In [1]:
import numpy as np
import math
import random
import pandas as pd
import os
import matplotlib.pyplot as plt
import cv2
import glob
from tqdm import tqdm
import pickle
import scipy.ndimage.interpolation as inter
from scipy.signal import medfilt
from scipy.spatial.distance import cdist
from keras.optimizers import *
from keras.models import Model
from keras.layers import *
from keras.layers.core import *
from tensorflow.keras.callbacks import *
from keras.layers.convolutional import *
from keras import backend as K
import tensorflow as tf
import google.colab.files
Using TensorFlow backend.
In [0]:
random.seed(1234)
class Config():
def __init__(self):
self.frame_l = 32 # the length of frames
self.joint_n = 15 # the number of joints
self.joint_d = 2 # the dimension of joints
self.clc_num = 21 # the number of class
self.feat_d = 105
self.filters = 64
C = Config()
In [0]:
# Temple resizing function
def zoom(p,target_l=64,joints_num=25,joints_dim=3):
l = p.shape[0]
p_new = np.empty([target_l,joints_num,joints_dim])
for m in range(joints_num):
for n in range(joints_dim):
p_new[:,m,n] = medfilt(p_new[:,m,n],3)
p_new[:,m,n] = inter.zoom(p[:,m,n],target_l/l)[:target_l]
return p_new
# Calculate JCD feature
def norm_scale(x):
return (x-np.mean(x))/np.mean(x)
def get_CG(p,C):
M = []
iu = np.triu_indices(C.joint_n,1,C.joint_n)
for f in range(C.frame_l):
d_m = cdist(p[f],p[f],'euclidean')
d_m = d_m[iu]
M.append(d_m)
M = np.stack(M)
M = norm_scale(M)
return M
# Genrate dataset
def data_generator(T,C,le):
X_0 = []
X_1 = []
X_2 = []
Y = []
limb_locs = [(0,2),(4,0),(3,0),(8,4),(7,3),(12,8),(11,7),(1,0),(6,1),(5,1),(10,6),(9,5),(14,10),(13,9)]
print('len_t_pose:',len(T['pose']))
for i in tqdm(range(len(T['pose']))):
p = np.copy(T['pose'][i])
q = np.copy(p)
q = np.pad(q,((0,0),(0,0),(0,1)), constant_values=0)
qc = np.zeros((q.shape[0],q.shape[1]-1,q.shape[2]))
normal_vec_pos = 0
for j1,j2 in limb_locs:
joint1_section = np.copy(q[:,j1,:])
joint2_section = np.copy(q[:,j2,:])
limb_mat = joint1_section - joint2_section
obs_vec_mat = np.copy(joint1_section)
#print('obs_vec_mat.size:',obs_vec_mat.size())
#print('opx.size:',self.opx.size())
obs_vec_mat[:,0] = obs_vec_mat[:,0] - 0.023900129681375948
obs_vec_mat[:,1] = obs_vec_mat[:,1] - 0.37827336942214096
obs_vec_mat[:,2] = obs_vec_mat[:,2] - 1
qc[:,normal_vec_pos,:] = np.cross(limb_mat, obs_vec_mat, axis=1)
normal_vec_pos += 1
if i == 0:
print('p.shape:',p.shape)
print('q.shape:',q.shape)
p = zoom(p,target_l=C.frame_l,joints_num=C.joint_n,joints_dim=C.joint_d)
qcz = zoom(qc,target_l=C.frame_l,joints_num=C.joint_n-1,joints_dim=C.joint_d+1)
if i == 0:
print('p1.shape:',p.shape)
print('q1.shape:',q.shape)
print('qc.shape:',qc.shape)
print('qcz.shape:',qcz.shape)
label = np.zeros(C.clc_num)
label[le.transform(T['label'])[i]-1] = 1
M = get_CG(p,C)
if i == 0:
print('M.shape:',M.shape)
X_0.append(M)
X_1.append(p)
X_2.append(qcz)
Y.append(label)
X_0 = np.stack(X_0)
X_1 = np.stack(X_1)
X_2 = np.stack(X_2)
Y = np.stack(Y)
return X_0,X_1,X_2,Y
In [0]:
def poses_diff(x):
H, W = x.get_shape()[1],x.get_shape()[2]
x = tf.subtract(x[:,1:,...],x[:,:-1,...])
x = tf.compat.v1.image.resize_nearest_neighbor(x,size=[H,W],align_corners=False) # should not alignment here
return x
def pose_motion(P,frame_l):
#print('pose_motion: P.shape:',P.shape)
P_diff_slow = Lambda(lambda x: poses_diff(x))(P)
#print('pose_motion: P_diff_slow.shape:',P_diff_slow.shape)
P_diff_slow = Reshape((frame_l,-1))(P_diff_slow)
#print('pose_motion: P_diff_slow1.shape:',P_diff_slow.shape)
P_fast = Lambda(lambda x: x[:,::2,...])(P)
#print('pose_motion: P_fast.shape:',P_fast.shape)
P_diff_fast = Lambda(lambda x: poses_diff(x))(P_fast)
#print('pose_motion: P_diff_fast.shape:',P_diff_fast.shape)
P_diff_fast = Reshape((int(frame_l/2),-1))(P_diff_fast)
#print('pose_motion: P_diff_fast1.shape:',P_diff_fast.shape)
return P_diff_slow,P_diff_fast
def c1D(x,filters,kernel):
#print('c1D - x.shape:',x.shape)
x = Conv1D(filters, kernel_size=kernel,padding='same',use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
return x
def block(x,filters):
x = c1D(x,filters,3)
x = c1D(x,filters,3)
return x
def d1D(x,filters):
x = Dense(filters,use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
return x
def build_FM(frame_l=32,joint_n=22,joint_d=2,feat_d=231,filters=16):
M = Input(shape=(frame_l,feat_d))
P = Input(shape=(frame_l,joint_n,joint_d))
Q = Input(shape=(frame_l,14,3))
diff_slow,diff_fast = pose_motion(P,frame_l)
#print('M.s:',M.shape)
x = c1D(M,filters*2,1)
x = SpatialDropout1D(0.1)(x)
x = c1D(x,filters,3)
x = SpatialDropout1D(0.1)(x)
x = c1D(x,filters,1)
x = MaxPooling1D(2)(x)
x = SpatialDropout1D(0.1)(x)
#print('P.s:',P.shape)
#print('diff_slow.s:',diff_slow.shape)
x_d_slow = c1D(diff_slow,filters*2,1)
x_d_slow = SpatialDropout1D(0.1)(x_d_slow)
x_d_slow = c1D(x_d_slow,filters,3)
x_d_slow = SpatialDropout1D(0.1)(x_d_slow)
x_d_slow = c1D(x_d_slow,filters,1)
x_d_slow = MaxPool1D(2)(x_d_slow)
x_d_slow = SpatialDropout1D(0.1)(x_d_slow)
#print('diff_fast.s:',diff_fast.shape)
x_d_fast = c1D(diff_fast,filters*2,1)
x_d_fast = SpatialDropout1D(0.1)(x_d_fast)
x_d_fast = c1D(x_d_fast,filters,3)
x_d_fast = SpatialDropout1D(0.1)(x_d_fast)
x_d_fast = c1D(x_d_fast,filters,1)
x_d_fast = SpatialDropout1D(0.1)(x_d_fast)
Q_reshaped = Reshape((frame_l,-1))(Q)
x_cp = c1D(Q_reshaped,filters*2,1)
x_cp = SpatialDropout1D(0.1)(x_cp)
x_cp = c1D(x_cp,filters,3)
x_cp = SpatialDropout1D(0.1)(x_cp)
x_cp = c1D(x_cp,filters,1)
x_cp = MaxPool1D(2)(x_cp)
x_cp = SpatialDropout1D(0.1)(x_cp)
x = concatenate([x,x_d_slow,x_d_fast, x_cp])
#x1 = concatenate([x,x_d_slow,x_d_fast, x_cp])
#print('mdl: x.shape:',x.shape,',x1.shape:', x1.shape)
x = block(x,filters*2)
x = MaxPool1D(2)(x)
x = SpatialDropout1D(0.1)(x)
x = block(x,filters*4)
x = MaxPool1D(2)(x)
x = SpatialDropout1D(0.1)(x)
x = block(x,filters*8)
x = SpatialDropout1D(0.1)(x)
return Model(inputs=[M,P,Q],outputs=x)
def build_DD_Net(C):
M = Input(name='M', shape=(C.frame_l,C.feat_d))
P = Input(name='P', shape=(C.frame_l,C.joint_n,C.joint_d))
Q = Input(name='Q', shape=(C.frame_l,14,3))
FM = build_FM(C.frame_l,C.joint_n,C.joint_d,C.feat_d,C.filters)
x = FM([M,P,Q])
x = GlobalMaxPool1D()(x)
x = d1D(x,128)
x = Dropout(0.5)(x)
x = d1D(x,128)
x = Dropout(0.5)(x)
x = Dense(C.clc_num, activation='softmax')(x)
######################Self-supervised part
model = Model(inputs=[M,P,Q],outputs=x)
return model
In [5]:
DD_Net = build_DD_Net(C)
DD_Net.summary()
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
M (InputLayer) (None, 32, 105) 0
__________________________________________________________________________________________________
P (InputLayer) (None, 32, 15, 2) 0
__________________________________________________________________________________________________
Q (InputLayer) (None, 32, 14, 3) 0
__________________________________________________________________________________________________
model_1 (Model) (None, 4, 512) 1774464 M[0][0]
P[0][0]
Q[0][0]
__________________________________________________________________________________________________
global_max_pooling1d_1 (GlobalM (None, 512) 0 model_1[1][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 128) 65536 global_max_pooling1d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 128) 512 dense_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU) (None, 128) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 128) 0 leaky_re_lu_19[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 128) 16384 dropout_1[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 128) 512 dense_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU) (None, 128) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 128) 0 leaky_re_lu_20[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 21) 2709 dropout_2[0][0]
==================================================================================================
Total params: 1,860,117
Trainable params: 1,853,973
Non-trainable params: 6,144
__________________________________________________________________________________________________
In [10]:
uploaded = google.colab.files.upload()
Saving GT_test_1.pkl to GT_test_1 (1).pkl
Saving GT_test_2.pkl to GT_test_2 (1).pkl
Saving GT_test_3.pkl to GT_test_3 (1).pkl
Saving GT_train_1.pkl to GT_train_1 (1).pkl
Saving GT_train_2.pkl to GT_train_2 (1).pkl
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-10-d36dcde5bd00> in <module>()
----> 1 uploaded = google.colab.files.upload()
/usr/local/lib/python3.6/dist-packages/google/colab/files.py in upload()
70 result = _output.eval_js(
71 'google.colab._files._uploadFilesContinue("{output_id}")'.format(
---> 72 output_id=output_id))
73 if result['action'] != 'append':
74 # JS side uses a generator of promises to process all of the files- some
/usr/local/lib/python3.6/dist-packages/google/colab/output/_js.py in eval_js(script, ignore_result)
37 if ignore_result:
38 return
---> 39 return _message.read_reply_from_input(request_id)
40
41
/usr/local/lib/python3.6/dist-packages/google/colab/_message.py in read_reply_from_input(message_id, timeout_sec)
99 reply = _read_next_input_message()
100 if reply == _NOT_READY or not isinstance(reply, dict):
--> 101 time.sleep(0.025)
102 continue
103 if (reply.get('type') == 'colab_reply' and
KeyboardInterrupt:
In [6]:
Train = pickle.load(open("GT_train_2.pkl", "rb"))
Test = pickle.load(open("GT_test_2.pkl", "rb"))
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(Train['label'])
X_0,X_1,X_2,Y = data_generator(Train,C,le)
print('X_0.shape:',X_0.shape,', X_1.shape:',X_1.shape)
X_test_0,X_test_1,X_test_2,Y_test = data_generator(Test,C,le)
2%|▏ | 11/658 [00:00<00:06, 105.58it/s]
len_t_pose: 658
p.shape: (40, 15, 2)
q.shape: (40, 15, 3)
p1.shape: (32, 15, 2)
q1.shape: (40, 15, 3)
qc.shape: (40, 14, 3)
qcz.shape: (32, 14, 3)
M.shape: (32, 105)
100%|██████████| 658/658 [00:06<00:00, 104.92it/s]
4%|▍ | 11/270 [00:00<00:02, 107.12it/s]
X_0.shape: (658, 32, 105) , X_1.shape: (658, 32, 15, 2)
len_t_pose: 270
p.shape: (40, 15, 2)
q.shape: (40, 15, 3)
p1.shape: (32, 15, 2)
q1.shape: (40, 15, 3)
qc.shape: (40, 14, 3)
qcz.shape: (32, 14, 3)
M.shape: (32, 105)
100%|██████████| 270/270 [00:02<00:00, 105.18it/s]
In [7]:
import keras
lr = 1e-3
DD_Net.compile(loss="categorical_crossentropy",optimizer=adam(lr),metrics=['accuracy'])
lrScheduler = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, cooldown=5, min_lr=5e-6)
history = DD_Net.fit([X_0,X_1,X_2],Y,
batch_size=len(Y),
epochs=600,
verbose=True,
shuffle=True,
callbacks=[lrScheduler],
validation_data=([X_test_0,X_test_1,X_test_2],Y_test)
)
lr = 1e-4
DD_Net.compile(loss="categorical_crossentropy",optimizer=adam(lr),metrics=['accuracy'])
lrScheduler = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, cooldown=5, min_lr=5e-6)
history = DD_Net.fit([X_0,X_1,X_2],Y,
batch_size=len(Y),
epochs=600,
verbose=True,
shuffle=True,
callbacks=[lrScheduler],
validation_data=([X_test_0,X_test_1,X_test_2],Y_test)
)
Train on 658 samples, validate on 270 samples
Epoch 1/600
658/658 [==============================] - 8s 13ms/step - loss: 3.7695 - accuracy: 0.0517 - val_loss: 3.0434 - val_accuracy: 0.0741
Epoch 2/600
658/658 [==============================] - 4s 6ms/step - loss: 3.3261 - accuracy: 0.0729 - val_loss: 3.0426 - val_accuracy: 0.0370
Epoch 3/600
658/658 [==============================] - 4s 6ms/step - loss: 3.0368 - accuracy: 0.1444 - val_loss: 3.0407 - val_accuracy: 0.0370
Epoch 4/600
658/658 [==============================] - 4s 6ms/step - loss: 2.8188 - accuracy: 0.1581 - val_loss: 3.0362 - val_accuracy: 0.1037
Epoch 5/600
658/658 [==============================] - 4s 6ms/step - loss: 2.7110 - accuracy: 0.1960 - val_loss: 3.0302 - val_accuracy: 0.1852
Epoch 6/600
658/658 [==============================] - 4s 6ms/step - loss: 2.5466 - accuracy: 0.2416 - val_loss: 3.0243 - val_accuracy: 0.1407
Epoch 7/600
658/658 [==============================] - 4s 6ms/step - loss: 2.4188 - accuracy: 0.2842 - val_loss: 3.0189 - val_accuracy: 0.1667
Epoch 8/600
658/658 [==============================] - 4s 6ms/step - loss: 2.2998 - accuracy: 0.2994 - val_loss: 3.0138 - val_accuracy: 0.1630
Epoch 9/600
658/658 [==============================] - 4s 6ms/step - loss: 2.1701 - accuracy: 0.3723 - val_loss: 3.0088 - val_accuracy: 0.1778
Epoch 10/600
658/658 [==============================] - 4s 6ms/step - loss: 2.0515 - accuracy: 0.3982 - val_loss: 3.0035 - val_accuracy: 0.1704
Epoch 11/600
658/658 [==============================] - 4s 6ms/step - loss: 1.9908 - accuracy: 0.4103 - val_loss: 2.9984 - val_accuracy: 0.1630
