
Iter-80430, train loss-1.7143, acc-0.5800, valid loss-1.7196, acc-0.5228, test loss-1.7204, acc-0.5104
Iter-80440, train loss-1.5729, acc-0.6400, valid loss-1.7196, acc-0.5228, test loss-1.7204, acc-0.5105
Iter-80450, train loss-1.8164, acc-0.4600, valid loss-1.7195, acc-0.5230, test loss-1.7204, acc-0.5104
Iter-80460, train loss-1.6884, acc-0.4600, valid loss-1.7195, acc-0.5230, test loss-1.7203, acc-0.5104
Iter-80470, train loss-1.6135, acc-0.6400, valid loss-1.7195, acc-0.5230, test loss-1.7203, acc-0.5104
Iter-80480, train loss-1.7571, acc-0.5400, valid loss-1.7194, acc-0.5230, test loss-1.7202, acc-0.5105
Iter-80490, train loss-1.6319, acc-0.5200, valid loss-1.7194, acc-0.5224, test loss-1.7202, acc-0.5105
Iter-80500, train loss-1.7994, acc-0.4200, valid loss-1.7194, acc-0.5226, test loss-1.7202, acc-0.5104
Iter-80510, train loss-1.7696, acc-0.4200, valid loss-1.7193, acc-0.5220, test loss-1.7201, acc-0.5105
Iter-80520, train loss-1.7630, acc-0.4800, valid loss-1.7193, acc-0.5220, test loss-1.7201, acc-0.5104
Iter-80530, train loss-1.7664, acc-0.5400, valid loss-1.7192, acc-0.5222, test loss-1.7201, acc-0.5104
Iter-80540, train loss-1.7560, acc-0.5000, valid loss-1.7192, acc-0.5224, test loss-1.7200, acc-0.5105
Iter-80550, train loss-1.7737, acc-0.5200, valid loss-1.7192, acc-0.5224, test loss-1.7200, acc-0.5104
Iter-80560, train loss-1.7667, acc-0.4600, valid loss-1.7191, acc-0.5224, test loss-1.7199, acc-0.5105
Iter-80570, train loss-1.7030, acc-0.5000, valid loss-1.7191, acc-0.5230, test loss-1.7199, acc-0.5105
Iter-80580, train loss-1.7894, acc-0.4600, valid loss-1.7191, acc-0.5234, test loss-1.7199, acc-0.5105
Iter-80590, train loss-1.7323, acc-0.5200, valid loss-1.7190, acc-0.5230, test loss-1.7198, acc-0.5104
Iter-80600, train loss-1.6290, acc-0.6000, valid loss-1.7190, acc-0.5228, test loss-1.7198, acc-0.5104
Iter-80610, train loss-1.8074, acc-0.4400, valid loss-1.7189, acc-0.5230, test loss-1.7198, acc-0.5103
Iter-80620, train loss-1.6965, acc-0.5200, valid loss-1.7189, acc-0.5230, test loss-1.7197, acc-0.5104
Iter-80630, train loss-1.7176, acc-0.4600, valid loss-1.7189, acc-0.5234, test loss-1.7197, acc-0.5104
Iter-80640, train loss-1.6640, acc-0.5600, valid loss-1.7188, acc-0.5228, test loss-1.7196, acc-0.5103
Iter-80650, train loss-1.6364, acc-0.5800, valid loss-1.7188, acc-0.5228, test loss-1.7196, acc-0.5103
Iter-80660, train loss-1.8368, acc-0.3400, valid loss-1.7187, acc-0.5226, test loss-1.7196, acc-0.5103
Iter-80670, train loss-1.8940, acc-0.3800, valid loss-1.7187, acc-0.5228, test loss-1.7195, acc-0.5104
Iter-80680, train loss-1.7656, acc-0.4600, valid loss-1.7187, acc-0.5230, test loss-1.7195, acc-0.5104
Iter-80690, train loss-1.7150, acc-0.4600, valid loss-1.7186, acc-0.5232, test loss-1.7195, acc-0.5104
Iter-80700, train loss-1.6946, acc-0.5200, valid loss-1.7186, acc-0.5228, test loss-1.7194, acc-0.5103
Iter-80710, train loss-1.6907, acc-0.5800, valid loss-1.7186, acc-0.5232, test loss-1.7194, acc-0.5105
Iter-80720, train loss-1.8243, acc-0.3600, valid loss-1.7185, acc-0.5232, test loss-1.7193, acc-0.5104
Iter-80730, train loss-1.6226, acc-0.6400, valid loss-1.7185, acc-0.5230, test loss-1.7193, acc-0.5104
Iter-80740, train loss-1.7253, acc-0.4600, valid loss-1.7184, acc-0.5228, test loss-1.7193, acc-0.5104
Iter-80750, train loss-1.7739, acc-0.4800, valid loss-1.7184, acc-0.5230, test loss-1.7192, acc-0.5104
Iter-80760, train loss-1.7153, acc-0.4000, valid loss-1.7184, acc-0.5230, test loss-1.7192, acc-0.5105
Iter-80770, train loss-1.8748, acc-0.4200, valid loss-1.7183, acc-0.5228, test loss-1.7192, acc-0.5105
Iter-80780, train loss-1.6943, acc-0.5200, valid loss-1.7183, acc-0.5234, test loss-1.7191, acc-0.5106
Iter-80790, train loss-1.7441, acc-0.4000, valid loss-1.7183, acc-0.5230, test loss-1.7191, acc-0.5104
Iter-80800, train loss-1.6591, acc-0.5000, valid loss-1.7182, acc-0.5228, test loss-1.7190, acc-0.5105
Iter-80810, train loss-1.7811, acc-0.5400, valid loss-1.7182, acc-0.5230, test loss-1.7190, acc-0.5104
Iter-80820, train loss-1.7357, acc-0.4400, valid loss-1.7181, acc-0.5228, test loss-1.7190, acc-0.5103
Iter-80830, train loss-1.7807, acc-0.4800, valid loss-1.7181, acc-0.5230, test loss-1.7189, acc-0.5104
Iter-80840, train loss-1.7279, acc-0.5200, valid loss-1.7181, acc-0.5230, test loss-1.7189, acc-0.5103
Iter-80850, train loss-1.6146, acc-0.6000, valid loss-1.7180, acc-0.5232, test loss-1.7189, acc-0.5104
Iter-80860, train loss-1.7206, acc-0.4800, valid loss-1.7180, acc-0.5230, test loss-1.7188, acc-0.5104
Iter-80870, train loss-1.7396, acc-0.4800, valid loss-1.7179, acc-0.5232, test loss-1.7188, acc-0.5103
Iter-80880, train loss-1.6310, acc-0.5200, valid loss-1.7179, acc-0.5234, test loss-1.7187, acc-0.5103
Iter-80890, train loss-1.9237, acc-0.3800, valid loss-1.7179, acc-0.5232, test loss-1.7187, acc-0.5104
Iter-80900, train loss-1.8466, acc-0.3800, valid loss-1.7178, acc-0.5232, test loss-1.7187, acc-0.5106
Iter-80910, train loss-1.6691, acc-0.6200, valid loss-1.7178, acc-0.5232, test loss-1.7186, acc-0.5104
Iter-80920, train loss-1.7605, acc-0.5000, valid loss-1.7178, acc-0.5232, test loss-1.7186, acc-0.5103
Iter-80930, train loss-1.5696, acc-0.5600, valid loss-1.7177, acc-0.5234, test loss-1.7186, acc-0.5103
Iter-80940, train loss-1.7013, acc-0.4800, valid loss-1.7177, acc-0.5236, test loss-1.7185, acc-0.5104
Iter-80950, train loss-1.6432, acc-0.5600, valid loss-1.7176, acc-0.5238, test loss-1.7185, acc-0.5105
Iter-80960, train loss-1.8032, acc-0.5200, valid loss-1.7176, acc-0.5238, test loss-1.7184, acc-0.5107
Iter-80970, train loss-1.7540, acc-0.5200, valid loss-1.7176, acc-0.5238, test loss-1.7184, acc-0.5108
Iter-80980, train loss-1.7778, acc-0.5200, valid loss-1.7175, acc-0.5236, test loss-1.7184, acc-0.5106
Iter-80990, train loss-1.7533, acc-0.5600, valid loss-1.7175, acc-0.5238, test loss-1.7183, acc-0.5106
Iter-81000, train loss-1.7284, acc-0.5000, valid loss-1.7175, acc-0.5236, test loss-1.7183, acc-0.5108
Iter-81010, train loss-1.7405, acc-0.5000, valid loss-1.7174, acc-0.5234, test loss-1.7183, acc-0.5107
Iter-81020, train loss-1.6764, acc-0.5400, valid loss-1.7174, acc-0.5234, test loss-1.7182, acc-0.5109
Iter-81030, train loss-1.6465, acc-0.5400, valid loss-1.7173, acc-0.5234, test loss-1.7182, acc-0.5110
Iter-81040, train loss-1.7256, acc-0.5000, valid loss-1.7173, acc-0.5234, test loss-1.7181, acc-0.5110
Iter-81050, train loss-1.6903, acc-0.5200, valid loss-1.7173, acc-0.5234, test loss-1.7181, acc-0.5111
Iter-81060, train loss-1.6476, acc-0.5000, valid loss-1.7172, acc-0.5232, test loss-1.7181, acc-0.5111
Iter-81070, train loss-1.7685, acc-0.4600, valid loss-1.7172, acc-0.5232, test loss-1.7180, acc-0.5111
Iter-81080, train loss-1.6477, acc-0.5600, valid loss-1.7172, acc-0.5232, test loss-1.7180, acc-0.5110
Iter-81090, train loss-1.7049, acc-0.5000, valid loss-1.7171, acc-0.5234, test loss-1.7179, acc-0.5110
Iter-81100, train loss-1.8566, acc-0.4400, valid loss-1.7171, acc-0.5236, test loss-1.7179, acc-0.5112
Iter-81110, train loss-1.7586, acc-0.4800, valid loss-1.7170, acc-0.5234, test loss-1.7179, acc-0.5110
Iter-81120, train loss-1.7864, acc-0.4600, valid loss-1.7170, acc-0.5232, test loss-1.7178, acc-0.5110
Iter-81130, train loss-1.7837, acc-0.4800, valid loss-1.7170, acc-0.5232, test loss-1.7178, acc-0.5111
Iter-81140, train loss-1.7570, acc-0.4600, valid loss-1.7169, acc-0.5234, test loss-1.7178, acc-0.5109
Iter-81150, train loss-1.8079, acc-0.4600, valid loss-1.7169, acc-0.5236, test loss-1.7177, acc-0.5111
Iter-81160, train loss-1.7268, acc-0.5200, valid loss-1.7169, acc-0.5236, test loss-1.7177, acc-0.5110
Iter-81170, train loss-1.7225, acc-0.5200, valid loss-1.7168, acc-0.5232, test loss-1.7176, acc-0.5110
Iter-81180, train loss-1.7660, acc-0.5600, valid loss-1.7168, acc-0.5234, test loss-1.7176, acc-0.5110
Iter-81190, train loss-1.6563, acc-0.5600, valid loss-1.7167, acc-0.5236, test loss-1.7176, acc-0.5111
Iter-81200, train loss-1.7980, acc-0.5000, valid loss-1.7167, acc-0.5236, test loss-1.7175, acc-0.5111
Iter-81210, train loss-1.8295, acc-0.4800, valid loss-1.7167, acc-0.5238, test loss-1.7175, acc-0.5111
Iter-81220, train loss-1.7403, acc-0.4800, valid loss-1.7166, acc-0.5238, test loss-1.7175, acc-0.5113
Iter-81230, train loss-1.7859, acc-0.4800, valid loss-1.7166, acc-0.5240, test loss-1.7174, acc-0.5114
Iter-81240, train loss-1.7826, acc-0.4800, valid loss-1.7165, acc-0.5240, test loss-1.7174, acc-0.5113
Iter-81250, train loss-1.6240, acc-0.6000, valid loss-1.7165, acc-0.5240, test loss-1.7173, acc-0.5112
Iter-81260, train loss-1.7418, acc-0.5600, valid loss-1.7165, acc-0.5240, test loss-1.7173, acc-0.5112
Iter-81270, train loss-1.7192, acc-0.6000, valid loss-1.7164, acc-0.5242, test loss-1.7173, acc-0.5112
Iter-81280, train loss-1.7243, acc-0.5000, valid loss-1.7164, acc-0.5242, test loss-1.7172, acc-0.5112
Iter-81290, train loss-1.6972, acc-0.4400, valid loss-1.7164, acc-0.5244, test loss-1.7172, acc-0.5112
Iter-81300, train loss-1.6327, acc-0.5600, valid loss-1.7163, acc-0.5242, test loss-1.7172, acc-0.5109
Iter-81310, train loss-1.6618, acc-0.6200, valid loss-1.7163, acc-0.5242, test loss-1.7171, acc-0.5109
Iter-81320, train loss-1.7194, acc-0.5200, valid loss-1.7162, acc-0.5238, test loss-1.7171, acc-0.5111
Iter-81330, train loss-1.6546, acc-0.5400, valid loss-1.7162, acc-0.5240, test loss-1.7170, acc-0.5112
Iter-81340, train loss-1.6767, acc-0.6000, valid loss-1.7162, acc-0.5240, test loss-1.7170, acc-0.5113
Iter-81350, train loss-1.7705, acc-0.4600, valid loss-1.7161, acc-0.5244, test loss-1.7170, acc-0.5112
Iter-81360, train loss-1.5709, acc-0.6000, valid loss-1.7161, acc-0.5242, test loss-1.7169, acc-0.5111
Iter-81370, train loss-1.5517, acc-0.6000, valid loss-1.7160, acc-0.5242, test loss-1.7169, acc-0.5109
Iter-81380, train loss-1.6027, acc-0.5000, valid loss-1.7160, acc-0.5238, test loss-1.7169, acc-0.5109
Iter-81390, train loss-1.7573, acc-0.5000, valid loss-1.7160, acc-0.5238, test loss-1.7168, acc-0.5107
Iter-81400, train loss-1.6953, acc-0.5400, valid loss-1.7159, acc-0.5238, test loss-1.7168, acc-0.5108
Iter-81410, train loss-1.7341, acc-0.5600, valid loss-1.7159, acc-0.5240, test loss-1.7167, acc-0.5108
Iter-81420, train loss-1.6977, acc-0.6000, valid loss-1.7159, acc-0.5240, test loss-1.7167, acc-0.5109
Iter-81430, train loss-1.6969, acc-0.5000, valid loss-1.7158, acc-0.5238, test loss-1.7167, acc-0.5110
Iter-81440, train loss-1.7233, acc-0.5200, valid loss-1.7158, acc-0.5240, test loss-1.7166, acc-0.5110
Iter-81450, train loss-1.6268, acc-0.5800, valid loss-1.7158, acc-0.5238, test loss-1.7166, acc-0.5110
Iter-81460, train loss-1.8944, acc-0.4000, valid loss-1.7157, acc-0.5238, test loss-1.7166, acc-0.5109
Iter-81470, train loss-1.6651, acc-0.5600, valid loss-1.7157, acc-0.5240, test loss-1.7165, acc-0.5108
Iter-81480, train loss-1.8029, acc-0.4200, valid loss-1.7156, acc-0.5240, test loss-1.7165, acc-0.5109
Iter-81490, train loss-1.6705, acc-0.5400, valid loss-1.7156, acc-0.5240, test loss-1.7165, acc-0.5107
Iter-81500, train loss-1.7011, acc-0.6000, valid loss-1.7156, acc-0.5240, test loss-1.7164, acc-0.5109
Iter-81510, train loss-1.8256, acc-0.5000, valid loss-1.7155, acc-0.5242, test loss-1.7164, acc-0.5111
Iter-81520, train loss-1.6589, acc-0.5200, valid loss-1.7155, acc-0.5240, test loss-1.7163, acc-0.5111
Iter-81530, train loss-1.8673, acc-0.4200, valid loss-1.7155, acc-0.5242, test loss-1.7163, acc-0.5111
Iter-81540, train loss-1.6275, acc-0.5600, valid loss-1.7154, acc-0.5242, test loss-1.7163, acc-0.5111
Iter-81550, train loss-1.6310, acc-0.5400, valid loss-1.7154, acc-0.5242, test loss-1.7162, acc-0.5111
Iter-81560, train loss-1.7615, acc-0.5600, valid loss-1.7154, acc-0.5240, test loss-1.7162, acc-0.5112
Iter-81570, train loss-1.8006, acc-0.5000, valid loss-1.7153, acc-0.5240, test loss-1.7162, acc-0.5112
Iter-81580, train loss-1.6455, acc-0.6400, valid loss-1.7153, acc-0.5240, test loss-1.7161, acc-0.5111
Iter-81590, train loss-1.7105, acc-0.5400, valid loss-1.7152, acc-0.5240, test loss-1.7161, acc-0.5111
Iter-81600, train loss-1.6871, acc-0.5200, valid loss-1.7152, acc-0.5242, test loss-1.7160, acc-0.5113
Iter-81610, train loss-1.7712, acc-0.5200, valid loss-1.7152, acc-0.5242, test loss-1.7160, acc-0.5112
Iter-81620, train loss-1.7777, acc-0.4800, valid loss-1.7151, acc-0.5240, test loss-1.7160, acc-0.5111
Iter-81630, train loss-1.7444, acc-0.4800, valid loss-1.7151, acc-0.5242, test loss-1.7159, acc-0.5111
Iter-81640, train loss-1.5707, acc-0.6000, valid loss-1.7151, acc-0.5244, test loss-1.7159, acc-0.5110
Iter-81650, train loss-1.6978, acc-0.5400, valid loss-1.7150, acc-0.5244, test loss-1.7159, acc-0.5111
Iter-81660, train loss-1.8286, acc-0.4200, valid loss-1.7150, acc-0.5242, test loss-1.7158, acc-0.5112
Iter-81670, train loss-1.6214, acc-0.5400, valid loss-1.7149, acc-0.5244, test loss-1.7158, acc-0.5112
Iter-81680, train loss-1.7089, acc-0.5200, valid loss-1.7149, acc-0.5244, test loss-1.7158, acc-0.5111
Iter-81690, train loss-1.7915, acc-0.5000, valid loss-1.7149, acc-0.5242, test loss-1.7157, acc-0.5111
Iter-81700, train loss-1.7265, acc-0.4400, valid loss-1.7148, acc-0.5242, test loss-1.7157, acc-0.5110
Iter-81710, train loss-1.6855, acc-0.5200, valid loss-1.7148, acc-0.5240, test loss-1.7156, acc-0.5111
Iter-81720, train loss-1.7001, acc-0.5400, valid loss-1.7148, acc-0.5242, test loss-1.7156, acc-0.5110
Iter-81730, train loss-1.7039, acc-0.5000, valid loss-1.7147, acc-0.5242, test loss-1.7156, acc-0.5110
Iter-81740, train loss-1.7935, acc-0.5400, valid loss-1.7147, acc-0.5240, test loss-1.7155, acc-0.5109
Iter-81750, train loss-1.6941, acc-0.5000, valid loss-1.7146, acc-0.5242, test loss-1.7155, acc-0.5109
Iter-81760, train loss-1.8135, acc-0.4600, valid loss-1.7146, acc-0.5242, test loss-1.7155, acc-0.5112
Iter-81770, train loss-1.5967, acc-0.6800, valid loss-1.7146, acc-0.5242, test loss-1.7154, acc-0.5111
Iter-81780, train loss-1.7259, acc-0.4200, valid loss-1.7145, acc-0.5242, test loss-1.7154, acc-0.5112
Iter-81790, train loss-1.7265, acc-0.5200, valid loss-1.7145, acc-0.5240, test loss-1.7153, acc-0.5110
Iter-81800, train loss-1.6034, acc-0.6400, valid loss-1.7144, acc-0.5242, test loss-1.7153, acc-0.5111
Iter-81810, train loss-1.8240, acc-0.4400, valid loss-1.7144, acc-0.5242, test loss-1.7153, acc-0.5111
Iter-81820, train loss-1.6606, acc-0.5000, valid loss-1.7144, acc-0.5242, test loss-1.7152, acc-0.5113
Iter-81830, train loss-1.6998, acc-0.6000, valid loss-1.7143, acc-0.5244, test loss-1.7152, acc-0.5112
Iter-81840, train loss-1.7396, acc-0.4400, valid loss-1.7143, acc-0.5240, test loss-1.7152, acc-0.5112
Iter-81850, train loss-1.7269, acc-0.5600, valid loss-1.7143, acc-0.5242, test loss-1.7151, acc-0.5111
Iter-81860, train loss-1.7232, acc-0.5000, valid loss-1.7142, acc-0.5242, test loss-1.7151, acc-0.5114
Iter-81870, train loss-1.7510, acc-0.5400, valid loss-1.7142, acc-0.5244, test loss-1.7150, acc-0.5112
Iter-81880, train loss-1.6907, acc-0.5600, valid loss-1.7141, acc-0.5242, test loss-1.7150, acc-0.5111
Iter-81890, train loss-1.7346, acc-0.5000, valid loss-1.7141, acc-0.5242, test loss-1.7150, acc-0.5111
Iter-81900, train loss-1.7572, acc-0.4400, valid loss-1.7141, acc-0.5242, test loss-1.7149, acc-0.5111
Iter-81910, train loss-1.5382, acc-0.6200, valid loss-1.7140, acc-0.5244, test loss-1.7149, acc-0.5110
Iter-81920, train loss-1.6727, acc-0.5800, valid loss-1.7140, acc-0.5246, test loss-1.7149, acc-0.5110
Iter-81930, train loss-1.6146, acc-0.6400, valid loss-1.7140, acc-0.5246, test loss-1.7148, acc-0.5110
Iter-81940, train loss-1.6711, acc-0.6000, valid loss-1.7139, acc-0.5246, test loss-1.7148, acc-0.5111
Iter-81950, train loss-1.6711, acc-0.5600, valid loss-1.7139, acc-0.5248, test loss-1.7148, acc-0.5113
Iter-81960, train loss-1.6971, acc-0.4800, valid loss-1.7138, acc-0.5244, test loss-1.7147, acc-0.5110
Iter-81970, train loss-1.7034, acc-0.5000, valid loss-1.7138, acc-0.5244, test loss-1.7147, acc-0.5111
Iter-81980, train loss-1.6761, acc-0.5400, valid loss-1.7138, acc-0.5242, test loss-1.7146, acc-0.5111
Iter-81990, train loss-1.7743, acc-0.4200, valid loss-1.7137, acc-0.5244, test loss-1.7146, acc-0.5111
Iter-82000, train loss-1.8991, acc-0.4400, valid loss-1.7137, acc-0.5242, test loss-1.7146, acc-0.5109
Iter-82010, train loss-1.7273, acc-0.4800, valid loss-1.7137, acc-0.5246, test loss-1.7145, acc-0.5110
Iter-82020, train loss-1.7525, acc-0.4400, valid loss-1.7136, acc-0.5246, test loss-1.7145, acc-0.5111
Iter-82030, train loss-1.8238, acc-0.5000, valid loss-1.7136, acc-0.5246, test loss-1.7145, acc-0.5109
Iter-82040, train loss-1.7688, acc-0.4600, valid loss-1.7136, acc-0.5244, test loss-1.7144, acc-0.5111
Iter-82050, train loss-1.6960, acc-0.5200, valid loss-1.7135, acc-0.5244, test loss-1.7144, acc-0.5113
Iter-82060, train loss-1.6371, acc-0.5800, valid loss-1.7135, acc-0.5246, test loss-1.7143, acc-0.5111
Iter-82070, train loss-1.6644, acc-0.5800, valid loss-1.7134, acc-0.5244, test loss-1.7143, acc-0.5111
Iter-82080, train loss-1.6639, acc-0.4600, valid loss-1.7134, acc-0.5244, test loss-1.7143, acc-0.5112
Iter-82090, train loss-1.7497, acc-0.4400, valid loss-1.7134, acc-0.5244, test loss-1.7142, acc-0.5112
Iter-82100, train loss-1.7190, acc-0.6000, valid loss-1.7133, acc-0.5244, test loss-1.7142, acc-0.5112
Iter-82110, train loss-1.7299, acc-0.4600, valid loss-1.7133, acc-0.5246, test loss-1.7142, acc-0.5112
Iter-82120, train loss-1.6957, acc-0.5600, valid loss-1.7133, acc-0.5246, test loss-1.7141, acc-0.5111
Iter-82130, train loss-1.7109, acc-0.5000, valid loss-1.7132, acc-0.5248, test loss-1.7141, acc-0.5112
Iter-82140, train loss-1.6900, acc-0.4800, valid loss-1.7132, acc-0.5244, test loss-1.7141, acc-0.5112
Iter-82150, train loss-1.6770, acc-0.5800, valid loss-1.7132, acc-0.5246, test loss-1.7140, acc-0.5112
Iter-82160, train loss-1.7071, acc-0.5400, valid loss-1.7131, acc-0.5244, test loss-1.7140, acc-0.5112
Iter-82170, train loss-1.6553, acc-0.5800, valid loss-1.7131, acc-0.5246, test loss-1.7139, acc-0.5112
Iter-82180, train loss-1.6776, acc-0.5600, valid loss-1.7130, acc-0.5246, test loss-1.7139, acc-0.5111
Iter-82190, train loss-1.7132, acc-0.5000, valid loss-1.7130, acc-0.5246, test loss-1.7139, acc-0.5111
Iter-82200, train loss-1.7501, acc-0.6000, valid loss-1.7130, acc-0.5246, test loss-1.7138, acc-0.5112
Iter-82210, train loss-1.8079, acc-0.4200, valid loss-1.7129, acc-0.5246, test loss-1.7138, acc-0.5111
Iter-82220, train loss-1.6846, acc-0.5600, valid loss-1.7129, acc-0.5246, test loss-1.7137, acc-0.5111
Iter-82230, train loss-1.8006, acc-0.4600, valid loss-1.7128, acc-0.5250, test loss-1.7137, acc-0.5112
Iter-82240, train loss-1.7176, acc-0.4800, valid loss-1.7128, acc-0.5248, test loss-1.7137, acc-0.5112
Iter-82250, train loss-1.7565, acc-0.5200, valid loss-1.7128, acc-0.5248, test loss-1.7136, acc-0.5114
Iter-82260, train loss-1.8011, acc-0.4200, valid loss-1.7127, acc-0.5248, test loss-1.7136, acc-0.5115
Iter-82270, train loss-1.6759, acc-0.5600, valid loss-1.7127, acc-0.5250, test loss-1.7136, acc-0.5116
Iter-82280, train loss-1.7913, acc-0.4400, valid loss-1.7127, acc-0.5250, test loss-1.7135, acc-0.5116
Iter-82290, train loss-1.8013, acc-0.4600, valid loss-1.7126, acc-0.5248, test loss-1.7135, acc-0.5115
Iter-82300, train loss-1.7183, acc-0.5200, valid loss-1.7126, acc-0.5248, test loss-1.7134, acc-0.5115
Iter-82310, train loss-1.7469, acc-0.4400, valid loss-1.7125, acc-0.5250, test loss-1.7134, acc-0.5116
Iter-82320, train loss-1.8323, acc-0.4200, valid loss-1.7125, acc-0.5250, test loss-1.7134, acc-0.5116
Iter-82330, train loss-1.7157, acc-0.5600, valid loss-1.7125, acc-0.5248, test loss-1.7133, acc-0.5117
Iter-82340, train loss-1.7968, acc-0.4400, valid loss-1.7124, acc-0.5246, test loss-1.7133, acc-0.5118
Iter-82350, train loss-1.7690, acc-0.5200, valid loss-1.7124, acc-0.5248, test loss-1.7133, acc-0.5117
Iter-82360, train loss-1.7808, acc-0.5000, valid loss-1.7124, acc-0.5248, test loss-1.7132, acc-0.5116
Iter-82370, train loss-1.7119, acc-0.5800, valid loss-1.7123, acc-0.5248, test loss-1.7132, acc-0.5118
Iter-82380, train loss-1.7514, acc-0.5400, valid loss-1.7123, acc-0.5248, test loss-1.7132, acc-0.5117
Iter-82390, train loss-1.7975, acc-0.4400, valid loss-1.7123, acc-0.5252, test loss-1.7131, acc-0.5120
Iter-82400, train loss-1.7378, acc-0.4800, valid loss-1.7122, acc-0.5252, test loss-1.7131, acc-0.5120
Iter-82410, train loss-1.8128, acc-0.4800, valid loss-1.7122, acc-0.5250, test loss-1.7130, acc-0.5122
Iter-82420, train loss-1.7921, acc-0.4600, valid loss-1.7122, acc-0.5252, test loss-1.7130, acc-0.5122
Iter-82430, train loss-1.6690, acc-0.5600, valid loss-1.7121, acc-0.5250, test loss-1.7130, acc-0.5122
Iter-82440, train loss-1.6678, acc-0.5800, valid loss-1.7121, acc-0.5252, test loss-1.7129, acc-0.5122
Iter-82450, train loss-1.6661, acc-0.5000, valid loss-1.7120, acc-0.5252, test loss-1.7129, acc-0.5122
Iter-82460, train loss-1.8027, acc-0.5600, valid loss-1.7120, acc-0.5254, test loss-1.7129, acc-0.5123
Iter-82470, train loss-1.6039, acc-0.6000, valid loss-1.7120, acc-0.5252, test loss-1.7128, acc-0.5121
Iter-82480, train loss-1.7590, acc-0.6000, valid loss-1.7119, acc-0.5252, test loss-1.7128, acc-0.5122
Iter-82490, train loss-1.8623, acc-0.4400, valid loss-1.7119, acc-0.5256, test loss-1.7128, acc-0.5124
Iter-82500, train loss-1.6426, acc-0.5200, valid loss-1.7119, acc-0.5256, test loss-1.7127, acc-0.5126
Iter-82510, train loss-1.6556, acc-0.5600, valid loss-1.7118, acc-0.5258, test loss-1.7127, acc-0.5124
Iter-82520, train loss-1.7087, acc-0.5800, valid loss-1.7118, acc-0.5256, test loss-1.7126, acc-0.5126
Iter-82530, train loss-1.7731, acc-0.4600, valid loss-1.7118, acc-0.5254, test loss-1.7126, acc-0.5128
Iter-82540, train loss-1.7069, acc-0.4800, valid loss-1.7117, acc-0.5254, test loss-1.7126, acc-0.5126
Iter-82550, train loss-1.7191, acc-0.5400, valid loss-1.7117, acc-0.5252, test loss-1.7125, acc-0.5126
Iter-82560, train loss-1.6221, acc-0.6800, valid loss-1.7116, acc-0.5254, test loss-1.7125, acc-0.5124
Iter-82570, train loss-1.7703, acc-0.5600, valid loss-1.7116, acc-0.5252, test loss-1.7125, acc-0.5126
Iter-82580, train loss-1.7753, acc-0.4600, valid loss-1.7116, acc-0.5252, test loss-1.7124, acc-0.5124
Iter-82590, train loss-1.7006, acc-0.5000, valid loss-1.7115, acc-0.5252, test loss-1.7124, acc-0.5122
Iter-82600, train loss-1.6896, acc-0.5400, valid loss-1.7115, acc-0.5252, test loss-1.7124, acc-0.5123
Iter-82610, train loss-1.7884, acc-0.5000, valid loss-1.7115, acc-0.5252, test loss-1.7123, acc-0.5124
Iter-82620, train loss-1.8193, acc-0.4600, valid loss-1.7114, acc-0.5252, test loss-1.7123, acc-0.5127
Iter-82630, train loss-1.7823, acc-0.5000, valid loss-1.7114, acc-0.5252, test loss-1.7122, acc-0.5127
Iter-82640, train loss-1.5538, acc-0.5400, valid loss-1.7114, acc-0.5252, test loss-1.7122, acc-0.5126
Iter-82650, train loss-1.7811, acc-0.4200, valid loss-1.7113, acc-0.5254, test loss-1.7122, acc-0.5125
Iter-82660, train loss-1.7402, acc-0.4800, valid loss-1.7113, acc-0.5254, test loss-1.7121, acc-0.5124
Iter-82670, train loss-1.7245, acc-0.5000, valid loss-1.7112, acc-0.5252, test loss-1.7121, acc-0.5123
Iter-82680, train loss-1.7888, acc-0.4800, valid loss-1.7112, acc-0.5252, test loss-1.7121, acc-0.5122
Iter-82690, train loss-1.7236, acc-0.5400, valid loss-1.7112, acc-0.5252, test loss-1.7120, acc-0.5121
Iter-82700, train loss-1.7755, acc-0.4000, valid loss-1.7111, acc-0.5256, test loss-1.7120, acc-0.5123
Iter-82710, train loss-1.7534, acc-0.4600, valid loss-1.7111, acc-0.5254, test loss-1.7120, acc-0.5122
Iter-82720, train loss-1.8375, acc-0.5000, valid loss-1.7111, acc-0.5254, test loss-1.7119, acc-0.5122
Iter-82730, train loss-1.8022, acc-0.5400, valid loss-1.7110, acc-0.5254, test loss-1.7119, acc-0.5123
Iter-82740, train loss-1.6517, acc-0.5400, valid loss-1.7110, acc-0.5256, test loss-1.7118, acc-0.5123
Iter-82750, train loss-1.7599, acc-0.4200, valid loss-1.7110, acc-0.5256, test loss-1.7118, acc-0.5123
Iter-82760, train loss-1.6549, acc-0.5200, valid loss-1.7109, acc-0.5254, test loss-1.7118, acc-0.5124
Iter-82770, train loss-1.8045, acc-0.4600, valid loss-1.7109, acc-0.5254, test loss-1.7117, acc-0.5123
Iter-82780, train loss-1.8508, acc-0.3800, valid loss-1.7109, acc-0.5254, test loss-1.7117, acc-0.5123
Iter-82790, train loss-1.6916, acc-0.4800, valid loss-1.7108, acc-0.5252, test loss-1.7117, acc-0.5123
Iter-82800, train loss-1.6629, acc-0.5200, valid loss-1.7108, acc-0.5252, test loss-1.7116, acc-0.5125
Iter-82810, train loss-1.8084, acc-0.4400, valid loss-1.7107, acc-0.5254, test loss-1.7116, acc-0.5124
Iter-82820, train loss-1.7371, acc-0.5200, valid loss-1.7107, acc-0.5258, test loss-1.7116, acc-0.5124
Iter-82830, train loss-1.7499, acc-0.4400, valid loss-1.7107, acc-0.5256, test loss-1.7115, acc-0.5125
Iter-82840, train loss-1.8103, acc-0.3800, valid loss-1.7106, acc-0.5256, test loss-1.7115, acc-0.5125
Iter-82850, train loss-1.7665, acc-0.5000, valid loss-1.7106, acc-0.5254, test loss-1.7114, acc-0.5125
Iter-82860, train loss-1.7945, acc-0.4000, valid loss-1.7106, acc-0.5256, test loss-1.7114, acc-0.5125
Iter-82870, train loss-1.8145, acc-0.3800, valid loss-1.7105, acc-0.5258, test loss-1.7114, acc-0.5125
Iter-82880, train loss-1.6699, acc-0.5200, valid loss-1.7105, acc-0.5256, test loss-1.7113, acc-0.5125
Iter-82890, train loss-1.6798, acc-0.5600, valid loss-1.7104, acc-0.5260, test loss-1.7113, acc-0.5125
Iter-82900, train loss-1.7312, acc-0.5000, valid loss-1.7104, acc-0.5258, test loss-1.7113, acc-0.5127
Iter-82910, train loss-1.7563, acc-0.4600, valid loss-1.7104, acc-0.5258, test loss-1.7112, acc-0.5126
Iter-82920, train loss-1.7061, acc-0.5800, valid loss-1.7103, acc-0.5256, test loss-1.7112, acc-0.5127
Iter-82930, train loss-1.7953, acc-0.4400, valid loss-1.7103, acc-0.5256, test loss-1.7112, acc-0.5128
Iter-82940, train loss-1.6257, acc-0.6000, valid loss-1.7103, acc-0.5256, test loss-1.7111, acc-0.5128
Iter-82950, train loss-1.7749, acc-0.4600, valid loss-1.7102, acc-0.5254, test loss-1.7111, acc-0.5128
Iter-82960, train loss-1.6665, acc-0.5600, valid loss-1.7102, acc-0.5256, test loss-1.7110, acc-0.5129
Iter-82970, train loss-1.7461, acc-0.4600, valid loss-1.7102, acc-0.5256, test loss-1.7110, acc-0.5126
Iter-82980, train loss-1.7828, acc-0.4600, valid loss-1.7101, acc-0.5254, test loss-1.7110, acc-0.5126
Iter-82990, train loss-1.7575, acc-0.4200, valid loss-1.7101, acc-0.5254, test loss-1.7109, acc-0.5126
Iter-83000, train loss-1.7758, acc-0.4800, valid loss-1.7100, acc-0.5256, test loss-1.7109, acc-0.5126
Iter-83010, train loss-1.7076, acc-0.5400, valid loss-1.7100, acc-0.5256, test loss-1.7109, acc-0.5126
Iter-83020, train loss-1.6319, acc-0.6000, valid loss-1.7100, acc-0.5258, test loss-1.7108, acc-0.5126
Iter-83030, train loss-1.7468, acc-0.5400, valid loss-1.7099, acc-0.5258, test loss-1.7108, acc-0.5126
Iter-83040, train loss-1.8513, acc-0.4000, valid loss-1.7099, acc-0.5258, test loss-1.7107, acc-0.5125
Iter-83050, train loss-1.7314, acc-0.6000, valid loss-1.7099, acc-0.5258, test loss-1.7107, acc-0.5124
Iter-83060, train loss-1.6485, acc-0.6600, valid loss-1.7098, acc-0.5260, test loss-1.7107, acc-0.5126
Iter-83070, train loss-1.6263, acc-0.5400, valid loss-1.7098, acc-0.5260, test loss-1.7106, acc-0.5125
Iter-83080, train loss-1.7286, acc-0.3800, valid loss-1.7097, acc-0.5260, test loss-1.7106, acc-0.5128
Iter-83090, train loss-1.6891, acc-0.5000, valid loss-1.7097, acc-0.5260, test loss-1.7106, acc-0.5130
Iter-83100, train loss-1.8051, acc-0.4800, valid loss-1.7097, acc-0.5260, test loss-1.7105, acc-0.5128
Iter-83110, train loss-1.7929, acc-0.4800, valid loss-1.7096, acc-0.5260, test loss-1.7105, acc-0.5129
Iter-83120, train loss-1.7268, acc-0.5000, valid loss-1.7096, acc-0.5260, test loss-1.7105, acc-0.5130
Iter-83130, train loss-1.6087, acc-0.6200, valid loss-1.7095, acc-0.5262, test loss-1.7104, acc-0.5130
Iter-83140, train loss-1.7113, acc-0.5000, valid loss-1.7095, acc-0.5260, test loss-1.7104, acc-0.5130
Iter-83150, train loss-1.7953, acc-0.4600, valid loss-1.7095, acc-0.5260, test loss-1.7103, acc-0.5129
Iter-83160, train loss-1.7578, acc-0.5000, valid loss-1.7094, acc-0.5258, test loss-1.7103, acc-0.5130
Iter-83170, train loss-1.6192, acc-0.6000, valid loss-1.7094, acc-0.5258, test loss-1.7103, acc-0.5129
Iter-83180, train loss-1.6861, acc-0.5600, valid loss-1.7094, acc-0.5258, test loss-1.7102, acc-0.5131
Iter-83190, train loss-1.5514, acc-0.6200, valid loss-1.7093, acc-0.5258, test loss-1.7102, acc-0.5130
Iter-83200, train loss-1.6225, acc-0.5800, valid loss-1.7093, acc-0.5258, test loss-1.7102, acc-0.5130
Iter-83210, train loss-1.6690, acc-0.6200, valid loss-1.7093, acc-0.5258, test loss-1.7101, acc-0.5130
Iter-83220, train loss-1.6106, acc-0.4400, valid loss-1.7092, acc-0.5258, test loss-1.7101, acc-0.5131
Iter-83230, train loss-1.6885, acc-0.5800, valid loss-1.7092, acc-0.5258, test loss-1.7100, acc-0.5130
Iter-83240, train loss-1.7719, acc-0.5600, valid loss-1.7092, acc-0.5258, test loss-1.7100, acc-0.5132
Iter-83250, train loss-1.6642, acc-0.6000, valid loss-1.7091, acc-0.5262, test loss-1.7100, acc-0.5131
Iter-83260, train loss-1.7444, acc-0.5400, valid loss-1.7091, acc-0.5260, test loss-1.7099, acc-0.5132
Iter-83270, train loss-1.6948, acc-0.5200, valid loss-1.7090, acc-0.5258, test loss-1.7099, acc-0.5131
Iter-83280, train loss-1.6824, acc-0.5600, valid loss-1.7090, acc-0.5258, test loss-1.7099, acc-0.5134
Iter-83290, train loss-1.7237, acc-0.5200, valid loss-1.7090, acc-0.5260, test loss-1.7098, acc-0.5134
Iter-83300, train loss-1.5755, acc-0.6400, valid loss-1.7089, acc-0.5258, test loss-1.7098, acc-0.5133
Iter-83310, train loss-1.7795, acc-0.4400, valid loss-1.7089, acc-0.5258, test loss-1.7098, acc-0.5135
Iter-83320, train loss-1.7572, acc-0.4400, valid loss-1.7089, acc-0.5258, test loss-1.7097, acc-0.5134
Iter-83330, train loss-1.8318, acc-0.4400, valid loss-1.7088, acc-0.5262, test loss-1.7097, acc-0.5134
Iter-83340, train loss-1.6504, acc-0.5600, valid loss-1.7088, acc-0.5262, test loss-1.7096, acc-0.5134
Iter-83350, train loss-1.7048, acc-0.4600, valid loss-1.7088, acc-0.5262, test loss-1.7096, acc-0.5135
Iter-83360, train loss-1.6108, acc-0.6200, valid loss-1.7087, acc-0.5262, test loss-1.7096, acc-0.5135
Iter-83370, train loss-1.7178, acc-0.5600, valid loss-1.7087, acc-0.5262, test loss-1.7095, acc-0.5134
Iter-83380, train loss-1.6475, acc-0.5600, valid loss-1.7086, acc-0.5262, test loss-1.7095, acc-0.5133
Iter-83390, train loss-1.6261, acc-0.6000, valid loss-1.7086, acc-0.5260, test loss-1.7095, acc-0.5134
Iter-83400, train loss-1.6874, acc-0.5200, valid loss-1.7086, acc-0.5260, test loss-1.7094, acc-0.5134
Iter-83410, train loss-1.8353, acc-0.3600, valid loss-1.7085, acc-0.5260, test loss-1.7094, acc-0.5136
Iter-83420, train loss-1.7318, acc-0.4400, valid loss-1.7085, acc-0.5260, test loss-1.7094, acc-0.5135
Iter-83430, train loss-1.6805, acc-0.5600, valid loss-1.7085, acc-0.5260, test loss-1.7093, acc-0.5135
Iter-83440, train loss-1.6974, acc-0.5600, valid loss-1.7084, acc-0.5258, test loss-1.7093, acc-0.5134
Iter-83450, train loss-1.6834, acc-0.4800, valid loss-1.7084, acc-0.5258, test loss-1.7093, acc-0.5134
Iter-83460, train loss-1.5801, acc-0.6200, valid loss-1.7083, acc-0.5260, test loss-1.7092, acc-0.5132
Iter-83470, train loss-1.6617, acc-0.6000, valid loss-1.7083, acc-0.5260, test loss-1.7092, acc-0.5135
Iter-83480, train loss-1.7672, acc-0.4400, valid loss-1.7083, acc-0.5260, test loss-1.7091, acc-0.5133
Iter-83490, train loss-1.6920, acc-0.5400, valid loss-1.7082, acc-0.5262, test loss-1.7091, acc-0.5133
Iter-83500, train loss-1.8264, acc-0.4400, valid loss-1.7082, acc-0.5262, test loss-1.7091, acc-0.5135
Iter-83510, train loss-1.7627, acc-0.5200, valid loss-1.7082, acc-0.5262, test loss-1.7090, acc-0.5135
Iter-83520, train loss-1.6627, acc-0.5200, valid loss-1.7081, acc-0.5262, test loss-1.7090, acc-0.5133
Iter-83530, train loss-1.8139, acc-0.4400, valid loss-1.7081, acc-0.5262, test loss-1.7090, acc-0.5133
Iter-83540, train loss-1.7114, acc-0.5400, valid loss-1.7081, acc-0.5262, test loss-1.7089, acc-0.5133
Iter-83550, train loss-1.7905, acc-0.4200, valid loss-1.7080, acc-0.5262, test loss-1.7089, acc-0.5133
Iter-83560, train loss-1.5838, acc-0.5800, valid loss-1.7080, acc-0.5262, test loss-1.7089, acc-0.5133
Iter-83570, train loss-1.7734, acc-0.4400, valid loss-1.7079, acc-0.5262, test loss-1.7088, acc-0.5132
Iter-83580, train loss-1.8173, acc-0.4200, valid loss-1.7079, acc-0.5262, test loss-1.7088, acc-0.5132
Iter-83590, train loss-1.7093, acc-0.5000, valid loss-1.7079, acc-0.5262, test loss-1.7088, acc-0.5133
Iter-83600, train loss-1.7683, acc-0.4600, valid loss-1.7078, acc-0.5262, test loss-1.7087, acc-0.5134
Iter-83610, train loss-1.7166, acc-0.4000, valid loss-1.7078, acc-0.5264, test loss-1.7087, acc-0.5135
Iter-83620, train loss-1.7718, acc-0.4800, valid loss-1.7078, acc-0.5266, test loss-1.7087, acc-0.5134
Iter-83630, train loss-1.6340, acc-0.5600, valid loss-1.7077, acc-0.5264, test loss-1.7086, acc-0.5134
Iter-83640, train loss-1.7340, acc-0.5200, valid loss-1.7077, acc-0.5264, test loss-1.7086, acc-0.5134
Iter-83650, train loss-1.8559, acc-0.3600, valid loss-1.7077, acc-0.5266, test loss-1.7085, acc-0.5134
Iter-83660, train loss-1.7338, acc-0.4600, valid loss-1.7076, acc-0.5266, test loss-1.7085, acc-0.5134
Iter-83670, train loss-1.7175, acc-0.4200, valid loss-1.7076, acc-0.5266, test loss-1.7085, acc-0.5133
Iter-83680, train loss-1.6562, acc-0.5800, valid loss-1.7075, acc-0.5266, test loss-1.7084, acc-0.5134
Iter-83690, train loss-1.7652, acc-0.4200, valid loss-1.7075, acc-0.5268, test loss-1.7084, acc-0.5133
Iter-83700, train loss-1.7571, acc-0.5200, valid loss-1.7075, acc-0.5268, test loss-1.7084, acc-0.5133
Iter-83710, train loss-1.6813, acc-0.4800, valid loss-1.7074, acc-0.5268, test loss-1.7083, acc-0.5133
Iter-83720, train loss-1.7227, acc-0.5000, valid loss-1.7074, acc-0.5268, test loss-1.7083, acc-0.5134
Iter-83730, train loss-1.7326, acc-0.4600, valid loss-1.7074, acc-0.5270, test loss-1.7083, acc-0.5135
Iter-83740, train loss-1.7492, acc-0.5200, valid loss-1.7073, acc-0.5266, test loss-1.7082, acc-0.5136
Iter-83750, train loss-1.6933, acc-0.4600, valid loss-1.7073, acc-0.5268, test loss-1.7082, acc-0.5135
Iter-83760, train loss-1.6664, acc-0.5800, valid loss-1.7073, acc-0.5266, test loss-1.7081, acc-0.5136
Iter-83770, train loss-1.6593, acc-0.5000, valid loss-1.7072, acc-0.5266, test loss-1.7081, acc-0.5137
Iter-83780, train loss-1.7697, acc-0.5400, valid loss-1.7072, acc-0.5268, test loss-1.7081, acc-0.5138
Iter-83790, train loss-1.7804, acc-0.4200, valid loss-1.7072, acc-0.5268, test loss-1.7080, acc-0.5137
Iter-83800, train loss-1.6663, acc-0.5400, valid loss-1.7071, acc-0.5268, test loss-1.7080, acc-0.5137
Iter-83810, train loss-1.7777, acc-0.4000, valid loss-1.7071, acc-0.5268, test loss-1.7080, acc-0.5137
Iter-83820, train loss-1.7052, acc-0.4800, valid loss-1.7071, acc-0.5268, test loss-1.7079, acc-0.5136
Iter-83830, train loss-1.7489, acc-0.5000, valid loss-1.7070, acc-0.5268, test loss-1.7079, acc-0.5137
Iter-83840, train loss-1.7437, acc-0.5000, valid loss-1.7070, acc-0.5268, test loss-1.7079, acc-0.5138
Iter-83850, train loss-1.7083, acc-0.4400, valid loss-1.7069, acc-0.5268, test loss-1.7078, acc-0.5136
Iter-83860, train loss-1.7996, acc-0.3800, valid loss-1.7069, acc-0.5268, test loss-1.7078, acc-0.5135
Iter-83870, train loss-1.7091, acc-0.6000, valid loss-1.7069, acc-0.5270, test loss-1.7078, acc-0.5138
Iter-83880, train loss-1.8630, acc-0.4800, valid loss-1.7068, acc-0.5270, test loss-1.7077, acc-0.5138
Iter-83890, train loss-1.6939, acc-0.5400, valid loss-1.7068, acc-0.5270, test loss-1.7077, acc-0.5135
Iter-83900, train loss-1.6953, acc-0.5200, valid loss-1.7068, acc-0.5270, test loss-1.7076, acc-0.5136
Iter-83910, train loss-1.6397, acc-0.5200, valid loss-1.7067, acc-0.5270, test loss-1.7076, acc-0.5135
Iter-83920, train loss-1.7522, acc-0.5000, valid loss-1.7067, acc-0.5270, test loss-1.7076, acc-0.5136
Iter-83930, train loss-1.6441, acc-0.6200, valid loss-1.7067, acc-0.5268, test loss-1.7075, acc-0.5137
Iter-83940, train loss-1.6911, acc-0.6000, valid loss-1.7066, acc-0.5268, test loss-1.7075, acc-0.5139
Iter-83950, train loss-1.7035, acc-0.6000, valid loss-1.7066, acc-0.5268, test loss-1.7075, acc-0.5139
Iter-83960, train loss-1.7144, acc-0.5400, valid loss-1.7066, acc-0.5268, test loss-1.7074, acc-0.5139
Iter-83970, train loss-1.6970, acc-0.4800, valid loss-1.7065, acc-0.5268, test loss-1.7074, acc-0.5138
Iter-83980, train loss-1.6924, acc-0.5600, valid loss-1.7065, acc-0.5268, test loss-1.7074, acc-0.5137
Iter-83990, train loss-1.6509, acc-0.6200, valid loss-1.7064, acc-0.5270, test loss-1.7073, acc-0.5137
Iter-84000, train loss-1.8488, acc-0.3200, valid loss-1.7064, acc-0.5266, test loss-1.7073, acc-0.5137
Iter-84010, train loss-1.7202, acc-0.4200, valid loss-1.7064, acc-0.5268, test loss-1.7073, acc-0.5137
Iter-84020, train loss-1.7090, acc-0.5600, valid loss-1.7063, acc-0.5268, test loss-1.7072, acc-0.5138
Iter-84030, train loss-1.6286, acc-0.5800, valid loss-1.7063, acc-0.5268, test loss-1.7072, acc-0.5139
Iter-84040, train loss-1.7602, acc-0.4800, valid loss-1.7063, acc-0.5270, test loss-1.7071, acc-0.5136
Iter-84050, train loss-1.7033, acc-0.5000, valid loss-1.7062, acc-0.5270, test loss-1.7071, acc-0.5136
Iter-84060, train loss-1.7651, acc-0.4000, valid loss-1.7062, acc-0.5270, test loss-1.7071, acc-0.5136
Iter-84070, train loss-1.7141, acc-0.4800, valid loss-1.7061, acc-0.5268, test loss-1.7070, acc-0.5138
Iter-84080, train loss-1.7574, acc-0.4600, valid loss-1.7061, acc-0.5268, test loss-1.7070, acc-0.5137
Iter-84090, train loss-1.5952, acc-0.6600, valid loss-1.7061, acc-0.5272, test loss-1.7070, acc-0.5138
Iter-84100, train loss-1.7407, acc-0.5400, valid loss-1.7060, acc-0.5270, test loss-1.7069, acc-0.5138
Iter-84110, train loss-1.8776, acc-0.4200, valid loss-1.7060, acc-0.5266, test loss-1.7069, acc-0.5138
Iter-84120, train loss-1.6795, acc-0.5200, valid loss-1.7060, acc-0.5268, test loss-1.7069, acc-0.5138
Iter-84130, train loss-1.6627, acc-0.6200, valid loss-1.7059, acc-0.5266, test loss-1.7068, acc-0.5137
Iter-84140, train loss-1.6123, acc-0.5800, valid loss-1.7059, acc-0.5266, test loss-1.7068, acc-0.5137
Iter-84150, train loss-1.6964, acc-0.5400, valid loss-1.7059, acc-0.5266, test loss-1.7068, acc-0.5137
Iter-84160, train loss-1.6747, acc-0.5600, valid loss-1.7058, acc-0.5268, test loss-1.7067, acc-0.5138
Iter-84170, train loss-1.7442, acc-0.4200, valid loss-1.7058, acc-0.5270, test loss-1.7067, acc-0.5138
Iter-84180, train loss-1.7713, acc-0.5000, valid loss-1.7057, acc-0.5268, test loss-1.7066, acc-0.5139
Iter-84190, train loss-1.8078, acc-0.4600, valid loss-1.7057, acc-0.5266, test loss-1.7066, acc-0.5137
Iter-84200, train loss-1.7099, acc-0.4800, valid loss-1.7057, acc-0.5264, test loss-1.7066, acc-0.5138
Iter-84210, train loss-1.6289, acc-0.6600, valid loss-1.7056, acc-0.5264, test loss-1.7065, acc-0.5138
Iter-84220, train loss-1.5972, acc-0.6600, valid loss-1.7056, acc-0.5266, test loss-1.7065, acc-0.5139
Iter-84230, train loss-1.8337, acc-0.5200, valid loss-1.7056, acc-0.5266, test loss-1.7065, acc-0.5138
Iter-84240, train loss-1.7523, acc-0.4400, valid loss-1.7055, acc-0.5266, test loss-1.7064, acc-0.5138
Iter-84250, train loss-1.7741, acc-0.5200, valid loss-1.7055, acc-0.5266, test loss-1.7064, acc-0.5139
Iter-84260, train loss-1.6627, acc-0.6000, valid loss-1.7054, acc-0.5268, test loss-1.7064, acc-0.5139
Iter-84270, train loss-1.6690, acc-0.5200, valid loss-1.7054, acc-0.5268, test loss-1.7063, acc-0.5139
Iter-84280, train loss-1.7830, acc-0.4200, valid loss-1.7054, acc-0.5270, test loss-1.7063, acc-0.5138
Iter-84290, train loss-1.6358, acc-0.5600, valid loss-1.7053, acc-0.5272, test loss-1.7062, acc-0.5138
Iter-84300, train loss-1.7411, acc-0.5200, valid loss-1.7053, acc-0.5270, test loss-1.7062, acc-0.5138
Iter-84310, train loss-1.6902, acc-0.5400, valid loss-1.7053, acc-0.5270, test loss-1.7062, acc-0.5138
Iter-84320, train loss-1.8301, acc-0.3400, valid loss-1.7052, acc-0.5272, test loss-1.7061, acc-0.5137
Iter-84330, train loss-1.6352, acc-0.6000, valid loss-1.7052, acc-0.5270, test loss-1.7061, acc-0.5137
Iter-84340, train loss-1.7339, acc-0.4800, valid loss-1.7052, acc-0.5270, test loss-1.7061, acc-0.5138
Iter-84350, train loss-1.6731, acc-0.6000, valid loss-1.7051, acc-0.5270, test loss-1.7060, acc-0.5137
Iter-84360, train loss-1.6563, acc-0.6600, valid loss-1.7051, acc-0.5270, test loss-1.7060, acc-0.5138
Iter-84370, train loss-1.7453, acc-0.4600, valid loss-1.7050, acc-0.5270, test loss-1.7060, acc-0.5139
Iter-84380, train loss-1.7220, acc-0.5400, valid loss-1.7050, acc-0.5270, test loss-1.7059, acc-0.5140
Iter-84390, train loss-1.6742, acc-0.5800, valid loss-1.7050, acc-0.5270, test loss-1.7059, acc-0.5141
Iter-84400, train loss-1.7929, acc-0.5600, valid loss-1.7049, acc-0.5272, test loss-1.7059, acc-0.5141
Iter-84410, train loss-1.6714, acc-0.5600, valid loss-1.7049, acc-0.5266, test loss-1.7058, acc-0.5140
Iter-84420, train loss-1.8901, acc-0.3800, valid loss-1.7049, acc-0.5268, test loss-1.7058, acc-0.5140
Iter-84430, train loss-1.6468, acc-0.6400, valid loss-1.7048, acc-0.5268, test loss-1.7057, acc-0.5141
Iter-84440, train loss-1.6633, acc-0.5600, valid loss-1.7048, acc-0.5268, test loss-1.7057, acc-0.5142
Iter-84450, train loss-1.8055, acc-0.4400, valid loss-1.7048, acc-0.5268, test loss-1.7057, acc-0.5140
Iter-84460, train loss-1.7020, acc-0.5200, valid loss-1.7047, acc-0.5268, test loss-1.7056, acc-0.5144
Iter-84470, train loss-1.7604, acc-0.4400, valid loss-1.7047, acc-0.5270, test loss-1.7056, acc-0.5142
Iter-84480, train loss-1.7684, acc-0.4200, valid loss-1.7047, acc-0.5268, test loss-1.7056, acc-0.5143
Iter-84490, train loss-1.6428, acc-0.5200, valid loss-1.7046, acc-0.5268, test loss-1.7055, acc-0.5141
Iter-84500, train loss-1.7975, acc-0.4200, valid loss-1.7046, acc-0.5268, test loss-1.7055, acc-0.5142
Iter-84510, train loss-1.7289, acc-0.4400, valid loss-1.7045, acc-0.5268, test loss-1.7055, acc-0.5142
Iter-84520, train loss-1.7466, acc-0.5200, valid loss-1.7045, acc-0.5268, test loss-1.7054, acc-0.5140
Iter-84530, train loss-1.7091, acc-0.4400, valid loss-1.7045, acc-0.5270, test loss-1.7054, acc-0.5142
Iter-84540, train loss-1.6763, acc-0.4800, valid loss-1.7044, acc-0.5268, test loss-1.7053, acc-0.5143
Iter-84550, train loss-1.6859, acc-0.6400, valid loss-1.7044, acc-0.5268, test loss-1.7053, acc-0.5143
Iter-84560, train loss-1.7533, acc-0.5200, valid loss-1.7044, acc-0.5266, test loss-1.7053, acc-0.5141
Iter-84570, train loss-1.7181, acc-0.4400, valid loss-1.7043, acc-0.5266, test loss-1.7052, acc-0.5141
Iter-84580, train loss-1.7734, acc-0.5400, valid loss-1.7043, acc-0.5266, test loss-1.7052, acc-0.5142
Iter-84590, train loss-1.6490, acc-0.6400, valid loss-1.7043, acc-0.5266, test loss-1.7052, acc-0.5141
Iter-84600, train loss-1.8099, acc-0.4600, valid loss-1.7042, acc-0.5268, test loss-1.7051, acc-0.5142
Iter-84610, train loss-1.7706, acc-0.5000, valid loss-1.7042, acc-0.5272, test loss-1.7051, acc-0.5144
Iter-84620, train loss-1.7471, acc-0.4000, valid loss-1.7041, acc-0.5274, test loss-1.7051, acc-0.5144
Iter-84630, train loss-1.7239, acc-0.5200, valid loss-1.7041, acc-0.5274, test loss-1.7050, acc-0.5144
Iter-84640, train loss-1.7541, acc-0.5400, valid loss-1.7041, acc-0.5274, test loss-1.7050, acc-0.5143
Iter-84650, train loss-1.7642, acc-0.4400, valid loss-1.7040, acc-0.5272, test loss-1.7050, acc-0.5143
Iter-84660, train loss-1.7359, acc-0.4400, valid loss-1.7040, acc-0.5272, test loss-1.7049, acc-0.5144
Iter-84670, train loss-1.6508, acc-0.5000, valid loss-1.7040, acc-0.5270, test loss-1.7049, acc-0.5143
Iter-84680, train loss-1.7808, acc-0.5000, valid loss-1.7039, acc-0.5272, test loss-1.7048, acc-0.5143
Iter-84690, train loss-1.6213, acc-0.6200, valid loss-1.7039, acc-0.5270, test loss-1.7048, acc-0.5143
Iter-84700, train loss-1.7722, acc-0.4200, valid loss-1.7039, acc-0.5270, test loss-1.7048, acc-0.5144
Iter-84710, train loss-1.7056, acc-0.6200, valid loss-1.7038, acc-0.5270, test loss-1.7047, acc-0.5143
Iter-84720, train loss-1.6692, acc-0.6200, valid loss-1.7038, acc-0.5274, test loss-1.7047, acc-0.5145
Iter-84730, train loss-1.8756, acc-0.3800, valid loss-1.7038, acc-0.5276, test loss-1.7047, acc-0.5143
Iter-84740, train loss-1.6465, acc-0.5800, valid loss-1.7037, acc-0.5276, test loss-1.7046, acc-0.5143
Iter-84750, train loss-1.7036, acc-0.4600, valid loss-1.7037, acc-0.5274, test loss-1.7046, acc-0.5146
Iter-84760, train loss-1.8030, acc-0.4800, valid loss-1.7036, acc-0.5276, test loss-1.7046, acc-0.5145
Iter-84770, train loss-1.6671, acc-0.5400, valid loss-1.7036, acc-0.5276, test loss-1.7045, acc-0.5145
Iter-84780, train loss-1.7134, acc-0.5800, valid loss-1.7036, acc-0.5276, test loss-1.7045, acc-0.5144
Iter-84790, train loss-1.6980, acc-0.5600, valid loss-1.7035, acc-0.5276, test loss-1.7044, acc-0.5145
Iter-84800, train loss-1.7363, acc-0.5000, valid loss-1.7035, acc-0.5276, test loss-1.7044, acc-0.5146
Iter-84810, train loss-1.7726, acc-0.4800, valid loss-1.7035, acc-0.5276, test loss-1.7044, acc-0.5145
Iter-84820, train loss-1.7463, acc-0.5000, valid loss-1.7034, acc-0.5274, test loss-1.7043, acc-0.5145
Iter-84830, train loss-1.7635, acc-0.4800, valid loss-1.7034, acc-0.5272, test loss-1.7043, acc-0.5145
Iter-84840, train loss-1.6276, acc-0.6000, valid loss-1.7034, acc-0.5272, test loss-1.7043, acc-0.5145
Iter-84850, train loss-1.5716, acc-0.6200, valid loss-1.7033, acc-0.5272, test loss-1.7042, acc-0.5145
Iter-84860, train loss-1.7037, acc-0.5200, valid loss-1.7033, acc-0.5272, test loss-1.7042, acc-0.5146
Iter-84870, train loss-1.6736, acc-0.6200, valid loss-1.7033, acc-0.5272, test loss-1.7042, acc-0.5146
Iter-84880, train loss-1.6639, acc-0.5600, valid loss-1.7032, acc-0.5272, test loss-1.7041, acc-0.5147
Iter-84890, train loss-1.5052, acc-0.7000, valid loss-1.7032, acc-0.5272, test loss-1.7041, acc-0.5146
Iter-84900, train loss-1.8580, acc-0.3600, valid loss-1.7032, acc-0.5272, test loss-1.7041, acc-0.5146
Iter-84910, train loss-1.6703, acc-0.5600, valid loss-1.7031, acc-0.5274, test loss-1.7040, acc-0.5145
Iter-84920, train loss-1.6547, acc-0.5200, valid loss-1.7031, acc-0.5274, test loss-1.7040, acc-0.5147
Iter-84930, train loss-1.8396, acc-0.4200, valid loss-1.7031, acc-0.5276, test loss-1.7040, acc-0.5146
Iter-84940, train loss-1.6256, acc-0.5600, valid loss-1.7030, acc-0.5276, test loss-1.7039, acc-0.5145
Iter-84950, train loss-1.8389, acc-0.3800, valid loss-1.7030, acc-0.5276, test loss-1.7039, acc-0.5145
Iter-84960, train loss-1.7684, acc-0.4600, valid loss-1.7029, acc-0.5274, test loss-1.7038, acc-0.5145
Iter-84970, train loss-1.6871, acc-0.4800, valid loss-1.7029, acc-0.5276, test loss-1.7038, acc-0.5145
Iter-84980, train loss-1.7449, acc-0.5400, valid loss-1.7029, acc-0.5276, test loss-1.7038, acc-0.5147
Iter-84990, train loss-1.6552, acc-0.5000, valid loss-1.7028, acc-0.5276, test loss-1.7037, acc-0.5147
Iter-85000, train loss-1.7551, acc-0.4200, valid loss-1.7028, acc-0.5274, test loss-1.7037, acc-0.5147
Iter-85010, train loss-1.6169, acc-0.6000, valid loss-1.7028, acc-0.5274, test loss-1.7037, acc-0.5146
Iter-85020, train loss-1.6355, acc-0.5600, valid loss-1.7027, acc-0.5274, test loss-1.7036, acc-0.5146
Iter-85030, train loss-1.7087, acc-0.4800, valid loss-1.7027, acc-0.5274, test loss-1.7036, acc-0.5146
Iter-85040, train loss-1.7960, acc-0.4400, valid loss-1.7027, acc-0.5274, test loss-1.7036, acc-0.5146
Iter-85050, train loss-1.6935, acc-0.5600, valid loss-1.7026, acc-0.5274, test loss-1.7035, acc-0.5146
Iter-85060, train loss-1.8208, acc-0.3600, valid loss-1.7026, acc-0.5274, test loss-1.7035, acc-0.5146
Iter-85070, train loss-1.7826, acc-0.4800, valid loss-1.7025, acc-0.5270, test loss-1.7035, acc-0.5146
Iter-85080, train loss-1.7739, acc-0.4000, valid loss-1.7025, acc-0.5276, test loss-1.7034, acc-0.5146
Iter-85090, train loss-1.7405, acc-0.5400, valid loss-1.7025, acc-0.5274, test loss-1.7034, acc-0.5146
Iter-85100, train loss-1.7276, acc-0.4600, valid loss-1.7024, acc-0.5274, test loss-1.7034, acc-0.5146
Iter-85110, train loss-1.7014, acc-0.5600, valid loss-1.7024, acc-0.5272, test loss-1.7033, acc-0.5146
Iter-85120, train loss-1.7813, acc-0.4400, valid loss-1.7024, acc-0.5272, test loss-1.7033, acc-0.5148
Iter-85130, train loss-1.7416, acc-0.5000, valid loss-1.7023, acc-0.5274, test loss-1.7032, acc-0.5148
Iter-85140, train loss-1.6857, acc-0.5800, valid loss-1.7023, acc-0.5276, test loss-1.7032, acc-0.5147
Iter-85150, train loss-1.7960, acc-0.5000, valid loss-1.7023, acc-0.5274, test loss-1.7032, acc-0.5148
Iter-85160, train loss-1.6835, acc-0.6000, valid loss-1.7022, acc-0.5274, test loss-1.7031, acc-0.5148
Iter-85170, train loss-1.7989, acc-0.4800, valid loss-1.7022, acc-0.5272, test loss-1.7031, acc-0.5149
Iter-85180, train loss-1.7629, acc-0.3800, valid loss-1.7022, acc-0.5274, test loss-1.7031, acc-0.5147
Iter-85190, train loss-1.7438, acc-0.5400, valid loss-1.7021, acc-0.5274, test loss-1.7030, acc-0.5149
Iter-85200, train loss-1.7144, acc-0.5600, valid loss-1.7021, acc-0.5274, test loss-1.7030, acc-0.5146
Iter-85210, train loss-1.7290, acc-0.5600, valid loss-1.7021, acc-0.5274, test loss-1.7030, acc-0.5148
Iter-85220, train loss-1.7275, acc-0.4600, valid loss-1.7020, acc-0.5274, test loss-1.7029, acc-0.5147
Iter-85230, train loss-1.6195, acc-0.6200, valid loss-1.7020, acc-0.5274, test loss-1.7029, acc-0.5147
Iter-85240, train loss-1.7784, acc-0.4400, valid loss-1.7019, acc-0.5274, test loss-1.7029, acc-0.5146
Iter-85250, train loss-1.7870, acc-0.5200, valid loss-1.7019, acc-0.5276, test loss-1.7028, acc-0.5146
Iter-85260, train loss-1.5989, acc-0.6400, valid loss-1.7019, acc-0.5276, test loss-1.7028, acc-0.5145
Iter-85270, train loss-1.7471, acc-0.4600, valid loss-1.7018, acc-0.5276, test loss-1.7027, acc-0.5146
Iter-85280, train loss-1.6134, acc-0.5600, valid loss-1.7018, acc-0.5278, test loss-1.7027, acc-0.5148
Iter-85290, train loss-1.6290, acc-0.5400, valid loss-1.7018, acc-0.5278, test loss-1.7027, acc-0.5148
Iter-85300, train loss-1.8082, acc-0.5000, valid loss-1.7017, acc-0.5278, test loss-1.7026, acc-0.5147
Iter-85310, train loss-1.6733, acc-0.6000, valid loss-1.7017, acc-0.5276, test loss-1.7026, acc-0.5148
Iter-85320, train loss-1.7645, acc-0.4600, valid loss-1.7016, acc-0.5278, test loss-1.7026, acc-0.5148
Iter-85330, train loss-1.7857, acc-0.4800, valid loss-1.7016, acc-0.5276, test loss-1.7025, acc-0.5148