Epoch 12/600
658/658 [==============================] - 4s 6ms/step - loss: 1.8779 - accuracy: 0.4225 - val_loss: 2.9924 - val_accuracy: 0.1593
Epoch 13/600
658/658 [==============================] - 4s 6ms/step - loss: 1.8004 - accuracy: 0.4848 - val_loss: 2.9863 - val_accuracy: 0.1519
Epoch 14/600
658/658 [==============================] - 4s 6ms/step - loss: 1.7169 - accuracy: 0.4985 - val_loss: 2.9795 - val_accuracy: 0.1481
Epoch 15/600
658/658 [==============================] - 4s 6ms/step - loss: 1.6371 - accuracy: 0.5517 - val_loss: 2.9724 - val_accuracy: 0.1519
Epoch 16/600
658/658 [==============================] - 4s 6ms/step - loss: 1.5400 - accuracy: 0.5547 - val_loss: 2.9656 - val_accuracy: 0.1593
Epoch 17/600
658/658 [==============================] - 4s 6ms/step - loss: 1.5369 - accuracy: 0.5699 - val_loss: 2.9592 - val_accuracy: 0.1593
Epoch 18/600
658/658 [==============================] - 4s 6ms/step - loss: 1.4190 - accuracy: 0.6125 - val_loss: 2.9528 - val_accuracy: 0.1593
Epoch 19/600
658/658 [==============================] - 4s 6ms/step - loss: 1.3886 - accuracy: 0.6246 - val_loss: 2.9465 - val_accuracy: 0.1593
Epoch 20/600
658/658 [==============================] - 4s 6ms/step - loss: 1.3706 - accuracy: 0.6322 - val_loss: 2.9417 - val_accuracy: 0.1630
Epoch 21/600
658/658 [==============================] - 4s 5ms/step - loss: 1.2827 - accuracy: 0.6672 - val_loss: 2.9375 - val_accuracy: 0.1630
Epoch 22/600
658/658 [==============================] - 4s 6ms/step - loss: 1.2435 - accuracy: 0.6626 - val_loss: 2.9328 - val_accuracy: 0.1593
Epoch 23/600
658/658 [==============================] - 4s 6ms/step - loss: 1.2078 - accuracy: 0.6748 - val_loss: 2.9309 - val_accuracy: 0.1630
Epoch 24/600
658/658 [==============================] - 4s 6ms/step - loss: 1.1600 - accuracy: 0.6717 - val_loss: 2.9292 - val_accuracy: 0.1667
Epoch 25/600
658/658 [==============================] - 4s 6ms/step - loss: 1.1568 - accuracy: 0.7006 - val_loss: 2.9268 - val_accuracy: 0.1667
Epoch 26/600
658/658 [==============================] - 4s 5ms/step - loss: 1.0777 - accuracy: 0.7067 - val_loss: 2.9258 - val_accuracy: 0.1630
Epoch 27/600
658/658 [==============================] - 4s 6ms/step - loss: 1.0539 - accuracy: 0.7234 - val_loss: 2.9253 - val_accuracy: 0.1667
Epoch 28/600
658/658 [==============================] - 4s 6ms/step - loss: 1.0153 - accuracy: 0.7416 - val_loss: 2.9253 - val_accuracy: 0.1667
Epoch 29/600
658/658 [==============================] - 4s 6ms/step - loss: 0.9858 - accuracy: 0.7386 - val_loss: 2.9300 - val_accuracy: 0.1704
Epoch 30/600
658/658 [==============================] - 4s 6ms/step - loss: 0.9014 - accuracy: 0.7857 - val_loss: 2.9353 - val_accuracy: 0.1704
Epoch 31/600
658/658 [==============================] - 4s 6ms/step - loss: 0.9040 - accuracy: 0.7720 - val_loss: 2.9413 - val_accuracy: 0.1704
Epoch 32/600
658/658 [==============================] - 4s 6ms/step - loss: 0.8826 - accuracy: 0.7629 - val_loss: 2.9467 - val_accuracy: 0.1667
Epoch 33/600
658/658 [==============================] - 4s 6ms/step - loss: 0.8299 - accuracy: 0.8100 - val_loss: 2.9509 - val_accuracy: 0.1630
Epoch 34/600
658/658 [==============================] - 4s 6ms/step - loss: 0.8073 - accuracy: 0.7994 - val_loss: 2.9512 - val_accuracy: 0.1593
Epoch 35/600
658/658 [==============================] - 4s 6ms/step - loss: 0.8233 - accuracy: 0.7994 - val_loss: 2.9484 - val_accuracy: 0.1556
Epoch 36/600
658/658 [==============================] - 4s 6ms/step - loss: 0.7665 - accuracy: 0.8100 - val_loss: 2.9422 - val_accuracy: 0.1593
Epoch 37/600
658/658 [==============================] - 4s 6ms/step - loss: 0.7664 - accuracy: 0.8207 - val_loss: 2.9386 - val_accuracy: 0.1704
Epoch 38/600
658/658 [==============================] - 4s 6ms/step - loss: 0.7210 - accuracy: 0.8283 - val_loss: 2.9331 - val_accuracy: 0.1741
Epoch 39/600
658/658 [==============================] - 4s 6ms/step - loss: 0.6799 - accuracy: 0.8511 - val_loss: 2.9331 - val_accuracy: 0.1741
Epoch 40/600
658/658 [==============================] - 4s 6ms/step - loss: 0.6435 - accuracy: 0.8359 - val_loss: 2.9361 - val_accuracy: 0.1741
Epoch 41/600
658/658 [==============================] - 4s 6ms/step - loss: 0.6652 - accuracy: 0.8495 - val_loss: 2.9417 - val_accuracy: 0.1778
Epoch 42/600
658/658 [==============================] - 4s 6ms/step - loss: 0.6025 - accuracy: 0.8708 - val_loss: 2.9520 - val_accuracy: 0.1778
Epoch 43/600
658/658 [==============================] - 4s 6ms/step - loss: 0.6186 - accuracy: 0.8678 - val_loss: 2.9678 - val_accuracy: 0.1778
Epoch 44/600
658/658 [==============================] - 4s 6ms/step - loss: 0.5628 - accuracy: 0.8815 - val_loss: 2.9891 - val_accuracy: 0.1778
Epoch 45/600
658/658 [==============================] - 4s 6ms/step - loss: 0.5337 - accuracy: 0.8967 - val_loss: 3.0046 - val_accuracy: 0.1778
Epoch 46/600
658/658 [==============================] - 4s 6ms/step - loss: 0.5320 - accuracy: 0.8982 - val_loss: 3.0259 - val_accuracy: 0.1778
Epoch 47/600
658/658 [==============================] - 4s 6ms/step - loss: 0.5134 - accuracy: 0.8906 - val_loss: 3.0465 - val_accuracy: 0.1815
Epoch 48/600
658/658 [==============================] - 4s 6ms/step - loss: 0.5059 - accuracy: 0.8967 - val_loss: 3.0673 - val_accuracy: 0.1815
Epoch 49/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4633 - accuracy: 0.8951 - val_loss: 3.0838 - val_accuracy: 0.1815
Epoch 50/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4715 - accuracy: 0.9027 - val_loss: 3.0942 - val_accuracy: 0.1815
Epoch 51/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4282 - accuracy: 0.9164 - val_loss: 3.1039 - val_accuracy: 0.1815
Epoch 52/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4409 - accuracy: 0.9119 - val_loss: 3.1174 - val_accuracy: 0.1815
Epoch 53/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4131 - accuracy: 0.9301 - val_loss: 3.1373 - val_accuracy: 0.1815
Epoch 54/600
658/658 [==============================] - 4s 6ms/step - loss: 0.4101 - accuracy: 0.9438 - val_loss: 3.1706 - val_accuracy: 0.1815
Epoch 55/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3736 - accuracy: 0.9331 - val_loss: 3.2076 - val_accuracy: 0.1815
Epoch 56/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3687 - accuracy: 0.9422 - val_loss: 3.2418 - val_accuracy: 0.1815
Epoch 57/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3512 - accuracy: 0.9529 - val_loss: 3.2608 - val_accuracy: 0.1815
Epoch 58/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3534 - accuracy: 0.9316 - val_loss: 3.2656 - val_accuracy: 0.1778
Epoch 59/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3327 - accuracy: 0.9407 - val_loss: 3.2623 - val_accuracy: 0.1778
Epoch 60/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3293 - accuracy: 0.9438 - val_loss: 3.2557 - val_accuracy: 0.1778
Epoch 61/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3196 - accuracy: 0.9347 - val_loss: 3.2486 - val_accuracy: 0.1815
Epoch 62/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3198 - accuracy: 0.9407 - val_loss: 3.2442 - val_accuracy: 0.1852
Epoch 63/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3102 - accuracy: 0.9453 - val_loss: 3.2401 - val_accuracy: 0.1852
Epoch 64/600
658/658 [==============================] - 4s 6ms/step - loss: 0.3097 - accuracy: 0.9590 - val_loss: 3.2342 - val_accuracy: 0.1852
Epoch 65/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2892 - accuracy: 0.9498 - val_loss: 3.2362 - val_accuracy: 0.1852
Epoch 66/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2598 - accuracy: 0.9620 - val_loss: 3.2522 - val_accuracy: 0.1815
Epoch 67/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2560 - accuracy: 0.9635 - val_loss: 3.2688 - val_accuracy: 0.1815
Epoch 68/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2408 - accuracy: 0.9666 - val_loss: 3.2903 - val_accuracy: 0.1778
Epoch 69/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2733 - accuracy: 0.9453 - val_loss: 3.3140 - val_accuracy: 0.1778
Epoch 70/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2540 - accuracy: 0.9590 - val_loss: 3.3313 - val_accuracy: 0.1778
Epoch 71/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2357 - accuracy: 0.9666 - val_loss: 3.3425 - val_accuracy: 0.1778
Epoch 72/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2209 - accuracy: 0.9681 - val_loss: 3.3577 - val_accuracy: 0.1815
Epoch 73/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2153 - accuracy: 0.9742 - val_loss: 3.3703 - val_accuracy: 0.1815
Epoch 74/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2147 - accuracy: 0.9726 - val_loss: 3.3789 - val_accuracy: 0.1815
Epoch 75/600
658/658 [==============================] - 4s 6ms/step - loss: 0.2134 - accuracy: 0.9711 - val_loss: 3.3896 - val_accuracy: 0.1815
Epoch 76/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1837 - accuracy: 0.9833 - val_loss: 3.4033 - val_accuracy: 0.1815
Epoch 77/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1996 - accuracy: 0.9711 - val_loss: 3.4140 - val_accuracy: 0.1815
Epoch 78/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1791 - accuracy: 0.9787 - val_loss: 3.4283 - val_accuracy: 0.1852
Epoch 79/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1644 - accuracy: 0.9818 - val_loss: 3.4429 - val_accuracy: 0.1889
Epoch 80/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1870 - accuracy: 0.9757 - val_loss: 3.4693 - val_accuracy: 0.1815
Epoch 81/600
658/658 [==============================] - 5s 8ms/step - loss: 0.1653 - accuracy: 0.9833 - val_loss: 3.5088 - val_accuracy: 0.1815
Epoch 82/600
658/658 [==============================] - 6s 9ms/step - loss: 0.1774 - accuracy: 0.9894 - val_loss: 3.5495 - val_accuracy: 0.1778
Epoch 83/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1677 - accuracy: 0.9878 - val_loss: 3.5901 - val_accuracy: 0.1815
Epoch 84/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1729 - accuracy: 0.9757 - val_loss: 3.6202 - val_accuracy: 0.1815
Epoch 85/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1598 - accuracy: 0.9848 - val_loss: 3.6371 - val_accuracy: 0.1815
Epoch 86/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1572 - accuracy: 0.9863 - val_loss: 3.6547 - val_accuracy: 0.1815
Epoch 87/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1608 - accuracy: 0.9848 - val_loss: 3.6688 - val_accuracy: 0.1815
Epoch 88/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1482 - accuracy: 0.9818 - val_loss: 3.6877 - val_accuracy: 0.1815
Epoch 89/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1483 - accuracy: 0.9863 - val_loss: 3.7039 - val_accuracy: 0.1852
Epoch 90/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1286 - accuracy: 0.9924 - val_loss: 3.7213 - val_accuracy: 0.1852
Epoch 91/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1522 - accuracy: 0.9818 - val_loss: 3.7352 - val_accuracy: 0.1852
Epoch 92/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1383 - accuracy: 0.9909 - val_loss: 3.7468 - val_accuracy: 0.1852
Epoch 93/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1458 - accuracy: 0.9787 - val_loss: 3.7580 - val_accuracy: 0.1852
Epoch 94/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1428 - accuracy: 0.9878 - val_loss: 3.7628 - val_accuracy: 0.1852
Epoch 95/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1356 - accuracy: 0.9909 - val_loss: 3.7619 - val_accuracy: 0.1852
Epoch 96/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1294 - accuracy: 0.9863 - val_loss: 3.7625 - val_accuracy: 0.1852
Epoch 97/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1332 - accuracy: 0.9909 - val_loss: 3.7634 - val_accuracy: 0.1815
Epoch 98/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1407 - accuracy: 0.9863 - val_loss: 3.7642 - val_accuracy: 0.1815
Epoch 99/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1297 - accuracy: 0.9878 - val_loss: 3.7649 - val_accuracy: 0.1815
Epoch 100/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1356 - accuracy: 0.9909 - val_loss: 3.7597 - val_accuracy: 0.1852
Epoch 101/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1277 - accuracy: 0.9909 - val_loss: 3.7559 - val_accuracy: 0.1926
Epoch 102/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1290 - accuracy: 0.9863 - val_loss: 3.7521 - val_accuracy: 0.1926
Epoch 103/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1306 - accuracy: 0.9833 - val_loss: 3.7549 - val_accuracy: 0.1926
Epoch 104/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1137 - accuracy: 0.9909 - val_loss: 3.7556 - val_accuracy: 0.1926