Iter-85340, train loss-1.7907, acc-0.5400, valid loss-1.7016, acc-0.5278, test loss-1.7025, acc-0.5148
Iter-85350, train loss-1.7572, acc-0.4400, valid loss-1.7015, acc-0.5276, test loss-1.7025, acc-0.5148
Iter-85360, train loss-1.7500, acc-0.4800, valid loss-1.7015, acc-0.5276, test loss-1.7024, acc-0.5147
Iter-85370, train loss-1.6321, acc-0.6200, valid loss-1.7015, acc-0.5276, test loss-1.7024, acc-0.5149
Iter-85380, train loss-1.7571, acc-0.4600, valid loss-1.7014, acc-0.5278, test loss-1.7023, acc-0.5148
Iter-85390, train loss-1.7161, acc-0.5000, valid loss-1.7014, acc-0.5276, test loss-1.7023, acc-0.5147
Iter-85400, train loss-1.7129, acc-0.5400, valid loss-1.7014, acc-0.5276, test loss-1.7023, acc-0.5148
Iter-85410, train loss-1.6519, acc-0.5800, valid loss-1.7013, acc-0.5276, test loss-1.7022, acc-0.5148
Iter-85420, train loss-1.7516, acc-0.5200, valid loss-1.7013, acc-0.5276, test loss-1.7022, acc-0.5148
Iter-85430, train loss-1.5610, acc-0.6200, valid loss-1.7012, acc-0.5276, test loss-1.7022, acc-0.5146
Iter-85440, train loss-1.7901, acc-0.4000, valid loss-1.7012, acc-0.5276, test loss-1.7021, acc-0.5147
Iter-85450, train loss-1.7434, acc-0.4800, valid loss-1.7012, acc-0.5274, test loss-1.7021, acc-0.5148
Iter-85460, train loss-1.7184, acc-0.5600, valid loss-1.7011, acc-0.5276, test loss-1.7021, acc-0.5146
Iter-85470, train loss-1.7246, acc-0.4200, valid loss-1.7011, acc-0.5278, test loss-1.7020, acc-0.5148
Iter-85480, train loss-1.6230, acc-0.6400, valid loss-1.7011, acc-0.5274, test loss-1.7020, acc-0.5149
Iter-85490, train loss-1.5916, acc-0.5000, valid loss-1.7010, acc-0.5274, test loss-1.7019, acc-0.5148
Iter-85500, train loss-1.6337, acc-0.5800, valid loss-1.7010, acc-0.5274, test loss-1.7019, acc-0.5147
Iter-85510, train loss-1.8361, acc-0.4000, valid loss-1.7010, acc-0.5276, test loss-1.7019, acc-0.5147
Iter-85520, train loss-1.6384, acc-0.5400, valid loss-1.7009, acc-0.5278, test loss-1.7018, acc-0.5147
Iter-85530, train loss-1.7285, acc-0.5200, valid loss-1.7009, acc-0.5278, test loss-1.7018, acc-0.5146
Iter-85540, train loss-1.7269, acc-0.4800, valid loss-1.7008, acc-0.5278, test loss-1.7018, acc-0.5148
Iter-85550, train loss-1.7449, acc-0.4600, valid loss-1.7008, acc-0.5278, test loss-1.7017, acc-0.5147
Iter-85560, train loss-1.5874, acc-0.5600, valid loss-1.7008, acc-0.5274, test loss-1.7017, acc-0.5146
Iter-85570, train loss-1.7393, acc-0.4600, valid loss-1.7007, acc-0.5276, test loss-1.7017, acc-0.5146
Iter-85580, train loss-1.7638, acc-0.4800, valid loss-1.7007, acc-0.5274, test loss-1.7016, acc-0.5146
Iter-85590, train loss-1.7395, acc-0.5200, valid loss-1.7007, acc-0.5274, test loss-1.7016, acc-0.5146
Iter-85600, train loss-1.6720, acc-0.5200, valid loss-1.7006, acc-0.5274, test loss-1.7016, acc-0.5146
Iter-85610, train loss-1.7331, acc-0.4400, valid loss-1.7006, acc-0.5278, test loss-1.7015, acc-0.5146
Iter-85620, train loss-1.7000, acc-0.6000, valid loss-1.7006, acc-0.5278, test loss-1.7015, acc-0.5148
Iter-85630, train loss-1.6834, acc-0.4600, valid loss-1.7005, acc-0.5274, test loss-1.7014, acc-0.5146
Iter-85640, train loss-1.7583, acc-0.4400, valid loss-1.7005, acc-0.5270, test loss-1.7014, acc-0.5146
Iter-85650, train loss-1.6803, acc-0.5600, valid loss-1.7005, acc-0.5272, test loss-1.7014, acc-0.5146
Iter-85660, train loss-1.7105, acc-0.5000, valid loss-1.7004, acc-0.5274, test loss-1.7013, acc-0.5145
Iter-85670, train loss-1.7175, acc-0.5400, valid loss-1.7004, acc-0.5274, test loss-1.7013, acc-0.5146
Iter-85680, train loss-1.8052, acc-0.4400, valid loss-1.7004, acc-0.5274, test loss-1.7013, acc-0.5148
Iter-85690, train loss-1.7437, acc-0.6000, valid loss-1.7003, acc-0.5274, test loss-1.7012, acc-0.5149
Iter-85700, train loss-1.7024, acc-0.5400, valid loss-1.7003, acc-0.5272, test loss-1.7012, acc-0.5145
Iter-85710, train loss-1.7071, acc-0.5000, valid loss-1.7002, acc-0.5274, test loss-1.7012, acc-0.5144
Iter-85720, train loss-1.7472, acc-0.5000, valid loss-1.7002, acc-0.5274, test loss-1.7011, acc-0.5145
Iter-85730, train loss-1.6700, acc-0.5400, valid loss-1.7002, acc-0.5278, test loss-1.7011, acc-0.5149
Iter-85740, train loss-1.6281, acc-0.5800, valid loss-1.7001, acc-0.5276, test loss-1.7011, acc-0.5148
Iter-85750, train loss-1.6832, acc-0.5800, valid loss-1.7001, acc-0.5278, test loss-1.7010, acc-0.5148
Iter-85760, train loss-1.6413, acc-0.5400, valid loss-1.7001, acc-0.5278, test loss-1.7010, acc-0.5149
Iter-85770, train loss-1.6943, acc-0.6000, valid loss-1.7000, acc-0.5276, test loss-1.7010, acc-0.5149
Iter-85780, train loss-1.7712, acc-0.5400, valid loss-1.7000, acc-0.5276, test loss-1.7009, acc-0.5152
Iter-85790, train loss-1.6569, acc-0.5400, valid loss-1.7000, acc-0.5282, test loss-1.7009, acc-0.5151
Iter-85800, train loss-1.8450, acc-0.4400, valid loss-1.6999, acc-0.5282, test loss-1.7008, acc-0.5152
Iter-85810, train loss-1.7247, acc-0.4600, valid loss-1.6999, acc-0.5284, test loss-1.7008, acc-0.5152
Iter-85820, train loss-1.7075, acc-0.4800, valid loss-1.6998, acc-0.5282, test loss-1.7008, acc-0.5152
Iter-85830, train loss-1.7148, acc-0.4600, valid loss-1.6998, acc-0.5284, test loss-1.7007, acc-0.5153
Iter-85840, train loss-1.6458, acc-0.6200, valid loss-1.6998, acc-0.5280, test loss-1.7007, acc-0.5151
Iter-85850, train loss-1.7024, acc-0.5200, valid loss-1.6997, acc-0.5282, test loss-1.7007, acc-0.5151
Iter-85860, train loss-1.5553, acc-0.6400, valid loss-1.6997, acc-0.5282, test loss-1.7006, acc-0.5149
Iter-85870, train loss-1.6685, acc-0.4600, valid loss-1.6997, acc-0.5286, test loss-1.7006, acc-0.5150
Iter-85880, train loss-1.7660, acc-0.5600, valid loss-1.6996, acc-0.5284, test loss-1.7006, acc-0.5150
Iter-85890, train loss-1.8438, acc-0.4600, valid loss-1.6996, acc-0.5282, test loss-1.7005, acc-0.5151
Iter-85900, train loss-1.7043, acc-0.5000, valid loss-1.6996, acc-0.5284, test loss-1.7005, acc-0.5152
Iter-85910, train loss-1.7063, acc-0.4600, valid loss-1.6995, acc-0.5280, test loss-1.7005, acc-0.5156
Iter-85920, train loss-1.7062, acc-0.4800, valid loss-1.6995, acc-0.5282, test loss-1.7004, acc-0.5155
Iter-85930, train loss-1.6174, acc-0.5400, valid loss-1.6995, acc-0.5280, test loss-1.7004, acc-0.5155
Iter-85940, train loss-1.5925, acc-0.6200, valid loss-1.6994, acc-0.5284, test loss-1.7003, acc-0.5153
Iter-85950, train loss-1.7136, acc-0.5800, valid loss-1.6994, acc-0.5284, test loss-1.7003, acc-0.5154
Iter-85960, train loss-1.8403, acc-0.3600, valid loss-1.6994, acc-0.5284, test loss-1.7003, acc-0.5153
Iter-85970, train loss-1.6385, acc-0.6000, valid loss-1.6993, acc-0.5282, test loss-1.7002, acc-0.5155
Iter-85980, train loss-1.7591, acc-0.5400, valid loss-1.6993, acc-0.5282, test loss-1.7002, acc-0.5156
Iter-85990, train loss-1.6837, acc-0.4800, valid loss-1.6992, acc-0.5284, test loss-1.7002, acc-0.5156
Iter-86000, train loss-1.6690, acc-0.5800, valid loss-1.6992, acc-0.5284, test loss-1.7001, acc-0.5156
Iter-86010, train loss-1.8616, acc-0.3800, valid loss-1.6992, acc-0.5282, test loss-1.7001, acc-0.5155
Iter-86020, train loss-1.5652, acc-0.5800, valid loss-1.6991, acc-0.5286, test loss-1.7001, acc-0.5156
Iter-86030, train loss-1.7233, acc-0.5600, valid loss-1.6991, acc-0.5286, test loss-1.7000, acc-0.5154
Iter-86040, train loss-1.6904, acc-0.4800, valid loss-1.6991, acc-0.5286, test loss-1.7000, acc-0.5155
Iter-86050, train loss-1.6103, acc-0.6400, valid loss-1.6990, acc-0.5286, test loss-1.7000, acc-0.5155
Iter-86060, train loss-1.6176, acc-0.6400, valid loss-1.6990, acc-0.5286, test loss-1.6999, acc-0.5155
Iter-86070, train loss-1.6172, acc-0.5800, valid loss-1.6990, acc-0.5286, test loss-1.6999, acc-0.5156
Iter-86080, train loss-1.7925, acc-0.4400, valid loss-1.6989, acc-0.5288, test loss-1.6999, acc-0.5157
Iter-86090, train loss-1.7340, acc-0.4800, valid loss-1.6989, acc-0.5284, test loss-1.6998, acc-0.5157
Iter-86100, train loss-1.6891, acc-0.5000, valid loss-1.6989, acc-0.5284, test loss-1.6998, acc-0.5155
Iter-86110, train loss-1.8310, acc-0.4200, valid loss-1.6988, acc-0.5284, test loss-1.6997, acc-0.5154
Iter-86120, train loss-1.7554, acc-0.5200, valid loss-1.6988, acc-0.5284, test loss-1.6997, acc-0.5154
Iter-86130, train loss-1.7944, acc-0.4200, valid loss-1.6988, acc-0.5284, test loss-1.6997, acc-0.5155
Iter-86140, train loss-1.7660, acc-0.5000, valid loss-1.6987, acc-0.5284, test loss-1.6996, acc-0.5155
Iter-86150, train loss-1.6627, acc-0.5000, valid loss-1.6987, acc-0.5284, test loss-1.6996, acc-0.5155
Iter-86160, train loss-1.7406, acc-0.5400, valid loss-1.6987, acc-0.5284, test loss-1.6996, acc-0.5156
Iter-86170, train loss-1.6857, acc-0.5600, valid loss-1.6986, acc-0.5284, test loss-1.6995, acc-0.5156
Iter-86180, train loss-1.6889, acc-0.5400, valid loss-1.6986, acc-0.5284, test loss-1.6995, acc-0.5156
Iter-86190, train loss-1.7432, acc-0.4200, valid loss-1.6985, acc-0.5284, test loss-1.6995, acc-0.5157
Iter-86200, train loss-1.6202, acc-0.6200, valid loss-1.6985, acc-0.5284, test loss-1.6994, acc-0.5155
Iter-86210, train loss-1.8396, acc-0.4600, valid loss-1.6985, acc-0.5284, test loss-1.6994, acc-0.5154
Iter-86220, train loss-1.6421, acc-0.5600, valid loss-1.6984, acc-0.5284, test loss-1.6994, acc-0.5154
Iter-86230, train loss-1.7255, acc-0.4800, valid loss-1.6984, acc-0.5284, test loss-1.6993, acc-0.5154
Iter-86240, train loss-1.6880, acc-0.5800, valid loss-1.6984, acc-0.5284, test loss-1.6993, acc-0.5154
Iter-86250, train loss-1.8060, acc-0.4800, valid loss-1.6983, acc-0.5284, test loss-1.6993, acc-0.5154
Iter-86260, train loss-1.6702, acc-0.5200, valid loss-1.6983, acc-0.5284, test loss-1.6992, acc-0.5155
Iter-86270, train loss-1.7309, acc-0.5000, valid loss-1.6983, acc-0.5284, test loss-1.6992, acc-0.5155
Iter-86280, train loss-1.6727, acc-0.4600, valid loss-1.6982, acc-0.5286, test loss-1.6992, acc-0.5156
Iter-86290, train loss-1.8096, acc-0.4000, valid loss-1.6982, acc-0.5286, test loss-1.6991, acc-0.5155
Iter-86300, train loss-1.7522, acc-0.5400, valid loss-1.6982, acc-0.5286, test loss-1.6991, acc-0.5156
Iter-86310, train loss-1.7150, acc-0.4000, valid loss-1.6981, acc-0.5286, test loss-1.6991, acc-0.5157
Iter-86320, train loss-1.7534, acc-0.4800, valid loss-1.6981, acc-0.5286, test loss-1.6990, acc-0.5157
Iter-86330, train loss-1.7078, acc-0.5400, valid loss-1.6981, acc-0.5288, test loss-1.6990, acc-0.5157
Iter-86340, train loss-1.7200, acc-0.6000, valid loss-1.6980, acc-0.5288, test loss-1.6990, acc-0.5156
Iter-86350, train loss-1.6813, acc-0.5400, valid loss-1.6980, acc-0.5288, test loss-1.6989, acc-0.5158
Iter-86360, train loss-1.7493, acc-0.5400, valid loss-1.6979, acc-0.5288, test loss-1.6989, acc-0.5157
Iter-86370, train loss-1.6642, acc-0.4600, valid loss-1.6979, acc-0.5286, test loss-1.6988, acc-0.5158
Iter-86380, train loss-1.6759, acc-0.5200, valid loss-1.6979, acc-0.5286, test loss-1.6988, acc-0.5158
Iter-86390, train loss-1.7229, acc-0.5400, valid loss-1.6978, acc-0.5286, test loss-1.6988, acc-0.5157
Iter-86400, train loss-1.7316, acc-0.5200, valid loss-1.6978, acc-0.5286, test loss-1.6987, acc-0.5157
Iter-86410, train loss-1.7354, acc-0.5000, valid loss-1.6978, acc-0.5286, test loss-1.6987, acc-0.5157
Iter-86420, train loss-1.6121, acc-0.5400, valid loss-1.6977, acc-0.5288, test loss-1.6987, acc-0.5157
Iter-86430, train loss-1.5973, acc-0.5800, valid loss-1.6977, acc-0.5288, test loss-1.6986, acc-0.5158
Iter-86440, train loss-1.6705, acc-0.5600, valid loss-1.6977, acc-0.5288, test loss-1.6986, acc-0.5159
Iter-86450, train loss-1.7904, acc-0.4400, valid loss-1.6976, acc-0.5288, test loss-1.6986, acc-0.5157
Iter-86460, train loss-1.6851, acc-0.5600, valid loss-1.6976, acc-0.5288, test loss-1.6985, acc-0.5157
Iter-86470, train loss-1.6441, acc-0.5600, valid loss-1.6976, acc-0.5288, test loss-1.6985, acc-0.5156
Iter-86480, train loss-1.7942, acc-0.4200, valid loss-1.6975, acc-0.5288, test loss-1.6985, acc-0.5155
Iter-86490, train loss-1.6896, acc-0.5800, valid loss-1.6975, acc-0.5288, test loss-1.6984, acc-0.5154
Iter-86500, train loss-1.6659, acc-0.4200, valid loss-1.6975, acc-0.5290, test loss-1.6984, acc-0.5156
Iter-86510, train loss-1.6006, acc-0.6000, valid loss-1.6974, acc-0.5290, test loss-1.6983, acc-0.5156
Iter-86520, train loss-1.7857, acc-0.3600, valid loss-1.6974, acc-0.5290, test loss-1.6983, acc-0.5155
Iter-86530, train loss-1.7498, acc-0.5200, valid loss-1.6973, acc-0.5292, test loss-1.6983, acc-0.5157
Iter-86540, train loss-1.7515, acc-0.4600, valid loss-1.6973, acc-0.5292, test loss-1.6982, acc-0.5158
Iter-86550, train loss-1.8880, acc-0.4400, valid loss-1.6973, acc-0.5292, test loss-1.6982, acc-0.5158
Iter-86560, train loss-1.7060, acc-0.5400, valid loss-1.6972, acc-0.5292, test loss-1.6982, acc-0.5158
Iter-86570, train loss-1.6963, acc-0.4000, valid loss-1.6972, acc-0.5290, test loss-1.6981, acc-0.5160
Iter-86580, train loss-1.6747, acc-0.5600, valid loss-1.6972, acc-0.5290, test loss-1.6981, acc-0.5161
Iter-86590, train loss-1.7119, acc-0.5600, valid loss-1.6971, acc-0.5290, test loss-1.6981, acc-0.5158
Iter-86600, train loss-1.7904, acc-0.4800, valid loss-1.6971, acc-0.5294, test loss-1.6980, acc-0.5159
Iter-86610, train loss-1.7605, acc-0.5400, valid loss-1.6971, acc-0.5294, test loss-1.6980, acc-0.5161
Iter-86620, train loss-1.7595, acc-0.5400, valid loss-1.6970, acc-0.5296, test loss-1.6980, acc-0.5161
Iter-86630, train loss-1.7979, acc-0.4600, valid loss-1.6970, acc-0.5296, test loss-1.6979, acc-0.5161
Iter-86640, train loss-1.6236, acc-0.5800, valid loss-1.6970, acc-0.5294, test loss-1.6979, acc-0.5160
Iter-86650, train loss-1.8096, acc-0.3800, valid loss-1.6969, acc-0.5294, test loss-1.6979, acc-0.5160
Iter-86660, train loss-1.6790, acc-0.5200, valid loss-1.6969, acc-0.5294, test loss-1.6978, acc-0.5160
Iter-86670, train loss-1.7075, acc-0.5200, valid loss-1.6969, acc-0.5294, test loss-1.6978, acc-0.5161
Iter-86680, train loss-1.6794, acc-0.5600, valid loss-1.6968, acc-0.5292, test loss-1.6978, acc-0.5162
Iter-86690, train loss-1.8175, acc-0.4000, valid loss-1.6968, acc-0.5294, test loss-1.6977, acc-0.5159
Iter-86700, train loss-1.6722, acc-0.6200, valid loss-1.6968, acc-0.5292, test loss-1.6977, acc-0.5160
Iter-86710, train loss-1.8627, acc-0.3800, valid loss-1.6967, acc-0.5290, test loss-1.6977, acc-0.5161
Iter-86720, train loss-1.7554, acc-0.4400, valid loss-1.6967, acc-0.5294, test loss-1.6976, acc-0.5164
Iter-86730, train loss-1.6896, acc-0.5000, valid loss-1.6967, acc-0.5296, test loss-1.6976, acc-0.5162
Iter-86740, train loss-1.6822, acc-0.5800, valid loss-1.6966, acc-0.5294, test loss-1.6976, acc-0.5164
Iter-86750, train loss-1.8026, acc-0.4400, valid loss-1.6966, acc-0.5296, test loss-1.6975, acc-0.5163
Iter-86760, train loss-1.7622, acc-0.5400, valid loss-1.6965, acc-0.5294, test loss-1.6975, acc-0.5163
Iter-86770, train loss-1.5911, acc-0.5200, valid loss-1.6965, acc-0.5294, test loss-1.6974, acc-0.5165
Iter-86780, train loss-1.9007, acc-0.3800, valid loss-1.6965, acc-0.5296, test loss-1.6974, acc-0.5163
Iter-86790, train loss-1.6520, acc-0.5000, valid loss-1.6964, acc-0.5296, test loss-1.6974, acc-0.5163
Iter-86800, train loss-1.7696, acc-0.4800, valid loss-1.6964, acc-0.5294, test loss-1.6973, acc-0.5164
Iter-86810, train loss-1.7629, acc-0.5200, valid loss-1.6964, acc-0.5294, test loss-1.6973, acc-0.5164
Iter-86820, train loss-1.7476, acc-0.4800, valid loss-1.6963, acc-0.5292, test loss-1.6973, acc-0.5163
Iter-86830, train loss-1.6906, acc-0.5400, valid loss-1.6963, acc-0.5292, test loss-1.6972, acc-0.5163
Iter-86840, train loss-1.7313, acc-0.4800, valid loss-1.6963, acc-0.5292, test loss-1.6972, acc-0.5163
Iter-86850, train loss-1.7020, acc-0.4800, valid loss-1.6962, acc-0.5292, test loss-1.6972, acc-0.5166
Iter-86860, train loss-1.8167, acc-0.4800, valid loss-1.6962, acc-0.5294, test loss-1.6971, acc-0.5165
Iter-86870, train loss-1.7332, acc-0.4600, valid loss-1.6962, acc-0.5294, test loss-1.6971, acc-0.5164
Iter-86880, train loss-1.6068, acc-0.6200, valid loss-1.6961, acc-0.5294, test loss-1.6971, acc-0.5164
Iter-86890, train loss-1.6171, acc-0.6400, valid loss-1.6961, acc-0.5296, test loss-1.6970, acc-0.5164
Iter-86900, train loss-1.7549, acc-0.4800, valid loss-1.6960, acc-0.5290, test loss-1.6970, acc-0.5165
Iter-86910, train loss-1.8000, acc-0.4200, valid loss-1.6960, acc-0.5292, test loss-1.6970, acc-0.5165
Iter-86920, train loss-1.6181, acc-0.5800, valid loss-1.6960, acc-0.5294, test loss-1.6969, acc-0.5164
Iter-86930, train loss-1.7556, acc-0.4400, valid loss-1.6959, acc-0.5292, test loss-1.6969, acc-0.5165
Iter-86940, train loss-1.6862, acc-0.6400, valid loss-1.6959, acc-0.5292, test loss-1.6969, acc-0.5165
Iter-86950, train loss-1.6187, acc-0.6000, valid loss-1.6959, acc-0.5290, test loss-1.6968, acc-0.5165
Iter-86960, train loss-1.7186, acc-0.5200, valid loss-1.6958, acc-0.5294, test loss-1.6968, acc-0.5164
Iter-86970, train loss-1.7265, acc-0.4400, valid loss-1.6958, acc-0.5296, test loss-1.6968, acc-0.5164
Iter-86980, train loss-1.6486, acc-0.5600, valid loss-1.6958, acc-0.5292, test loss-1.6967, acc-0.5164
Iter-86990, train loss-1.8018, acc-0.3800, valid loss-1.6957, acc-0.5294, test loss-1.6967, acc-0.5163
Iter-87000, train loss-1.6939, acc-0.5200, valid loss-1.6957, acc-0.5292, test loss-1.6967, acc-0.5164
Iter-87010, train loss-1.6540, acc-0.5600, valid loss-1.6957, acc-0.5292, test loss-1.6966, acc-0.5164
Iter-87020, train loss-1.5888, acc-0.6400, valid loss-1.6956, acc-0.5290, test loss-1.6966, acc-0.5166
Iter-87030, train loss-1.7111, acc-0.5000, valid loss-1.6956, acc-0.5290, test loss-1.6966, acc-0.5166
Iter-87040, train loss-1.7543, acc-0.4800, valid loss-1.6956, acc-0.5292, test loss-1.6965, acc-0.5165
Iter-87050, train loss-1.5602, acc-0.5800, valid loss-1.6955, acc-0.5292, test loss-1.6965, acc-0.5166
Iter-87060, train loss-1.7026, acc-0.4400, valid loss-1.6955, acc-0.5292, test loss-1.6964, acc-0.5166
Iter-87070, train loss-1.6806, acc-0.5600, valid loss-1.6954, acc-0.5292, test loss-1.6964, acc-0.5165
Iter-87080, train loss-1.6894, acc-0.5800, valid loss-1.6954, acc-0.5294, test loss-1.6964, acc-0.5166
Iter-87090, train loss-1.6741, acc-0.5600, valid loss-1.6954, acc-0.5292, test loss-1.6963, acc-0.5166
Iter-87100, train loss-1.5803, acc-0.5400, valid loss-1.6953, acc-0.5292, test loss-1.6963, acc-0.5165
Iter-87110, train loss-1.7306, acc-0.4800, valid loss-1.6953, acc-0.5292, test loss-1.6963, acc-0.5164
Iter-87120, train loss-1.7107, acc-0.4800, valid loss-1.6953, acc-0.5292, test loss-1.6962, acc-0.5165
Iter-87130, train loss-1.7376, acc-0.6000, valid loss-1.6952, acc-0.5294, test loss-1.6962, acc-0.5164
Iter-87140, train loss-1.7406, acc-0.4800, valid loss-1.6952, acc-0.5290, test loss-1.6962, acc-0.5165
Iter-87150, train loss-1.7175, acc-0.5000, valid loss-1.6952, acc-0.5292, test loss-1.6961, acc-0.5166
Iter-87160, train loss-1.7772, acc-0.5000, valid loss-1.6951, acc-0.5292, test loss-1.6961, acc-0.5166
Iter-87170, train loss-1.7319, acc-0.4800, valid loss-1.6951, acc-0.5294, test loss-1.6961, acc-0.5165
Iter-87180, train loss-1.7521, acc-0.3600, valid loss-1.6951, acc-0.5296, test loss-1.6960, acc-0.5168
Iter-87190, train loss-1.6197, acc-0.6400, valid loss-1.6950, acc-0.5294, test loss-1.6960, acc-0.5168
Iter-87200, train loss-1.6938, acc-0.5600, valid loss-1.6950, acc-0.5298, test loss-1.6960, acc-0.5167
Iter-87210, train loss-1.7079, acc-0.6200, valid loss-1.6950, acc-0.5296, test loss-1.6959, acc-0.5166
Iter-87220, train loss-1.6837, acc-0.5200, valid loss-1.6949, acc-0.5296, test loss-1.6959, acc-0.5166
Iter-87230, train loss-1.7380, acc-0.5600, valid loss-1.6949, acc-0.5296, test loss-1.6958, acc-0.5167
Iter-87240, train loss-1.6698, acc-0.5200, valid loss-1.6949, acc-0.5298, test loss-1.6958, acc-0.5167
Iter-87250, train loss-1.7348, acc-0.5200, valid loss-1.6948, acc-0.5298, test loss-1.6958, acc-0.5167
Iter-87260, train loss-1.5942, acc-0.6000, valid loss-1.6948, acc-0.5298, test loss-1.6957, acc-0.5167
Iter-87270, train loss-1.7195, acc-0.4800, valid loss-1.6948, acc-0.5298, test loss-1.6957, acc-0.5167
Iter-87280, train loss-1.6744, acc-0.5400, valid loss-1.6947, acc-0.5298, test loss-1.6957, acc-0.5167
Iter-87290, train loss-1.7014, acc-0.5800, valid loss-1.6947, acc-0.5298, test loss-1.6956, acc-0.5166
Iter-87300, train loss-1.7513, acc-0.5800, valid loss-1.6947, acc-0.5298, test loss-1.6956, acc-0.5167
Iter-87310, train loss-1.6083, acc-0.6600, valid loss-1.6946, acc-0.5296, test loss-1.6956, acc-0.5167
Iter-87320, train loss-1.6347, acc-0.5600, valid loss-1.6946, acc-0.5296, test loss-1.6955, acc-0.5168
Iter-87330, train loss-1.8404, acc-0.5400, valid loss-1.6946, acc-0.5296, test loss-1.6955, acc-0.5168
Iter-87340, train loss-1.7648, acc-0.4600, valid loss-1.6945, acc-0.5292, test loss-1.6955, acc-0.5170
Iter-87350, train loss-1.6747, acc-0.5600, valid loss-1.6945, acc-0.5294, test loss-1.6954, acc-0.5170
Iter-87360, train loss-1.6982, acc-0.5600, valid loss-1.6944, acc-0.5296, test loss-1.6954, acc-0.5170
Iter-87370, train loss-1.6286, acc-0.6800, valid loss-1.6944, acc-0.5296, test loss-1.6953, acc-0.5169
Iter-87380, train loss-1.7351, acc-0.5400, valid loss-1.6944, acc-0.5298, test loss-1.6953, acc-0.5170
Iter-87390, train loss-1.6345, acc-0.5800, valid loss-1.6943, acc-0.5296, test loss-1.6953, acc-0.5170
Iter-87400, train loss-1.8154, acc-0.4800, valid loss-1.6943, acc-0.5296, test loss-1.6952, acc-0.5170
Iter-87410, train loss-1.6522, acc-0.5800, valid loss-1.6943, acc-0.5298, test loss-1.6952, acc-0.5170
Iter-87420, train loss-1.8066, acc-0.4000, valid loss-1.6942, acc-0.5300, test loss-1.6952, acc-0.5171
Iter-87430, train loss-1.8123, acc-0.4600, valid loss-1.6942, acc-0.5298, test loss-1.6952, acc-0.5170
Iter-87440, train loss-1.8175, acc-0.3800, valid loss-1.6942, acc-0.5298, test loss-1.6951, acc-0.5169
Iter-87450, train loss-1.7525, acc-0.5200, valid loss-1.6941, acc-0.5300, test loss-1.6951, acc-0.5169
Iter-87460, train loss-1.6910, acc-0.5200, valid loss-1.6941, acc-0.5298, test loss-1.6950, acc-0.5169
Iter-87470, train loss-1.8300, acc-0.4600, valid loss-1.6941, acc-0.5296, test loss-1.6950, acc-0.5169
Iter-87480, train loss-1.7460, acc-0.5200, valid loss-1.6940, acc-0.5294, test loss-1.6950, acc-0.5169
Iter-87490, train loss-1.7660, acc-0.4200, valid loss-1.6940, acc-0.5294, test loss-1.6949, acc-0.5169
Iter-87500, train loss-1.7998, acc-0.4800, valid loss-1.6940, acc-0.5294, test loss-1.6949, acc-0.5169
Iter-87510, train loss-1.7960, acc-0.4200, valid loss-1.6939, acc-0.5294, test loss-1.6949, acc-0.5167
Iter-87520, train loss-1.6890, acc-0.5200, valid loss-1.6939, acc-0.5294, test loss-1.6948, acc-0.5168
Iter-87530, train loss-1.7597, acc-0.4600, valid loss-1.6939, acc-0.5296, test loss-1.6948, acc-0.5170
Iter-87540, train loss-1.8418, acc-0.4200, valid loss-1.6938, acc-0.5294, test loss-1.6948, acc-0.5170
Iter-87550, train loss-1.8305, acc-0.4200, valid loss-1.6938, acc-0.5298, test loss-1.6947, acc-0.5170
Iter-87560, train loss-1.7626, acc-0.4800, valid loss-1.6938, acc-0.5294, test loss-1.6947, acc-0.5170
Iter-87570, train loss-1.8274, acc-0.5400, valid loss-1.6937, acc-0.5294, test loss-1.6947, acc-0.5170
Iter-87580, train loss-1.7456, acc-0.5200, valid loss-1.6937, acc-0.5294, test loss-1.6946, acc-0.5170
Iter-87590, train loss-1.6370, acc-0.6200, valid loss-1.6937, acc-0.5294, test loss-1.6946, acc-0.5170
Iter-87600, train loss-1.6954, acc-0.6400, valid loss-1.6936, acc-0.5298, test loss-1.6946, acc-0.5171
Iter-87610, train loss-1.7273, acc-0.5000, valid loss-1.6936, acc-0.5298, test loss-1.6945, acc-0.5172
Iter-87620, train loss-1.7093, acc-0.4600, valid loss-1.6936, acc-0.5296, test loss-1.6945, acc-0.5171
Iter-87630, train loss-1.5313, acc-0.6800, valid loss-1.6935, acc-0.5294, test loss-1.6945, acc-0.5169
Iter-87640, train loss-1.6900, acc-0.4800, valid loss-1.6935, acc-0.5294, test loss-1.6944, acc-0.5171
Iter-87650, train loss-1.7221, acc-0.5800, valid loss-1.6934, acc-0.5296, test loss-1.6944, acc-0.5170
Iter-87660, train loss-1.6719, acc-0.5400, valid loss-1.6934, acc-0.5296, test loss-1.6944, acc-0.5169
Iter-87670, train loss-1.8689, acc-0.3600, valid loss-1.6934, acc-0.5294, test loss-1.6943, acc-0.5170
Iter-87680, train loss-1.7229, acc-0.5200, valid loss-1.6933, acc-0.5292, test loss-1.6943, acc-0.5171
Iter-87690, train loss-1.7536, acc-0.5000, valid loss-1.6933, acc-0.5294, test loss-1.6943, acc-0.5170
Iter-87700, train loss-1.7418, acc-0.4400, valid loss-1.6933, acc-0.5294, test loss-1.6942, acc-0.5171
Iter-87710, train loss-1.6435, acc-0.5800, valid loss-1.6932, acc-0.5294, test loss-1.6942, acc-0.5170
Iter-87720, train loss-1.7429, acc-0.4600, valid loss-1.6932, acc-0.5294, test loss-1.6942, acc-0.5169
Iter-87730, train loss-1.6275, acc-0.5800, valid loss-1.6932, acc-0.5294, test loss-1.6941, acc-0.5171
Iter-87740, train loss-1.7246, acc-0.5200, valid loss-1.6931, acc-0.5294, test loss-1.6941, acc-0.5171
Iter-87750, train loss-1.7428, acc-0.4600, valid loss-1.6931, acc-0.5296, test loss-1.6941, acc-0.5170
Iter-87760, train loss-1.6191, acc-0.5800, valid loss-1.6931, acc-0.5296, test loss-1.6940, acc-0.5170
Iter-87770, train loss-1.7781, acc-0.5200, valid loss-1.6930, acc-0.5294, test loss-1.6940, acc-0.5170
Iter-87780, train loss-1.6743, acc-0.5600, valid loss-1.6930, acc-0.5294, test loss-1.6939, acc-0.5171
Iter-87790, train loss-1.6386, acc-0.6200, valid loss-1.6930, acc-0.5292, test loss-1.6939, acc-0.5171
Iter-87800, train loss-1.6182, acc-0.5800, valid loss-1.6929, acc-0.5294, test loss-1.6939, acc-0.5171
Iter-87810, train loss-1.7494, acc-0.4800, valid loss-1.6929, acc-0.5296, test loss-1.6938, acc-0.5170
Iter-87820, train loss-1.6972, acc-0.6200, valid loss-1.6929, acc-0.5298, test loss-1.6938, acc-0.5170
Iter-87830, train loss-1.6622, acc-0.5600, valid loss-1.6928, acc-0.5296, test loss-1.6938, acc-0.5170
Iter-87840, train loss-1.7762, acc-0.4400, valid loss-1.6928, acc-0.5296, test loss-1.6937, acc-0.5171
Iter-87850, train loss-1.6310, acc-0.5400, valid loss-1.6927, acc-0.5296, test loss-1.6937, acc-0.5172
Iter-87860, train loss-1.8005, acc-0.4200, valid loss-1.6927, acc-0.5296, test loss-1.6937, acc-0.5172
Iter-87870, train loss-1.6810, acc-0.4200, valid loss-1.6927, acc-0.5296, test loss-1.6936, acc-0.5171
Iter-87880, train loss-1.7267, acc-0.5800, valid loss-1.6926, acc-0.5298, test loss-1.6936, acc-0.5171
Iter-87890, train loss-1.7258, acc-0.5200, valid loss-1.6926, acc-0.5296, test loss-1.6936, acc-0.5171
Iter-87900, train loss-1.6221, acc-0.5800, valid loss-1.6926, acc-0.5294, test loss-1.6935, acc-0.5171
Iter-87910, train loss-1.7106, acc-0.4800, valid loss-1.6925, acc-0.5294, test loss-1.6935, acc-0.5171
Iter-87920, train loss-1.6587, acc-0.5200, valid loss-1.6925, acc-0.5296, test loss-1.6935, acc-0.5171
Iter-87930, train loss-1.7770, acc-0.4800, valid loss-1.6925, acc-0.5292, test loss-1.6934, acc-0.5171
Iter-87940, train loss-1.6519, acc-0.5800, valid loss-1.6924, acc-0.5300, test loss-1.6934, acc-0.5171
Iter-87950, train loss-1.7554, acc-0.5400, valid loss-1.6924, acc-0.5298, test loss-1.6934, acc-0.5172
Iter-87960, train loss-1.7898, acc-0.4600, valid loss-1.6924, acc-0.5296, test loss-1.6933, acc-0.5171
Iter-87970, train loss-1.6228, acc-0.6600, valid loss-1.6923, acc-0.5294, test loss-1.6933, acc-0.5172
Iter-87980, train loss-1.7233, acc-0.4600, valid loss-1.6923, acc-0.5294, test loss-1.6933, acc-0.5171
Iter-87990, train loss-1.6614, acc-0.5800, valid loss-1.6923, acc-0.5294, test loss-1.6932, acc-0.5171
Iter-88000, train loss-1.7557, acc-0.4000, valid loss-1.6922, acc-0.5298, test loss-1.6932, acc-0.5171
Iter-88010, train loss-1.7625, acc-0.5000, valid loss-1.6922, acc-0.5298, test loss-1.6932, acc-0.5170
Iter-88020, train loss-1.8042, acc-0.4600, valid loss-1.6921, acc-0.5298, test loss-1.6931, acc-0.5171
Iter-88030, train loss-1.8059, acc-0.3600, valid loss-1.6921, acc-0.5294, test loss-1.6931, acc-0.5172
Iter-88040, train loss-1.7915, acc-0.4400, valid loss-1.6921, acc-0.5292, test loss-1.6931, acc-0.5173
Iter-88050, train loss-1.7667, acc-0.4800, valid loss-1.6920, acc-0.5292, test loss-1.6930, acc-0.5172
Iter-88060, train loss-1.8135, acc-0.3600, valid loss-1.6920, acc-0.5292, test loss-1.6930, acc-0.5172
Iter-88070, train loss-1.6550, acc-0.6200, valid loss-1.6920, acc-0.5296, test loss-1.6930, acc-0.5172
Iter-88080, train loss-1.6091, acc-0.5600, valid loss-1.6920, acc-0.5298, test loss-1.6929, acc-0.5173
Iter-88090, train loss-1.7683, acc-0.4200, valid loss-1.6919, acc-0.5298, test loss-1.6929, acc-0.5173
Iter-88100, train loss-1.7471, acc-0.5000, valid loss-1.6919, acc-0.5296, test loss-1.6928, acc-0.5174
Iter-88110, train loss-1.7678, acc-0.4200, valid loss-1.6918, acc-0.5296, test loss-1.6928, acc-0.5174
Iter-88120, train loss-1.6530, acc-0.5000, valid loss-1.6918, acc-0.5296, test loss-1.6928, acc-0.5176
Iter-88130, train loss-1.7806, acc-0.4800, valid loss-1.6918, acc-0.5296, test loss-1.6928, acc-0.5175
Iter-88140, train loss-1.8261, acc-0.4000, valid loss-1.6918, acc-0.5296, test loss-1.6927, acc-0.5176
Iter-88150, train loss-1.7283, acc-0.5200, valid loss-1.6917, acc-0.5296, test loss-1.6927, acc-0.5176
Iter-88160, train loss-1.6152, acc-0.4800, valid loss-1.6917, acc-0.5296, test loss-1.6927, acc-0.5175
Iter-88170, train loss-1.7410, acc-0.4200, valid loss-1.6916, acc-0.5298, test loss-1.6926, acc-0.5175
Iter-88180, train loss-1.6392, acc-0.5400, valid loss-1.6916, acc-0.5298, test loss-1.6926, acc-0.5175
Iter-88190, train loss-1.7894, acc-0.4400, valid loss-1.6916, acc-0.5298, test loss-1.6925, acc-0.5176
Iter-88200, train loss-1.7949, acc-0.4400, valid loss-1.6915, acc-0.5298, test loss-1.6925, acc-0.5176
Iter-88210, train loss-1.7400, acc-0.4600, valid loss-1.6915, acc-0.5300, test loss-1.6925, acc-0.5176
Iter-88220, train loss-1.7141, acc-0.4800, valid loss-1.6915, acc-0.5296, test loss-1.6924, acc-0.5175
Iter-88230, train loss-1.6384, acc-0.6000, valid loss-1.6914, acc-0.5296, test loss-1.6924, acc-0.5175
Iter-88240, train loss-1.6336, acc-0.6000, valid loss-1.6914, acc-0.5294, test loss-1.6924, acc-0.5174
Iter-88250, train loss-1.7195, acc-0.4600, valid loss-1.6914, acc-0.5294, test loss-1.6923, acc-0.5175
Iter-88260, train loss-1.7594, acc-0.4000, valid loss-1.6913, acc-0.5294, test loss-1.6923, acc-0.5175
Iter-88270, train loss-1.7354, acc-0.5000, valid loss-1.6913, acc-0.5294, test loss-1.6923, acc-0.5175
Iter-88280, train loss-1.7481, acc-0.5000, valid loss-1.6913, acc-0.5294, test loss-1.6922, acc-0.5175
Iter-88290, train loss-1.5940, acc-0.6200, valid loss-1.6912, acc-0.5294, test loss-1.6922, acc-0.5173
Iter-88300, train loss-1.6479, acc-0.5800, valid loss-1.6912, acc-0.5296, test loss-1.6922, acc-0.5173
Iter-88310, train loss-1.6820, acc-0.5600, valid loss-1.6912, acc-0.5298, test loss-1.6921, acc-0.5173
Iter-88320, train loss-1.7955, acc-0.4600, valid loss-1.6911, acc-0.5298, test loss-1.6921, acc-0.5176
Iter-88330, train loss-1.7452, acc-0.4800, valid loss-1.6911, acc-0.5296, test loss-1.6921, acc-0.5174
Iter-88340, train loss-1.6558, acc-0.6000, valid loss-1.6911, acc-0.5296, test loss-1.6920, acc-0.5175
Iter-88350, train loss-1.7412, acc-0.6400, valid loss-1.6910, acc-0.5294, test loss-1.6920, acc-0.5175
Iter-88360, train loss-1.7884, acc-0.4600, valid loss-1.6910, acc-0.5294, test loss-1.6920, acc-0.5175
Iter-88370, train loss-1.5848, acc-0.5600, valid loss-1.6910, acc-0.5294, test loss-1.6919, acc-0.5176
Iter-88380, train loss-1.8719, acc-0.4200, valid loss-1.6909, acc-0.5292, test loss-1.6919, acc-0.5173
Iter-88390, train loss-1.7252, acc-0.4400, valid loss-1.6909, acc-0.5294, test loss-1.6919, acc-0.5173
Iter-88400, train loss-1.8003, acc-0.5000, valid loss-1.6909, acc-0.5294, test loss-1.6918, acc-0.5173
Iter-88410, train loss-1.7545, acc-0.5400, valid loss-1.6908, acc-0.5294, test loss-1.6918, acc-0.5173
Iter-88420, train loss-1.8059, acc-0.5000, valid loss-1.6908, acc-0.5294, test loss-1.6918, acc-0.5173
Iter-88430, train loss-1.8054, acc-0.4200, valid loss-1.6907, acc-0.5292, test loss-1.6917, acc-0.5173
Iter-88440, train loss-1.7153, acc-0.4400, valid loss-1.6907, acc-0.5290, test loss-1.6917, acc-0.5173
Iter-88450, train loss-1.6023, acc-0.6200, valid loss-1.6907, acc-0.5292, test loss-1.6917, acc-0.5173
Iter-88460, train loss-1.7496, acc-0.5000, valid loss-1.6907, acc-0.5294, test loss-1.6916, acc-0.5173
Iter-88470, train loss-1.6738, acc-0.5600, valid loss-1.6906, acc-0.5292, test loss-1.6916, acc-0.5174
Iter-88480, train loss-1.6619, acc-0.5400, valid loss-1.6906, acc-0.5294, test loss-1.6916, acc-0.5174
Iter-88490, train loss-1.6940, acc-0.5000, valid loss-1.6906, acc-0.5290, test loss-1.6915, acc-0.5174
Iter-88500, train loss-1.6905, acc-0.5000, valid loss-1.6905, acc-0.5290, test loss-1.6915, acc-0.5173
Iter-88510, train loss-1.6629, acc-0.4800, valid loss-1.6905, acc-0.5290, test loss-1.6915, acc-0.5173
Iter-88520, train loss-1.7578, acc-0.5600, valid loss-1.6904, acc-0.5290, test loss-1.6914, acc-0.5173
Iter-88530, train loss-1.7467, acc-0.4800, valid loss-1.6904, acc-0.5290, test loss-1.6914, acc-0.5174
Iter-88540, train loss-1.7210, acc-0.6200, valid loss-1.6904, acc-0.5290, test loss-1.6913, acc-0.5174
Iter-88550, train loss-1.6208, acc-0.6200, valid loss-1.6903, acc-0.5294, test loss-1.6913, acc-0.5174
Iter-88560, train loss-1.6382, acc-0.5200, valid loss-1.6903, acc-0.5296, test loss-1.6913, acc-0.5174
Iter-88570, train loss-1.5791, acc-0.5200, valid loss-1.6903, acc-0.5298, test loss-1.6912, acc-0.5173
Iter-88580, train loss-1.6750, acc-0.5400, valid loss-1.6902, acc-0.5296, test loss-1.6912, acc-0.5173
Iter-88590, train loss-1.7222, acc-0.5200, valid loss-1.6902, acc-0.5298, test loss-1.6912, acc-0.5171
Iter-88600, train loss-1.6116, acc-0.6400, valid loss-1.6902, acc-0.5296, test loss-1.6911, acc-0.5172
Iter-88610, train loss-1.6646, acc-0.5800, valid loss-1.6901, acc-0.5296, test loss-1.6911, acc-0.5172
Iter-88620, train loss-1.6986, acc-0.4400, valid loss-1.6901, acc-0.5298, test loss-1.6911, acc-0.5174
Iter-88630, train loss-1.6715, acc-0.5600, valid loss-1.6901, acc-0.5300, test loss-1.6910, acc-0.5174
Iter-88640, train loss-1.7629, acc-0.4400, valid loss-1.6900, acc-0.5294, test loss-1.6910, acc-0.5174
Iter-88650, train loss-1.6268, acc-0.5800, valid loss-1.6900, acc-0.5294, test loss-1.6910, acc-0.5174
Iter-88660, train loss-1.7063, acc-0.5000, valid loss-1.6900, acc-0.5294, test loss-1.6909, acc-0.5174
Iter-88670, train loss-1.7210, acc-0.5400, valid loss-1.6899, acc-0.5292, test loss-1.6909, acc-0.5176
Iter-88680, train loss-1.5790, acc-0.6000, valid loss-1.6899, acc-0.5292, test loss-1.6909, acc-0.5175
Iter-88690, train loss-1.7392, acc-0.5400, valid loss-1.6899, acc-0.5292, test loss-1.6908, acc-0.5175
Iter-88700, train loss-1.8172, acc-0.3800, valid loss-1.6898, acc-0.5294, test loss-1.6908, acc-0.5174
Iter-88710, train loss-1.6734, acc-0.6000, valid loss-1.6898, acc-0.5296, test loss-1.6908, acc-0.5173
Iter-88720, train loss-1.7420, acc-0.3800, valid loss-1.6898, acc-0.5298, test loss-1.6907, acc-0.5172
Iter-88730, train loss-1.7224, acc-0.5600, valid loss-1.6897, acc-0.5298, test loss-1.6907, acc-0.5173
Iter-88740, train loss-1.7495, acc-0.4600, valid loss-1.6897, acc-0.5298, test loss-1.6907, acc-0.5172
Iter-88750, train loss-1.6489, acc-0.5600, valid loss-1.6896, acc-0.5294, test loss-1.6906, acc-0.5174
Iter-88760, train loss-1.7237, acc-0.5600, valid loss-1.6896, acc-0.5292, test loss-1.6906, acc-0.5173
Iter-88770, train loss-1.6320, acc-0.5400, valid loss-1.6896, acc-0.5292, test loss-1.6906, acc-0.5174
Iter-88780, train loss-1.8172, acc-0.4000, valid loss-1.6895, acc-0.5292, test loss-1.6905, acc-0.5174
Iter-88790, train loss-1.7425, acc-0.4600, valid loss-1.6895, acc-0.5294, test loss-1.6905, acc-0.5175
Iter-88800, train loss-1.7170, acc-0.4600, valid loss-1.6895, acc-0.5294, test loss-1.6905, acc-0.5175
Iter-88810, train loss-1.6059, acc-0.6600, valid loss-1.6894, acc-0.5294, test loss-1.6904, acc-0.5175
Iter-88820, train loss-1.7964, acc-0.3800, valid loss-1.6894, acc-0.5294, test loss-1.6904, acc-0.5174
Iter-88830, train loss-1.6037, acc-0.6000, valid loss-1.6894, acc-0.5294, test loss-1.6904, acc-0.5178
Iter-88840, train loss-1.7305, acc-0.4600, valid loss-1.6893, acc-0.5294, test loss-1.6903, acc-0.5178
Iter-88850, train loss-1.6949, acc-0.5600, valid loss-1.6893, acc-0.5296, test loss-1.6903, acc-0.5177
Iter-88860, train loss-1.7642, acc-0.4800, valid loss-1.6893, acc-0.5294, test loss-1.6903, acc-0.5179
Iter-88870, train loss-1.7069, acc-0.4800, valid loss-1.6892, acc-0.5294, test loss-1.6902, acc-0.5178
Iter-88880, train loss-1.7503, acc-0.5200, valid loss-1.6892, acc-0.5294, test loss-1.6902, acc-0.5179
Iter-88890, train loss-1.6935, acc-0.5200, valid loss-1.6892, acc-0.5294, test loss-1.6902, acc-0.5179
Iter-88900, train loss-1.6418, acc-0.4800, valid loss-1.6891, acc-0.5294, test loss-1.6901, acc-0.5179
Iter-88910, train loss-1.7561, acc-0.4200, valid loss-1.6891, acc-0.5294, test loss-1.6901, acc-0.5178
Iter-88920, train loss-1.7439, acc-0.5000, valid loss-1.6891, acc-0.5294, test loss-1.6900, acc-0.5178
Iter-88930, train loss-1.6763, acc-0.4600, valid loss-1.6890, acc-0.5294, test loss-1.6900, acc-0.5178
Iter-88940, train loss-1.6662, acc-0.5800, valid loss-1.6890, acc-0.5294, test loss-1.6900, acc-0.5178
Iter-88950, train loss-1.7431, acc-0.4800, valid loss-1.6890, acc-0.5294, test loss-1.6899, acc-0.5178
Iter-88960, train loss-1.7950, acc-0.4000, valid loss-1.6889, acc-0.5294, test loss-1.6899, acc-0.5177
Iter-88970, train loss-1.8128, acc-0.4200, valid loss-1.6889, acc-0.5294, test loss-1.6899, acc-0.5177
Iter-88980, train loss-1.5800, acc-0.6200, valid loss-1.6889, acc-0.5296, test loss-1.6898, acc-0.5176
Iter-88990, train loss-1.6811, acc-0.5200, valid loss-1.6888, acc-0.5300, test loss-1.6898, acc-0.5175
Iter-89000, train loss-1.7253, acc-0.5600, valid loss-1.6888, acc-0.5298, test loss-1.6898, acc-0.5177
Iter-89010, train loss-1.6245, acc-0.5600, valid loss-1.6888, acc-0.5300, test loss-1.6897, acc-0.5177
Iter-89020, train loss-1.7372, acc-0.3800, valid loss-1.6887, acc-0.5300, test loss-1.6897, acc-0.5177
Iter-89030, train loss-1.6815, acc-0.6000, valid loss-1.6887, acc-0.5298, test loss-1.6897, acc-0.5177
Iter-89040, train loss-1.7492, acc-0.5200, valid loss-1.6886, acc-0.5298, test loss-1.6896, acc-0.5176
Iter-89050, train loss-1.7051, acc-0.5800, valid loss-1.6886, acc-0.5298, test loss-1.6896, acc-0.5177
Iter-89060, train loss-1.6564, acc-0.5600, valid loss-1.6886, acc-0.5298, test loss-1.6896, acc-0.5177
Iter-89070, train loss-1.7119, acc-0.5000, valid loss-1.6885, acc-0.5298, test loss-1.6895, acc-0.5176
Iter-89080, train loss-1.6446, acc-0.5400, valid loss-1.6885, acc-0.5298, test loss-1.6895, acc-0.5175
Iter-89090, train loss-1.6888, acc-0.5400, valid loss-1.6885, acc-0.5296, test loss-1.6895, acc-0.5175
Iter-89100, train loss-1.5662, acc-0.6600, valid loss-1.6884, acc-0.5298, test loss-1.6894, acc-0.5173
Iter-89110, train loss-1.5765, acc-0.5600, valid loss-1.6884, acc-0.5300, test loss-1.6894, acc-0.5174
Iter-89120, train loss-1.7017, acc-0.5000, valid loss-1.6884, acc-0.5300, test loss-1.6894, acc-0.5174
Iter-89130, train loss-1.7139, acc-0.5600, valid loss-1.6883, acc-0.5298, test loss-1.6893, acc-0.5174
Iter-89140, train loss-1.8223, acc-0.4200, valid loss-1.6883, acc-0.5300, test loss-1.6893, acc-0.5175
Iter-89150, train loss-1.7804, acc-0.4600, valid loss-1.6883, acc-0.5300, test loss-1.6893, acc-0.5176
Iter-89160, train loss-1.8067, acc-0.4800, valid loss-1.6882, acc-0.5304, test loss-1.6892, acc-0.5177
Iter-89170, train loss-1.6584, acc-0.6000, valid loss-1.6882, acc-0.5302, test loss-1.6892, acc-0.5175
Iter-89180, train loss-1.8080, acc-0.5400, valid loss-1.6882, acc-0.5302, test loss-1.6891, acc-0.5174
Iter-89190, train loss-1.5481, acc-0.6000, valid loss-1.6881, acc-0.5306, test loss-1.6891, acc-0.5175
Iter-89200, train loss-1.6893, acc-0.5000, valid loss-1.6881, acc-0.5298, test loss-1.6891, acc-0.5176
Iter-89210, train loss-1.8163, acc-0.5200, valid loss-1.6881, acc-0.5300, test loss-1.6890, acc-0.5178
Iter-89220, train loss-1.6352, acc-0.6000, valid loss-1.6880, acc-0.5304, test loss-1.6890, acc-0.5178
Iter-89230, train loss-1.7407, acc-0.5200, valid loss-1.6880, acc-0.5304, test loss-1.6890, acc-0.5178
Iter-89240, train loss-1.6741, acc-0.5800, valid loss-1.6880, acc-0.5304, test loss-1.6889, acc-0.5177
Iter-89250, train loss-1.8315, acc-0.4200, valid loss-1.6879, acc-0.5304, test loss-1.6889, acc-0.5176
Iter-89260, train loss-1.6933, acc-0.5400, valid loss-1.6879, acc-0.5306, test loss-1.6889, acc-0.5175
Iter-89270, train loss-1.7218, acc-0.5200, valid loss-1.6879, acc-0.5302, test loss-1.6888, acc-0.5177
Iter-89280, train loss-1.6462, acc-0.5000, valid loss-1.6878, acc-0.5304, test loss-1.6888, acc-0.5176
Iter-89290, train loss-1.6834, acc-0.5000, valid loss-1.6878, acc-0.5302, test loss-1.6888, acc-0.5178
Iter-89300, train loss-1.6676, acc-0.5400, valid loss-1.6878, acc-0.5300, test loss-1.6887, acc-0.5178
Iter-89310, train loss-1.7487, acc-0.5600, valid loss-1.6877, acc-0.5300, test loss-1.6887, acc-0.5178
Iter-89320, train loss-1.6189, acc-0.6000, valid loss-1.6877, acc-0.5304, test loss-1.6887, acc-0.5175
Iter-89330, train loss-1.7359, acc-0.5200, valid loss-1.6877, acc-0.5302, test loss-1.6886, acc-0.5176
Iter-89340, train loss-1.6421, acc-0.5000, valid loss-1.6876, acc-0.5300, test loss-1.6886, acc-0.5176
Iter-89350, train loss-1.7443, acc-0.5200, valid loss-1.6876, acc-0.5302, test loss-1.6886, acc-0.5176
Iter-89360, train loss-1.6668, acc-0.6200, valid loss-1.6876, acc-0.5300, test loss-1.6885, acc-0.5179
Iter-89370, train loss-1.6766, acc-0.5200, valid loss-1.6875, acc-0.5300, test loss-1.6885, acc-0.5177
Iter-89380, train loss-1.6941, acc-0.5000, valid loss-1.6875, acc-0.5300, test loss-1.6885, acc-0.5177
Iter-89390, train loss-1.7229, acc-0.5600, valid loss-1.6875, acc-0.5300, test loss-1.6884, acc-0.5177
Iter-89400, train loss-1.6064, acc-0.5600, valid loss-1.6874, acc-0.5300, test loss-1.6884, acc-0.5181
Iter-89410, train loss-1.7481, acc-0.5200, valid loss-1.6874, acc-0.5302, test loss-1.6884, acc-0.5180
Iter-89420, train loss-1.8510, acc-0.4400, valid loss-1.6874, acc-0.5302, test loss-1.6883, acc-0.5180
Iter-89430, train loss-1.6774, acc-0.5600, valid loss-1.6873, acc-0.5300, test loss-1.6883, acc-0.5180
Iter-89440, train loss-1.6903, acc-0.5600, valid loss-1.6873, acc-0.5302, test loss-1.6883, acc-0.5181
Iter-89450, train loss-1.6650, acc-0.4600, valid loss-1.6873, acc-0.5302, test loss-1.6882, acc-0.5182
Iter-89460, train loss-1.6900, acc-0.5400, valid loss-1.6872, acc-0.5302, test loss-1.6882, acc-0.5181
Iter-89470, train loss-1.6740, acc-0.5400, valid loss-1.6872, acc-0.5300, test loss-1.6882, acc-0.5181
Iter-89480, train loss-1.7767, acc-0.3200, valid loss-1.6872, acc-0.5302, test loss-1.6881, acc-0.5180
Iter-89490, train loss-1.7571, acc-0.4400, valid loss-1.6871, acc-0.5302, test loss-1.6881, acc-0.5181
Iter-89500, train loss-1.7320, acc-0.4200, valid loss-1.6871, acc-0.5302, test loss-1.6881, acc-0.5181
Iter-89510, train loss-1.7375, acc-0.4600, valid loss-1.6871, acc-0.5302, test loss-1.6880, acc-0.5181
Iter-89520, train loss-1.6244, acc-0.5600, valid loss-1.6870, acc-0.5304, test loss-1.6880, acc-0.5181
Iter-89530, train loss-1.6652, acc-0.5800, valid loss-1.6870, acc-0.5304, test loss-1.6880, acc-0.5182
Iter-89540, train loss-1.6999, acc-0.4200, valid loss-1.6870, acc-0.5304, test loss-1.6879, acc-0.5181
Iter-89550, train loss-1.5852, acc-0.6000, valid loss-1.6869, acc-0.5304, test loss-1.6879, acc-0.5181
Iter-89560, train loss-1.6638, acc-0.4400, valid loss-1.6869, acc-0.5304, test loss-1.6879, acc-0.5180
Iter-89570, train loss-1.6376, acc-0.5000, valid loss-1.6869, acc-0.5304, test loss-1.6878, acc-0.5181
Iter-89580, train loss-1.5667, acc-0.5200, valid loss-1.6868, acc-0.5304, test loss-1.6878, acc-0.5181
Iter-89590, train loss-1.6114, acc-0.6600, valid loss-1.6868, acc-0.5302, test loss-1.6878, acc-0.5181
Iter-89600, train loss-1.6773, acc-0.5400, valid loss-1.6868, acc-0.5302, test loss-1.6877, acc-0.5180
Iter-89610, train loss-1.6748, acc-0.6200, valid loss-1.6867, acc-0.5304, test loss-1.6877, acc-0.5181
Iter-89620, train loss-1.8110, acc-0.4800, valid loss-1.6867, acc-0.5304, test loss-1.6877, acc-0.5181
Iter-89630, train loss-1.7682, acc-0.5600, valid loss-1.6867, acc-0.5302, test loss-1.6876, acc-0.5181
Iter-89640, train loss-1.8126, acc-0.4400, valid loss-1.6866, acc-0.5302, test loss-1.6876, acc-0.5181
Iter-89650, train loss-1.8771, acc-0.3800, valid loss-1.6866, acc-0.5302, test loss-1.6876, acc-0.5179
Iter-89660, train loss-1.6688, acc-0.6800, valid loss-1.6866, acc-0.5302, test loss-1.6875, acc-0.5180
Iter-89670, train loss-1.7133, acc-0.5600, valid loss-1.6865, acc-0.5302, test loss-1.6875, acc-0.5179
Iter-89680, train loss-1.6539, acc-0.5400, valid loss-1.6865, acc-0.5302, test loss-1.6875, acc-0.5180
Iter-89690, train loss-1.6675, acc-0.5600, valid loss-1.6865, acc-0.5302, test loss-1.6874, acc-0.5178
Iter-89700, train loss-1.7838, acc-0.4400, valid loss-1.6864, acc-0.5302, test loss-1.6874, acc-0.5181
Iter-89710, train loss-1.7218, acc-0.4600, valid loss-1.6864, acc-0.5302, test loss-1.6874, acc-0.5180
Iter-89720, train loss-1.7497, acc-0.5400, valid loss-1.6864, acc-0.5302, test loss-1.6873, acc-0.5180
Iter-89730, train loss-1.6472, acc-0.5600, valid loss-1.6863, acc-0.5302, test loss-1.6873, acc-0.5180
Iter-89740, train loss-1.7137, acc-0.5400, valid loss-1.6863, acc-0.5304, test loss-1.6873, acc-0.5181
Iter-89750, train loss-1.6151, acc-0.5200, valid loss-1.6863, acc-0.5304, test loss-1.6872, acc-0.5180
Iter-89760, train loss-1.7506, acc-0.4600, valid loss-1.6862, acc-0.5304, test loss-1.6872, acc-0.5180
Iter-89770, train loss-1.7382, acc-0.4800, valid loss-1.6862, acc-0.5304, test loss-1.6872, acc-0.5181
Iter-89780, train loss-1.6997, acc-0.5800, valid loss-1.6862, acc-0.5304, test loss-1.6871, acc-0.5181
Iter-89790, train loss-1.7562, acc-0.3600, valid loss-1.6861, acc-0.5304, test loss-1.6871, acc-0.5182
Iter-89800, train loss-1.7034, acc-0.5000, valid loss-1.6861, acc-0.5304, test loss-1.6871, acc-0.5180
Iter-89810, train loss-1.7149, acc-0.5000, valid loss-1.6861, acc-0.5304, test loss-1.6870, acc-0.5179
Iter-89820, train loss-1.6679, acc-0.5400, valid loss-1.6860, acc-0.5306, test loss-1.6870, acc-0.5179
Iter-89830, train loss-1.6975, acc-0.6000, valid loss-1.6860, acc-0.5306, test loss-1.6870, acc-0.5179
Iter-89840, train loss-1.6911, acc-0.4800, valid loss-1.6860, acc-0.5304, test loss-1.6869, acc-0.5180
Iter-89850, train loss-1.7124, acc-0.4600, valid loss-1.6859, acc-0.5306, test loss-1.6869, acc-0.5179
Iter-89860, train loss-1.8011, acc-0.3800, valid loss-1.6859, acc-0.5306, test loss-1.6869, acc-0.5179
Iter-89870, train loss-1.6622, acc-0.6000, valid loss-1.6859, acc-0.5308, test loss-1.6868, acc-0.5181
Iter-89880, train loss-1.7009, acc-0.5800, valid loss-1.6858, acc-0.5306, test loss-1.6868, acc-0.5180
Iter-89890, train loss-1.7977, acc-0.4600, valid loss-1.6858, acc-0.5306, test loss-1.6868, acc-0.5180
Iter-89900, train loss-1.5980, acc-0.6200, valid loss-1.6858, acc-0.5304, test loss-1.6867, acc-0.5178
Iter-89910, train loss-1.6828, acc-0.6000, valid loss-1.6857, acc-0.5306, test loss-1.6867, acc-0.5178
Iter-89920, train loss-1.6608, acc-0.5600, valid loss-1.6857, acc-0.5306, test loss-1.6867, acc-0.5180
Iter-89930, train loss-1.6576, acc-0.5400, valid loss-1.6857, acc-0.5306, test loss-1.6866, acc-0.5179
Iter-89940, train loss-1.7062, acc-0.5800, valid loss-1.6856, acc-0.5306, test loss-1.6866, acc-0.5179
Iter-89950, train loss-1.7335, acc-0.4200, valid loss-1.6856, acc-0.5306, test loss-1.6866, acc-0.5180
Iter-89960, train loss-1.6488, acc-0.5800, valid loss-1.6856, acc-0.5306, test loss-1.6865, acc-0.5180
Iter-89970, train loss-1.7171, acc-0.5400, valid loss-1.6855, acc-0.5306, test loss-1.6865, acc-0.5179
Iter-89980, train loss-1.7575, acc-0.4200, valid loss-1.6855, acc-0.5306, test loss-1.6865, acc-0.5181
Iter-89990, train loss-1.7537, acc-0.4400, valid loss-1.6855, acc-0.5306, test loss-1.6864, acc-0.5180
Iter-90000, train loss-1.7493, acc-0.5200, valid loss-1.6854, acc-0.5306, test loss-1.6864, acc-0.5181
Iter-90010, train loss-1.7567, acc-0.4400, valid loss-1.6854, acc-0.5306, test loss-1.6864, acc-0.5180
Iter-90020, train loss-1.7366, acc-0.4400, valid loss-1.6854, acc-0.5306, test loss-1.6863, acc-0.5180
Iter-90030, train loss-1.7530, acc-0.4600, valid loss-1.6853, acc-0.5306, test loss-1.6863, acc-0.5179
Iter-90040, train loss-1.6426, acc-0.5200, valid loss-1.6853, acc-0.5306, test loss-1.6863, acc-0.5180
Iter-90050, train loss-1.6498, acc-0.5200, valid loss-1.6853, acc-0.5306, test loss-1.6862, acc-0.5180
Iter-90060, train loss-1.6054, acc-0.5400, valid loss-1.6852, acc-0.5306, test loss-1.6862, acc-0.5181
Iter-90070, train loss-1.7198, acc-0.5000, valid loss-1.6852, acc-0.5306, test loss-1.6861, acc-0.5182
Iter-90080, train loss-1.6731, acc-0.5600, valid loss-1.6852, acc-0.5306, test loss-1.6861, acc-0.5182
Iter-90090, train loss-1.6813, acc-0.4600, valid loss-1.6851, acc-0.5306, test loss-1.6861, acc-0.5183
Iter-90100, train loss-1.9138, acc-0.3400, valid loss-1.6851, acc-0.5306, test loss-1.6860, acc-0.5181
Iter-90110, train loss-1.6431, acc-0.5800, valid loss-1.6851, acc-0.5306, test loss-1.6860, acc-0.5181
Iter-90120, train loss-1.6572, acc-0.6400, valid loss-1.6850, acc-0.5306, test loss-1.6860, acc-0.5181
Iter-90130, train loss-1.6843, acc-0.5000, valid loss-1.6850, acc-0.5306, test loss-1.6859, acc-0.5181
Iter-90140, train loss-1.8087, acc-0.3800, valid loss-1.6849, acc-0.5306, test loss-1.6859, acc-0.5181
Iter-90150, train loss-1.5958, acc-0.5400, valid loss-1.6849, acc-0.5306, test loss-1.6859, acc-0.5182
Iter-90160, train loss-1.6961, acc-0.4200, valid loss-1.6849, acc-0.5306, test loss-1.6858, acc-0.5181
Iter-90170, train loss-1.6648, acc-0.5400, valid loss-1.6848, acc-0.5306, test loss-1.6858, acc-0.5182
Iter-90180, train loss-1.8217, acc-0.4600, valid loss-1.6848, acc-0.5306, test loss-1.6858, acc-0.5182
Iter-90190, train loss-1.6335, acc-0.5600, valid loss-1.6848, acc-0.5306, test loss-1.6857, acc-0.5182
Iter-90200, train loss-1.7137, acc-0.4200, valid loss-1.6847, acc-0.5306, test loss-1.6857, acc-0.5182
Iter-90210, train loss-1.6381, acc-0.5000, valid loss-1.6847, acc-0.5306, test loss-1.6857, acc-0.5181
Iter-90220, train loss-1.8578, acc-0.3400, valid loss-1.6847, acc-0.5306, test loss-1.6856, acc-0.5183
Iter-90230, train loss-1.7190, acc-0.4800, valid loss-1.6846, acc-0.5306, test loss-1.6856, acc-0.5182
Iter-90240, train loss-1.6594, acc-0.4600, valid loss-1.6846, acc-0.5306, test loss-1.6856, acc-0.5181