Epoch 105/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1170 - accuracy: 0.9924 - val_loss: 3.7523 - val_accuracy: 0.1926
Epoch 106/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1295 - accuracy: 0.9863 - val_loss: 3.7477 - val_accuracy: 0.1926
Epoch 107/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1189 - accuracy: 0.9878 - val_loss: 3.7464 - val_accuracy: 0.1963
Epoch 108/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1237 - accuracy: 0.9894 - val_loss: 3.7451 - val_accuracy: 0.1963
Epoch 109/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1204 - accuracy: 0.9939 - val_loss: 3.7469 - val_accuracy: 0.1963
Epoch 110/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1081 - accuracy: 0.9909 - val_loss: 3.7492 - val_accuracy: 0.1963
Epoch 111/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1399 - accuracy: 0.9818 - val_loss: 3.7519 - val_accuracy: 0.1889
Epoch 112/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1346 - accuracy: 0.9878 - val_loss: 3.7562 - val_accuracy: 0.1889
Epoch 113/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1336 - accuracy: 0.9818 - val_loss: 3.7596 - val_accuracy: 0.1889
Epoch 114/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1208 - accuracy: 0.9878 - val_loss: 3.7617 - val_accuracy: 0.1889
Epoch 115/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1104 - accuracy: 0.9970 - val_loss: 3.7656 - val_accuracy: 0.1889
Epoch 116/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1083 - accuracy: 0.9909 - val_loss: 3.7699 - val_accuracy: 0.1926
Epoch 117/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1222 - accuracy: 0.9848 - val_loss: 3.7762 - val_accuracy: 0.1926
Epoch 118/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1244 - accuracy: 0.9833 - val_loss: 3.7830 - val_accuracy: 0.1926
Epoch 119/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1266 - accuracy: 0.9863 - val_loss: 3.7866 - val_accuracy: 0.1926
Epoch 120/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1264 - accuracy: 0.9863 - val_loss: 3.7894 - val_accuracy: 0.1926
Epoch 121/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1123 - accuracy: 0.9863 - val_loss: 3.7916 - val_accuracy: 0.1926
Epoch 122/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1224 - accuracy: 0.9894 - val_loss: 3.7941 - val_accuracy: 0.1926
Epoch 123/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1099 - accuracy: 0.9939 - val_loss: 3.7979 - val_accuracy: 0.1963
Epoch 124/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1185 - accuracy: 0.9924 - val_loss: 3.8014 - val_accuracy: 0.1963
Epoch 125/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1360 - accuracy: 0.9802 - val_loss: 3.8046 - val_accuracy: 0.1963
Epoch 126/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1304 - accuracy: 0.9909 - val_loss: 3.8076 - val_accuracy: 0.1963
Epoch 127/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1274 - accuracy: 0.9909 - val_loss: 3.8099 - val_accuracy: 0.2000
Epoch 128/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1137 - accuracy: 0.9894 - val_loss: 3.8111 - val_accuracy: 0.2000
Epoch 129/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1287 - accuracy: 0.9909 - val_loss: 3.8141 - val_accuracy: 0.2000
Epoch 130/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1122 - accuracy: 0.9954 - val_loss: 3.8163 - val_accuracy: 0.2000
Epoch 131/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1262 - accuracy: 0.9863 - val_loss: 3.8187 - val_accuracy: 0.2000
Epoch 132/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1169 - accuracy: 0.9894 - val_loss: 3.8206 - val_accuracy: 0.2000
Epoch 133/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1285 - accuracy: 0.9894 - val_loss: 3.8226 - val_accuracy: 0.2000
Epoch 134/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1073 - accuracy: 0.9909 - val_loss: 3.8240 - val_accuracy: 0.2000
Epoch 135/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1089 - accuracy: 0.9939 - val_loss: 3.8271 - val_accuracy: 0.1963
Epoch 136/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1113 - accuracy: 0.9909 - val_loss: 3.8293 - val_accuracy: 0.1963
Epoch 137/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1124 - accuracy: 0.9909 - val_loss: 3.8314 - val_accuracy: 0.1963
Epoch 138/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1156 - accuracy: 0.9909 - val_loss: 3.8345 - val_accuracy: 0.2000
Epoch 139/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1241 - accuracy: 0.9878 - val_loss: 3.8359 - val_accuracy: 0.1963
Epoch 140/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1286 - accuracy: 0.9863 - val_loss: 3.8372 - val_accuracy: 0.1963
Epoch 141/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1354 - accuracy: 0.9833 - val_loss: 3.8384 - val_accuracy: 0.1963
Epoch 142/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1228 - accuracy: 0.9863 - val_loss: 3.8387 - val_accuracy: 0.1963
Epoch 143/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1327 - accuracy: 0.9818 - val_loss: 3.8389 - val_accuracy: 0.1963
Epoch 144/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1161 - accuracy: 0.9863 - val_loss: 3.8387 - val_accuracy: 0.1963
Epoch 145/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1234 - accuracy: 0.9863 - val_loss: 3.8384 - val_accuracy: 0.1963
Epoch 146/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1167 - accuracy: 0.9909 - val_loss: 3.8383 - val_accuracy: 0.1963
Epoch 147/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1149 - accuracy: 0.9909 - val_loss: 3.8382 - val_accuracy: 0.1963
Epoch 148/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9954 - val_loss: 3.8388 - val_accuracy: 0.1926
Epoch 149/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1164 - accuracy: 0.9924 - val_loss: 3.8389 - val_accuracy: 0.1926
Epoch 150/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1158 - accuracy: 0.9894 - val_loss: 3.8379 - val_accuracy: 0.1926
Epoch 151/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1263 - accuracy: 0.9863 - val_loss: 3.8377 - val_accuracy: 0.1926
Epoch 152/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1289 - accuracy: 0.9878 - val_loss: 3.8362 - val_accuracy: 0.1926
Epoch 153/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1314 - accuracy: 0.9848 - val_loss: 3.8350 - val_accuracy: 0.1926
Epoch 154/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1201 - accuracy: 0.9939 - val_loss: 3.8335 - val_accuracy: 0.1926
Epoch 155/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1093 - accuracy: 0.9939 - val_loss: 3.8328 - val_accuracy: 0.1963
Epoch 156/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1157 - accuracy: 0.9924 - val_loss: 3.8314 - val_accuracy: 0.1963
Epoch 157/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1002 - accuracy: 0.9939 - val_loss: 3.8300 - val_accuracy: 0.1963
Epoch 158/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1118 - accuracy: 0.9878 - val_loss: 3.8289 - val_accuracy: 0.1926
Epoch 159/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1012 - accuracy: 0.9909 - val_loss: 3.8271 - val_accuracy: 0.1926
Epoch 160/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1021 - accuracy: 0.9909 - val_loss: 3.8259 - val_accuracy: 0.1926
Epoch 161/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9924 - val_loss: 3.8247 - val_accuracy: 0.1926
Epoch 162/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1110 - accuracy: 0.9909 - val_loss: 3.8234 - val_accuracy: 0.1926
Epoch 163/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1228 - accuracy: 0.9878 - val_loss: 3.8216 - val_accuracy: 0.1889
Epoch 164/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1120 - accuracy: 0.9924 - val_loss: 3.8200 - val_accuracy: 0.1889
Epoch 165/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1105 - accuracy: 0.9939 - val_loss: 3.8182 - val_accuracy: 0.1889
Epoch 166/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1177 - accuracy: 0.9924 - val_loss: 3.8171 - val_accuracy: 0.1889
Epoch 167/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1210 - accuracy: 0.9863 - val_loss: 3.8150 - val_accuracy: 0.1889
Epoch 168/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1360 - accuracy: 0.9772 - val_loss: 3.8130 - val_accuracy: 0.1889
Epoch 169/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1251 - accuracy: 0.9848 - val_loss: 3.8112 - val_accuracy: 0.1889
Epoch 170/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1135 - accuracy: 0.9878 - val_loss: 3.8094 - val_accuracy: 0.1889
Epoch 171/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1137 - accuracy: 0.9924 - val_loss: 3.8070 - val_accuracy: 0.1889
Epoch 172/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1176 - accuracy: 0.9894 - val_loss: 3.8043 - val_accuracy: 0.1889
Epoch 173/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1041 - accuracy: 0.9924 - val_loss: 3.8023 - val_accuracy: 0.1889
Epoch 174/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1088 - accuracy: 0.9924 - val_loss: 3.7997 - val_accuracy: 0.1889
Epoch 175/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1235 - accuracy: 0.9894 - val_loss: 3.7972 - val_accuracy: 0.1889
Epoch 176/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1243 - accuracy: 0.9909 - val_loss: 3.7942 - val_accuracy: 0.1889
Epoch 177/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0988 - accuracy: 0.9954 - val_loss: 3.7916 - val_accuracy: 0.1889
Epoch 178/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1268 - accuracy: 0.9863 - val_loss: 3.7884 - val_accuracy: 0.1889
Epoch 179/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1424 - accuracy: 0.9818 - val_loss: 3.7861 - val_accuracy: 0.1926
Epoch 180/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1244 - accuracy: 0.9894 - val_loss: 3.7823 - val_accuracy: 0.1926
Epoch 181/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1291 - accuracy: 0.9848 - val_loss: 3.7794 - val_accuracy: 0.2000
Epoch 182/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9924 - val_loss: 3.7764 - val_accuracy: 0.2037
Epoch 183/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1155 - accuracy: 0.9939 - val_loss: 3.7731 - val_accuracy: 0.2037
Epoch 184/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1162 - accuracy: 0.9909 - val_loss: 3.7694 - val_accuracy: 0.2037
Epoch 185/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1197 - accuracy: 0.9894 - val_loss: 3.7660 - val_accuracy: 0.2074
Epoch 186/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1094 - accuracy: 0.9924 - val_loss: 3.7625 - val_accuracy: 0.2074
Epoch 187/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1172 - accuracy: 0.9833 - val_loss: 3.7589 - val_accuracy: 0.2074
Epoch 188/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1127 - accuracy: 0.9924 - val_loss: 3.7559 - val_accuracy: 0.2111
Epoch 189/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1204 - accuracy: 0.9894 - val_loss: 3.7523 - val_accuracy: 0.2111
Epoch 190/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0995 - accuracy: 0.9924 - val_loss: 3.7486 - val_accuracy: 0.2111
Epoch 191/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1220 - accuracy: 0.9894 - val_loss: 3.7453 - val_accuracy: 0.2111
Epoch 192/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1147 - accuracy: 0.9894 - val_loss: 3.7416 - val_accuracy: 0.2148
Epoch 193/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1141 - accuracy: 0.9894 - val_loss: 3.7371 - val_accuracy: 0.2148
Epoch 194/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1219 - accuracy: 0.9924 - val_loss: 3.7332 - val_accuracy: 0.2148
Epoch 195/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1151 - accuracy: 0.9878 - val_loss: 3.7289 - val_accuracy: 0.2148
Epoch 196/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1236 - accuracy: 0.9894 - val_loss: 3.7253 - val_accuracy: 0.2148
Epoch 197/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1101 - accuracy: 0.9970 - val_loss: 3.7219 - val_accuracy: 0.2111
Epoch 198/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1079 - accuracy: 0.9939 - val_loss: 3.7177 - val_accuracy: 0.2111
Epoch 199/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1231 - accuracy: 0.9878 - val_loss: 3.7140 - val_accuracy: 0.2148
Epoch 200/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1026 - accuracy: 0.9939 - val_loss: 3.7097 - val_accuracy: 0.2148
Epoch 201/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1017 - accuracy: 0.9939 - val_loss: 3.7054 - val_accuracy: 0.2148