Iter-90250, train loss-1.7672, acc-0.4800, valid loss-1.6846, acc-0.5306, test loss-1.6855, acc-0.5183
Iter-90260, train loss-1.7220, acc-0.4400, valid loss-1.6845, acc-0.5306, test loss-1.6855, acc-0.5182
Iter-90270, train loss-1.8614, acc-0.4600, valid loss-1.6845, acc-0.5306, test loss-1.6855, acc-0.5182
Iter-90280, train loss-1.7212, acc-0.5000, valid loss-1.6845, acc-0.5306, test loss-1.6854, acc-0.5184
Iter-90290, train loss-1.6641, acc-0.5400, valid loss-1.6844, acc-0.5306, test loss-1.6854, acc-0.5183
Iter-90300, train loss-1.6053, acc-0.6000, valid loss-1.6844, acc-0.5306, test loss-1.6854, acc-0.5183
Iter-90310, train loss-1.6940, acc-0.5000, valid loss-1.6844, acc-0.5304, test loss-1.6853, acc-0.5183
Iter-90320, train loss-1.6710, acc-0.5400, valid loss-1.6843, acc-0.5302, test loss-1.6853, acc-0.5183
Iter-90330, train loss-1.6866, acc-0.5000, valid loss-1.6843, acc-0.5304, test loss-1.6853, acc-0.5182
Iter-90340, train loss-1.6751, acc-0.5600, valid loss-1.6843, acc-0.5302, test loss-1.6852, acc-0.5181
Iter-90350, train loss-1.6269, acc-0.5000, valid loss-1.6842, acc-0.5302, test loss-1.6852, acc-0.5182
Iter-90360, train loss-1.6802, acc-0.6000, valid loss-1.6842, acc-0.5302, test loss-1.6852, acc-0.5182
Iter-90370, train loss-1.7086, acc-0.4800, valid loss-1.6842, acc-0.5302, test loss-1.6851, acc-0.5183
Iter-90380, train loss-1.6192, acc-0.6000, valid loss-1.6841, acc-0.5304, test loss-1.6851, acc-0.5184
Iter-90390, train loss-1.7839, acc-0.4600, valid loss-1.6841, acc-0.5306, test loss-1.6851, acc-0.5183
Iter-90400, train loss-1.6879, acc-0.5400, valid loss-1.6841, acc-0.5306, test loss-1.6850, acc-0.5183
Iter-90410, train loss-1.6681, acc-0.6000, valid loss-1.6840, acc-0.5306, test loss-1.6850, acc-0.5184
Iter-90420, train loss-1.8008, acc-0.5000, valid loss-1.6840, acc-0.5304, test loss-1.6850, acc-0.5184
Iter-90430, train loss-1.6231, acc-0.7200, valid loss-1.6840, acc-0.5302, test loss-1.6849, acc-0.5185
Iter-90440, train loss-1.7118, acc-0.5200, valid loss-1.6839, acc-0.5302, test loss-1.6849, acc-0.5185
Iter-90450, train loss-1.6485, acc-0.6000, valid loss-1.6839, acc-0.5302, test loss-1.6849, acc-0.5184
Iter-90460, train loss-1.5935, acc-0.5800, valid loss-1.6839, acc-0.5306, test loss-1.6848, acc-0.5185
Iter-90470, train loss-1.8737, acc-0.4200, valid loss-1.6838, acc-0.5304, test loss-1.6848, acc-0.5184
Iter-90480, train loss-1.8023, acc-0.4600, valid loss-1.6838, acc-0.5304, test loss-1.6848, acc-0.5183
Iter-90490, train loss-1.7487, acc-0.5000, valid loss-1.6838, acc-0.5306, test loss-1.6847, acc-0.5183
Iter-90500, train loss-1.7798, acc-0.4600, valid loss-1.6837, acc-0.5306, test loss-1.6847, acc-0.5183
Iter-90510, train loss-1.5996, acc-0.5800, valid loss-1.6837, acc-0.5306, test loss-1.6847, acc-0.5183
Iter-90520, train loss-1.7950, acc-0.4400, valid loss-1.6837, acc-0.5304, test loss-1.6846, acc-0.5183
Iter-90530, train loss-1.7453, acc-0.4400, valid loss-1.6836, acc-0.5304, test loss-1.6846, acc-0.5182
Iter-90540, train loss-1.6790, acc-0.5400, valid loss-1.6836, acc-0.5304, test loss-1.6846, acc-0.5182
Iter-90550, train loss-1.7378, acc-0.5200, valid loss-1.6836, acc-0.5306, test loss-1.6845, acc-0.5184
Iter-90560, train loss-1.5742, acc-0.6000, valid loss-1.6835, acc-0.5306, test loss-1.6845, acc-0.5183
Iter-90570, train loss-1.5731, acc-0.6600, valid loss-1.6835, acc-0.5306, test loss-1.6845, acc-0.5183
Iter-90580, train loss-1.5928, acc-0.6000, valid loss-1.6835, acc-0.5304, test loss-1.6844, acc-0.5182
Iter-90590, train loss-1.7729, acc-0.4400, valid loss-1.6834, acc-0.5304, test loss-1.6844, acc-0.5185
Iter-90600, train loss-1.8503, acc-0.4000, valid loss-1.6834, acc-0.5308, test loss-1.6844, acc-0.5183
Iter-90610, train loss-1.6592, acc-0.6000, valid loss-1.6834, acc-0.5306, test loss-1.6843, acc-0.5185
Iter-90620, train loss-1.6619, acc-0.5400, valid loss-1.6833, acc-0.5306, test loss-1.6843, acc-0.5185
Iter-90630, train loss-1.5662, acc-0.5400, valid loss-1.6833, acc-0.5306, test loss-1.6843, acc-0.5185
Iter-90640, train loss-1.6436, acc-0.5200, valid loss-1.6833, acc-0.5306, test loss-1.6842, acc-0.5186
Iter-90650, train loss-1.7623, acc-0.4200, valid loss-1.6832, acc-0.5306, test loss-1.6842, acc-0.5186
Iter-90660, train loss-1.7493, acc-0.5000, valid loss-1.6832, acc-0.5306, test loss-1.6842, acc-0.5187
Iter-90670, train loss-1.7568, acc-0.5000, valid loss-1.6832, acc-0.5306, test loss-1.6841, acc-0.5188
Iter-90680, train loss-1.7505, acc-0.4800, valid loss-1.6832, acc-0.5306, test loss-1.6841, acc-0.5188
Iter-90690, train loss-1.7243, acc-0.5000, valid loss-1.6831, acc-0.5306, test loss-1.6841, acc-0.5189
Iter-90700, train loss-1.8113, acc-0.3600, valid loss-1.6831, acc-0.5306, test loss-1.6840, acc-0.5188
Iter-90710, train loss-1.7310, acc-0.5400, valid loss-1.6831, acc-0.5306, test loss-1.6840, acc-0.5190
Iter-90720, train loss-1.7104, acc-0.5200, valid loss-1.6830, acc-0.5306, test loss-1.6840, acc-0.5189
Iter-90730, train loss-1.6939, acc-0.4800, valid loss-1.6830, acc-0.5306, test loss-1.6839, acc-0.5189
Iter-90740, train loss-1.7404, acc-0.4800, valid loss-1.6830, acc-0.5306, test loss-1.6839, acc-0.5189
Iter-90750, train loss-1.6822, acc-0.5600, valid loss-1.6829, acc-0.5306, test loss-1.6839, acc-0.5187
Iter-90760, train loss-1.7338, acc-0.5200, valid loss-1.6829, acc-0.5306, test loss-1.6838, acc-0.5188
Iter-90770, train loss-1.6339, acc-0.6200, valid loss-1.6829, acc-0.5306, test loss-1.6838, acc-0.5187
Iter-90780, train loss-1.6611, acc-0.5600, valid loss-1.6828, acc-0.5304, test loss-1.6838, acc-0.5188
Iter-90790, train loss-1.6995, acc-0.5600, valid loss-1.6828, acc-0.5304, test loss-1.6837, acc-0.5188
Iter-90800, train loss-1.7342, acc-0.5800, valid loss-1.6828, acc-0.5304, test loss-1.6837, acc-0.5187
Iter-90810, train loss-1.8076, acc-0.4400, valid loss-1.6827, acc-0.5304, test loss-1.6837, acc-0.5187
Iter-90820, train loss-1.5943, acc-0.5000, valid loss-1.6827, acc-0.5304, test loss-1.6837, acc-0.5187
Iter-90830, train loss-1.7582, acc-0.4600, valid loss-1.6827, acc-0.5302, test loss-1.6836, acc-0.5187
Iter-90840, train loss-1.6921, acc-0.4800, valid loss-1.6826, acc-0.5300, test loss-1.6836, acc-0.5187
Iter-90850, train loss-1.6740, acc-0.5200, valid loss-1.6826, acc-0.5304, test loss-1.6836, acc-0.5188
Iter-90860, train loss-1.6433, acc-0.5400, valid loss-1.6826, acc-0.5300, test loss-1.6835, acc-0.5188
Iter-90870, train loss-1.7237, acc-0.5200, valid loss-1.6825, acc-0.5304, test loss-1.6835, acc-0.5189
Iter-90880, train loss-1.5976, acc-0.6400, valid loss-1.6825, acc-0.5306, test loss-1.6835, acc-0.5189
Iter-90890, train loss-1.7959, acc-0.5000, valid loss-1.6825, acc-0.5306, test loss-1.6834, acc-0.5189
Iter-90900, train loss-1.5752, acc-0.6600, valid loss-1.6824, acc-0.5306, test loss-1.6834, acc-0.5189
Iter-90910, train loss-1.7605, acc-0.5000, valid loss-1.6824, acc-0.5306, test loss-1.6834, acc-0.5191
Iter-90920, train loss-1.6678, acc-0.5800, valid loss-1.6824, acc-0.5306, test loss-1.6833, acc-0.5191
Iter-90930, train loss-1.7334, acc-0.5000, valid loss-1.6823, acc-0.5308, test loss-1.6833, acc-0.5190
Iter-90940, train loss-1.5540, acc-0.6000, valid loss-1.6823, acc-0.5306, test loss-1.6833, acc-0.5190
Iter-90950, train loss-1.7023, acc-0.5000, valid loss-1.6823, acc-0.5308, test loss-1.6832, acc-0.5189
Iter-90960, train loss-1.5224, acc-0.6000, valid loss-1.6822, acc-0.5310, test loss-1.6832, acc-0.5190
Iter-90970, train loss-1.6997, acc-0.5800, valid loss-1.6822, acc-0.5314, test loss-1.6832, acc-0.5190
Iter-90980, train loss-1.7798, acc-0.5000, valid loss-1.6822, acc-0.5314, test loss-1.6831, acc-0.5190
Iter-90990, train loss-1.6951, acc-0.5600, valid loss-1.6821, acc-0.5316, test loss-1.6831, acc-0.5190
Iter-91000, train loss-1.7329, acc-0.6000, valid loss-1.6821, acc-0.5318, test loss-1.6831, acc-0.5190
Iter-91010, train loss-1.7353, acc-0.5800, valid loss-1.6821, acc-0.5318, test loss-1.6830, acc-0.5190
Iter-91020, train loss-1.6893, acc-0.4800, valid loss-1.6820, acc-0.5318, test loss-1.6830, acc-0.5190
Iter-91030, train loss-1.8166, acc-0.3600, valid loss-1.6820, acc-0.5318, test loss-1.6830, acc-0.5191
Iter-91040, train loss-1.5821, acc-0.6400, valid loss-1.6820, acc-0.5318, test loss-1.6829, acc-0.5191
Iter-91050, train loss-1.6592, acc-0.5400, valid loss-1.6819, acc-0.5318, test loss-1.6829, acc-0.5192
Iter-91060, train loss-1.7335, acc-0.4800, valid loss-1.6819, acc-0.5318, test loss-1.6829, acc-0.5193
Iter-91070, train loss-1.6510, acc-0.5400, valid loss-1.6819, acc-0.5318, test loss-1.6828, acc-0.5194
Iter-91080, train loss-1.8427, acc-0.3600, valid loss-1.6818, acc-0.5318, test loss-1.6828, acc-0.5192
Iter-91090, train loss-1.6346, acc-0.5800, valid loss-1.6818, acc-0.5316, test loss-1.6828, acc-0.5192
Iter-91100, train loss-1.6113, acc-0.5600, valid loss-1.6818, acc-0.5316, test loss-1.6827, acc-0.5192
Iter-91110, train loss-1.5847, acc-0.5400, valid loss-1.6817, acc-0.5316, test loss-1.6827, acc-0.5195
Iter-91120, train loss-1.7390, acc-0.5200, valid loss-1.6817, acc-0.5316, test loss-1.6827, acc-0.5196
Iter-91130, train loss-1.7433, acc-0.5400, valid loss-1.6817, acc-0.5316, test loss-1.6826, acc-0.5196
Iter-91140, train loss-1.7651, acc-0.4400, valid loss-1.6816, acc-0.5316, test loss-1.6826, acc-0.5196
Iter-91150, train loss-1.6757, acc-0.5200, valid loss-1.6816, acc-0.5316, test loss-1.6826, acc-0.5196
Iter-91160, train loss-1.6912, acc-0.5200, valid loss-1.6816, acc-0.5316, test loss-1.6825, acc-0.5196
Iter-91170, train loss-1.7028, acc-0.5000, valid loss-1.6815, acc-0.5316, test loss-1.6825, acc-0.5196
Iter-91180, train loss-1.7820, acc-0.5000, valid loss-1.6815, acc-0.5316, test loss-1.6825, acc-0.5196
Iter-91190, train loss-1.7343, acc-0.5200, valid loss-1.6815, acc-0.5318, test loss-1.6824, acc-0.5195
Iter-91200, train loss-1.6248, acc-0.5400, valid loss-1.6814, acc-0.5316, test loss-1.6824, acc-0.5196
Iter-91210, train loss-1.7157, acc-0.5600, valid loss-1.6814, acc-0.5316, test loss-1.6824, acc-0.5196
Iter-91220, train loss-1.6478, acc-0.5800, valid loss-1.6814, acc-0.5316, test loss-1.6823, acc-0.5196
Iter-91230, train loss-1.6526, acc-0.5400, valid loss-1.6813, acc-0.5316, test loss-1.6823, acc-0.5196
Iter-91240, train loss-1.6382, acc-0.6200, valid loss-1.6813, acc-0.5316, test loss-1.6823, acc-0.5196
Iter-91250, train loss-1.7152, acc-0.4600, valid loss-1.6813, acc-0.5318, test loss-1.6822, acc-0.5197
Iter-91260, train loss-1.7297, acc-0.5400, valid loss-1.6812, acc-0.5320, test loss-1.6822, acc-0.5196
Iter-91270, train loss-1.7009, acc-0.5600, valid loss-1.6812, acc-0.5318, test loss-1.6822, acc-0.5195
Iter-91280, train loss-1.6471, acc-0.6000, valid loss-1.6812, acc-0.5318, test loss-1.6821, acc-0.5196
Iter-91290, train loss-1.7482, acc-0.4600, valid loss-1.6811, acc-0.5318, test loss-1.6821, acc-0.5196
Iter-91300, train loss-1.7256, acc-0.3800, valid loss-1.6811, acc-0.5320, test loss-1.6821, acc-0.5197
Iter-91310, train loss-1.6451, acc-0.6000, valid loss-1.6811, acc-0.5320, test loss-1.6820, acc-0.5197
Iter-91320, train loss-1.7184, acc-0.5000, valid loss-1.6810, acc-0.5320, test loss-1.6820, acc-0.5197
Iter-91330, train loss-1.6287, acc-0.5000, valid loss-1.6810, acc-0.5322, test loss-1.6820, acc-0.5198
Iter-91340, train loss-1.5679, acc-0.6200, valid loss-1.6810, acc-0.5322, test loss-1.6819, acc-0.5196
Iter-91350, train loss-1.7378, acc-0.6200, valid loss-1.6809, acc-0.5322, test loss-1.6819, acc-0.5197
Iter-91360, train loss-1.6601, acc-0.5600, valid loss-1.6809, acc-0.5322, test loss-1.6819, acc-0.5198
Iter-91370, train loss-1.6289, acc-0.5400, valid loss-1.6809, acc-0.5322, test loss-1.6818, acc-0.5198
Iter-91380, train loss-1.6901, acc-0.5200, valid loss-1.6808, acc-0.5322, test loss-1.6818, acc-0.5198
Iter-91390, train loss-1.7001, acc-0.5200, valid loss-1.6808, acc-0.5324, test loss-1.6818, acc-0.5198
Iter-91400, train loss-1.7597, acc-0.4800, valid loss-1.6808, acc-0.5324, test loss-1.6817, acc-0.5197
Iter-91410, train loss-1.7251, acc-0.5600, valid loss-1.6807, acc-0.5326, test loss-1.6817, acc-0.5197
Iter-91420, train loss-1.8336, acc-0.4200, valid loss-1.6807, acc-0.5326, test loss-1.6817, acc-0.5198
Iter-91430, train loss-1.7457, acc-0.5400, valid loss-1.6807, acc-0.5328, test loss-1.6816, acc-0.5197
Iter-91440, train loss-1.5162, acc-0.6200, valid loss-1.6806, acc-0.5324, test loss-1.6816, acc-0.5198
Iter-91450, train loss-1.6199, acc-0.5200, valid loss-1.6806, acc-0.5324, test loss-1.6816, acc-0.5196
Iter-91460, train loss-1.7262, acc-0.4400, valid loss-1.6806, acc-0.5324, test loss-1.6815, acc-0.5197
Iter-91470, train loss-1.6651, acc-0.5600, valid loss-1.6805, acc-0.5324, test loss-1.6815, acc-0.5198
Iter-91480, train loss-1.6721, acc-0.4000, valid loss-1.6805, acc-0.5324, test loss-1.6815, acc-0.5196
Iter-91490, train loss-1.6562, acc-0.5400, valid loss-1.6805, acc-0.5326, test loss-1.6814, acc-0.5196
Iter-91500, train loss-1.6678, acc-0.5600, valid loss-1.6804, acc-0.5324, test loss-1.6814, acc-0.5197
Iter-91510, train loss-1.7473, acc-0.4200, valid loss-1.6804, acc-0.5326, test loss-1.6814, acc-0.5197
Iter-91520, train loss-1.7855, acc-0.4600, valid loss-1.6804, acc-0.5326, test loss-1.6813, acc-0.5199
Iter-91530, train loss-1.6715, acc-0.5600, valid loss-1.6803, acc-0.5330, test loss-1.6813, acc-0.5199
Iter-91540, train loss-1.7890, acc-0.4600, valid loss-1.6803, acc-0.5326, test loss-1.6813, acc-0.5199
Iter-91550, train loss-1.6434, acc-0.4400, valid loss-1.6803, acc-0.5326, test loss-1.6812, acc-0.5199
Iter-91560, train loss-1.7448, acc-0.5200, valid loss-1.6802, acc-0.5328, test loss-1.6812, acc-0.5198
Iter-91570, train loss-1.7282, acc-0.4800, valid loss-1.6802, acc-0.5330, test loss-1.6812, acc-0.5198
Iter-91580, train loss-1.7246, acc-0.5000, valid loss-1.6802, acc-0.5330, test loss-1.6811, acc-0.5198
Iter-91590, train loss-1.6580, acc-0.5000, valid loss-1.6801, acc-0.5326, test loss-1.6811, acc-0.5198
Iter-91600, train loss-1.7789, acc-0.5000, valid loss-1.6801, acc-0.5328, test loss-1.6811, acc-0.5198
Iter-91610, train loss-1.6970, acc-0.4600, valid loss-1.6801, acc-0.5330, test loss-1.6811, acc-0.5198
Iter-91620, train loss-1.7451, acc-0.4600, valid loss-1.6800, acc-0.5330, test loss-1.6810, acc-0.5197
Iter-91630, train loss-1.7310, acc-0.5200, valid loss-1.6800, acc-0.5330, test loss-1.6810, acc-0.5197
Iter-91640, train loss-1.8273, acc-0.4600, valid loss-1.6800, acc-0.5328, test loss-1.6810, acc-0.5197
Iter-91650, train loss-1.6694, acc-0.5600, valid loss-1.6799, acc-0.5330, test loss-1.6809, acc-0.5198
Iter-91660, train loss-1.7193, acc-0.6400, valid loss-1.6799, acc-0.5330, test loss-1.6809, acc-0.5198
Iter-91670, train loss-1.7109, acc-0.4800, valid loss-1.6799, acc-0.5330, test loss-1.6809, acc-0.5198
Iter-91680, train loss-1.5837, acc-0.6600, valid loss-1.6798, acc-0.5328, test loss-1.6808, acc-0.5196
Iter-91690, train loss-1.7203, acc-0.5200, valid loss-1.6798, acc-0.5330, test loss-1.6808, acc-0.5197
Iter-91700, train loss-1.6648, acc-0.5000, valid loss-1.6797, acc-0.5328, test loss-1.6808, acc-0.5196
Iter-91710, train loss-1.7011, acc-0.5200, valid loss-1.6797, acc-0.5328, test loss-1.6807, acc-0.5195
Iter-91720, train loss-1.5233, acc-0.6200, valid loss-1.6797, acc-0.5328, test loss-1.6807, acc-0.5198
Iter-91730, train loss-1.7112, acc-0.4400, valid loss-1.6797, acc-0.5328, test loss-1.6807, acc-0.5198
Iter-91740, train loss-1.8056, acc-0.4400, valid loss-1.6796, acc-0.5326, test loss-1.6806, acc-0.5198
Iter-91750, train loss-1.6238, acc-0.6000, valid loss-1.6796, acc-0.5328, test loss-1.6806, acc-0.5199
Iter-91760, train loss-1.7334, acc-0.5000, valid loss-1.6796, acc-0.5328, test loss-1.6806, acc-0.5199
Iter-91770, train loss-1.6591, acc-0.4400, valid loss-1.6795, acc-0.5328, test loss-1.6805, acc-0.5198
Iter-91780, train loss-1.6845, acc-0.5400, valid loss-1.6795, acc-0.5328, test loss-1.6805, acc-0.5199
Iter-91790, train loss-1.6760, acc-0.5000, valid loss-1.6795, acc-0.5328, test loss-1.6805, acc-0.5199
Iter-91800, train loss-1.6874, acc-0.4600, valid loss-1.6794, acc-0.5328, test loss-1.6804, acc-0.5198
Iter-91810, train loss-1.7172, acc-0.5800, valid loss-1.6794, acc-0.5328, test loss-1.6804, acc-0.5199
Iter-91820, train loss-1.6166, acc-0.5200, valid loss-1.6794, acc-0.5328, test loss-1.6804, acc-0.5200
Iter-91830, train loss-1.6665, acc-0.5200, valid loss-1.6793, acc-0.5328, test loss-1.6803, acc-0.5200
Iter-91840, train loss-1.5668, acc-0.5600, valid loss-1.6793, acc-0.5328, test loss-1.6803, acc-0.5201
Iter-91850, train loss-1.7254, acc-0.5200, valid loss-1.6793, acc-0.5330, test loss-1.6803, acc-0.5201
Iter-91860, train loss-1.6136, acc-0.5200, valid loss-1.6792, acc-0.5330, test loss-1.6802, acc-0.5201
Iter-91870, train loss-1.7043, acc-0.5200, valid loss-1.6792, acc-0.5330, test loss-1.6802, acc-0.5201
Iter-91880, train loss-1.6020, acc-0.6400, valid loss-1.6792, acc-0.5330, test loss-1.6802, acc-0.5201
Iter-91890, train loss-1.6835, acc-0.5200, valid loss-1.6791, acc-0.5330, test loss-1.6801, acc-0.5201
Iter-91900, train loss-1.7904, acc-0.4800, valid loss-1.6791, acc-0.5332, test loss-1.6801, acc-0.5203
Iter-91910, train loss-1.8307, acc-0.3800, valid loss-1.6791, acc-0.5334, test loss-1.6801, acc-0.5203
Iter-91920, train loss-1.7000, acc-0.4600, valid loss-1.6790, acc-0.5332, test loss-1.6800, acc-0.5201
Iter-91930, train loss-1.6992, acc-0.5800, valid loss-1.6790, acc-0.5332, test loss-1.6800, acc-0.5202
Iter-91940, train loss-1.6369, acc-0.5200, valid loss-1.6790, acc-0.5334, test loss-1.6800, acc-0.5201
Iter-91950, train loss-1.6301, acc-0.5800, valid loss-1.6789, acc-0.5334, test loss-1.6799, acc-0.5201
Iter-91960, train loss-1.8029, acc-0.4200, valid loss-1.6789, acc-0.5332, test loss-1.6799, acc-0.5202
Iter-91970, train loss-1.7837, acc-0.4400, valid loss-1.6789, acc-0.5330, test loss-1.6799, acc-0.5203
Iter-91980, train loss-1.6225, acc-0.5000, valid loss-1.6788, acc-0.5330, test loss-1.6798, acc-0.5204
Iter-91990, train loss-1.6780, acc-0.5800, valid loss-1.6788, acc-0.5330, test loss-1.6798, acc-0.5204
Iter-92000, train loss-1.7312, acc-0.5400, valid loss-1.6788, acc-0.5330, test loss-1.6798, acc-0.5200
Iter-92010, train loss-1.5890, acc-0.5800, valid loss-1.6788, acc-0.5330, test loss-1.6797, acc-0.5202
Iter-92020, train loss-1.6756, acc-0.5800, valid loss-1.6787, acc-0.5332, test loss-1.6797, acc-0.5203
Iter-92030, train loss-1.6655, acc-0.6000, valid loss-1.6787, acc-0.5332, test loss-1.6797, acc-0.5203
Iter-92040, train loss-1.7258, acc-0.4200, valid loss-1.6786, acc-0.5332, test loss-1.6796, acc-0.5202
Iter-92050, train loss-1.6595, acc-0.5200, valid loss-1.6786, acc-0.5332, test loss-1.6796, acc-0.5202
Iter-92060, train loss-1.7022, acc-0.5000, valid loss-1.6786, acc-0.5332, test loss-1.6796, acc-0.5202
Iter-92070, train loss-1.6760, acc-0.5800, valid loss-1.6785, acc-0.5332, test loss-1.6795, acc-0.5204
Iter-92080, train loss-1.7726, acc-0.4600, valid loss-1.6785, acc-0.5332, test loss-1.6795, acc-0.5203
Iter-92090, train loss-1.6907, acc-0.5000, valid loss-1.6785, acc-0.5330, test loss-1.6795, acc-0.5204
Iter-92100, train loss-1.6809, acc-0.6200, valid loss-1.6785, acc-0.5330, test loss-1.6794, acc-0.5204
Iter-92110, train loss-1.7348, acc-0.4000, valid loss-1.6784, acc-0.5330, test loss-1.6794, acc-0.5204
Iter-92120, train loss-1.6524, acc-0.6000, valid loss-1.6784, acc-0.5330, test loss-1.6794, acc-0.5205
Iter-92130, train loss-1.6023, acc-0.6200, valid loss-1.6784, acc-0.5332, test loss-1.6793, acc-0.5205
Iter-92140, train loss-1.7971, acc-0.4000, valid loss-1.6783, acc-0.5332, test loss-1.6793, acc-0.5205
Iter-92150, train loss-1.7046, acc-0.6200, valid loss-1.6783, acc-0.5332, test loss-1.6793, acc-0.5204
Iter-92160, train loss-1.6592, acc-0.5200, valid loss-1.6783, acc-0.5332, test loss-1.6792, acc-0.5205
Iter-92170, train loss-1.6290, acc-0.6000, valid loss-1.6782, acc-0.5332, test loss-1.6792, acc-0.5205
Iter-92180, train loss-1.6403, acc-0.5200, valid loss-1.6782, acc-0.5332, test loss-1.6792, acc-0.5205
Iter-92190, train loss-1.7535, acc-0.4400, valid loss-1.6782, acc-0.5328, test loss-1.6791, acc-0.5204
Iter-92200, train loss-1.6340, acc-0.6600, valid loss-1.6781, acc-0.5328, test loss-1.6791, acc-0.5204
Iter-92210, train loss-1.8195, acc-0.3400, valid loss-1.6781, acc-0.5326, test loss-1.6791, acc-0.5202
Iter-92220, train loss-1.7575, acc-0.4200, valid loss-1.6781, acc-0.5326, test loss-1.6791, acc-0.5201
Iter-92230, train loss-1.7033, acc-0.4400, valid loss-1.6780, acc-0.5326, test loss-1.6790, acc-0.5201
Iter-92240, train loss-1.5933, acc-0.5800, valid loss-1.6780, acc-0.5324, test loss-1.6790, acc-0.5202
Iter-92250, train loss-1.5726, acc-0.5600, valid loss-1.6780, acc-0.5322, test loss-1.6790, acc-0.5201
Iter-92260, train loss-1.7773, acc-0.4000, valid loss-1.6779, acc-0.5322, test loss-1.6789, acc-0.5202
Iter-92270, train loss-1.6835, acc-0.5600, valid loss-1.6779, acc-0.5322, test loss-1.6789, acc-0.5201
Iter-92280, train loss-1.7554, acc-0.5200, valid loss-1.6779, acc-0.5322, test loss-1.6789, acc-0.5201
Iter-92290, train loss-1.6928, acc-0.3400, valid loss-1.6778, acc-0.5322, test loss-1.6788, acc-0.5201
Iter-92300, train loss-1.7279, acc-0.5200, valid loss-1.6778, acc-0.5322, test loss-1.6788, acc-0.5201
Iter-92310, train loss-1.7508, acc-0.4600, valid loss-1.6778, acc-0.5322, test loss-1.6788, acc-0.5202
Iter-92320, train loss-1.6581, acc-0.5600, valid loss-1.6777, acc-0.5324, test loss-1.6787, acc-0.5204
Iter-92330, train loss-1.7412, acc-0.4600, valid loss-1.6777, acc-0.5324, test loss-1.6787, acc-0.5205
Iter-92340, train loss-1.6429, acc-0.5600, valid loss-1.6777, acc-0.5324, test loss-1.6787, acc-0.5205
Iter-92350, train loss-1.6913, acc-0.4400, valid loss-1.6776, acc-0.5328, test loss-1.6786, acc-0.5204
Iter-92360, train loss-1.5753, acc-0.5800, valid loss-1.6776, acc-0.5330, test loss-1.6786, acc-0.5203
Iter-92370, train loss-1.7066, acc-0.5000, valid loss-1.6776, acc-0.5330, test loss-1.6786, acc-0.5204
Iter-92380, train loss-1.7459, acc-0.4200, valid loss-1.6775, acc-0.5328, test loss-1.6785, acc-0.5204
Iter-92390, train loss-1.6848, acc-0.4800, valid loss-1.6775, acc-0.5330, test loss-1.6785, acc-0.5203
Iter-92400, train loss-1.7529, acc-0.4000, valid loss-1.6775, acc-0.5330, test loss-1.6785, acc-0.5204
Iter-92410, train loss-1.7459, acc-0.4800, valid loss-1.6774, acc-0.5328, test loss-1.6784, acc-0.5205
Iter-92420, train loss-1.7448, acc-0.4400, valid loss-1.6774, acc-0.5330, test loss-1.6784, acc-0.5203
Iter-92430, train loss-1.7355, acc-0.4800, valid loss-1.6774, acc-0.5330, test loss-1.6784, acc-0.5205
Iter-92440, train loss-1.8029, acc-0.3800, valid loss-1.6774, acc-0.5330, test loss-1.6783, acc-0.5205
Iter-92450, train loss-1.6356, acc-0.5400, valid loss-1.6773, acc-0.5330, test loss-1.6783, acc-0.5203
Iter-92460, train loss-1.7759, acc-0.4200, valid loss-1.6773, acc-0.5330, test loss-1.6783, acc-0.5205
Iter-92470, train loss-1.6917, acc-0.5800, valid loss-1.6773, acc-0.5328, test loss-1.6782, acc-0.5203
Iter-92480, train loss-1.6931, acc-0.4600, valid loss-1.6772, acc-0.5328, test loss-1.6782, acc-0.5202
Iter-92490, train loss-1.6714, acc-0.5600, valid loss-1.6772, acc-0.5330, test loss-1.6782, acc-0.5206
Iter-92500, train loss-1.6646, acc-0.5400, valid loss-1.6772, acc-0.5330, test loss-1.6781, acc-0.5206
Iter-92510, train loss-1.8140, acc-0.4400, valid loss-1.6771, acc-0.5330, test loss-1.6781, acc-0.5205
Iter-92520, train loss-1.7080, acc-0.5200, valid loss-1.6771, acc-0.5328, test loss-1.6781, acc-0.5205
Iter-92530, train loss-1.7741, acc-0.4200, valid loss-1.6771, acc-0.5328, test loss-1.6781, acc-0.5206
Iter-92540, train loss-1.6347, acc-0.5200, valid loss-1.6770, acc-0.5328, test loss-1.6780, acc-0.5206
Iter-92550, train loss-1.6315, acc-0.5800, valid loss-1.6770, acc-0.5328, test loss-1.6780, acc-0.5208
Iter-92560, train loss-1.6306, acc-0.6200, valid loss-1.6770, acc-0.5328, test loss-1.6780, acc-0.5206
Iter-92570, train loss-1.7121, acc-0.5200, valid loss-1.6769, acc-0.5330, test loss-1.6779, acc-0.5206
Iter-92580, train loss-1.6202, acc-0.6400, valid loss-1.6769, acc-0.5330, test loss-1.6779, acc-0.5205
Iter-92590, train loss-1.6794, acc-0.6000, valid loss-1.6769, acc-0.5330, test loss-1.6779, acc-0.5205
Iter-92600, train loss-1.7637, acc-0.4400, valid loss-1.6768, acc-0.5330, test loss-1.6778, acc-0.5205
Iter-92610, train loss-1.7383, acc-0.4600, valid loss-1.6768, acc-0.5330, test loss-1.6778, acc-0.5205
Iter-92620, train loss-1.7300, acc-0.5800, valid loss-1.6768, acc-0.5330, test loss-1.6778, acc-0.5205
Iter-92630, train loss-1.6578, acc-0.5800, valid loss-1.6767, acc-0.5324, test loss-1.6777, acc-0.5205
Iter-92640, train loss-1.7826, acc-0.5000, valid loss-1.6767, acc-0.5326, test loss-1.6777, acc-0.5205
Iter-92650, train loss-1.7858, acc-0.3800, valid loss-1.6767, acc-0.5328, test loss-1.6777, acc-0.5205
Iter-92660, train loss-1.7810, acc-0.4400, valid loss-1.6766, acc-0.5324, test loss-1.6776, acc-0.5206
Iter-92670, train loss-1.7584, acc-0.5800, valid loss-1.6766, acc-0.5328, test loss-1.6776, acc-0.5206
Iter-92680, train loss-1.8010, acc-0.4400, valid loss-1.6766, acc-0.5328, test loss-1.6776, acc-0.5206
Iter-92690, train loss-1.6473, acc-0.6000, valid loss-1.6765, acc-0.5326, test loss-1.6775, acc-0.5206