Epoch 202/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1100 - accuracy: 0.9954 - val_loss: 3.7010 - val_accuracy: 0.2148
Epoch 203/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1085 - accuracy: 0.9939 - val_loss: 3.6965 - val_accuracy: 0.2148
Epoch 204/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1152 - accuracy: 0.9863 - val_loss: 3.6921 - val_accuracy: 0.2148
Epoch 205/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1108 - accuracy: 0.9924 - val_loss: 3.6879 - val_accuracy: 0.2148
Epoch 206/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1024 - accuracy: 0.9924 - val_loss: 3.6833 - val_accuracy: 0.2148
Epoch 207/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0976 - accuracy: 0.9939 - val_loss: 3.6786 - val_accuracy: 0.2148
Epoch 208/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1031 - accuracy: 0.9985 - val_loss: 3.6738 - val_accuracy: 0.2185
Epoch 209/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1139 - accuracy: 0.9848 - val_loss: 3.6692 - val_accuracy: 0.2185
Epoch 210/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1152 - accuracy: 0.9924 - val_loss: 3.6642 - val_accuracy: 0.2185
Epoch 211/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1228 - accuracy: 0.9924 - val_loss: 3.6595 - val_accuracy: 0.2185
Epoch 212/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1110 - accuracy: 0.9894 - val_loss: 3.6542 - val_accuracy: 0.2185
Epoch 213/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1026 - accuracy: 0.9954 - val_loss: 3.6494 - val_accuracy: 0.2222
Epoch 214/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9924 - val_loss: 3.6439 - val_accuracy: 0.2222
Epoch 215/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1100 - accuracy: 0.9894 - val_loss: 3.6390 - val_accuracy: 0.2222
Epoch 216/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1161 - accuracy: 0.9909 - val_loss: 3.6332 - val_accuracy: 0.2222
Epoch 217/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1110 - accuracy: 0.9894 - val_loss: 3.6279 - val_accuracy: 0.2259
Epoch 218/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1115 - accuracy: 0.9909 - val_loss: 3.6222 - val_accuracy: 0.2259
Epoch 219/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1210 - accuracy: 0.9924 - val_loss: 3.6167 - val_accuracy: 0.2259
Epoch 220/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1050 - accuracy: 0.9954 - val_loss: 3.6113 - val_accuracy: 0.2259
Epoch 221/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1008 - accuracy: 0.9924 - val_loss: 3.6059 - val_accuracy: 0.2259
Epoch 222/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1093 - accuracy: 0.9954 - val_loss: 3.6004 - val_accuracy: 0.2259
Epoch 223/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9939 - val_loss: 3.5939 - val_accuracy: 0.2259
Epoch 224/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1226 - accuracy: 0.9878 - val_loss: 3.5881 - val_accuracy: 0.2259
Epoch 225/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1086 - accuracy: 0.9985 - val_loss: 3.5822 - val_accuracy: 0.2296
Epoch 226/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1231 - accuracy: 0.9818 - val_loss: 3.5761 - val_accuracy: 0.2296
Epoch 227/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1087 - accuracy: 0.9894 - val_loss: 3.5701 - val_accuracy: 0.2296
Epoch 228/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9954 - val_loss: 3.5641 - val_accuracy: 0.2296
Epoch 229/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1260 - accuracy: 0.9878 - val_loss: 3.5580 - val_accuracy: 0.2333
Epoch 230/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1079 - accuracy: 0.9939 - val_loss: 3.5515 - val_accuracy: 0.2333
Epoch 231/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1173 - accuracy: 0.9894 - val_loss: 3.5451 - val_accuracy: 0.2333
Epoch 232/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1048 - accuracy: 0.9939 - val_loss: 3.5393 - val_accuracy: 0.2370
Epoch 233/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1121 - accuracy: 0.9909 - val_loss: 3.5330 - val_accuracy: 0.2333
Epoch 234/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1196 - accuracy: 0.9894 - val_loss: 3.5269 - val_accuracy: 0.2333
Epoch 235/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1190 - accuracy: 0.9894 - val_loss: 3.5212 - val_accuracy: 0.2296
Epoch 236/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1210 - accuracy: 0.9848 - val_loss: 3.5152 - val_accuracy: 0.2296
Epoch 237/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1032 - accuracy: 0.9954 - val_loss: 3.5089 - val_accuracy: 0.2296
Epoch 238/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1222 - accuracy: 0.9909 - val_loss: 3.5034 - val_accuracy: 0.2296
Epoch 239/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1052 - accuracy: 0.9924 - val_loss: 3.4971 - val_accuracy: 0.2370
Epoch 240/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0988 - accuracy: 0.9954 - val_loss: 3.4911 - val_accuracy: 0.2444
Epoch 241/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1147 - accuracy: 0.9894 - val_loss: 3.4845 - val_accuracy: 0.2481
Epoch 242/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1187 - accuracy: 0.9894 - val_loss: 3.4790 - val_accuracy: 0.2481
Epoch 243/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1163 - accuracy: 0.9954 - val_loss: 3.4727 - val_accuracy: 0.2481
Epoch 244/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1122 - accuracy: 0.9939 - val_loss: 3.4663 - val_accuracy: 0.2481
Epoch 245/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1095 - accuracy: 0.9894 - val_loss: 3.4602 - val_accuracy: 0.2519
Epoch 246/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1202 - accuracy: 0.9909 - val_loss: 3.4534 - val_accuracy: 0.2519
Epoch 247/600
658/658 [==============================] - 6s 10ms/step - loss: 0.1284 - accuracy: 0.9863 - val_loss: 3.4464 - val_accuracy: 0.2519
Epoch 248/600
658/658 [==============================] - 6s 9ms/step - loss: 0.1110 - accuracy: 0.9924 - val_loss: 3.4399 - val_accuracy: 0.2556
Epoch 249/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1085 - accuracy: 0.9939 - val_loss: 3.4334 - val_accuracy: 0.2556
Epoch 250/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1128 - accuracy: 0.9924 - val_loss: 3.4265 - val_accuracy: 0.2556
Epoch 251/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1087 - accuracy: 0.9939 - val_loss: 3.4191 - val_accuracy: 0.2593
Epoch 252/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1280 - accuracy: 0.9878 - val_loss: 3.4122 - val_accuracy: 0.2593
Epoch 253/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1079 - accuracy: 0.9924 - val_loss: 3.4050 - val_accuracy: 0.2593
Epoch 254/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1142 - accuracy: 0.9894 - val_loss: 3.3979 - val_accuracy: 0.2593
Epoch 255/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1027 - accuracy: 0.9924 - val_loss: 3.3908 - val_accuracy: 0.2593
Epoch 256/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1195 - accuracy: 0.9954 - val_loss: 3.3834 - val_accuracy: 0.2593
Epoch 257/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1077 - accuracy: 0.9939 - val_loss: 3.3765 - val_accuracy: 0.2630
Epoch 258/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9909 - val_loss: 3.3692 - val_accuracy: 0.2630
Epoch 259/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1094 - accuracy: 0.9939 - val_loss: 3.3624 - val_accuracy: 0.2667
Epoch 260/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1144 - accuracy: 0.9848 - val_loss: 3.3553 - val_accuracy: 0.2741
Epoch 261/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1197 - accuracy: 0.9878 - val_loss: 3.3479 - val_accuracy: 0.2704
Epoch 262/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1177 - accuracy: 0.9863 - val_loss: 3.3406 - val_accuracy: 0.2704
Epoch 263/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1142 - accuracy: 0.9909 - val_loss: 3.3337 - val_accuracy: 0.2704
Epoch 264/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1095 - accuracy: 0.9924 - val_loss: 3.3261 - val_accuracy: 0.2704
Epoch 265/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1137 - accuracy: 0.9939 - val_loss: 3.3187 - val_accuracy: 0.2704
Epoch 266/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1075 - accuracy: 0.9924 - val_loss: 3.3119 - val_accuracy: 0.2704
Epoch 267/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9939 - val_loss: 3.3047 - val_accuracy: 0.2704
Epoch 268/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1299 - accuracy: 0.9894 - val_loss: 3.2962 - val_accuracy: 0.2704
Epoch 269/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1131 - accuracy: 0.9894 - val_loss: 3.2894 - val_accuracy: 0.2704
Epoch 270/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9909 - val_loss: 3.2820 - val_accuracy: 0.2741
Epoch 271/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1107 - accuracy: 0.9924 - val_loss: 3.2742 - val_accuracy: 0.2741
Epoch 272/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1187 - accuracy: 0.9909 - val_loss: 3.2674 - val_accuracy: 0.2778
Epoch 273/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1335 - accuracy: 0.9848 - val_loss: 3.2602 - val_accuracy: 0.2778
Epoch 274/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1232 - accuracy: 0.9878 - val_loss: 3.2530 - val_accuracy: 0.2778
Epoch 275/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1196 - accuracy: 0.9909 - val_loss: 3.2458 - val_accuracy: 0.2778
Epoch 276/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1073 - accuracy: 0.9954 - val_loss: 3.2379 - val_accuracy: 0.2778
Epoch 277/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1238 - accuracy: 0.9909 - val_loss: 3.2308 - val_accuracy: 0.2778
Epoch 278/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1217 - accuracy: 0.9894 - val_loss: 3.2236 - val_accuracy: 0.2778
Epoch 279/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1401 - accuracy: 0.9802 - val_loss: 3.2159 - val_accuracy: 0.2741
Epoch 280/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1206 - accuracy: 0.9939 - val_loss: 3.2083 - val_accuracy: 0.2741
Epoch 281/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9924 - val_loss: 3.2002 - val_accuracy: 0.2741
Epoch 282/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1124 - accuracy: 0.9878 - val_loss: 3.1924 - val_accuracy: 0.2741
Epoch 283/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9863 - val_loss: 3.1850 - val_accuracy: 0.2741
Epoch 284/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1361 - accuracy: 0.9833 - val_loss: 3.1775 - val_accuracy: 0.2741
Epoch 285/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1099 - accuracy: 0.9894 - val_loss: 3.1701 - val_accuracy: 0.2741
Epoch 286/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1149 - accuracy: 0.9863 - val_loss: 3.1625 - val_accuracy: 0.2778
Epoch 287/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1061 - accuracy: 0.9939 - val_loss: 3.1552 - val_accuracy: 0.2778
Epoch 288/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1087 - accuracy: 0.9939 - val_loss: 3.1476 - val_accuracy: 0.2778
Epoch 289/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1298 - accuracy: 0.9878 - val_loss: 3.1398 - val_accuracy: 0.2815
Epoch 290/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1015 - accuracy: 0.9939 - val_loss: 3.1319 - val_accuracy: 0.2852
Epoch 291/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0998 - accuracy: 0.9954 - val_loss: 3.1243 - val_accuracy: 0.2852
Epoch 292/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1164 - accuracy: 0.9878 - val_loss: 3.1166 - val_accuracy: 0.2852
Epoch 293/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1284 - accuracy: 0.9863 - val_loss: 3.1080 - val_accuracy: 0.2852
Epoch 294/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1148 - accuracy: 0.9863 - val_loss: 3.0992 - val_accuracy: 0.2889
Epoch 295/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9894 - val_loss: 3.0913 - val_accuracy: 0.2889
Epoch 296/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1127 - accuracy: 0.9954 - val_loss: 3.0816 - val_accuracy: 0.2889
Epoch 297/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1207 - accuracy: 0.9878 - val_loss: 3.0733 - val_accuracy: 0.2926
Epoch 298/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1024 - accuracy: 0.9939 - val_loss: 3.0640 - val_accuracy: 0.2963
Epoch 299/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1205 - accuracy: 0.9878 - val_loss: 3.0559 - val_accuracy: 0.2963
Epoch 300/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9939 - val_loss: 3.0478 - val_accuracy: 0.3000