Iter-92700, train loss-1.7813, acc-0.5200, valid loss-1.6765, acc-0.5328, test loss-1.6775, acc-0.5206
Iter-92710, train loss-1.8089, acc-0.4400, valid loss-1.6765, acc-0.5328, test loss-1.6775, acc-0.5207
Iter-92720, train loss-1.6960, acc-0.5400, valid loss-1.6765, acc-0.5330, test loss-1.6774, acc-0.5208
Iter-92730, train loss-1.7010, acc-0.5200, valid loss-1.6764, acc-0.5332, test loss-1.6774, acc-0.5209
Iter-92740, train loss-1.6564, acc-0.4600, valid loss-1.6764, acc-0.5332, test loss-1.6774, acc-0.5209
Iter-92750, train loss-1.5605, acc-0.5600, valid loss-1.6763, acc-0.5326, test loss-1.6773, acc-0.5209
Iter-92760, train loss-1.7501, acc-0.4800, valid loss-1.6763, acc-0.5328, test loss-1.6773, acc-0.5208
Iter-92770, train loss-1.5851, acc-0.6200, valid loss-1.6763, acc-0.5328, test loss-1.6773, acc-0.5209
Iter-92780, train loss-1.6717, acc-0.5400, valid loss-1.6762, acc-0.5326, test loss-1.6772, acc-0.5210
Iter-92790, train loss-1.7229, acc-0.4800, valid loss-1.6762, acc-0.5326, test loss-1.6772, acc-0.5209
Iter-92800, train loss-1.6704, acc-0.5400, valid loss-1.6762, acc-0.5326, test loss-1.6772, acc-0.5209
Iter-92810, train loss-1.7138, acc-0.4400, valid loss-1.6762, acc-0.5324, test loss-1.6771, acc-0.5209
Iter-92820, train loss-1.7567, acc-0.4800, valid loss-1.6761, acc-0.5326, test loss-1.6771, acc-0.5208
Iter-92830, train loss-1.7706, acc-0.4600, valid loss-1.6761, acc-0.5328, test loss-1.6771, acc-0.5209
Iter-92840, train loss-1.7522, acc-0.4800, valid loss-1.6761, acc-0.5328, test loss-1.6770, acc-0.5209
Iter-92850, train loss-1.6702, acc-0.5000, valid loss-1.6760, acc-0.5328, test loss-1.6770, acc-0.5209
Iter-92860, train loss-1.6492, acc-0.5800, valid loss-1.6760, acc-0.5328, test loss-1.6770, acc-0.5209
Iter-92870, train loss-1.7222, acc-0.4800, valid loss-1.6760, acc-0.5330, test loss-1.6769, acc-0.5209
Iter-92880, train loss-1.6264, acc-0.5400, valid loss-1.6759, acc-0.5330, test loss-1.6769, acc-0.5209
Iter-92890, train loss-1.6639, acc-0.6200, valid loss-1.6759, acc-0.5330, test loss-1.6769, acc-0.5209
Iter-92900, train loss-1.6082, acc-0.5600, valid loss-1.6759, acc-0.5330, test loss-1.6768, acc-0.5209
Iter-92910, train loss-1.7924, acc-0.5800, valid loss-1.6758, acc-0.5328, test loss-1.6768, acc-0.5209
Iter-92920, train loss-1.6555, acc-0.6400, valid loss-1.6758, acc-0.5326, test loss-1.6768, acc-0.5209
Iter-92930, train loss-1.6906, acc-0.4600, valid loss-1.6758, acc-0.5326, test loss-1.6767, acc-0.5209
Iter-92940, train loss-1.6409, acc-0.5400, valid loss-1.6757, acc-0.5326, test loss-1.6767, acc-0.5209
Iter-92950, train loss-1.7324, acc-0.5000, valid loss-1.6757, acc-0.5326, test loss-1.6767, acc-0.5210
Iter-92960, train loss-1.7453, acc-0.5400, valid loss-1.6757, acc-0.5326, test loss-1.6767, acc-0.5209
Iter-92970, train loss-1.7513, acc-0.4600, valid loss-1.6756, acc-0.5326, test loss-1.6766, acc-0.5209
Iter-92980, train loss-1.7031, acc-0.4800, valid loss-1.6756, acc-0.5326, test loss-1.6766, acc-0.5209
Iter-92990, train loss-1.8666, acc-0.4200, valid loss-1.6756, acc-0.5326, test loss-1.6766, acc-0.5209
Iter-93000, train loss-1.7707, acc-0.4800, valid loss-1.6755, acc-0.5324, test loss-1.6765, acc-0.5209
Iter-93010, train loss-1.5989, acc-0.5400, valid loss-1.6755, acc-0.5324, test loss-1.6765, acc-0.5210
Iter-93020, train loss-1.7641, acc-0.4400, valid loss-1.6755, acc-0.5324, test loss-1.6765, acc-0.5210
Iter-93030, train loss-1.6823, acc-0.5600, valid loss-1.6754, acc-0.5324, test loss-1.6764, acc-0.5209
Iter-93040, train loss-1.6117, acc-0.5800, valid loss-1.6754, acc-0.5324, test loss-1.6764, acc-0.5209
Iter-93050, train loss-1.7304, acc-0.4800, valid loss-1.6754, acc-0.5324, test loss-1.6764, acc-0.5209
Iter-93060, train loss-1.6343, acc-0.4800, valid loss-1.6753, acc-0.5322, test loss-1.6763, acc-0.5209
Iter-93070, train loss-1.7825, acc-0.4000, valid loss-1.6753, acc-0.5324, test loss-1.6763, acc-0.5210
Iter-93080, train loss-1.8185, acc-0.4200, valid loss-1.6753, acc-0.5324, test loss-1.6763, acc-0.5210
Iter-93090, train loss-1.7005, acc-0.4000, valid loss-1.6752, acc-0.5322, test loss-1.6762, acc-0.5210
Iter-93100, train loss-1.7028, acc-0.5000, valid loss-1.6752, acc-0.5326, test loss-1.6762, acc-0.5211
Iter-93110, train loss-1.6773, acc-0.5200, valid loss-1.6752, acc-0.5324, test loss-1.6762, acc-0.5210
Iter-93120, train loss-1.6761, acc-0.5000, valid loss-1.6751, acc-0.5322, test loss-1.6761, acc-0.5211
Iter-93130, train loss-1.6940, acc-0.5600, valid loss-1.6751, acc-0.5324, test loss-1.6761, acc-0.5212
Iter-93140, train loss-1.6209, acc-0.4800, valid loss-1.6751, acc-0.5324, test loss-1.6761, acc-0.5211
Iter-93150, train loss-1.7274, acc-0.4400, valid loss-1.6750, acc-0.5322, test loss-1.6760, acc-0.5212
Iter-93160, train loss-1.7113, acc-0.4000, valid loss-1.6750, acc-0.5322, test loss-1.6760, acc-0.5214
Iter-93170, train loss-1.6932, acc-0.5200, valid loss-1.6750, acc-0.5322, test loss-1.6760, acc-0.5211
Iter-93180, train loss-1.7542, acc-0.4800, valid loss-1.6749, acc-0.5320, test loss-1.6759, acc-0.5211
Iter-93190, train loss-1.7308, acc-0.5000, valid loss-1.6749, acc-0.5322, test loss-1.6759, acc-0.5212
Iter-93200, train loss-1.7326, acc-0.4600, valid loss-1.6749, acc-0.5326, test loss-1.6759, acc-0.5211
Iter-93210, train loss-1.7005, acc-0.5000, valid loss-1.6748, acc-0.5326, test loss-1.6758, acc-0.5210
Iter-93220, train loss-1.7776, acc-0.4400, valid loss-1.6748, acc-0.5326, test loss-1.6758, acc-0.5211
Iter-93230, train loss-1.5833, acc-0.6600, valid loss-1.6748, acc-0.5326, test loss-1.6758, acc-0.5212
Iter-93240, train loss-1.7132, acc-0.4800, valid loss-1.6747, acc-0.5326, test loss-1.6758, acc-0.5211
Iter-93250, train loss-1.6217, acc-0.5200, valid loss-1.6747, acc-0.5326, test loss-1.6757, acc-0.5212
Iter-93260, train loss-1.7537, acc-0.5000, valid loss-1.6747, acc-0.5326, test loss-1.6757, acc-0.5211
Iter-93270, train loss-1.8418, acc-0.4400, valid loss-1.6747, acc-0.5326, test loss-1.6757, acc-0.5212
Iter-93280, train loss-1.6547, acc-0.5000, valid loss-1.6746, acc-0.5326, test loss-1.6756, acc-0.5211
Iter-93290, train loss-1.6568, acc-0.4400, valid loss-1.6746, acc-0.5326, test loss-1.6756, acc-0.5211
Iter-93300, train loss-1.6287, acc-0.5600, valid loss-1.6746, acc-0.5326, test loss-1.6756, acc-0.5211
Iter-93310, train loss-1.6545, acc-0.5000, valid loss-1.6745, acc-0.5326, test loss-1.6755, acc-0.5212
Iter-93320, train loss-1.7440, acc-0.4800, valid loss-1.6745, acc-0.5326, test loss-1.6755, acc-0.5212
Iter-93330, train loss-1.5739, acc-0.5000, valid loss-1.6745, acc-0.5326, test loss-1.6755, acc-0.5212
Iter-93340, train loss-1.7757, acc-0.4000, valid loss-1.6744, acc-0.5328, test loss-1.6754, acc-0.5212
Iter-93350, train loss-1.8254, acc-0.4600, valid loss-1.6744, acc-0.5328, test loss-1.6754, acc-0.5211
Iter-93360, train loss-1.6762, acc-0.6000, valid loss-1.6744, acc-0.5326, test loss-1.6754, acc-0.5211
Iter-93370, train loss-1.5917, acc-0.5800, valid loss-1.6743, acc-0.5326, test loss-1.6753, acc-0.5210
Iter-93380, train loss-1.7171, acc-0.5200, valid loss-1.6743, acc-0.5326, test loss-1.6753, acc-0.5211
Iter-93390, train loss-1.6887, acc-0.4400, valid loss-1.6743, acc-0.5326, test loss-1.6753, acc-0.5214
Iter-93400, train loss-1.6238, acc-0.5000, valid loss-1.6742, acc-0.5326, test loss-1.6752, acc-0.5213
Iter-93410, train loss-1.6793, acc-0.5200, valid loss-1.6742, acc-0.5326, test loss-1.6752, acc-0.5213
Iter-93420, train loss-1.7217, acc-0.4800, valid loss-1.6742, acc-0.5326, test loss-1.6752, acc-0.5213
Iter-93430, train loss-1.7595, acc-0.4000, valid loss-1.6741, acc-0.5326, test loss-1.6751, acc-0.5214
Iter-93440, train loss-1.5994, acc-0.5000, valid loss-1.6741, acc-0.5328, test loss-1.6751, acc-0.5213
Iter-93450, train loss-1.7316, acc-0.5000, valid loss-1.6741, acc-0.5326, test loss-1.6751, acc-0.5212
Iter-93460, train loss-1.7669, acc-0.4200, valid loss-1.6740, acc-0.5328, test loss-1.6750, acc-0.5212
Iter-93470, train loss-1.6426, acc-0.5600, valid loss-1.6740, acc-0.5326, test loss-1.6750, acc-0.5212
Iter-93480, train loss-1.7498, acc-0.5200, valid loss-1.6740, acc-0.5326, test loss-1.6750, acc-0.5211
Iter-93490, train loss-1.5965, acc-0.6000, valid loss-1.6739, acc-0.5326, test loss-1.6749, acc-0.5211
Iter-93500, train loss-1.5936, acc-0.6000, valid loss-1.6739, acc-0.5326, test loss-1.6749, acc-0.5211
Iter-93510, train loss-1.7310, acc-0.5400, valid loss-1.6739, acc-0.5326, test loss-1.6749, acc-0.5212
Iter-93520, train loss-1.6383, acc-0.6400, valid loss-1.6738, acc-0.5326, test loss-1.6748, acc-0.5212
Iter-93530, train loss-1.6462, acc-0.5400, valid loss-1.6738, acc-0.5330, test loss-1.6748, acc-0.5212
Iter-93540, train loss-1.6613, acc-0.5000, valid loss-1.6738, acc-0.5330, test loss-1.6748, acc-0.5213
Iter-93550, train loss-1.6751, acc-0.5000, valid loss-1.6737, acc-0.5330, test loss-1.6747, acc-0.5211
Iter-93560, train loss-1.7006, acc-0.4800, valid loss-1.6737, acc-0.5330, test loss-1.6747, acc-0.5211
Iter-93570, train loss-1.7440, acc-0.6600, valid loss-1.6737, acc-0.5330, test loss-1.6747, acc-0.5211
Iter-93580, train loss-1.6535, acc-0.5200, valid loss-1.6736, acc-0.5330, test loss-1.6746, acc-0.5211
Iter-93590, train loss-1.7082, acc-0.4600, valid loss-1.6736, acc-0.5330, test loss-1.6746, acc-0.5212
Iter-93600, train loss-1.6493, acc-0.5600, valid loss-1.6736, acc-0.5332, test loss-1.6746, acc-0.5211
Iter-93610, train loss-1.7349, acc-0.4600, valid loss-1.6735, acc-0.5330, test loss-1.6745, acc-0.5211
Iter-93620, train loss-1.7663, acc-0.4800, valid loss-1.6735, acc-0.5330, test loss-1.6745, acc-0.5210
Iter-93630, train loss-1.6290, acc-0.6000, valid loss-1.6735, acc-0.5330, test loss-1.6745, acc-0.5211
Iter-93640, train loss-1.7020, acc-0.5200, valid loss-1.6734, acc-0.5330, test loss-1.6745, acc-0.5210
Iter-93650, train loss-1.7348, acc-0.4400, valid loss-1.6734, acc-0.5328, test loss-1.6744, acc-0.5211
Iter-93660, train loss-1.6270, acc-0.6000, valid loss-1.6734, acc-0.5330, test loss-1.6744, acc-0.5212
Iter-93670, train loss-1.6713, acc-0.5600, valid loss-1.6734, acc-0.5330, test loss-1.6744, acc-0.5213
Iter-93680, train loss-1.6142, acc-0.4800, valid loss-1.6733, acc-0.5332, test loss-1.6743, acc-0.5210
Iter-93690, train loss-1.6746, acc-0.5000, valid loss-1.6733, acc-0.5332, test loss-1.6743, acc-0.5210
Iter-93700, train loss-1.5951, acc-0.6000, valid loss-1.6732, acc-0.5334, test loss-1.6743, acc-0.5211
Iter-93710, train loss-1.5875, acc-0.6000, valid loss-1.6732, acc-0.5334, test loss-1.6742, acc-0.5211
Iter-93720, train loss-1.7249, acc-0.5000, valid loss-1.6732, acc-0.5334, test loss-1.6742, acc-0.5211
Iter-93730, train loss-1.6854, acc-0.5800, valid loss-1.6732, acc-0.5336, test loss-1.6742, acc-0.5210
Iter-93740, train loss-1.6789, acc-0.5000, valid loss-1.6731, acc-0.5336, test loss-1.6741, acc-0.5211
Iter-93750, train loss-1.6950, acc-0.4600, valid loss-1.6731, acc-0.5338, test loss-1.6741, acc-0.5213
Iter-93760, train loss-1.6418, acc-0.6200, valid loss-1.6731, acc-0.5336, test loss-1.6741, acc-0.5212
Iter-93770, train loss-1.7431, acc-0.4600, valid loss-1.6730, acc-0.5336, test loss-1.6740, acc-0.5212
Iter-93780, train loss-1.7421, acc-0.5200, valid loss-1.6730, acc-0.5336, test loss-1.6740, acc-0.5212
Iter-93790, train loss-1.6898, acc-0.5200, valid loss-1.6730, acc-0.5336, test loss-1.6740, acc-0.5213
Iter-93800, train loss-1.7088, acc-0.4600, valid loss-1.6729, acc-0.5336, test loss-1.6739, acc-0.5212
Iter-93810, train loss-1.6540, acc-0.4600, valid loss-1.6729, acc-0.5336, test loss-1.6739, acc-0.5214
Iter-93820, train loss-1.5950, acc-0.6600, valid loss-1.6729, acc-0.5334, test loss-1.6739, acc-0.5214
Iter-93830, train loss-1.6563, acc-0.6400, valid loss-1.6728, acc-0.5338, test loss-1.6738, acc-0.5214
Iter-93840, train loss-1.5747, acc-0.6600, valid loss-1.6728, acc-0.5336, test loss-1.6738, acc-0.5217
Iter-93850, train loss-1.6556, acc-0.5200, valid loss-1.6728, acc-0.5334, test loss-1.6738, acc-0.5216
Iter-93860, train loss-1.7296, acc-0.4800, valid loss-1.6727, acc-0.5334, test loss-1.6737, acc-0.5215
Iter-93870, train loss-1.7301, acc-0.5400, valid loss-1.6727, acc-0.5334, test loss-1.6737, acc-0.5217
Iter-93880, train loss-1.6556, acc-0.5000, valid loss-1.6727, acc-0.5334, test loss-1.6737, acc-0.5218
Iter-93890, train loss-1.6197, acc-0.6200, valid loss-1.6726, acc-0.5334, test loss-1.6737, acc-0.5218
Iter-93900, train loss-1.7258, acc-0.5000, valid loss-1.6726, acc-0.5336, test loss-1.6736, acc-0.5220
Iter-93910, train loss-1.6079, acc-0.6000, valid loss-1.6726, acc-0.5332, test loss-1.6736, acc-0.5221
Iter-93920, train loss-1.7272, acc-0.4800, valid loss-1.6725, acc-0.5334, test loss-1.6736, acc-0.5220
Iter-93930, train loss-1.6962, acc-0.5600, valid loss-1.6725, acc-0.5334, test loss-1.6735, acc-0.5220
Iter-93940, train loss-1.6863, acc-0.6000, valid loss-1.6725, acc-0.5334, test loss-1.6735, acc-0.5221
Iter-93950, train loss-1.6479, acc-0.4800, valid loss-1.6724, acc-0.5332, test loss-1.6735, acc-0.5222
Iter-93960, train loss-1.7655, acc-0.5800, valid loss-1.6724, acc-0.5334, test loss-1.6734, acc-0.5220
Iter-93970, train loss-1.6015, acc-0.5000, valid loss-1.6724, acc-0.5334, test loss-1.6734, acc-0.5220
Iter-93980, train loss-1.7477, acc-0.5200, valid loss-1.6723, acc-0.5334, test loss-1.6734, acc-0.5220
Iter-93990, train loss-1.7249, acc-0.5200, valid loss-1.6723, acc-0.5334, test loss-1.6733, acc-0.5220
Iter-94000, train loss-1.6820, acc-0.5200, valid loss-1.6723, acc-0.5334, test loss-1.6733, acc-0.5221
Iter-94010, train loss-1.6151, acc-0.5600, valid loss-1.6722, acc-0.5334, test loss-1.6733, acc-0.5222
Iter-94020, train loss-1.6821, acc-0.5400, valid loss-1.6722, acc-0.5334, test loss-1.6732, acc-0.5221
Iter-94030, train loss-1.7454, acc-0.5400, valid loss-1.6722, acc-0.5332, test loss-1.6732, acc-0.5221
Iter-94040, train loss-1.6419, acc-0.5200, valid loss-1.6722, acc-0.5332, test loss-1.6732, acc-0.5222
Iter-94050, train loss-1.6925, acc-0.4800, valid loss-1.6721, acc-0.5330, test loss-1.6731, acc-0.5221
Iter-94060, train loss-1.8157, acc-0.4200, valid loss-1.6721, acc-0.5334, test loss-1.6731, acc-0.5221
Iter-94070, train loss-1.6724, acc-0.5000, valid loss-1.6721, acc-0.5334, test loss-1.6731, acc-0.5221
Iter-94080, train loss-1.7860, acc-0.4600, valid loss-1.6720, acc-0.5336, test loss-1.6730, acc-0.5221
Iter-94090, train loss-1.6571, acc-0.5000, valid loss-1.6720, acc-0.5338, test loss-1.6730, acc-0.5222
Iter-94100, train loss-1.6504, acc-0.5600, valid loss-1.6720, acc-0.5338, test loss-1.6730, acc-0.5221
Iter-94110, train loss-1.7113, acc-0.5600, valid loss-1.6719, acc-0.5338, test loss-1.6729, acc-0.5223
Iter-94120, train loss-1.7148, acc-0.5200, valid loss-1.6719, acc-0.5336, test loss-1.6729, acc-0.5225
Iter-94130, train loss-1.7737, acc-0.4400, valid loss-1.6719, acc-0.5336, test loss-1.6729, acc-0.5225
Iter-94140, train loss-1.8585, acc-0.3800, valid loss-1.6718, acc-0.5336, test loss-1.6728, acc-0.5224
Iter-94150, train loss-1.6231, acc-0.4800, valid loss-1.6718, acc-0.5336, test loss-1.6728, acc-0.5223
Iter-94160, train loss-1.6531, acc-0.5800, valid loss-1.6718, acc-0.5338, test loss-1.6728, acc-0.5223
Iter-94170, train loss-1.6475, acc-0.6000, valid loss-1.6717, acc-0.5338, test loss-1.6728, acc-0.5221
Iter-94180, train loss-1.7305, acc-0.4600, valid loss-1.6717, acc-0.5338, test loss-1.6727, acc-0.5221
Iter-94190, train loss-1.6070, acc-0.5800, valid loss-1.6717, acc-0.5336, test loss-1.6727, acc-0.5222
Iter-94200, train loss-1.8015, acc-0.4200, valid loss-1.6716, acc-0.5338, test loss-1.6727, acc-0.5221
Iter-94210, train loss-1.7545, acc-0.5000, valid loss-1.6716, acc-0.5340, test loss-1.6726, acc-0.5223
Iter-94220, train loss-1.6394, acc-0.5400, valid loss-1.6716, acc-0.5336, test loss-1.6726, acc-0.5223
Iter-94230, train loss-1.6306, acc-0.5200, valid loss-1.6715, acc-0.5336, test loss-1.6726, acc-0.5223
Iter-94240, train loss-1.6271, acc-0.6000, valid loss-1.6715, acc-0.5336, test loss-1.6725, acc-0.5223
Iter-94250, train loss-1.6183, acc-0.4200, valid loss-1.6715, acc-0.5338, test loss-1.6725, acc-0.5223
Iter-94260, train loss-1.6846, acc-0.6000, valid loss-1.6714, acc-0.5336, test loss-1.6725, acc-0.5223
Iter-94270, train loss-1.6334, acc-0.6400, valid loss-1.6714, acc-0.5336, test loss-1.6724, acc-0.5223
Iter-94280, train loss-1.6235, acc-0.5400, valid loss-1.6714, acc-0.5336, test loss-1.6724, acc-0.5222
Iter-94290, train loss-1.6413, acc-0.5400, valid loss-1.6713, acc-0.5336, test loss-1.6724, acc-0.5223
Iter-94300, train loss-1.7243, acc-0.4800, valid loss-1.6713, acc-0.5334, test loss-1.6723, acc-0.5223
Iter-94310, train loss-1.7720, acc-0.4200, valid loss-1.6713, acc-0.5338, test loss-1.6723, acc-0.5223
Iter-94320, train loss-1.6379, acc-0.6000, valid loss-1.6712, acc-0.5336, test loss-1.6723, acc-0.5222
Iter-94330, train loss-1.6390, acc-0.5400, valid loss-1.6712, acc-0.5334, test loss-1.6722, acc-0.5222
Iter-94340, train loss-1.6064, acc-0.5800, valid loss-1.6712, acc-0.5336, test loss-1.6722, acc-0.5222
Iter-94350, train loss-1.6092, acc-0.5000, valid loss-1.6711, acc-0.5334, test loss-1.6722, acc-0.5222
Iter-94360, train loss-1.7655, acc-0.5200, valid loss-1.6711, acc-0.5334, test loss-1.6721, acc-0.5223
Iter-94370, train loss-1.7437, acc-0.4800, valid loss-1.6711, acc-0.5336, test loss-1.6721, acc-0.5221
Iter-94380, train loss-1.6748, acc-0.5600, valid loss-1.6711, acc-0.5336, test loss-1.6721, acc-0.5221
Iter-94390, train loss-1.5584, acc-0.6400, valid loss-1.6710, acc-0.5338, test loss-1.6721, acc-0.5221
Iter-94400, train loss-1.7341, acc-0.5200, valid loss-1.6710, acc-0.5336, test loss-1.6720, acc-0.5221
Iter-94410, train loss-1.7446, acc-0.5200, valid loss-1.6710, acc-0.5336, test loss-1.6720, acc-0.5221
Iter-94420, train loss-1.6462, acc-0.5800, valid loss-1.6709, acc-0.5338, test loss-1.6720, acc-0.5221
Iter-94430, train loss-1.7666, acc-0.5000, valid loss-1.6709, acc-0.5336, test loss-1.6719, acc-0.5221
Iter-94440, train loss-1.5545, acc-0.5800, valid loss-1.6709, acc-0.5336, test loss-1.6719, acc-0.5222
Iter-94450, train loss-1.6663, acc-0.5000, valid loss-1.6708, acc-0.5336, test loss-1.6719, acc-0.5224
Iter-94460, train loss-1.6754, acc-0.5400, valid loss-1.6708, acc-0.5336, test loss-1.6718, acc-0.5224
Iter-94470, train loss-1.6715, acc-0.4600, valid loss-1.6708, acc-0.5336, test loss-1.6718, acc-0.5225
Iter-94480, train loss-1.6390, acc-0.5800, valid loss-1.6707, acc-0.5336, test loss-1.6718, acc-0.5225
Iter-94490, train loss-1.7662, acc-0.4800, valid loss-1.6707, acc-0.5336, test loss-1.6717, acc-0.5224
Iter-94500, train loss-1.7550, acc-0.3600, valid loss-1.6707, acc-0.5338, test loss-1.6717, acc-0.5226
Iter-94510, train loss-1.5960, acc-0.5800, valid loss-1.6706, acc-0.5338, test loss-1.6717, acc-0.5225
Iter-94520, train loss-1.6570, acc-0.5400, valid loss-1.6706, acc-0.5336, test loss-1.6716, acc-0.5224
Iter-94530, train loss-1.6020, acc-0.5400, valid loss-1.6706, acc-0.5338, test loss-1.6716, acc-0.5224
Iter-94540, train loss-1.7446, acc-0.5200, valid loss-1.6705, acc-0.5338, test loss-1.6716, acc-0.5226
Iter-94550, train loss-1.7386, acc-0.4000, valid loss-1.6705, acc-0.5338, test loss-1.6716, acc-0.5227
Iter-94560, train loss-1.7340, acc-0.5000, valid loss-1.6705, acc-0.5336, test loss-1.6715, acc-0.5222
Iter-94570, train loss-1.8281, acc-0.4600, valid loss-1.6705, acc-0.5338, test loss-1.6715, acc-0.5225
Iter-94580, train loss-1.6191, acc-0.5600, valid loss-1.6704, acc-0.5340, test loss-1.6715, acc-0.5224
Iter-94590, train loss-1.6892, acc-0.4400, valid loss-1.6704, acc-0.5338, test loss-1.6714, acc-0.5226
Iter-94600, train loss-1.7399, acc-0.4800, valid loss-1.6704, acc-0.5338, test loss-1.6714, acc-0.5225
Iter-94610, train loss-1.7449, acc-0.4200, valid loss-1.6703, acc-0.5338, test loss-1.6714, acc-0.5226
Iter-94620, train loss-1.6839, acc-0.4200, valid loss-1.6703, acc-0.5338, test loss-1.6713, acc-0.5226
Iter-94630, train loss-1.7517, acc-0.5000, valid loss-1.6703, acc-0.5338, test loss-1.6713, acc-0.5226
Iter-94640, train loss-1.7344, acc-0.5400, valid loss-1.6702, acc-0.5336, test loss-1.6713, acc-0.5226
Iter-94650, train loss-1.6461, acc-0.5400, valid loss-1.6702, acc-0.5340, test loss-1.6712, acc-0.5226
Iter-94660, train loss-1.8350, acc-0.4400, valid loss-1.6702, acc-0.5338, test loss-1.6712, acc-0.5226
Iter-94670, train loss-1.6617, acc-0.5200, valid loss-1.6701, acc-0.5336, test loss-1.6712, acc-0.5227
Iter-94680, train loss-1.5798, acc-0.5600, valid loss-1.6701, acc-0.5338, test loss-1.6711, acc-0.5226
Iter-94690, train loss-1.6332, acc-0.5200, valid loss-1.6701, acc-0.5338, test loss-1.6711, acc-0.5225
Iter-94700, train loss-1.7651, acc-0.4400, valid loss-1.6700, acc-0.5340, test loss-1.6711, acc-0.5227
Iter-94710, train loss-1.6925, acc-0.5600, valid loss-1.6700, acc-0.5340, test loss-1.6710, acc-0.5226
Iter-94720, train loss-1.5856, acc-0.5800, valid loss-1.6700, acc-0.5340, test loss-1.6710, acc-0.5226
Iter-94730, train loss-1.5204, acc-0.6400, valid loss-1.6699, acc-0.5340, test loss-1.6710, acc-0.5226
Iter-94740, train loss-1.5807, acc-0.5800, valid loss-1.6699, acc-0.5338, test loss-1.6709, acc-0.5226
Iter-94750, train loss-1.6123, acc-0.5600, valid loss-1.6699, acc-0.5340, test loss-1.6709, acc-0.5225
Iter-94760, train loss-1.7383, acc-0.5000, valid loss-1.6698, acc-0.5342, test loss-1.6709, acc-0.5226
Iter-94770, train loss-1.6950, acc-0.5600, valid loss-1.6698, acc-0.5340, test loss-1.6708, acc-0.5225
Iter-94780, train loss-1.7394, acc-0.4200, valid loss-1.6698, acc-0.5340, test loss-1.6708, acc-0.5226
Iter-94790, train loss-1.8133, acc-0.5000, valid loss-1.6697, acc-0.5340, test loss-1.6708, acc-0.5225
Iter-94800, train loss-1.6883, acc-0.4800, valid loss-1.6697, acc-0.5340, test loss-1.6708, acc-0.5226
Iter-94810, train loss-1.7266, acc-0.4800, valid loss-1.6697, acc-0.5340, test loss-1.6707, acc-0.5226
Iter-94820, train loss-1.5825, acc-0.5800, valid loss-1.6697, acc-0.5338, test loss-1.6707, acc-0.5225
Iter-94830, train loss-1.6780, acc-0.6200, valid loss-1.6696, acc-0.5336, test loss-1.6707, acc-0.5226
Iter-94840, train loss-1.7141, acc-0.4800, valid loss-1.6696, acc-0.5336, test loss-1.6706, acc-0.5227
Iter-94850, train loss-1.8167, acc-0.4800, valid loss-1.6696, acc-0.5336, test loss-1.6706, acc-0.5227
Iter-94860, train loss-1.6170, acc-0.6200, valid loss-1.6695, acc-0.5336, test loss-1.6706, acc-0.5227
Iter-94870, train loss-1.7337, acc-0.4600, valid loss-1.6695, acc-0.5336, test loss-1.6705, acc-0.5226
Iter-94880, train loss-1.6514, acc-0.5400, valid loss-1.6695, acc-0.5336, test loss-1.6705, acc-0.5227
Iter-94890, train loss-1.7949, acc-0.5000, valid loss-1.6694, acc-0.5338, test loss-1.6705, acc-0.5227
Iter-94900, train loss-1.7964, acc-0.5000, valid loss-1.6694, acc-0.5338, test loss-1.6704, acc-0.5227
Iter-94910, train loss-1.6386, acc-0.5800, valid loss-1.6694, acc-0.5336, test loss-1.6704, acc-0.5228
Iter-94920, train loss-1.6829, acc-0.5800, valid loss-1.6693, acc-0.5338, test loss-1.6704, acc-0.5226
Iter-94930, train loss-1.6160, acc-0.5200, valid loss-1.6693, acc-0.5338, test loss-1.6703, acc-0.5227
Iter-94940, train loss-1.6328, acc-0.5600, valid loss-1.6693, acc-0.5340, test loss-1.6703, acc-0.5226
Iter-94950, train loss-1.6348, acc-0.5600, valid loss-1.6693, acc-0.5340, test loss-1.6703, acc-0.5229
Iter-94960, train loss-1.5866, acc-0.5000, valid loss-1.6692, acc-0.5340, test loss-1.6702, acc-0.5229
Iter-94970, train loss-1.7481, acc-0.4400, valid loss-1.6692, acc-0.5340, test loss-1.6702, acc-0.5229
Iter-94980, train loss-1.6681, acc-0.5400, valid loss-1.6692, acc-0.5342, test loss-1.6702, acc-0.5230
Iter-94990, train loss-1.8501, acc-0.3600, valid loss-1.6691, acc-0.5342, test loss-1.6702, acc-0.5230
Iter-95000, train loss-1.6892, acc-0.5400, valid loss-1.6691, acc-0.5340, test loss-1.6701, acc-0.5229
Iter-95010, train loss-1.6579, acc-0.5800, valid loss-1.6691, acc-0.5342, test loss-1.6701, acc-0.5229
Iter-95020, train loss-1.6188, acc-0.5600, valid loss-1.6690, acc-0.5342, test loss-1.6701, acc-0.5230
Iter-95030, train loss-1.6334, acc-0.5800, valid loss-1.6690, acc-0.5340, test loss-1.6700, acc-0.5230
Iter-95040, train loss-1.7236, acc-0.4400, valid loss-1.6690, acc-0.5340, test loss-1.6700, acc-0.5231
Iter-95050, train loss-1.6491, acc-0.6400, valid loss-1.6689, acc-0.5340, test loss-1.6700, acc-0.5231
Iter-95060, train loss-1.7230, acc-0.4400, valid loss-1.6689, acc-0.5342, test loss-1.6699, acc-0.5232
Iter-95070, train loss-1.8450, acc-0.4000, valid loss-1.6689, acc-0.5342, test loss-1.6699, acc-0.5231
Iter-95080, train loss-1.6419, acc-0.5600, valid loss-1.6688, acc-0.5342, test loss-1.6699, acc-0.5230
Iter-95090, train loss-1.7613, acc-0.5600, valid loss-1.6688, acc-0.5342, test loss-1.6698, acc-0.5231
Iter-95100, train loss-1.6859, acc-0.5600, valid loss-1.6688, acc-0.5340, test loss-1.6698, acc-0.5231
Iter-95110, train loss-1.6037, acc-0.6400, valid loss-1.6687, acc-0.5340, test loss-1.6698, acc-0.5231
Iter-95120, train loss-1.6045, acc-0.4800, valid loss-1.6687, acc-0.5340, test loss-1.6697, acc-0.5231
Iter-95130, train loss-1.6901, acc-0.5400, valid loss-1.6687, acc-0.5340, test loss-1.6697, acc-0.5231
Iter-95140, train loss-1.6697, acc-0.5000, valid loss-1.6686, acc-0.5342, test loss-1.6697, acc-0.5232
Iter-95150, train loss-1.6579, acc-0.6000, valid loss-1.6686, acc-0.5344, test loss-1.6696, acc-0.5232
Iter-95160, train loss-1.7232, acc-0.5000, valid loss-1.6686, acc-0.5342, test loss-1.6696, acc-0.5232
Iter-95170, train loss-1.5973, acc-0.6600, valid loss-1.6686, acc-0.5342, test loss-1.6696, acc-0.5232
Iter-95180, train loss-1.7168, acc-0.5200, valid loss-1.6685, acc-0.5342, test loss-1.6695, acc-0.5232
Iter-95190, train loss-1.4816, acc-0.5400, valid loss-1.6685, acc-0.5342, test loss-1.6695, acc-0.5232
Iter-95200, train loss-1.5361, acc-0.6400, valid loss-1.6685, acc-0.5342, test loss-1.6695, acc-0.5235
Iter-95210, train loss-1.6594, acc-0.4800, valid loss-1.6684, acc-0.5340, test loss-1.6694, acc-0.5234
Iter-95220, train loss-1.5113, acc-0.6200, valid loss-1.6684, acc-0.5340, test loss-1.6694, acc-0.5233
Iter-95230, train loss-1.7042, acc-0.5200, valid loss-1.6684, acc-0.5338, test loss-1.6694, acc-0.5235
Iter-95240, train loss-1.7927, acc-0.4600, valid loss-1.6683, acc-0.5340, test loss-1.6693, acc-0.5234
Iter-95250, train loss-1.6059, acc-0.5200, valid loss-1.6683, acc-0.5340, test loss-1.6693, acc-0.5235
Iter-95260, train loss-1.7087, acc-0.4400, valid loss-1.6683, acc-0.5340, test loss-1.6693, acc-0.5235
Iter-95270, train loss-1.7234, acc-0.4800, valid loss-1.6682, acc-0.5340, test loss-1.6693, acc-0.5233
Iter-95280, train loss-1.7462, acc-0.6000, valid loss-1.6682, acc-0.5338, test loss-1.6692, acc-0.5233
Iter-95290, train loss-1.7604, acc-0.4400, valid loss-1.6682, acc-0.5338, test loss-1.6692, acc-0.5233
Iter-95300, train loss-1.7082, acc-0.5600, valid loss-1.6681, acc-0.5338, test loss-1.6692, acc-0.5232
Iter-95310, train loss-1.7676, acc-0.4800, valid loss-1.6681, acc-0.5340, test loss-1.6691, acc-0.5234
Iter-95320, train loss-1.7609, acc-0.5200, valid loss-1.6681, acc-0.5342, test loss-1.6691, acc-0.5235
Iter-95330, train loss-1.7265, acc-0.5400, valid loss-1.6680, acc-0.5344, test loss-1.6691, acc-0.5233
Iter-95340, train loss-1.7059, acc-0.5600, valid loss-1.6680, acc-0.5342, test loss-1.6690, acc-0.5233
Iter-95350, train loss-1.5371, acc-0.6600, valid loss-1.6680, acc-0.5342, test loss-1.6690, acc-0.5234
Iter-95360, train loss-1.6400, acc-0.6200, valid loss-1.6679, acc-0.5342, test loss-1.6690, acc-0.5234
Iter-95370, train loss-1.6972, acc-0.5000, valid loss-1.6679, acc-0.5342, test loss-1.6689, acc-0.5234
Iter-95380, train loss-1.6652, acc-0.5200, valid loss-1.6679, acc-0.5342, test loss-1.6689, acc-0.5234
Iter-95390, train loss-1.6763, acc-0.5400, valid loss-1.6679, acc-0.5342, test loss-1.6689, acc-0.5234
Iter-95400, train loss-1.6793, acc-0.5600, valid loss-1.6678, acc-0.5342, test loss-1.6689, acc-0.5234
Iter-95410, train loss-1.7600, acc-0.4600, valid loss-1.6678, acc-0.5342, test loss-1.6688, acc-0.5234
Iter-95420, train loss-1.8464, acc-0.4600, valid loss-1.6678, acc-0.5340, test loss-1.6688, acc-0.5234
Iter-95430, train loss-1.7175, acc-0.4600, valid loss-1.6677, acc-0.5340, test loss-1.6688, acc-0.5234
Iter-95440, train loss-1.7564, acc-0.4400, valid loss-1.6677, acc-0.5342, test loss-1.6687, acc-0.5233
Iter-95450, train loss-1.6009, acc-0.5600, valid loss-1.6677, acc-0.5340, test loss-1.6687, acc-0.5233
Iter-95460, train loss-1.6659, acc-0.5400, valid loss-1.6676, acc-0.5342, test loss-1.6687, acc-0.5233
Iter-95470, train loss-1.6940, acc-0.5000, valid loss-1.6676, acc-0.5342, test loss-1.6686, acc-0.5235
Iter-95480, train loss-1.7273, acc-0.5600, valid loss-1.6676, acc-0.5342, test loss-1.6686, acc-0.5233
Iter-95490, train loss-1.5902, acc-0.5600, valid loss-1.6675, acc-0.5342, test loss-1.6686, acc-0.5235
Iter-95500, train loss-1.6655, acc-0.4800, valid loss-1.6675, acc-0.5342, test loss-1.6685, acc-0.5236
Iter-95510, train loss-1.7123, acc-0.5200, valid loss-1.6675, acc-0.5342, test loss-1.6685, acc-0.5234
Iter-95520, train loss-1.5763, acc-0.5800, valid loss-1.6674, acc-0.5342, test loss-1.6685, acc-0.5235
Iter-95530, train loss-1.6089, acc-0.6000, valid loss-1.6674, acc-0.5342, test loss-1.6684, acc-0.5235
Iter-95540, train loss-1.6638, acc-0.5200, valid loss-1.6674, acc-0.5342, test loss-1.6684, acc-0.5235
Iter-95550, train loss-1.6576, acc-0.4800, valid loss-1.6673, acc-0.5342, test loss-1.6684, acc-0.5235
Iter-95560, train loss-1.6652, acc-0.5400, valid loss-1.6673, acc-0.5342, test loss-1.6683, acc-0.5235
Iter-95570, train loss-1.5572, acc-0.6200, valid loss-1.6673, acc-0.5342, test loss-1.6683, acc-0.5235
Iter-95580, train loss-1.7634, acc-0.4600, valid loss-1.6673, acc-0.5342, test loss-1.6683, acc-0.5235
Iter-95590, train loss-1.6817, acc-0.5400, valid loss-1.6672, acc-0.5340, test loss-1.6682, acc-0.5235
Iter-95600, train loss-1.7080, acc-0.5800, valid loss-1.6672, acc-0.5342, test loss-1.6682, acc-0.5234
Iter-95610, train loss-1.6581, acc-0.5400, valid loss-1.6672, acc-0.5342, test loss-1.6682, acc-0.5234
Iter-95620, train loss-1.7175, acc-0.4600, valid loss-1.6671, acc-0.5342, test loss-1.6681, acc-0.5235
Iter-95630, train loss-1.6649, acc-0.5200, valid loss-1.6671, acc-0.5342, test loss-1.6681, acc-0.5234
Iter-95640, train loss-1.6145, acc-0.6000, valid loss-1.6671, acc-0.5342, test loss-1.6681, acc-0.5234
Iter-95650, train loss-1.6583, acc-0.5200, valid loss-1.6670, acc-0.5340, test loss-1.6681, acc-0.5234
Iter-95660, train loss-1.7102, acc-0.4600, valid loss-1.6670, acc-0.5340, test loss-1.6680, acc-0.5234
Iter-95670, train loss-1.6594, acc-0.6400, valid loss-1.6670, acc-0.5340, test loss-1.6680, acc-0.5234
Iter-95680, train loss-1.6404, acc-0.5600, valid loss-1.6669, acc-0.5340, test loss-1.6680, acc-0.5237
Iter-95690, train loss-1.7349, acc-0.4400, valid loss-1.6669, acc-0.5340, test loss-1.6679, acc-0.5235
Iter-95700, train loss-1.5912, acc-0.5600, valid loss-1.6669, acc-0.5340, test loss-1.6679, acc-0.5235
Iter-95710, train loss-1.6599, acc-0.5000, valid loss-1.6668, acc-0.5338, test loss-1.6679, acc-0.5235
Iter-95720, train loss-1.7926, acc-0.4200, valid loss-1.6668, acc-0.5340, test loss-1.6678, acc-0.5234
Iter-95730, train loss-1.6562, acc-0.5600, valid loss-1.6668, acc-0.5342, test loss-1.6678, acc-0.5235
Iter-95740, train loss-1.5993, acc-0.5000, valid loss-1.6667, acc-0.5342, test loss-1.6678, acc-0.5235
Iter-95750, train loss-1.6707, acc-0.4800, valid loss-1.6667, acc-0.5342, test loss-1.6677, acc-0.5235
Iter-95760, train loss-1.7526, acc-0.5200, valid loss-1.6667, acc-0.5342, test loss-1.6677, acc-0.5235
Iter-95770, train loss-1.7283, acc-0.5000, valid loss-1.6666, acc-0.5342, test loss-1.6677, acc-0.5235
Iter-95780, train loss-1.6783, acc-0.5000, valid loss-1.6666, acc-0.5342, test loss-1.6676, acc-0.5234
Iter-95790, train loss-1.7135, acc-0.5000, valid loss-1.6666, acc-0.5342, test loss-1.6676, acc-0.5234
Iter-95800, train loss-1.6911, acc-0.4800, valid loss-1.6666, acc-0.5342, test loss-1.6676, acc-0.5233
Iter-95810, train loss-1.7179, acc-0.4600, valid loss-1.6665, acc-0.5342, test loss-1.6675, acc-0.5233
Iter-95820, train loss-1.6848, acc-0.5000, valid loss-1.6665, acc-0.5342, test loss-1.6675, acc-0.5233
Iter-95830, train loss-1.6461, acc-0.5600, valid loss-1.6665, acc-0.5342, test loss-1.6675, acc-0.5233
Iter-95840, train loss-1.7822, acc-0.4400, valid loss-1.6664, acc-0.5346, test loss-1.6674, acc-0.5233
Iter-95850, train loss-1.7943, acc-0.4600, valid loss-1.6664, acc-0.5346, test loss-1.6674, acc-0.5234
Iter-95860, train loss-1.6735, acc-0.4600, valid loss-1.6664, acc-0.5348, test loss-1.6674, acc-0.5235
Iter-95870, train loss-1.7781, acc-0.4600, valid loss-1.6664, acc-0.5348, test loss-1.6674, acc-0.5236
Iter-95880, train loss-1.6574, acc-0.5800, valid loss-1.6663, acc-0.5350, test loss-1.6673, acc-0.5238
Iter-95890, train loss-1.6180, acc-0.6600, valid loss-1.6663, acc-0.5346, test loss-1.6673, acc-0.5238
Iter-95900, train loss-1.6285, acc-0.6000, valid loss-1.6663, acc-0.5346, test loss-1.6673, acc-0.5239
Iter-95910, train loss-1.5727, acc-0.5600, valid loss-1.6662, acc-0.5348, test loss-1.6672, acc-0.5238
Iter-95920, train loss-1.6811, acc-0.5200, valid loss-1.6662, acc-0.5348, test loss-1.6672, acc-0.5239
Iter-95930, train loss-1.6435, acc-0.5200, valid loss-1.6662, acc-0.5346, test loss-1.6672, acc-0.5239
Iter-95940, train loss-1.6382, acc-0.5200, valid loss-1.6661, acc-0.5348, test loss-1.6671, acc-0.5240
Iter-95950, train loss-1.6871, acc-0.5000, valid loss-1.6661, acc-0.5348, test loss-1.6671, acc-0.5240
Iter-95960, train loss-1.6198, acc-0.5400, valid loss-1.6661, acc-0.5348, test loss-1.6671, acc-0.5239
Iter-95970, train loss-1.6674, acc-0.5400, valid loss-1.6661, acc-0.5348, test loss-1.6670, acc-0.5239
Iter-95980, train loss-1.7409, acc-0.4200, valid loss-1.6660, acc-0.5346, test loss-1.6670, acc-0.5240
Iter-95990, train loss-1.6190, acc-0.5800, valid loss-1.6660, acc-0.5348, test loss-1.6670, acc-0.5236
Iter-96000, train loss-1.7585, acc-0.4400, valid loss-1.6660, acc-0.5348, test loss-1.6669, acc-0.5236
Iter-96010, train loss-1.7265, acc-0.5800, valid loss-1.6659, acc-0.5348, test loss-1.6669, acc-0.5237
Iter-96020, train loss-1.6909, acc-0.4800, valid loss-1.6659, acc-0.5350, test loss-1.6669, acc-0.5235
Iter-96030, train loss-1.7762, acc-0.4800, valid loss-1.6659, acc-0.5346, test loss-1.6668, acc-0.5235
Iter-96040, train loss-1.7767, acc-0.5600, valid loss-1.6658, acc-0.5348, test loss-1.6668, acc-0.5237
Iter-96050, train loss-1.5723, acc-0.6600, valid loss-1.6658, acc-0.5350, test loss-1.6668, acc-0.5237
Iter-96060, train loss-1.6150, acc-0.5200, valid loss-1.6658, acc-0.5348, test loss-1.6668, acc-0.5238
Iter-96070, train loss-1.6786, acc-0.5000, valid loss-1.6657, acc-0.5348, test loss-1.6667, acc-0.5238
Iter-96080, train loss-1.7303, acc-0.5800, valid loss-1.6657, acc-0.5350, test loss-1.6667, acc-0.5237
Iter-96090, train loss-1.6643, acc-0.6400, valid loss-1.6657, acc-0.5350, test loss-1.6667, acc-0.5237
Iter-96100, train loss-1.6832, acc-0.4800, valid loss-1.6656, acc-0.5348, test loss-1.6666, acc-0.5239
Iter-96110, train loss-1.7223, acc-0.4800, valid loss-1.6656, acc-0.5348, test loss-1.6666, acc-0.5237
Iter-96120, train loss-1.6399, acc-0.5800, valid loss-1.6656, acc-0.5348, test loss-1.6666, acc-0.5237
Iter-96130, train loss-1.7550, acc-0.4800, valid loss-1.6655, acc-0.5350, test loss-1.6665, acc-0.5237
Iter-96140, train loss-1.7510, acc-0.4600, valid loss-1.6655, acc-0.5350, test loss-1.6665, acc-0.5236
Iter-96150, train loss-1.5909, acc-0.6200, valid loss-1.6655, acc-0.5348, test loss-1.6665, acc-0.5236
Iter-96160, train loss-1.6115, acc-0.5600, valid loss-1.6655, acc-0.5346, test loss-1.6664, acc-0.5238
Iter-96170, train loss-1.6234, acc-0.5400, valid loss-1.6654, acc-0.5346, test loss-1.6664, acc-0.5238
Iter-96180, train loss-1.6067, acc-0.6200, valid loss-1.6654, acc-0.5350, test loss-1.6664, acc-0.5237
Iter-96190, train loss-1.7269, acc-0.6000, valid loss-1.6654, acc-0.5350, test loss-1.6663, acc-0.5237
Iter-96200, train loss-1.7396, acc-0.5000, valid loss-1.6653, acc-0.5348, test loss-1.6663, acc-0.5236
Iter-96210, train loss-1.5897, acc-0.5800, valid loss-1.6653, acc-0.5348, test loss-1.6663, acc-0.5237
Iter-96220, train loss-1.7625, acc-0.5200, valid loss-1.6653, acc-0.5350, test loss-1.6662, acc-0.5236
Iter-96230, train loss-1.7326, acc-0.5200, valid loss-1.6652, acc-0.5350, test loss-1.6662, acc-0.5236
Iter-96240, train loss-1.4904, acc-0.5800, valid loss-1.6652, acc-0.5356, test loss-1.6662, acc-0.5234
Iter-96250, train loss-1.6555, acc-0.6000, valid loss-1.6652, acc-0.5352, test loss-1.6662, acc-0.5234
Iter-96260, train loss-1.6111, acc-0.6400, valid loss-1.6651, acc-0.5356, test loss-1.6661, acc-0.5234
Iter-96270, train loss-1.6399, acc-0.6000, valid loss-1.6651, acc-0.5354, test loss-1.6661, acc-0.5234
Iter-96280, train loss-1.7245, acc-0.5000, valid loss-1.6651, acc-0.5354, test loss-1.6661, acc-0.5236
Iter-96290, train loss-1.6551, acc-0.6000, valid loss-1.6651, acc-0.5352, test loss-1.6660, acc-0.5235
Iter-96300, train loss-1.6070, acc-0.5800, valid loss-1.6650, acc-0.5350, test loss-1.6660, acc-0.5236
Iter-96310, train loss-1.6266, acc-0.4800, valid loss-1.6650, acc-0.5350, test loss-1.6660, acc-0.5236
Iter-96320, train loss-1.6839, acc-0.5400, valid loss-1.6650, acc-0.5348, test loss-1.6659, acc-0.5236
Iter-96330, train loss-1.7266, acc-0.4400, valid loss-1.6649, acc-0.5348, test loss-1.6659, acc-0.5236
Iter-96340, train loss-1.6140, acc-0.6200, valid loss-1.6649, acc-0.5348, test loss-1.6659, acc-0.5237
Iter-96350, train loss-1.6117, acc-0.5600, valid loss-1.6649, acc-0.5350, test loss-1.6658, acc-0.5235
Iter-96360, train loss-1.6583, acc-0.5800, valid loss-1.6648, acc-0.5348, test loss-1.6658, acc-0.5235
Iter-96370, train loss-1.6625, acc-0.5600, valid loss-1.6648, acc-0.5350, test loss-1.6658, acc-0.5235
Iter-96380, train loss-1.6921, acc-0.5400, valid loss-1.6648, acc-0.5350, test loss-1.6657, acc-0.5235
Iter-96390, train loss-1.6231, acc-0.5800, valid loss-1.6647, acc-0.5350, test loss-1.6657, acc-0.5236
Iter-96400, train loss-1.6970, acc-0.4800, valid loss-1.6647, acc-0.5350, test loss-1.6657, acc-0.5236
Iter-96410, train loss-1.6583, acc-0.5400, valid loss-1.6647, acc-0.5348, test loss-1.6657, acc-0.5236
Iter-96420, train loss-1.7309, acc-0.4800, valid loss-1.6646, acc-0.5350, test loss-1.6656, acc-0.5236
Iter-96430, train loss-1.8057, acc-0.3600, valid loss-1.6646, acc-0.5348, test loss-1.6656, acc-0.5236
Iter-96440, train loss-1.6897, acc-0.5400, valid loss-1.6646, acc-0.5350, test loss-1.6656, acc-0.5236
Iter-96450, train loss-1.7186, acc-0.4800, valid loss-1.6645, acc-0.5350, test loss-1.6655, acc-0.5236
Iter-96460, train loss-1.7796, acc-0.5000, valid loss-1.6645, acc-0.5348, test loss-1.6655, acc-0.5235
Iter-96470, train loss-1.6452, acc-0.5200, valid loss-1.6645, acc-0.5348, test loss-1.6655, acc-0.5235
Iter-96480, train loss-1.6773, acc-0.5400, valid loss-1.6644, acc-0.5348, test loss-1.6654, acc-0.5235
Iter-96490, train loss-1.6291, acc-0.5200, valid loss-1.6644, acc-0.5348, test loss-1.6654, acc-0.5234
Iter-96500, train loss-1.6452, acc-0.5600, valid loss-1.6644, acc-0.5348, test loss-1.6654, acc-0.5235
Iter-96510, train loss-1.6448, acc-0.5400, valid loss-1.6643, acc-0.5350, test loss-1.6653, acc-0.5235
Iter-96520, train loss-1.5596, acc-0.6200, valid loss-1.6643, acc-0.5350, test loss-1.6653, acc-0.5235
Iter-96530, train loss-1.6890, acc-0.5400, valid loss-1.6643, acc-0.5350, test loss-1.6653, acc-0.5235
Iter-96540, train loss-1.6170, acc-0.6200, valid loss-1.6642, acc-0.5350, test loss-1.6652, acc-0.5236
Iter-96550, train loss-1.7536, acc-0.4200, valid loss-1.6642, acc-0.5350, test loss-1.6652, acc-0.5237
Iter-96560, train loss-1.5381, acc-0.6000, valid loss-1.6642, acc-0.5350, test loss-1.6652, acc-0.5238
Iter-96570, train loss-1.7014, acc-0.4000, valid loss-1.6641, acc-0.5352, test loss-1.6652, acc-0.5238
Iter-96580, train loss-1.5883, acc-0.5800, valid loss-1.6641, acc-0.5352, test loss-1.6651, acc-0.5237
Iter-96590, train loss-1.6181, acc-0.6400, valid loss-1.6641, acc-0.5350, test loss-1.6651, acc-0.5237
Iter-96600, train loss-1.6730, acc-0.5400, valid loss-1.6641, acc-0.5354, test loss-1.6651, acc-0.5237
Iter-96610, train loss-1.6296, acc-0.5400, valid loss-1.6640, acc-0.5354, test loss-1.6650, acc-0.5237
Iter-96620, train loss-1.5745, acc-0.5400, valid loss-1.6640, acc-0.5352, test loss-1.6650, acc-0.5237
Iter-96630, train loss-1.6350, acc-0.5000, valid loss-1.6640, acc-0.5352, test loss-1.6650, acc-0.5237
Iter-96640, train loss-1.7292, acc-0.4600, valid loss-1.6639, acc-0.5352, test loss-1.6649, acc-0.5237
Iter-96650, train loss-1.7663, acc-0.4200, valid loss-1.6639, acc-0.5354, test loss-1.6649, acc-0.5237
Iter-96660, train loss-1.7103, acc-0.4800, valid loss-1.6639, acc-0.5354, test loss-1.6649, acc-0.5237
Iter-96670, train loss-1.7056, acc-0.4200, valid loss-1.6638, acc-0.5352, test loss-1.6648, acc-0.5237
Iter-96680, train loss-1.6421, acc-0.5200, valid loss-1.6638, acc-0.5352, test loss-1.6648, acc-0.5237
Iter-96690, train loss-1.6671, acc-0.5200, valid loss-1.6638, acc-0.5352, test loss-1.6648, acc-0.5237
Iter-96700, train loss-1.5690, acc-0.7000, valid loss-1.6637, acc-0.5352, test loss-1.6647, acc-0.5237
Iter-96710, train loss-1.7069, acc-0.4800, valid loss-1.6637, acc-0.5352, test loss-1.6647, acc-0.5237
Iter-96720, train loss-1.6200, acc-0.6200, valid loss-1.6637, acc-0.5352, test loss-1.6647, acc-0.5237
Iter-96730, train loss-1.7851, acc-0.4400, valid loss-1.6636, acc-0.5354, test loss-1.6647, acc-0.5237
Iter-96740, train loss-1.6464, acc-0.5800, valid loss-1.6636, acc-0.5354, test loss-1.6646, acc-0.5237
Iter-96750, train loss-1.6938, acc-0.4800, valid loss-1.6636, acc-0.5354, test loss-1.6646, acc-0.5238
Iter-96760, train loss-1.7249, acc-0.4600, valid loss-1.6635, acc-0.5352, test loss-1.6646, acc-0.5238
Iter-96770, train loss-1.7236, acc-0.5400, valid loss-1.6635, acc-0.5352, test loss-1.6645, acc-0.5238
Iter-96780, train loss-1.7046, acc-0.5400, valid loss-1.6635, acc-0.5354, test loss-1.6645, acc-0.5237
Iter-96790, train loss-1.6569, acc-0.5200, valid loss-1.6635, acc-0.5354, test loss-1.6645, acc-0.5237
Iter-96800, train loss-1.6947, acc-0.5000, valid loss-1.6634, acc-0.5354, test loss-1.6644, acc-0.5237
Iter-96810, train loss-1.7083, acc-0.4800, valid loss-1.6634, acc-0.5352, test loss-1.6644, acc-0.5237
Iter-96820, train loss-1.6994, acc-0.5400, valid loss-1.6634, acc-0.5354, test loss-1.6644, acc-0.5237
Iter-96830, train loss-1.6175, acc-0.5000, valid loss-1.6633, acc-0.5352, test loss-1.6643, acc-0.5237
Iter-96840, train loss-1.5922, acc-0.6200, valid loss-1.6633, acc-0.5354, test loss-1.6643, acc-0.5235
Iter-96850, train loss-1.8542, acc-0.4200, valid loss-1.6633, acc-0.5354, test loss-1.6643, acc-0.5235
Iter-96860, train loss-1.7402, acc-0.5000, valid loss-1.6632, acc-0.5354, test loss-1.6642, acc-0.5236
Iter-96870, train loss-1.6647, acc-0.5200, valid loss-1.6632, acc-0.5354, test loss-1.6642, acc-0.5235
Iter-96880, train loss-1.5027, acc-0.5800, valid loss-1.6632, acc-0.5354, test loss-1.6642, acc-0.5235
Iter-96890, train loss-1.6490, acc-0.5200, valid loss-1.6631, acc-0.5354, test loss-1.6641, acc-0.5235
Iter-96900, train loss-1.6797, acc-0.6000, valid loss-1.6631, acc-0.5354, test loss-1.6641, acc-0.5234
Iter-96910, train loss-1.6780, acc-0.6000, valid loss-1.6631, acc-0.5352, test loss-1.6641, acc-0.5234
Iter-96920, train loss-1.6029, acc-0.5000, valid loss-1.6631, acc-0.5352, test loss-1.6641, acc-0.5234
Iter-96930, train loss-1.6852, acc-0.4400, valid loss-1.6630, acc-0.5352, test loss-1.6640, acc-0.5234
Iter-96940, train loss-1.6672, acc-0.6400, valid loss-1.6630, acc-0.5350, test loss-1.6640, acc-0.5235
Iter-96950, train loss-1.7050, acc-0.5000, valid loss-1.6630, acc-0.5350, test loss-1.6640, acc-0.5236
Iter-96960, train loss-1.6921, acc-0.6600, valid loss-1.6629, acc-0.5350, test loss-1.6639, acc-0.5236
Iter-96970, train loss-1.6930, acc-0.5400, valid loss-1.6629, acc-0.5350, test loss-1.6639, acc-0.5236
Iter-96980, train loss-1.6752, acc-0.5600, valid loss-1.6629, acc-0.5348, test loss-1.6639, acc-0.5236
Iter-96990, train loss-1.6535, acc-0.4600, valid loss-1.6628, acc-0.5350, test loss-1.6638, acc-0.5236
Iter-97000, train loss-1.7335, acc-0.4800, valid loss-1.6628, acc-0.5350, test loss-1.6638, acc-0.5237
Iter-97010, train loss-1.6973, acc-0.5000, valid loss-1.6628, acc-0.5350, test loss-1.6638, acc-0.5235
Iter-97020, train loss-1.6260, acc-0.5800, valid loss-1.6627, acc-0.5352, test loss-1.6637, acc-0.5236
Iter-97030, train loss-1.6075, acc-0.6400, valid loss-1.6627, acc-0.5350, test loss-1.6637, acc-0.5237
Iter-97040, train loss-1.5822, acc-0.5600, valid loss-1.6627, acc-0.5350, test loss-1.6637, acc-0.5236
Iter-97050, train loss-1.6762, acc-0.6000, valid loss-1.6627, acc-0.5350, test loss-1.6636, acc-0.5238
Iter-97060, train loss-1.8267, acc-0.4200, valid loss-1.6626, acc-0.5352, test loss-1.6636, acc-0.5239
Iter-97070, train loss-1.5983, acc-0.6000, valid loss-1.6626, acc-0.5352, test loss-1.6636, acc-0.5238
Iter-97080, train loss-1.6014, acc-0.5200, valid loss-1.6626, acc-0.5352, test loss-1.6636, acc-0.5242
Iter-97090, train loss-1.6681, acc-0.5400, valid loss-1.6625, acc-0.5352, test loss-1.6635, acc-0.5240
Iter-97100, train loss-1.7764, acc-0.4400, valid loss-1.6625, acc-0.5352, test loss-1.6635, acc-0.5239
Iter-97110, train loss-1.6753, acc-0.5200, valid loss-1.6625, acc-0.5350, test loss-1.6635, acc-0.5240
Iter-97120, train loss-1.8006, acc-0.4400, valid loss-1.6624, acc-0.5350, test loss-1.6634, acc-0.5240
Iter-97130, train loss-1.7020, acc-0.5200, valid loss-1.6624, acc-0.5350, test loss-1.6634, acc-0.5240
Iter-97140, train loss-1.6432, acc-0.5200, valid loss-1.6624, acc-0.5350, test loss-1.6634, acc-0.5239
Iter-97150, train loss-1.7316, acc-0.5400, valid loss-1.6623, acc-0.5350, test loss-1.6633, acc-0.5240
Iter-97160, train loss-1.6926, acc-0.4800, valid loss-1.6623, acc-0.5350, test loss-1.6633, acc-0.5240
Iter-97170, train loss-1.7116, acc-0.4800, valid loss-1.6623, acc-0.5350, test loss-1.6633, acc-0.5239
Iter-97180, train loss-1.6782, acc-0.5200, valid loss-1.6622, acc-0.5350, test loss-1.6632, acc-0.5239
Iter-97190, train loss-1.6497, acc-0.6200, valid loss-1.6622, acc-0.5352, test loss-1.6632, acc-0.5239
Iter-97200, train loss-1.6845, acc-0.5400, valid loss-1.6622, acc-0.5350, test loss-1.6632, acc-0.5238
Iter-97210, train loss-1.7854, acc-0.4800, valid loss-1.6622, acc-0.5350, test loss-1.6632, acc-0.5240
Iter-97220, train loss-1.6083, acc-0.6200, valid loss-1.6621, acc-0.5352, test loss-1.6631, acc-0.5238
Iter-97230, train loss-1.7397, acc-0.4400, valid loss-1.6621, acc-0.5352, test loss-1.6631, acc-0.5239
Iter-97240, train loss-1.5603, acc-0.5800, valid loss-1.6621, acc-0.5352, test loss-1.6631, acc-0.5237
Iter-97250, train loss-1.6215, acc-0.5400, valid loss-1.6620, acc-0.5352, test loss-1.6630, acc-0.5237
Iter-97260, train loss-1.6519, acc-0.5400, valid loss-1.6620, acc-0.5352, test loss-1.6630, acc-0.5239
Iter-97270, train loss-1.6660, acc-0.5200, valid loss-1.6620, acc-0.5352, test loss-1.6630, acc-0.5238
Iter-97280, train loss-1.6940, acc-0.5800, valid loss-1.6619, acc-0.5350, test loss-1.6629, acc-0.5240
Iter-97290, train loss-1.6669, acc-0.5000, valid loss-1.6619, acc-0.5352, test loss-1.6629, acc-0.5239
Iter-97300, train loss-1.8212, acc-0.4000, valid loss-1.6619, acc-0.5352, test loss-1.6629, acc-0.5239
Iter-97310, train loss-1.7071, acc-0.5200, valid loss-1.6618, acc-0.5352, test loss-1.6628, acc-0.5240
Iter-97320, train loss-1.7483, acc-0.4800, valid loss-1.6618, acc-0.5352, test loss-1.6628, acc-0.5239
Iter-97330, train loss-1.7937, acc-0.4800, valid loss-1.6618, acc-0.5352, test loss-1.6628, acc-0.5239
Iter-97340, train loss-1.7429, acc-0.5000, valid loss-1.6618, acc-0.5352, test loss-1.6627, acc-0.5237
Iter-97350, train loss-1.7529, acc-0.4600, valid loss-1.6617, acc-0.5352, test loss-1.6627, acc-0.5237
Iter-97360, train loss-1.6470, acc-0.4800, valid loss-1.6617, acc-0.5352, test loss-1.6627, acc-0.5237
Iter-97370, train loss-1.7285, acc-0.5000, valid loss-1.6617, acc-0.5352, test loss-1.6626, acc-0.5237
Iter-97380, train loss-1.7540, acc-0.4200, valid loss-1.6616, acc-0.5352, test loss-1.6626, acc-0.5239
Iter-97390, train loss-1.5794, acc-0.5800, valid loss-1.6616, acc-0.5352, test loss-1.6626, acc-0.5238
Iter-97400, train loss-1.7153, acc-0.5000, valid loss-1.6616, acc-0.5350, test loss-1.6626, acc-0.5241
Iter-97410, train loss-1.5541, acc-0.5600, valid loss-1.6615, acc-0.5350, test loss-1.6625, acc-0.5241
Iter-97420, train loss-1.6565, acc-0.5200, valid loss-1.6615, acc-0.5350, test loss-1.6625, acc-0.5240
Iter-97430, train loss-1.5338, acc-0.6600, valid loss-1.6615, acc-0.5352, test loss-1.6625, acc-0.5239
Iter-97440, train loss-1.6476, acc-0.5200, valid loss-1.6614, acc-0.5352, test loss-1.6624, acc-0.5240
Iter-97450, train loss-1.7067, acc-0.5600, valid loss-1.6614, acc-0.5350, test loss-1.6624, acc-0.5240
Iter-97460, train loss-1.5667, acc-0.6200, valid loss-1.6614, acc-0.5352, test loss-1.6624, acc-0.5241
Iter-97470, train loss-1.7161, acc-0.5600, valid loss-1.6613, acc-0.5352, test loss-1.6623, acc-0.5242
Iter-97480, train loss-1.6386, acc-0.5400, valid loss-1.6613, acc-0.5352, test loss-1.6623, acc-0.5242
Iter-97490, train loss-1.7227, acc-0.4800, valid loss-1.6613, acc-0.5352, test loss-1.6623, acc-0.5241
Iter-97500, train loss-1.8623, acc-0.3600, valid loss-1.6612, acc-0.5352, test loss-1.6622, acc-0.5242
Iter-97510, train loss-1.6055, acc-0.5600, valid loss-1.6612, acc-0.5352, test loss-1.6622, acc-0.5243
Iter-97520, train loss-1.6690, acc-0.5600, valid loss-1.6612, acc-0.5352, test loss-1.6622, acc-0.5243
Iter-97530, train loss-1.6785, acc-0.4600, valid loss-1.6612, acc-0.5352, test loss-1.6622, acc-0.5243
Iter-97540, train loss-1.5960, acc-0.6000, valid loss-1.6611, acc-0.5350, test loss-1.6621, acc-0.5243
Iter-97550, train loss-1.6816, acc-0.5800, valid loss-1.6611, acc-0.5350, test loss-1.6621, acc-0.5243
Iter-97560, train loss-1.7347, acc-0.4400, valid loss-1.6611, acc-0.5352, test loss-1.6621, acc-0.5243
Iter-97570, train loss-1.5617, acc-0.6200, valid loss-1.6610, acc-0.5352, test loss-1.6620, acc-0.5244
Iter-97580, train loss-1.6471, acc-0.5400, valid loss-1.6610, acc-0.5354, test loss-1.6620, acc-0.5243
Iter-97590, train loss-1.6444, acc-0.5200, valid loss-1.6610, acc-0.5354, test loss-1.6620, acc-0.5244
Iter-97600, train loss-1.6103, acc-0.6000, valid loss-1.6609, acc-0.5352, test loss-1.6619, acc-0.5243