Epoch 301/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1305 - accuracy: 0.9894 - val_loss: 3.0390 - val_accuracy: 0.3000
Epoch 302/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1075 - accuracy: 0.9909 - val_loss: 3.0308 - val_accuracy: 0.3037
Epoch 303/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1202 - accuracy: 0.9894 - val_loss: 3.0225 - val_accuracy: 0.3037
Epoch 304/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0986 - accuracy: 0.9954 - val_loss: 3.0133 - val_accuracy: 0.3074
Epoch 305/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1150 - accuracy: 0.9954 - val_loss: 3.0047 - val_accuracy: 0.3074
Epoch 306/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0954 - accuracy: 0.9939 - val_loss: 2.9963 - val_accuracy: 0.3111
Epoch 307/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9894 - val_loss: 2.9883 - val_accuracy: 0.3111
Epoch 308/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1068 - accuracy: 0.9909 - val_loss: 2.9799 - val_accuracy: 0.3111
Epoch 309/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1167 - accuracy: 0.9894 - val_loss: 2.9714 - val_accuracy: 0.3111
Epoch 310/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1079 - accuracy: 0.9924 - val_loss: 2.9636 - val_accuracy: 0.3111
Epoch 311/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1247 - accuracy: 0.9848 - val_loss: 2.9548 - val_accuracy: 0.3111
Epoch 312/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1203 - accuracy: 0.9894 - val_loss: 2.9464 - val_accuracy: 0.3111
Epoch 313/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1250 - accuracy: 0.9863 - val_loss: 2.9384 - val_accuracy: 0.3111
Epoch 314/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1115 - accuracy: 0.9970 - val_loss: 2.9299 - val_accuracy: 0.3148
Epoch 315/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1268 - accuracy: 0.9909 - val_loss: 2.9209 - val_accuracy: 0.3148
Epoch 316/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1096 - accuracy: 0.9909 - val_loss: 2.9123 - val_accuracy: 0.3148
Epoch 317/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1139 - accuracy: 0.9909 - val_loss: 2.9031 - val_accuracy: 0.3148
Epoch 318/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0975 - accuracy: 0.9924 - val_loss: 2.8939 - val_accuracy: 0.3148
Epoch 319/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1128 - accuracy: 0.9894 - val_loss: 2.8857 - val_accuracy: 0.3148
Epoch 320/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9894 - val_loss: 2.8768 - val_accuracy: 0.3185
Epoch 321/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1201 - accuracy: 0.9924 - val_loss: 2.8681 - val_accuracy: 0.3185
Epoch 322/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1171 - accuracy: 0.9894 - val_loss: 2.8593 - val_accuracy: 0.3185
Epoch 323/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1034 - accuracy: 0.9939 - val_loss: 2.8510 - val_accuracy: 0.3222
Epoch 324/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1188 - accuracy: 0.9924 - val_loss: 2.8428 - val_accuracy: 0.3259
Epoch 325/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1052 - accuracy: 0.9939 - val_loss: 2.8339 - val_accuracy: 0.3259
Epoch 326/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1094 - accuracy: 0.9924 - val_loss: 2.8253 - val_accuracy: 0.3259
Epoch 327/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1082 - accuracy: 0.9894 - val_loss: 2.8167 - val_accuracy: 0.3259
Epoch 328/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1305 - accuracy: 0.9909 - val_loss: 2.8080 - val_accuracy: 0.3259
Epoch 329/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0971 - accuracy: 0.9909 - val_loss: 2.7993 - val_accuracy: 0.3259
Epoch 330/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1164 - accuracy: 0.9924 - val_loss: 2.7906 - val_accuracy: 0.3259
Epoch 331/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9970 - val_loss: 2.7816 - val_accuracy: 0.3259
Epoch 332/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1214 - accuracy: 0.9894 - val_loss: 2.7726 - val_accuracy: 0.3296
Epoch 333/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1141 - accuracy: 0.9924 - val_loss: 2.7634 - val_accuracy: 0.3296
Epoch 334/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1094 - accuracy: 0.9924 - val_loss: 2.7543 - val_accuracy: 0.3296
Epoch 335/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1227 - accuracy: 0.9863 - val_loss: 2.7453 - val_accuracy: 0.3296
Epoch 336/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1178 - accuracy: 0.9802 - val_loss: 2.7364 - val_accuracy: 0.3296
Epoch 337/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0982 - accuracy: 0.9970 - val_loss: 2.7271 - val_accuracy: 0.3296
Epoch 338/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1193 - accuracy: 0.9878 - val_loss: 2.7179 - val_accuracy: 0.3296
Epoch 339/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1233 - accuracy: 0.9878 - val_loss: 2.7084 - val_accuracy: 0.3296
Epoch 340/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1130 - accuracy: 0.9909 - val_loss: 2.6993 - val_accuracy: 0.3296
Epoch 341/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1161 - accuracy: 0.9878 - val_loss: 2.6902 - val_accuracy: 0.3370
Epoch 342/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0998 - accuracy: 0.9894 - val_loss: 2.6806 - val_accuracy: 0.3407
Epoch 343/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1303 - accuracy: 0.9863 - val_loss: 2.6710 - val_accuracy: 0.3407
Epoch 344/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1111 - accuracy: 0.9909 - val_loss: 2.6622 - val_accuracy: 0.3481
Epoch 345/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1153 - accuracy: 0.9939 - val_loss: 2.6531 - val_accuracy: 0.3481
Epoch 346/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1124 - accuracy: 0.9939 - val_loss: 2.6442 - val_accuracy: 0.3556
Epoch 347/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1192 - accuracy: 0.9909 - val_loss: 2.6357 - val_accuracy: 0.3556
Epoch 348/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9909 - val_loss: 2.6272 - val_accuracy: 0.3556
Epoch 349/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1248 - accuracy: 0.9909 - val_loss: 2.6186 - val_accuracy: 0.3556
Epoch 350/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1045 - accuracy: 0.9894 - val_loss: 2.6100 - val_accuracy: 0.3556
Epoch 351/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1176 - accuracy: 0.9909 - val_loss: 2.6011 - val_accuracy: 0.3630
Epoch 352/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1143 - accuracy: 0.9924 - val_loss: 2.5923 - val_accuracy: 0.3630
Epoch 353/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1173 - accuracy: 0.9863 - val_loss: 2.5840 - val_accuracy: 0.3630
Epoch 354/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1113 - accuracy: 0.9909 - val_loss: 2.5750 - val_accuracy: 0.3630
Epoch 355/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1117 - accuracy: 0.9924 - val_loss: 2.5661 - val_accuracy: 0.3630
Epoch 356/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1218 - accuracy: 0.9863 - val_loss: 2.5571 - val_accuracy: 0.3630
Epoch 357/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1229 - accuracy: 0.9909 - val_loss: 2.5477 - val_accuracy: 0.3630
Epoch 358/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1070 - accuracy: 0.9878 - val_loss: 2.5390 - val_accuracy: 0.3667
Epoch 359/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1116 - accuracy: 0.9924 - val_loss: 2.5298 - val_accuracy: 0.3741
Epoch 360/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1077 - accuracy: 0.9924 - val_loss: 2.5202 - val_accuracy: 0.3741
Epoch 361/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1086 - accuracy: 0.9878 - val_loss: 2.5109 - val_accuracy: 0.3778
Epoch 362/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1222 - accuracy: 0.9863 - val_loss: 2.5014 - val_accuracy: 0.3778
Epoch 363/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1234 - accuracy: 0.9894 - val_loss: 2.4924 - val_accuracy: 0.3778
Epoch 364/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1201 - accuracy: 0.9894 - val_loss: 2.4833 - val_accuracy: 0.3778
Epoch 365/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9954 - val_loss: 2.4745 - val_accuracy: 0.3778
Epoch 366/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0978 - accuracy: 0.9970 - val_loss: 2.4655 - val_accuracy: 0.3815
Epoch 367/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1190 - accuracy: 0.9863 - val_loss: 2.4569 - val_accuracy: 0.3815
Epoch 368/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1101 - accuracy: 0.9924 - val_loss: 2.4476 - val_accuracy: 0.3815
Epoch 369/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1217 - accuracy: 0.9878 - val_loss: 2.4387 - val_accuracy: 0.3852
Epoch 370/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1094 - accuracy: 0.9909 - val_loss: 2.4299 - val_accuracy: 0.3852
Epoch 371/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1096 - accuracy: 0.9909 - val_loss: 2.4217 - val_accuracy: 0.3852
Epoch 372/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1052 - accuracy: 0.9924 - val_loss: 2.4133 - val_accuracy: 0.3889
Epoch 373/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1135 - accuracy: 0.9924 - val_loss: 2.4043 - val_accuracy: 0.3852
Epoch 374/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1203 - accuracy: 0.9802 - val_loss: 2.3953 - val_accuracy: 0.3889
Epoch 375/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1204 - accuracy: 0.9909 - val_loss: 2.3867 - val_accuracy: 0.3926
Epoch 376/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1134 - accuracy: 0.9894 - val_loss: 2.3771 - val_accuracy: 0.3926
Epoch 377/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1067 - accuracy: 0.9909 - val_loss: 2.3675 - val_accuracy: 0.3963
Epoch 378/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1059 - accuracy: 0.9924 - val_loss: 2.3584 - val_accuracy: 0.4074
Epoch 379/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1183 - accuracy: 0.9878 - val_loss: 2.3497 - val_accuracy: 0.4111
Epoch 380/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1107 - accuracy: 0.9878 - val_loss: 2.3406 - val_accuracy: 0.4111
Epoch 381/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1259 - accuracy: 0.9863 - val_loss: 2.3311 - val_accuracy: 0.4111
Epoch 382/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1059 - accuracy: 0.9939 - val_loss: 2.3225 - val_accuracy: 0.4148
Epoch 383/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1110 - accuracy: 0.9924 - val_loss: 2.3134 - val_accuracy: 0.4148
Epoch 384/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1114 - accuracy: 0.9939 - val_loss: 2.3049 - val_accuracy: 0.4148
Epoch 385/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1082 - accuracy: 0.9924 - val_loss: 2.2960 - val_accuracy: 0.4148
Epoch 386/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1125 - accuracy: 0.9894 - val_loss: 2.2876 - val_accuracy: 0.4148
Epoch 387/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1039 - accuracy: 0.9954 - val_loss: 2.2793 - val_accuracy: 0.4148
Epoch 388/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1290 - accuracy: 0.9878 - val_loss: 2.2710 - val_accuracy: 0.4148
Epoch 389/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1183 - accuracy: 0.9939 - val_loss: 2.2625 - val_accuracy: 0.4148
Epoch 390/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1132 - accuracy: 0.9833 - val_loss: 2.2542 - val_accuracy: 0.4185
Epoch 391/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1053 - accuracy: 0.9924 - val_loss: 2.2457 - val_accuracy: 0.4222
Epoch 392/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0961 - accuracy: 0.9939 - val_loss: 2.2370 - val_accuracy: 0.4259
Epoch 393/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1067 - accuracy: 0.9909 - val_loss: 2.2290 - val_accuracy: 0.4259
Epoch 394/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1214 - accuracy: 0.9894 - val_loss: 2.2205 - val_accuracy: 0.4296
Epoch 395/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1111 - accuracy: 0.9924 - val_loss: 2.2118 - val_accuracy: 0.4296
Epoch 396/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1063 - accuracy: 0.9924 - val_loss: 2.2034 - val_accuracy: 0.4333
Epoch 397/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1187 - accuracy: 0.9909 - val_loss: 2.1950 - val_accuracy: 0.4370
Epoch 398/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1084 - accuracy: 0.9939 - val_loss: 2.1863 - val_accuracy: 0.4407