Iter-97610, train loss-1.6830, acc-0.5200, valid loss-1.6609, acc-0.5352, test loss-1.6619, acc-0.5243
Iter-97620, train loss-1.6949, acc-0.5000, valid loss-1.6609, acc-0.5352, test loss-1.6619, acc-0.5244
Iter-97630, train loss-1.7612, acc-0.4000, valid loss-1.6609, acc-0.5354, test loss-1.6618, acc-0.5243
Iter-97640, train loss-1.7398, acc-0.4400, valid loss-1.6608, acc-0.5354, test loss-1.6618, acc-0.5244
Iter-97650, train loss-1.7857, acc-0.3600, valid loss-1.6608, acc-0.5352, test loss-1.6618, acc-0.5244
Iter-97660, train loss-1.6588, acc-0.5200, valid loss-1.6608, acc-0.5352, test loss-1.6617, acc-0.5244
Iter-97670, train loss-1.7008, acc-0.4800, valid loss-1.6607, acc-0.5352, test loss-1.6617, acc-0.5243
Iter-97680, train loss-1.6339, acc-0.5600, valid loss-1.6607, acc-0.5352, test loss-1.6617, acc-0.5243
Iter-97690, train loss-1.7944, acc-0.4600, valid loss-1.6607, acc-0.5350, test loss-1.6617, acc-0.5244
Iter-97700, train loss-1.6070, acc-0.6000, valid loss-1.6606, acc-0.5350, test loss-1.6616, acc-0.5244
Iter-97710, train loss-1.6780, acc-0.5800, valid loss-1.6606, acc-0.5350, test loss-1.6616, acc-0.5243
Iter-97720, train loss-1.6456, acc-0.5800, valid loss-1.6606, acc-0.5352, test loss-1.6616, acc-0.5243
Iter-97730, train loss-1.6643, acc-0.5000, valid loss-1.6605, acc-0.5352, test loss-1.6615, acc-0.5244
Iter-97740, train loss-1.6666, acc-0.5200, valid loss-1.6605, acc-0.5354, test loss-1.6615, acc-0.5243
Iter-97750, train loss-1.7444, acc-0.4000, valid loss-1.6605, acc-0.5354, test loss-1.6615, acc-0.5245
Iter-97760, train loss-1.5991, acc-0.6200, valid loss-1.6604, acc-0.5354, test loss-1.6614, acc-0.5246
Iter-97770, train loss-1.6313, acc-0.5000, valid loss-1.6604, acc-0.5354, test loss-1.6614, acc-0.5245
Iter-97780, train loss-1.6438, acc-0.6000, valid loss-1.6604, acc-0.5352, test loss-1.6614, acc-0.5244
Iter-97790, train loss-1.6225, acc-0.5400, valid loss-1.6604, acc-0.5354, test loss-1.6613, acc-0.5244
Iter-97800, train loss-1.7372, acc-0.4600, valid loss-1.6603, acc-0.5354, test loss-1.6613, acc-0.5244
Iter-97810, train loss-1.7952, acc-0.4000, valid loss-1.6603, acc-0.5354, test loss-1.6613, acc-0.5244
Iter-97820, train loss-1.5708, acc-0.6200, valid loss-1.6603, acc-0.5352, test loss-1.6612, acc-0.5243
Iter-97830, train loss-1.6586, acc-0.5800, valid loss-1.6602, acc-0.5354, test loss-1.6612, acc-0.5244
Iter-97840, train loss-1.5706, acc-0.5400, valid loss-1.6602, acc-0.5354, test loss-1.6612, acc-0.5244
Iter-97850, train loss-1.7842, acc-0.4400, valid loss-1.6602, acc-0.5356, test loss-1.6612, acc-0.5244
Iter-97860, train loss-1.6503, acc-0.5800, valid loss-1.6601, acc-0.5354, test loss-1.6611, acc-0.5245
Iter-97870, train loss-1.5684, acc-0.5800, valid loss-1.6601, acc-0.5358, test loss-1.6611, acc-0.5243
Iter-97880, train loss-1.6696, acc-0.5600, valid loss-1.6601, acc-0.5360, test loss-1.6611, acc-0.5242
Iter-97890, train loss-1.7395, acc-0.4800, valid loss-1.6600, acc-0.5356, test loss-1.6610, acc-0.5242
Iter-97900, train loss-1.6552, acc-0.5600, valid loss-1.6600, acc-0.5356, test loss-1.6610, acc-0.5243
Iter-97910, train loss-1.7377, acc-0.4400, valid loss-1.6600, acc-0.5358, test loss-1.6610, acc-0.5242
Iter-97920, train loss-1.5956, acc-0.5600, valid loss-1.6599, acc-0.5354, test loss-1.6609, acc-0.5242
Iter-97930, train loss-1.7001, acc-0.5000, valid loss-1.6599, acc-0.5354, test loss-1.6609, acc-0.5243
Iter-97940, train loss-1.6400, acc-0.5600, valid loss-1.6599, acc-0.5356, test loss-1.6609, acc-0.5242
Iter-97950, train loss-1.7394, acc-0.5000, valid loss-1.6599, acc-0.5356, test loss-1.6608, acc-0.5242
Iter-97960, train loss-1.7626, acc-0.5000, valid loss-1.6598, acc-0.5354, test loss-1.6608, acc-0.5244
Iter-97970, train loss-1.6581, acc-0.4400, valid loss-1.6598, acc-0.5352, test loss-1.6608, acc-0.5244
Iter-97980, train loss-1.7495, acc-0.4600, valid loss-1.6598, acc-0.5354, test loss-1.6608, acc-0.5245
Iter-97990, train loss-1.6641, acc-0.5600, valid loss-1.6597, acc-0.5354, test loss-1.6607, acc-0.5245
Iter-98000, train loss-1.7700, acc-0.4400, valid loss-1.6597, acc-0.5356, test loss-1.6607, acc-0.5245
Iter-98010, train loss-1.6230, acc-0.5600, valid loss-1.6597, acc-0.5358, test loss-1.6607, acc-0.5244
Iter-98020, train loss-1.7252, acc-0.5200, valid loss-1.6596, acc-0.5358, test loss-1.6606, acc-0.5244
Iter-98030, train loss-1.6938, acc-0.4400, valid loss-1.6596, acc-0.5360, test loss-1.6606, acc-0.5244
Iter-98040, train loss-1.5949, acc-0.6000, valid loss-1.6596, acc-0.5360, test loss-1.6606, acc-0.5246
Iter-98050, train loss-1.7075, acc-0.4600, valid loss-1.6596, acc-0.5360, test loss-1.6605, acc-0.5246
Iter-98060, train loss-1.7764, acc-0.4600, valid loss-1.6595, acc-0.5360, test loss-1.6605, acc-0.5245
Iter-98070, train loss-1.6622, acc-0.5200, valid loss-1.6595, acc-0.5358, test loss-1.6605, acc-0.5247
Iter-98080, train loss-1.5701, acc-0.5600, valid loss-1.6595, acc-0.5358, test loss-1.6604, acc-0.5246
Iter-98090, train loss-1.6572, acc-0.4200, valid loss-1.6594, acc-0.5360, test loss-1.6604, acc-0.5246
Iter-98100, train loss-1.5749, acc-0.5400, valid loss-1.6594, acc-0.5360, test loss-1.6604, acc-0.5247
Iter-98110, train loss-1.5973, acc-0.6200, valid loss-1.6594, acc-0.5360, test loss-1.6604, acc-0.5247
Iter-98120, train loss-1.6824, acc-0.5200, valid loss-1.6593, acc-0.5362, test loss-1.6603, acc-0.5247
Iter-98130, train loss-1.7174, acc-0.5000, valid loss-1.6593, acc-0.5362, test loss-1.6603, acc-0.5248
Iter-98140, train loss-1.8342, acc-0.4000, valid loss-1.6593, acc-0.5364, test loss-1.6603, acc-0.5249
Iter-98150, train loss-1.6510, acc-0.5400, valid loss-1.6593, acc-0.5364, test loss-1.6602, acc-0.5250
Iter-98160, train loss-1.7830, acc-0.4800, valid loss-1.6592, acc-0.5362, test loss-1.6602, acc-0.5249
Iter-98170, train loss-1.6650, acc-0.5400, valid loss-1.6592, acc-0.5364, test loss-1.6602, acc-0.5249
Iter-98180, train loss-1.7581, acc-0.4800, valid loss-1.6592, acc-0.5364, test loss-1.6601, acc-0.5250
Iter-98190, train loss-1.6819, acc-0.5200, valid loss-1.6591, acc-0.5364, test loss-1.6601, acc-0.5250
Iter-98200, train loss-1.5940, acc-0.6000, valid loss-1.6591, acc-0.5362, test loss-1.6601, acc-0.5250
Iter-98210, train loss-1.6554, acc-0.5400, valid loss-1.6591, acc-0.5362, test loss-1.6600, acc-0.5249
Iter-98220, train loss-1.6278, acc-0.5200, valid loss-1.6590, acc-0.5360, test loss-1.6600, acc-0.5249
Iter-98230, train loss-1.6905, acc-0.5400, valid loss-1.6590, acc-0.5360, test loss-1.6600, acc-0.5248
Iter-98240, train loss-1.7791, acc-0.3600, valid loss-1.6590, acc-0.5360, test loss-1.6600, acc-0.5247
Iter-98250, train loss-1.7511, acc-0.5000, valid loss-1.6589, acc-0.5358, test loss-1.6599, acc-0.5246
Iter-98260, train loss-1.6485, acc-0.5400, valid loss-1.6589, acc-0.5360, test loss-1.6599, acc-0.5246
Iter-98270, train loss-1.7710, acc-0.5600, valid loss-1.6589, acc-0.5360, test loss-1.6599, acc-0.5248
Iter-98280, train loss-1.7685, acc-0.4200, valid loss-1.6589, acc-0.5360, test loss-1.6598, acc-0.5249
Iter-98290, train loss-1.8036, acc-0.5000, valid loss-1.6588, acc-0.5362, test loss-1.6598, acc-0.5249
Iter-98300, train loss-1.6063, acc-0.5600, valid loss-1.6588, acc-0.5362, test loss-1.6598, acc-0.5249
Iter-98310, train loss-1.8558, acc-0.2800, valid loss-1.6588, acc-0.5362, test loss-1.6597, acc-0.5249
Iter-98320, train loss-1.8188, acc-0.4400, valid loss-1.6587, acc-0.5362, test loss-1.6597, acc-0.5251
Iter-98330, train loss-1.7233, acc-0.4400, valid loss-1.6587, acc-0.5360, test loss-1.6597, acc-0.5250
Iter-98340, train loss-1.7031, acc-0.5200, valid loss-1.6587, acc-0.5360, test loss-1.6596, acc-0.5250
Iter-98350, train loss-1.7361, acc-0.5600, valid loss-1.6586, acc-0.5362, test loss-1.6596, acc-0.5251
Iter-98360, train loss-1.7795, acc-0.4600, valid loss-1.6586, acc-0.5362, test loss-1.6596, acc-0.5251
Iter-98370, train loss-1.5698, acc-0.6200, valid loss-1.6586, acc-0.5362, test loss-1.6596, acc-0.5250
Iter-98380, train loss-1.7168, acc-0.4800, valid loss-1.6585, acc-0.5360, test loss-1.6595, acc-0.5253
Iter-98390, train loss-1.6910, acc-0.4400, valid loss-1.6585, acc-0.5360, test loss-1.6595, acc-0.5252
Iter-98400, train loss-1.6356, acc-0.5600, valid loss-1.6585, acc-0.5360, test loss-1.6595, acc-0.5250
Iter-98410, train loss-1.8282, acc-0.3600, valid loss-1.6585, acc-0.5360, test loss-1.6594, acc-0.5253
Iter-98420, train loss-1.5789, acc-0.5600, valid loss-1.6584, acc-0.5360, test loss-1.6594, acc-0.5251
Iter-98430, train loss-1.6988, acc-0.4800, valid loss-1.6584, acc-0.5358, test loss-1.6594, acc-0.5252
Iter-98440, train loss-1.6014, acc-0.4600, valid loss-1.6584, acc-0.5358, test loss-1.6593, acc-0.5249
Iter-98450, train loss-1.5951, acc-0.5600, valid loss-1.6583, acc-0.5362, test loss-1.6593, acc-0.5249
Iter-98460, train loss-1.6592, acc-0.5400, valid loss-1.6583, acc-0.5360, test loss-1.6593, acc-0.5249
Iter-98470, train loss-1.6139, acc-0.6600, valid loss-1.6583, acc-0.5360, test loss-1.6592, acc-0.5249
Iter-98480, train loss-1.7036, acc-0.4800, valid loss-1.6582, acc-0.5360, test loss-1.6592, acc-0.5249
Iter-98490, train loss-1.6948, acc-0.5600, valid loss-1.6582, acc-0.5360, test loss-1.6592, acc-0.5249
Iter-98500, train loss-1.6351, acc-0.5800, valid loss-1.6582, acc-0.5360, test loss-1.6592, acc-0.5250
Iter-98510, train loss-1.6796, acc-0.5600, valid loss-1.6582, acc-0.5358, test loss-1.6591, acc-0.5249
Iter-98520, train loss-1.7143, acc-0.4200, valid loss-1.6581, acc-0.5358, test loss-1.6591, acc-0.5250
Iter-98530, train loss-1.5804, acc-0.5400, valid loss-1.6581, acc-0.5358, test loss-1.6591, acc-0.5249
Iter-98540, train loss-1.7941, acc-0.5000, valid loss-1.6581, acc-0.5358, test loss-1.6590, acc-0.5249
Iter-98550, train loss-1.6244, acc-0.4600, valid loss-1.6580, acc-0.5362, test loss-1.6590, acc-0.5249
Iter-98560, train loss-1.7568, acc-0.5000, valid loss-1.6580, acc-0.5360, test loss-1.6590, acc-0.5249
Iter-98570, train loss-1.6327, acc-0.5800, valid loss-1.6580, acc-0.5358, test loss-1.6589, acc-0.5250
Iter-98580, train loss-1.6058, acc-0.5800, valid loss-1.6579, acc-0.5356, test loss-1.6589, acc-0.5250
Iter-98590, train loss-1.5269, acc-0.7400, valid loss-1.6579, acc-0.5354, test loss-1.6589, acc-0.5248
Iter-98600, train loss-1.7075, acc-0.5200, valid loss-1.6579, acc-0.5360, test loss-1.6589, acc-0.5249
Iter-98610, train loss-1.5534, acc-0.6200, valid loss-1.6579, acc-0.5360, test loss-1.6588, acc-0.5249
Iter-98620, train loss-1.7385, acc-0.4400, valid loss-1.6578, acc-0.5358, test loss-1.6588, acc-0.5250
Iter-98630, train loss-1.9262, acc-0.2800, valid loss-1.6578, acc-0.5356, test loss-1.6588, acc-0.5250
Iter-98640, train loss-1.7741, acc-0.4200, valid loss-1.6578, acc-0.5358, test loss-1.6587, acc-0.5250
Iter-98650, train loss-1.7853, acc-0.6000, valid loss-1.6577, acc-0.5356, test loss-1.6587, acc-0.5250
Iter-98660, train loss-1.7031, acc-0.4800, valid loss-1.6577, acc-0.5358, test loss-1.6587, acc-0.5250
Iter-98670, train loss-1.5957, acc-0.6200, valid loss-1.6577, acc-0.5358, test loss-1.6586, acc-0.5250
Iter-98680, train loss-1.8001, acc-0.4800, valid loss-1.6576, acc-0.5358, test loss-1.6586, acc-0.5249
Iter-98690, train loss-1.7252, acc-0.4400, valid loss-1.6576, acc-0.5358, test loss-1.6586, acc-0.5251
Iter-98700, train loss-1.6864, acc-0.4400, valid loss-1.6576, acc-0.5358, test loss-1.6586, acc-0.5252
Iter-98710, train loss-1.6164, acc-0.5800, valid loss-1.6576, acc-0.5356, test loss-1.6585, acc-0.5254
Iter-98720, train loss-1.6433, acc-0.4800, valid loss-1.6575, acc-0.5356, test loss-1.6585, acc-0.5254
Iter-98730, train loss-1.6380, acc-0.5600, valid loss-1.6575, acc-0.5358, test loss-1.6585, acc-0.5252
Iter-98740, train loss-1.7307, acc-0.5000, valid loss-1.6575, acc-0.5358, test loss-1.6584, acc-0.5252
Iter-98750, train loss-1.7290, acc-0.5200, valid loss-1.6574, acc-0.5358, test loss-1.6584, acc-0.5254
Iter-98760, train loss-1.7619, acc-0.4200, valid loss-1.6574, acc-0.5358, test loss-1.6584, acc-0.5254
Iter-98770, train loss-1.6047, acc-0.5600, valid loss-1.6574, acc-0.5358, test loss-1.6583, acc-0.5254
Iter-98780, train loss-1.5558, acc-0.5800, valid loss-1.6573, acc-0.5360, test loss-1.6583, acc-0.5251
Iter-98790, train loss-1.6383, acc-0.6200, valid loss-1.6573, acc-0.5358, test loss-1.6583, acc-0.5253
Iter-98800, train loss-1.6293, acc-0.4800, valid loss-1.6573, acc-0.5358, test loss-1.6582, acc-0.5254
Iter-98810, train loss-1.6033, acc-0.4600, valid loss-1.6572, acc-0.5358, test loss-1.6582, acc-0.5255
Iter-98820, train loss-1.6297, acc-0.5400, valid loss-1.6572, acc-0.5358, test loss-1.6582, acc-0.5254
Iter-98830, train loss-1.6372, acc-0.5200, valid loss-1.6572, acc-0.5358, test loss-1.6582, acc-0.5252
Iter-98840, train loss-1.7126, acc-0.5400, valid loss-1.6571, acc-0.5358, test loss-1.6581, acc-0.5251
Iter-98850, train loss-1.7679, acc-0.5400, valid loss-1.6571, acc-0.5358, test loss-1.6581, acc-0.5251
Iter-98860, train loss-1.6927, acc-0.5800, valid loss-1.6571, acc-0.5358, test loss-1.6581, acc-0.5251
Iter-98870, train loss-1.8176, acc-0.4200, valid loss-1.6571, acc-0.5358, test loss-1.6580, acc-0.5251
Iter-98880, train loss-1.6699, acc-0.4800, valid loss-1.6570, acc-0.5358, test loss-1.6580, acc-0.5253
Iter-98890, train loss-1.6909, acc-0.5200, valid loss-1.6570, acc-0.5360, test loss-1.6580, acc-0.5254
Iter-98900, train loss-1.6190, acc-0.6400, valid loss-1.6570, acc-0.5358, test loss-1.6579, acc-0.5253
Iter-98910, train loss-1.5874, acc-0.6200, valid loss-1.6569, acc-0.5358, test loss-1.6579, acc-0.5254
Iter-98920, train loss-1.7046, acc-0.4600, valid loss-1.6569, acc-0.5358, test loss-1.6579, acc-0.5254
Iter-98930, train loss-1.6798, acc-0.5800, valid loss-1.6569, acc-0.5358, test loss-1.6579, acc-0.5254
Iter-98940, train loss-1.6638, acc-0.5800, valid loss-1.6568, acc-0.5358, test loss-1.6578, acc-0.5254
Iter-98950, train loss-1.6331, acc-0.5400, valid loss-1.6568, acc-0.5358, test loss-1.6578, acc-0.5254
Iter-98960, train loss-1.5865, acc-0.5400, valid loss-1.6568, acc-0.5356, test loss-1.6578, acc-0.5255
Iter-98970, train loss-1.6145, acc-0.5800, valid loss-1.6567, acc-0.5356, test loss-1.6577, acc-0.5256
Iter-98980, train loss-1.7339, acc-0.3800, valid loss-1.6567, acc-0.5356, test loss-1.6577, acc-0.5256
Iter-98990, train loss-1.8171, acc-0.4400, valid loss-1.6567, acc-0.5356, test loss-1.6577, acc-0.5257
Iter-99000, train loss-1.6642, acc-0.5000, valid loss-1.6567, acc-0.5356, test loss-1.6576, acc-0.5258
Iter-99010, train loss-1.6869, acc-0.4200, valid loss-1.6566, acc-0.5356, test loss-1.6576, acc-0.5257
Iter-99020, train loss-1.5987, acc-0.5600, valid loss-1.6566, acc-0.5358, test loss-1.6576, acc-0.5257
Iter-99030, train loss-1.7056, acc-0.5600, valid loss-1.6566, acc-0.5358, test loss-1.6575, acc-0.5259
Iter-99040, train loss-1.5608, acc-0.6200, valid loss-1.6565, acc-0.5354, test loss-1.6575, acc-0.5258
Iter-99050, train loss-1.7135, acc-0.4200, valid loss-1.6565, acc-0.5354, test loss-1.6575, acc-0.5258
Iter-99060, train loss-1.6806, acc-0.5000, valid loss-1.6565, acc-0.5358, test loss-1.6575, acc-0.5258
Iter-99070, train loss-1.8237, acc-0.4200, valid loss-1.6564, acc-0.5358, test loss-1.6574, acc-0.5258
Iter-99080, train loss-1.7300, acc-0.5000, valid loss-1.6564, acc-0.5356, test loss-1.6574, acc-0.5257
Iter-99090, train loss-1.7781, acc-0.4600, valid loss-1.6564, acc-0.5356, test loss-1.6574, acc-0.5257
Iter-99100, train loss-1.6156, acc-0.5400, valid loss-1.6564, acc-0.5358, test loss-1.6573, acc-0.5258
Iter-99110, train loss-1.6535, acc-0.5600, valid loss-1.6563, acc-0.5356, test loss-1.6573, acc-0.5260
Iter-99120, train loss-1.8170, acc-0.3600, valid loss-1.6563, acc-0.5356, test loss-1.6573, acc-0.5261
Iter-99130, train loss-1.5261, acc-0.7800, valid loss-1.6563, acc-0.5356, test loss-1.6573, acc-0.5260
Iter-99140, train loss-1.5746, acc-0.5400, valid loss-1.6562, acc-0.5356, test loss-1.6572, acc-0.5260
Iter-99150, train loss-1.7252, acc-0.4800, valid loss-1.6562, acc-0.5356, test loss-1.6572, acc-0.5260
Iter-99160, train loss-1.7166, acc-0.4800, valid loss-1.6562, acc-0.5354, test loss-1.6572, acc-0.5262
Iter-99170, train loss-1.6651, acc-0.5400, valid loss-1.6561, acc-0.5356, test loss-1.6571, acc-0.5261
Iter-99180, train loss-1.7245, acc-0.4600, valid loss-1.6561, acc-0.5354, test loss-1.6571, acc-0.5259
Iter-99190, train loss-1.6338, acc-0.6000, valid loss-1.6561, acc-0.5356, test loss-1.6571, acc-0.5259
Iter-99200, train loss-1.7196, acc-0.5000, valid loss-1.6560, acc-0.5354, test loss-1.6570, acc-0.5261
Iter-99210, train loss-1.7010, acc-0.5200, valid loss-1.6560, acc-0.5356, test loss-1.6570, acc-0.5261
Iter-99220, train loss-1.6608, acc-0.5200, valid loss-1.6560, acc-0.5356, test loss-1.6570, acc-0.5261
Iter-99230, train loss-1.5847, acc-0.5600, valid loss-1.6560, acc-0.5356, test loss-1.6569, acc-0.5261
Iter-99240, train loss-1.5936, acc-0.5800, valid loss-1.6559, acc-0.5356, test loss-1.6569, acc-0.5262
Iter-99250, train loss-1.5916, acc-0.5600, valid loss-1.6559, acc-0.5356, test loss-1.6569, acc-0.5263
Iter-99260, train loss-1.6160, acc-0.5200, valid loss-1.6559, acc-0.5358, test loss-1.6569, acc-0.5261
Iter-99270, train loss-1.7047, acc-0.5200, valid loss-1.6558, acc-0.5358, test loss-1.6568, acc-0.5261
Iter-99280, train loss-1.7221, acc-0.5000, valid loss-1.6558, acc-0.5360, test loss-1.6568, acc-0.5262
Iter-99290, train loss-1.6253, acc-0.5800, valid loss-1.6558, acc-0.5358, test loss-1.6568, acc-0.5262
Iter-99300, train loss-1.6760, acc-0.5800, valid loss-1.6557, acc-0.5356, test loss-1.6567, acc-0.5263
Iter-99310, train loss-1.7428, acc-0.4200, valid loss-1.6557, acc-0.5358, test loss-1.6567, acc-0.5262
Iter-99320, train loss-1.6097, acc-0.6000, valid loss-1.6557, acc-0.5356, test loss-1.6567, acc-0.5264
Iter-99330, train loss-1.6845, acc-0.6000, valid loss-1.6556, acc-0.5354, test loss-1.6566, acc-0.5263
Iter-99340, train loss-1.6456, acc-0.5000, valid loss-1.6556, acc-0.5354, test loss-1.6566, acc-0.5262
Iter-99350, train loss-1.6510, acc-0.5200, valid loss-1.6556, acc-0.5354, test loss-1.6566, acc-0.5262
Iter-99360, train loss-1.6550, acc-0.5600, valid loss-1.6555, acc-0.5356, test loss-1.6566, acc-0.5266
Iter-99370, train loss-1.6770, acc-0.5000, valid loss-1.6555, acc-0.5352, test loss-1.6565, acc-0.5264
Iter-99380, train loss-1.6005, acc-0.6400, valid loss-1.6555, acc-0.5354, test loss-1.6565, acc-0.5265
Iter-99390, train loss-1.5987, acc-0.5200, valid loss-1.6555, acc-0.5356, test loss-1.6565, acc-0.5264
Iter-99400, train loss-1.6357, acc-0.5600, valid loss-1.6554, acc-0.5354, test loss-1.6564, acc-0.5263
Iter-99410, train loss-1.6914, acc-0.5200, valid loss-1.6554, acc-0.5354, test loss-1.6564, acc-0.5262
Iter-99420, train loss-1.6545, acc-0.5200, valid loss-1.6554, acc-0.5354, test loss-1.6564, acc-0.5262
Iter-99430, train loss-1.6553, acc-0.5200, valid loss-1.6553, acc-0.5354, test loss-1.6563, acc-0.5261
Iter-99440, train loss-1.8029, acc-0.4000, valid loss-1.6553, acc-0.5354, test loss-1.6563, acc-0.5262
Iter-99450, train loss-1.5863, acc-0.7200, valid loss-1.6553, acc-0.5354, test loss-1.6563, acc-0.5262
Iter-99460, train loss-1.6838, acc-0.5400, valid loss-1.6552, acc-0.5354, test loss-1.6563, acc-0.5261
Iter-99470, train loss-1.7066, acc-0.5400, valid loss-1.6552, acc-0.5354, test loss-1.6562, acc-0.5264
Iter-99480, train loss-1.6925, acc-0.5600, valid loss-1.6552, acc-0.5354, test loss-1.6562, acc-0.5262
Iter-99490, train loss-1.6731, acc-0.5400, valid loss-1.6552, acc-0.5354, test loss-1.6562, acc-0.5262
Iter-99500, train loss-1.6291, acc-0.6800, valid loss-1.6551, acc-0.5354, test loss-1.6561, acc-0.5262
Iter-99510, train loss-1.5468, acc-0.6000, valid loss-1.6551, acc-0.5354, test loss-1.6561, acc-0.5262
Iter-99520, train loss-1.7279, acc-0.5200, valid loss-1.6551, acc-0.5354, test loss-1.6561, acc-0.5261
Iter-99530, train loss-1.6266, acc-0.5600, valid loss-1.6550, acc-0.5352, test loss-1.6561, acc-0.5261
Iter-99540, train loss-1.6471, acc-0.5600, valid loss-1.6550, acc-0.5348, test loss-1.6560, acc-0.5261
Iter-99550, train loss-1.6710, acc-0.5000, valid loss-1.6550, acc-0.5348, test loss-1.6560, acc-0.5261
Iter-99560, train loss-1.6901, acc-0.5200, valid loss-1.6550, acc-0.5348, test loss-1.6560, acc-0.5261
Iter-99570, train loss-1.7220, acc-0.4400, valid loss-1.6549, acc-0.5348, test loss-1.6559, acc-0.5261
Iter-99580, train loss-1.6968, acc-0.5400, valid loss-1.6549, acc-0.5346, test loss-1.6559, acc-0.5263
Iter-99590, train loss-1.6991, acc-0.4800, valid loss-1.6549, acc-0.5346, test loss-1.6559, acc-0.5262
Iter-99600, train loss-1.8098, acc-0.4800, valid loss-1.6548, acc-0.5346, test loss-1.6559, acc-0.5262
Iter-99610, train loss-1.6541, acc-0.5400, valid loss-1.6548, acc-0.5346, test loss-1.6558, acc-0.5262
Iter-99620, train loss-1.7085, acc-0.4400, valid loss-1.6548, acc-0.5346, test loss-1.6558, acc-0.5261
Iter-99630, train loss-1.5575, acc-0.6000, valid loss-1.6547, acc-0.5346, test loss-1.6558, acc-0.5261
Iter-99640, train loss-1.5992, acc-0.5200, valid loss-1.6547, acc-0.5346, test loss-1.6557, acc-0.5261
Iter-99650, train loss-1.5991, acc-0.5200, valid loss-1.6547, acc-0.5346, test loss-1.6557, acc-0.5262
Iter-99660, train loss-1.6469, acc-0.5200, valid loss-1.6546, acc-0.5346, test loss-1.6557, acc-0.5261
Iter-99670, train loss-1.7094, acc-0.5600, valid loss-1.6546, acc-0.5344, test loss-1.6556, acc-0.5261
Iter-99680, train loss-1.6624, acc-0.4800, valid loss-1.6546, acc-0.5346, test loss-1.6556, acc-0.5262
Iter-99690, train loss-1.8082, acc-0.4200, valid loss-1.6546, acc-0.5346, test loss-1.6556, acc-0.5263
Iter-99700, train loss-1.7924, acc-0.4200, valid loss-1.6545, acc-0.5346, test loss-1.6556, acc-0.5262
Iter-99710, train loss-1.6060, acc-0.5200, valid loss-1.6545, acc-0.5346, test loss-1.6555, acc-0.5264
Iter-99720, train loss-1.5224, acc-0.5800, valid loss-1.6545, acc-0.5346, test loss-1.6555, acc-0.5263
Iter-99730, train loss-1.7465, acc-0.5000, valid loss-1.6544, acc-0.5348, test loss-1.6555, acc-0.5263
Iter-99740, train loss-1.7945, acc-0.4600, valid loss-1.6544, acc-0.5348, test loss-1.6554, acc-0.5262
Iter-99750, train loss-1.6408, acc-0.5600, valid loss-1.6544, acc-0.5346, test loss-1.6554, acc-0.5263
Iter-99760, train loss-1.7235, acc-0.4600, valid loss-1.6543, acc-0.5344, test loss-1.6554, acc-0.5263
Iter-99770, train loss-1.6984, acc-0.5200, valid loss-1.6543, acc-0.5346, test loss-1.6553, acc-0.5262
Iter-99780, train loss-1.6542, acc-0.5200, valid loss-1.6543, acc-0.5346, test loss-1.6553, acc-0.5263
Iter-99790, train loss-1.8239, acc-0.4400, valid loss-1.6543, acc-0.5346, test loss-1.6553, acc-0.5262
Iter-99800, train loss-1.6519, acc-0.6200, valid loss-1.6542, acc-0.5344, test loss-1.6552, acc-0.5263
Iter-99810, train loss-1.6421, acc-0.5600, valid loss-1.6542, acc-0.5346, test loss-1.6552, acc-0.5263
Iter-99820, train loss-1.6817, acc-0.4800, valid loss-1.6542, acc-0.5346, test loss-1.6552, acc-0.5262
Iter-99830, train loss-1.6281, acc-0.6000, valid loss-1.6541, acc-0.5346, test loss-1.6552, acc-0.5261
Iter-99840, train loss-1.6548, acc-0.4200, valid loss-1.6541, acc-0.5346, test loss-1.6551, acc-0.5262
Iter-99850, train loss-1.6037, acc-0.5600, valid loss-1.6541, acc-0.5346, test loss-1.6551, acc-0.5262
Iter-99860, train loss-1.6737, acc-0.5600, valid loss-1.6540, acc-0.5348, test loss-1.6551, acc-0.5262
Iter-99870, train loss-1.6519, acc-0.4800, valid loss-1.6540, acc-0.5346, test loss-1.6550, acc-0.5262
Iter-99880, train loss-1.6721, acc-0.5200, valid loss-1.6540, acc-0.5348, test loss-1.6550, acc-0.5264
Iter-99890, train loss-1.5443, acc-0.6000, valid loss-1.6540, acc-0.5346, test loss-1.6550, acc-0.5262
Iter-99900, train loss-1.5888, acc-0.6200, valid loss-1.6539, acc-0.5348, test loss-1.6549, acc-0.5262
Iter-99910, train loss-1.7764, acc-0.4200, valid loss-1.6539, acc-0.5346, test loss-1.6549, acc-0.5262
Iter-99920, train loss-1.6889, acc-0.5000, valid loss-1.6539, acc-0.5346, test loss-1.6549, acc-0.5262
Iter-99930, train loss-1.6296, acc-0.6400, valid loss-1.6538, acc-0.5346, test loss-1.6549, acc-0.5261
Iter-99940, train loss-1.6910, acc-0.4800, valid loss-1.6538, acc-0.5348, test loss-1.6548, acc-0.5262
Iter-99950, train loss-1.6889, acc-0.5200, valid loss-1.6538, acc-0.5348, test loss-1.6548, acc-0.5260
Iter-99960, train loss-1.7671, acc-0.5200, valid loss-1.6538, acc-0.5348, test loss-1.6548, acc-0.5264
Iter-99970, train loss-1.6109, acc-0.5800, valid loss-1.6537, acc-0.5348, test loss-1.6547, acc-0.5261
Iter-99980, train loss-1.6699, acc-0.5600, valid loss-1.6537, acc-0.5352, test loss-1.6547, acc-0.5264
Iter-99990, train loss-1.6320, acc-0.5200, valid loss-1.6537, acc-0.5352, test loss-1.6547, acc-0.5264
Iter-100000, train loss-1.5977, acc-0.4800, valid loss-1.6536, acc-0.5350, test loss-1.6546, acc-0.5261
In [5]:
# # Display the learning curve and losses for training, validation, and testing
# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
plt.plot(nn.losses['train'], label='Train loss')
plt.plot(nn.losses['valid'], label='Valid loss')
plt.plot(nn.losses['test'], label='Test loss')
plt.legend()
plt.show()
In [6]:
plt.plot(nn.losses['train_acc'], label='Train accuracy')
plt.plot(nn.losses['valid_acc'], label='Valid accuracy')
plt.plot(nn.losses['test_acc'], label='Test accuracy')
plt.legend()
plt.show()
In [ ]:
In [ ]:
Content source: arasdar/DL
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