Epoch 399/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1147 - accuracy: 0.9863 - val_loss: 2.1783 - val_accuracy: 0.4407
Epoch 400/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1016 - accuracy: 0.9939 - val_loss: 2.1701 - val_accuracy: 0.4407
Epoch 401/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1313 - accuracy: 0.9802 - val_loss: 2.1614 - val_accuracy: 0.4407
Epoch 402/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1138 - accuracy: 0.9863 - val_loss: 2.1530 - val_accuracy: 0.4407
Epoch 403/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1138 - accuracy: 0.9939 - val_loss: 2.1447 - val_accuracy: 0.4407
Epoch 404/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1133 - accuracy: 0.9863 - val_loss: 2.1364 - val_accuracy: 0.4444
Epoch 405/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1326 - accuracy: 0.9863 - val_loss: 2.1282 - val_accuracy: 0.4481
Epoch 406/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1003 - accuracy: 0.9924 - val_loss: 2.1204 - val_accuracy: 0.4481
Epoch 407/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1070 - accuracy: 0.9939 - val_loss: 2.1120 - val_accuracy: 0.4481
Epoch 408/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1077 - accuracy: 0.9909 - val_loss: 2.1039 - val_accuracy: 0.4481
Epoch 409/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9909 - val_loss: 2.0957 - val_accuracy: 0.4519
Epoch 410/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1115 - accuracy: 0.9939 - val_loss: 2.0878 - val_accuracy: 0.4519
Epoch 411/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1391 - accuracy: 0.9787 - val_loss: 2.0799 - val_accuracy: 0.4519
Epoch 412/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1079 - accuracy: 0.9894 - val_loss: 2.0719 - val_accuracy: 0.4556
Epoch 413/600
658/658 [==============================] - 6s 10ms/step - loss: 0.1177 - accuracy: 0.9878 - val_loss: 2.0644 - val_accuracy: 0.4556
Epoch 414/600
658/658 [==============================] - 6s 8ms/step - loss: 0.1071 - accuracy: 0.9939 - val_loss: 2.0566 - val_accuracy: 0.4556
Epoch 415/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0951 - accuracy: 0.9924 - val_loss: 2.0490 - val_accuracy: 0.4593
Epoch 416/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1141 - accuracy: 0.9939 - val_loss: 2.0414 - val_accuracy: 0.4593
Epoch 417/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1017 - accuracy: 0.9985 - val_loss: 2.0337 - val_accuracy: 0.4667
Epoch 418/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1123 - accuracy: 0.9878 - val_loss: 2.0262 - val_accuracy: 0.4667
Epoch 419/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9924 - val_loss: 2.0190 - val_accuracy: 0.4704
Epoch 420/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0964 - accuracy: 0.9924 - val_loss: 2.0118 - val_accuracy: 0.4741
Epoch 421/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1146 - accuracy: 0.9924 - val_loss: 2.0043 - val_accuracy: 0.4778
Epoch 422/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1160 - accuracy: 0.9878 - val_loss: 1.9969 - val_accuracy: 0.4815
Epoch 423/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1042 - accuracy: 0.9954 - val_loss: 1.9895 - val_accuracy: 0.4815
Epoch 424/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1124 - accuracy: 0.9878 - val_loss: 1.9822 - val_accuracy: 0.4852
Epoch 425/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1051 - accuracy: 0.9909 - val_loss: 1.9751 - val_accuracy: 0.4889
Epoch 426/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1137 - accuracy: 0.9924 - val_loss: 1.9682 - val_accuracy: 0.4889
Epoch 427/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1202 - accuracy: 0.9894 - val_loss: 1.9612 - val_accuracy: 0.4889
Epoch 428/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1155 - accuracy: 0.9924 - val_loss: 1.9539 - val_accuracy: 0.4926
Epoch 429/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0983 - accuracy: 0.9985 - val_loss: 1.9466 - val_accuracy: 0.4963
Epoch 430/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1122 - accuracy: 0.9878 - val_loss: 1.9396 - val_accuracy: 0.5000
Epoch 431/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1026 - accuracy: 0.9924 - val_loss: 1.9324 - val_accuracy: 0.5000
Epoch 432/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1014 - accuracy: 0.9924 - val_loss: 1.9254 - val_accuracy: 0.5000
Epoch 433/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1036 - accuracy: 0.9954 - val_loss: 1.9185 - val_accuracy: 0.5000
Epoch 434/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1066 - accuracy: 0.9939 - val_loss: 1.9115 - val_accuracy: 0.5000
Epoch 435/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1200 - accuracy: 0.9833 - val_loss: 1.9046 - val_accuracy: 0.5000
Epoch 436/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1213 - accuracy: 0.9863 - val_loss: 1.8973 - val_accuracy: 0.5037
Epoch 437/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1040 - accuracy: 0.9954 - val_loss: 1.8906 - val_accuracy: 0.5074
Epoch 438/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1143 - accuracy: 0.9863 - val_loss: 1.8835 - val_accuracy: 0.5111
Epoch 439/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9939 - val_loss: 1.8769 - val_accuracy: 0.5111
Epoch 440/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1006 - accuracy: 0.9970 - val_loss: 1.8699 - val_accuracy: 0.5111
Epoch 441/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1145 - accuracy: 0.9939 - val_loss: 1.8634 - val_accuracy: 0.5148
Epoch 442/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1089 - accuracy: 0.9894 - val_loss: 1.8568 - val_accuracy: 0.5148
Epoch 443/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1085 - accuracy: 0.9954 - val_loss: 1.8504 - val_accuracy: 0.5148
Epoch 444/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1078 - accuracy: 0.9939 - val_loss: 1.8442 - val_accuracy: 0.5185
Epoch 445/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1231 - accuracy: 0.9894 - val_loss: 1.8380 - val_accuracy: 0.5222
Epoch 446/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1011 - accuracy: 0.9924 - val_loss: 1.8315 - val_accuracy: 0.5296
Epoch 447/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1244 - accuracy: 0.9878 - val_loss: 1.8253 - val_accuracy: 0.5296
Epoch 448/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1201 - accuracy: 0.9863 - val_loss: 1.8189 - val_accuracy: 0.5370
Epoch 449/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1148 - accuracy: 0.9878 - val_loss: 1.8126 - val_accuracy: 0.5370
Epoch 450/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1293 - accuracy: 0.9848 - val_loss: 1.8062 - val_accuracy: 0.5370
Epoch 451/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1033 - accuracy: 0.9924 - val_loss: 1.8001 - val_accuracy: 0.5370
Epoch 452/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1047 - accuracy: 0.9954 - val_loss: 1.7940 - val_accuracy: 0.5444
Epoch 453/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1099 - accuracy: 0.9863 - val_loss: 1.7881 - val_accuracy: 0.5444
Epoch 454/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1166 - accuracy: 0.9939 - val_loss: 1.7820 - val_accuracy: 0.5444
Epoch 455/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1105 - accuracy: 0.9909 - val_loss: 1.7757 - val_accuracy: 0.5444
Epoch 456/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0991 - accuracy: 0.9909 - val_loss: 1.7699 - val_accuracy: 0.5444
Epoch 457/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1019 - accuracy: 0.9924 - val_loss: 1.7635 - val_accuracy: 0.5519
Epoch 458/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1055 - accuracy: 0.9909 - val_loss: 1.7576 - val_accuracy: 0.5519
Epoch 459/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1021 - accuracy: 0.9924 - val_loss: 1.7513 - val_accuracy: 0.5519
Epoch 460/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1136 - accuracy: 0.9954 - val_loss: 1.7454 - val_accuracy: 0.5556
Epoch 461/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1011 - accuracy: 0.9924 - val_loss: 1.7392 - val_accuracy: 0.5556
Epoch 462/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1028 - accuracy: 0.9924 - val_loss: 1.7331 - val_accuracy: 0.5556
Epoch 463/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1065 - accuracy: 0.9939 - val_loss: 1.7272 - val_accuracy: 0.5556
Epoch 464/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1147 - accuracy: 0.9939 - val_loss: 1.7218 - val_accuracy: 0.5593
Epoch 465/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1070 - accuracy: 0.9909 - val_loss: 1.7159 - val_accuracy: 0.5593
Epoch 466/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1069 - accuracy: 0.9909 - val_loss: 1.7105 - val_accuracy: 0.5593
Epoch 467/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1054 - accuracy: 0.9954 - val_loss: 1.7049 - val_accuracy: 0.5630
Epoch 468/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1130 - accuracy: 0.9863 - val_loss: 1.6994 - val_accuracy: 0.5630
Epoch 469/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1006 - accuracy: 0.9954 - val_loss: 1.6940 - val_accuracy: 0.5667
Epoch 470/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1030 - accuracy: 0.9939 - val_loss: 1.6884 - val_accuracy: 0.5741
Epoch 471/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1248 - accuracy: 0.9894 - val_loss: 1.6832 - val_accuracy: 0.5778
Epoch 472/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1192 - accuracy: 0.9848 - val_loss: 1.6779 - val_accuracy: 0.5815
Epoch 473/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1235 - accuracy: 0.9894 - val_loss: 1.6722 - val_accuracy: 0.5815
Epoch 474/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1076 - accuracy: 0.9878 - val_loss: 1.6671 - val_accuracy: 0.5852
Epoch 475/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1036 - accuracy: 0.9909 - val_loss: 1.6622 - val_accuracy: 0.5852
Epoch 476/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1025 - accuracy: 0.9894 - val_loss: 1.6567 - val_accuracy: 0.5852
Epoch 477/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1021 - accuracy: 0.9970 - val_loss: 1.6518 - val_accuracy: 0.5852
Epoch 478/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1006 - accuracy: 0.9970 - val_loss: 1.6466 - val_accuracy: 0.5852
Epoch 479/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1020 - accuracy: 0.9924 - val_loss: 1.6416 - val_accuracy: 0.5852
Epoch 480/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1064 - accuracy: 0.9909 - val_loss: 1.6364 - val_accuracy: 0.5852
Epoch 481/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1232 - accuracy: 0.9833 - val_loss: 1.6309 - val_accuracy: 0.5889
Epoch 482/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1099 - accuracy: 0.9924 - val_loss: 1.6259 - val_accuracy: 0.5889
Epoch 483/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1001 - accuracy: 0.9924 - val_loss: 1.6211 - val_accuracy: 0.5926
Epoch 484/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1046 - accuracy: 0.9909 - val_loss: 1.6161 - val_accuracy: 0.5926
Epoch 485/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1059 - accuracy: 0.9924 - val_loss: 1.6114 - val_accuracy: 0.5963
Epoch 486/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1082 - accuracy: 0.9939 - val_loss: 1.6067 - val_accuracy: 0.5963
Epoch 487/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1136 - accuracy: 0.9909 - val_loss: 1.6019 - val_accuracy: 0.5963
Epoch 488/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0974 - accuracy: 0.9924 - val_loss: 1.5972 - val_accuracy: 0.5963
Epoch 489/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0925 - accuracy: 0.9939 - val_loss: 1.5925 - val_accuracy: 0.5963
Epoch 490/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1189 - accuracy: 0.9848 - val_loss: 1.5881 - val_accuracy: 0.5963
Epoch 491/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1038 - accuracy: 0.9985 - val_loss: 1.5835 - val_accuracy: 0.6000
Epoch 492/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1144 - accuracy: 0.9863 - val_loss: 1.5789 - val_accuracy: 0.6037
Epoch 493/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1172 - accuracy: 0.9909 - val_loss: 1.5742 - val_accuracy: 0.6037
Epoch 494/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1211 - accuracy: 0.9863 - val_loss: 1.5694 - val_accuracy: 0.6037
Epoch 495/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1093 - accuracy: 0.9909 - val_loss: 1.5648 - val_accuracy: 0.6074
Epoch 496/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1193 - accuracy: 0.9878 - val_loss: 1.5603 - val_accuracy: 0.6074
Epoch 497/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1092 - accuracy: 0.9909 - val_loss: 1.5556 - val_accuracy: 0.6074
Epoch 498/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1041 - accuracy: 0.9894 - val_loss: 1.5506 - val_accuracy: 0.6074
Epoch 499/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1070 - accuracy: 0.9939 - val_loss: 1.5461 - val_accuracy: 0.6037
Epoch 500/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1099 - accuracy: 0.9909 - val_loss: 1.5415 - val_accuracy: 0.6037
Epoch 501/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0967 - accuracy: 0.9954 - val_loss: 1.5369 - val_accuracy: 0.6074
Epoch 502/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1044 - accuracy: 0.9954 - val_loss: 1.5326 - val_accuracy: 0.6074
Epoch 503/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1199 - accuracy: 0.9878 - val_loss: 1.5282 - val_accuracy: 0.6111
Epoch 504/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9954 - val_loss: 1.5238 - val_accuracy: 0.6185
Epoch 505/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1049 - accuracy: 0.9909 - val_loss: 1.5195 - val_accuracy: 0.6185
Epoch 506/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0994 - accuracy: 0.9939 - val_loss: 1.5152 - val_accuracy: 0.6185
Epoch 507/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1212 - accuracy: 0.9878 - val_loss: 1.5108 - val_accuracy: 0.6259
Epoch 508/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0975 - accuracy: 0.9939 - val_loss: 1.5064 - val_accuracy: 0.6296
Epoch 509/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1045 - accuracy: 0.9909 - val_loss: 1.5021 - val_accuracy: 0.6333
Epoch 510/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1065 - accuracy: 0.9954 - val_loss: 1.4977 - val_accuracy: 0.6333
Epoch 511/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1020 - accuracy: 0.9939 - val_loss: 1.4932 - val_accuracy: 0.6370
Epoch 512/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0986 - accuracy: 0.9954 - val_loss: 1.4891 - val_accuracy: 0.6370
Epoch 513/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1095 - accuracy: 0.9909 - val_loss: 1.4849 - val_accuracy: 0.6407
Epoch 514/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1127 - accuracy: 0.9954 - val_loss: 1.4810 - val_accuracy: 0.6407
Epoch 515/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1171 - accuracy: 0.9878 - val_loss: 1.4769 - val_accuracy: 0.6481
Epoch 516/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1029 - accuracy: 0.9939 - val_loss: 1.4729 - val_accuracy: 0.6481
Epoch 517/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0964 - accuracy: 0.9909 - val_loss: 1.4688 - val_accuracy: 0.6481
Epoch 518/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1031 - accuracy: 0.9939 - val_loss: 1.4647 - val_accuracy: 0.6481
Epoch 519/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1087 - accuracy: 0.9909 - val_loss: 1.4607 - val_accuracy: 0.6481
Epoch 520/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1064 - accuracy: 0.9939 - val_loss: 1.4567 - val_accuracy: 0.6481
Epoch 521/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1136 - accuracy: 0.9909 - val_loss: 1.4528 - val_accuracy: 0.6481
Epoch 522/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9909 - val_loss: 1.4491 - val_accuracy: 0.6481
Epoch 523/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9939 - val_loss: 1.4451 - val_accuracy: 0.6519
Epoch 524/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1214 - accuracy: 0.9894 - val_loss: 1.4414 - val_accuracy: 0.6519
Epoch 525/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1136 - accuracy: 0.9909 - val_loss: 1.4377 - val_accuracy: 0.6519
Epoch 526/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1133 - accuracy: 0.9909 - val_loss: 1.4339 - val_accuracy: 0.6519
Epoch 527/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1058 - accuracy: 0.9894 - val_loss: 1.4302 - val_accuracy: 0.6519
Epoch 528/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1049 - accuracy: 0.9954 - val_loss: 1.4268 - val_accuracy: 0.6519
Epoch 529/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1185 - accuracy: 0.9878 - val_loss: 1.4234 - val_accuracy: 0.6519
Epoch 530/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1210 - accuracy: 0.9894 - val_loss: 1.4199 - val_accuracy: 0.6519
Epoch 531/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1116 - accuracy: 0.9878 - val_loss: 1.4164 - val_accuracy: 0.6519
Epoch 532/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1207 - accuracy: 0.9924 - val_loss: 1.4127 - val_accuracy: 0.6519
Epoch 533/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0985 - accuracy: 0.9985 - val_loss: 1.4089 - val_accuracy: 0.6519
Epoch 534/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1328 - accuracy: 0.9833 - val_loss: 1.4054 - val_accuracy: 0.6593
Epoch 535/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1118 - accuracy: 0.9924 - val_loss: 1.4021 - val_accuracy: 0.6593
Epoch 536/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1133 - accuracy: 0.9878 - val_loss: 1.3985 - val_accuracy: 0.6593
Epoch 537/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0940 - accuracy: 0.9954 - val_loss: 1.3949 - val_accuracy: 0.6593
Epoch 538/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1040 - accuracy: 0.9939 - val_loss: 1.3915 - val_accuracy: 0.6593
Epoch 539/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9924 - val_loss: 1.3883 - val_accuracy: 0.6593
Epoch 540/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1114 - accuracy: 0.9878 - val_loss: 1.3847 - val_accuracy: 0.6593
Epoch 541/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1242 - accuracy: 0.9863 - val_loss: 1.3810 - val_accuracy: 0.6593
Epoch 542/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9894 - val_loss: 1.3777 - val_accuracy: 0.6593
Epoch 543/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1092 - accuracy: 0.9939 - val_loss: 1.3742 - val_accuracy: 0.6593
Epoch 544/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0958 - accuracy: 0.9894 - val_loss: 1.3709 - val_accuracy: 0.6593
Epoch 545/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9939 - val_loss: 1.3674 - val_accuracy: 0.6630
Epoch 546/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1246 - accuracy: 0.9848 - val_loss: 1.3640 - val_accuracy: 0.6630
Epoch 547/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1010 - accuracy: 0.9909 - val_loss: 1.3608 - val_accuracy: 0.6630
Epoch 548/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1012 - accuracy: 0.9863 - val_loss: 1.3573 - val_accuracy: 0.6630
Epoch 549/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1154 - accuracy: 0.9878 - val_loss: 1.3538 - val_accuracy: 0.6630
Epoch 550/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1098 - accuracy: 0.9909 - val_loss: 1.3506 - val_accuracy: 0.6667
Epoch 551/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1029 - accuracy: 0.9954 - val_loss: 1.3472 - val_accuracy: 0.6667
Epoch 552/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1117 - accuracy: 0.9863 - val_loss: 1.3440 - val_accuracy: 0.6667
Epoch 553/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1120 - accuracy: 0.9894 - val_loss: 1.3410 - val_accuracy: 0.6667
Epoch 554/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1237 - accuracy: 0.9863 - val_loss: 1.3377 - val_accuracy: 0.6667
Epoch 555/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1105 - accuracy: 0.9894 - val_loss: 1.3345 - val_accuracy: 0.6667
Epoch 556/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1114 - accuracy: 0.9894 - val_loss: 1.3314 - val_accuracy: 0.6667
Epoch 557/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0955 - accuracy: 0.9970 - val_loss: 1.3284 - val_accuracy: 0.6667
Epoch 558/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1299 - accuracy: 0.9894 - val_loss: 1.3253 - val_accuracy: 0.6667
Epoch 559/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9939 - val_loss: 1.3224 - val_accuracy: 0.6667
Epoch 560/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1269 - accuracy: 0.9863 - val_loss: 1.3194 - val_accuracy: 0.6630
Epoch 561/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1035 - accuracy: 0.9939 - val_loss: 1.3166 - val_accuracy: 0.6630
Epoch 562/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1077 - accuracy: 0.9924 - val_loss: 1.3138 - val_accuracy: 0.6630
Epoch 563/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1135 - accuracy: 0.9939 - val_loss: 1.3112 - val_accuracy: 0.6630
Epoch 564/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1183 - accuracy: 0.9863 - val_loss: 1.3085 - val_accuracy: 0.6630
Epoch 565/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1021 - accuracy: 0.9939 - val_loss: 1.3056 - val_accuracy: 0.6630
Epoch 566/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1166 - accuracy: 0.9878 - val_loss: 1.3031 - val_accuracy: 0.6630
Epoch 567/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1022 - accuracy: 0.9909 - val_loss: 1.3003 - val_accuracy: 0.6630
Epoch 568/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1101 - accuracy: 0.9894 - val_loss: 1.2976 - val_accuracy: 0.6667
Epoch 569/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0987 - accuracy: 0.9970 - val_loss: 1.2947 - val_accuracy: 0.6704
Epoch 570/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1071 - accuracy: 0.9924 - val_loss: 1.2920 - val_accuracy: 0.6704
Epoch 571/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0953 - accuracy: 0.9954 - val_loss: 1.2890 - val_accuracy: 0.6704
Epoch 572/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1090 - accuracy: 0.9894 - val_loss: 1.2860 - val_accuracy: 0.6704
Epoch 573/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1135 - accuracy: 0.9878 - val_loss: 1.2830 - val_accuracy: 0.6704
Epoch 574/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1056 - accuracy: 0.9924 - val_loss: 1.2800 - val_accuracy: 0.6741
Epoch 575/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0980 - accuracy: 0.9909 - val_loss: 1.2769 - val_accuracy: 0.6741
Epoch 576/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1001 - accuracy: 0.9939 - val_loss: 1.2740 - val_accuracy: 0.6741
Epoch 577/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1032 - accuracy: 0.9894 - val_loss: 1.2711 - val_accuracy: 0.6741
Epoch 578/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0988 - accuracy: 0.9954 - val_loss: 1.2683 - val_accuracy: 0.6741
Epoch 579/600
658/658 [==============================] - 6s 9ms/step - loss: 0.1049 - accuracy: 0.9924 - val_loss: 1.2657 - val_accuracy: 0.6778
Epoch 580/600
658/658 [==============================] - 6s 9ms/step - loss: 0.0910 - accuracy: 0.9924 - val_loss: 1.2631 - val_accuracy: 0.6778
Epoch 581/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1054 - accuracy: 0.9954 - val_loss: 1.2605 - val_accuracy: 0.6778
Epoch 582/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1085 - accuracy: 0.9939 - val_loss: 1.2578 - val_accuracy: 0.6741
Epoch 583/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0996 - accuracy: 0.9970 - val_loss: 1.2551 - val_accuracy: 0.6741
Epoch 584/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1058 - accuracy: 0.9909 - val_loss: 1.2526 - val_accuracy: 0.6778
Epoch 585/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0909 - accuracy: 0.9909 - val_loss: 1.2497 - val_accuracy: 0.6778
Epoch 586/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1140 - accuracy: 0.9924 - val_loss: 1.2473 - val_accuracy: 0.6778
Epoch 587/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1254 - accuracy: 0.9909 - val_loss: 1.2448 - val_accuracy: 0.6778
Epoch 588/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1077 - accuracy: 0.9863 - val_loss: 1.2422 - val_accuracy: 0.6778
Epoch 589/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1150 - accuracy: 0.9878 - val_loss: 1.2396 - val_accuracy: 0.6778
Epoch 590/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0959 - accuracy: 0.9939 - val_loss: 1.2369 - val_accuracy: 0.6778
Epoch 591/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9894 - val_loss: 1.2343 - val_accuracy: 0.6815
Epoch 592/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1140 - accuracy: 0.9970 - val_loss: 1.2316 - val_accuracy: 0.6852
Epoch 593/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0916 - accuracy: 0.9954 - val_loss: 1.2291 - val_accuracy: 0.6889
Epoch 594/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1130 - accuracy: 0.9924 - val_loss: 1.2266 - val_accuracy: 0.6889
Epoch 595/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1131 - accuracy: 0.9909 - val_loss: 1.2244 - val_accuracy: 0.6889
Epoch 596/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1009 - accuracy: 0.9924 - val_loss: 1.2219 - val_accuracy: 0.6926
Epoch 597/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9939 - val_loss: 1.2192 - val_accuracy: 0.6963
Epoch 598/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1078 - accuracy: 0.9954 - val_loss: 1.2166 - val_accuracy: 0.6963
Epoch 599/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1098 - accuracy: 0.9863 - val_loss: 1.2141 - val_accuracy: 0.7000
Epoch 600/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1034 - accuracy: 0.9939 - val_loss: 1.2115 - val_accuracy: 0.7000
Train on 658 samples, validate on 270 samples
Epoch 1/600
658/658 [==============================] - 9s 13ms/step - loss: 0.0990 - accuracy: 0.9970 - val_loss: 1.2135 - val_accuracy: 0.7074
Epoch 2/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1126 - accuracy: 0.9894 - val_loss: 1.2063 - val_accuracy: 0.7111
Epoch 3/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1049 - accuracy: 0.9954 - val_loss: 1.1920 - val_accuracy: 0.7111
Epoch 4/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1134 - accuracy: 0.9924 - val_loss: 1.1858 - val_accuracy: 0.7037
Epoch 5/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1112 - accuracy: 0.9878 - val_loss: 1.1813 - val_accuracy: 0.6963
Epoch 6/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1072 - accuracy: 0.9939 - val_loss: 1.1828 - val_accuracy: 0.7037
Epoch 7/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0992 - accuracy: 0.9939 - val_loss: 1.1830 - val_accuracy: 0.7037
Epoch 8/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0984 - accuracy: 0.9970 - val_loss: 1.1839 - val_accuracy: 0.7000
Epoch 9/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1070 - accuracy: 0.9924 - val_loss: 1.1853 - val_accuracy: 0.7037
Epoch 10/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9924 - val_loss: 1.1846 - val_accuracy: 0.7037
Epoch 11/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1062 - accuracy: 0.9939 - val_loss: 1.1822 - val_accuracy: 0.7037
Epoch 12/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1109 - accuracy: 0.9939 - val_loss: 1.1785 - val_accuracy: 0.7037
Epoch 13/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1035 - accuracy: 0.9924 - val_loss: 1.1753 - val_accuracy: 0.7000
Epoch 14/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1149 - accuracy: 0.9863 - val_loss: 1.1714 - val_accuracy: 0.7037
Epoch 15/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1090 - accuracy: 0.9863 - val_loss: 1.1683 - val_accuracy: 0.7037
Epoch 16/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0995 - accuracy: 0.9924 - val_loss: 1.1661 - val_accuracy: 0.7074
Epoch 17/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0975 - accuracy: 0.9970 - val_loss: 1.1642 - val_accuracy: 0.7074
Epoch 18/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1022 - accuracy: 0.9939 - val_loss: 1.1615 - val_accuracy: 0.7074
Epoch 19/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1043 - accuracy: 0.9954 - val_loss: 1.1592 - val_accuracy: 0.7074
Epoch 20/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0947 - accuracy: 0.9924 - val_loss: 1.1571 - val_accuracy: 0.7111
Epoch 21/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0963 - accuracy: 0.9970 - val_loss: 1.1546 - val_accuracy: 0.7111
Epoch 22/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1106 - accuracy: 0.9939 - val_loss: 1.1527 - val_accuracy: 0.7074
Epoch 23/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0940 - accuracy: 0.9954 - val_loss: 1.1507 - val_accuracy: 0.7074
Epoch 24/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1074 - accuracy: 0.9909 - val_loss: 1.1477 - val_accuracy: 0.7074
Epoch 25/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0925 - accuracy: 0.9939 - val_loss: 1.1448 - val_accuracy: 0.7074
Epoch 26/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0843 - accuracy: 0.9954 - val_loss: 1.1425 - val_accuracy: 0.7074
Epoch 27/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1004 - accuracy: 0.9954 - val_loss: 1.1404 - val_accuracy: 0.7111
Epoch 28/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1013 - accuracy: 0.9939 - val_loss: 1.1382 - val_accuracy: 0.7148
Epoch 29/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0998 - accuracy: 0.9878 - val_loss: 1.1360 - val_accuracy: 0.7148
Epoch 30/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1011 - accuracy: 0.9939 - val_loss: 1.1335 - val_accuracy: 0.7148
Epoch 31/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0924 - accuracy: 0.9985 - val_loss: 1.1312 - val_accuracy: 0.7148
Epoch 32/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1059 - accuracy: 0.9939 - val_loss: 1.1286 - val_accuracy: 0.7148
Epoch 33/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1054 - accuracy: 0.9924 - val_loss: 1.1264 - val_accuracy: 0.7074
Epoch 34/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0932 - accuracy: 0.9939 - val_loss: 1.1243 - val_accuracy: 0.7074
Epoch 35/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1014 - accuracy: 0.9970 - val_loss: 1.1222 - val_accuracy: 0.7074
Epoch 36/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0906 - accuracy: 0.9939 - val_loss: 1.1207 - val_accuracy: 0.7074
Epoch 37/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0964 - accuracy: 0.9863 - val_loss: 1.1194 - val_accuracy: 0.7074
Epoch 38/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1031 - accuracy: 0.9878 - val_loss: 1.1181 - val_accuracy: 0.7074
Epoch 39/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0924 - accuracy: 0.9954 - val_loss: 1.1165 - val_accuracy: 0.7111
Epoch 40/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0961 - accuracy: 0.9863 - val_loss: 1.1147 - val_accuracy: 0.7111
Epoch 41/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0926 - accuracy: 0.9939 - val_loss: 1.1128 - val_accuracy: 0.7111
Epoch 42/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1003 - accuracy: 0.9954 - val_loss: 1.1111 - val_accuracy: 0.7111
Epoch 43/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1046 - accuracy: 0.9894 - val_loss: 1.1092 - val_accuracy: 0.7111
Epoch 44/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0898 - accuracy: 0.9970 - val_loss: 1.1076 - val_accuracy: 0.7111
Epoch 45/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1046 - accuracy: 0.9878 - val_loss: 1.1057 - val_accuracy: 0.7148
Epoch 46/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1105 - accuracy: 0.9878 - val_loss: 1.1038 - val_accuracy: 0.7148
Epoch 47/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1026 - accuracy: 0.9954 - val_loss: 1.1021 - val_accuracy: 0.7185
Epoch 48/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0963 - accuracy: 0.9985 - val_loss: 1.1004 - val_accuracy: 0.7185
Epoch 49/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9909 - val_loss: 1.0988 - val_accuracy: 0.7185
Epoch 50/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1045 - accuracy: 0.9924 - val_loss: 1.0973 - val_accuracy: 0.7185
Epoch 51/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9970 - val_loss: 1.0958 - val_accuracy: 0.7185
Epoch 52/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0969 - accuracy: 0.9909 - val_loss: 1.0944 - val_accuracy: 0.7185
Epoch 53/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1002 - accuracy: 0.9878 - val_loss: 1.0931 - val_accuracy: 0.7185
Epoch 54/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0953 - accuracy: 0.9878 - val_loss: 1.0918 - val_accuracy: 0.7185
Epoch 55/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0978 - accuracy: 0.9954 - val_loss: 1.0904 - val_accuracy: 0.7185
Epoch 56/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9924 - val_loss: 1.0891 - val_accuracy: 0.7222
Epoch 57/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9909 - val_loss: 1.0877 - val_accuracy: 0.7222
Epoch 58/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9939 - val_loss: 1.0864 - val_accuracy: 0.7222
Epoch 59/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9909 - val_loss: 1.0852 - val_accuracy: 0.7222
Epoch 60/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0804 - accuracy: 0.9970 - val_loss: 1.0840 - val_accuracy: 0.7259
Epoch 61/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1057 - accuracy: 0.9924 - val_loss: 1.0826 - val_accuracy: 0.7259
Epoch 62/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0849 - accuracy: 0.9970 - val_loss: 1.0813 - val_accuracy: 0.7259
Epoch 63/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0853 - accuracy: 0.9970 - val_loss: 1.0800 - val_accuracy: 0.7259
Epoch 64/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1048 - accuracy: 0.9924 - val_loss: 1.0787 - val_accuracy: 0.7259
Epoch 65/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0851 - accuracy: 1.0000 - val_loss: 1.0772 - val_accuracy: 0.7259
Epoch 66/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0903 - accuracy: 0.9954 - val_loss: 1.0759 - val_accuracy: 0.7259
Epoch 67/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0962 - accuracy: 0.9939 - val_loss: 1.0743 - val_accuracy: 0.7259
Epoch 68/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1017 - accuracy: 0.9909 - val_loss: 1.0730 - val_accuracy: 0.7259
Epoch 69/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1001 - accuracy: 0.9970 - val_loss: 1.0716 - val_accuracy: 0.7259
Epoch 70/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0974 - accuracy: 0.9954 - val_loss: 1.0702 - val_accuracy: 0.7259
Epoch 71/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1034 - accuracy: 0.9954 - val_loss: 1.0689 - val_accuracy: 0.7259
Epoch 72/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9924 - val_loss: 1.0676 - val_accuracy: 0.7259
Epoch 73/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0912 - accuracy: 0.9924 - val_loss: 1.0661 - val_accuracy: 0.7259
Epoch 74/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0837 - accuracy: 0.9939 - val_loss: 1.0648 - val_accuracy: 0.7259
Epoch 75/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1027 - accuracy: 0.9909 - val_loss: 1.0633 - val_accuracy: 0.7259
Epoch 76/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0943 - accuracy: 0.9939 - val_loss: 1.0620 - val_accuracy: 0.7259
Epoch 77/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0791 - accuracy: 1.0000 - val_loss: 1.0606 - val_accuracy: 0.7259
Epoch 78/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9924 - val_loss: 1.0592 - val_accuracy: 0.7259
Epoch 79/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0796 - accuracy: 0.9970 - val_loss: 1.0578 - val_accuracy: 0.7259
Epoch 80/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1046 - accuracy: 0.9909 - val_loss: 1.0564 - val_accuracy: 0.7259
Epoch 81/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0977 - accuracy: 0.9924 - val_loss: 1.0551 - val_accuracy: 0.7259
Epoch 82/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0954 - accuracy: 0.9954 - val_loss: 1.0535 - val_accuracy: 0.7259
Epoch 83/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9970 - val_loss: 1.0520 - val_accuracy: 0.7259
Epoch 84/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1023 - accuracy: 0.9924 - val_loss: 1.0506 - val_accuracy: 0.7259
Epoch 85/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0955 - accuracy: 0.9954 - val_loss: 1.0492 - val_accuracy: 0.7259
Epoch 86/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0967 - accuracy: 0.9924 - val_loss: 1.0477 - val_accuracy: 0.7259
Epoch 87/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1018 - accuracy: 0.9924 - val_loss: 1.0463 - val_accuracy: 0.7259
Epoch 88/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0910 - accuracy: 0.9954 - val_loss: 1.0447 - val_accuracy: 0.7259
Epoch 89/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9985 - val_loss: 1.0434 - val_accuracy: 0.7259
Epoch 90/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0921 - accuracy: 0.9970 - val_loss: 1.0420 - val_accuracy: 0.7259
Epoch 91/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0902 - accuracy: 0.9939 - val_loss: 1.0405 - val_accuracy: 0.7259
Epoch 92/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1045 - accuracy: 0.9954 - val_loss: 1.0391 - val_accuracy: 0.7259
Epoch 93/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1029 - accuracy: 0.9894 - val_loss: 1.0377 - val_accuracy: 0.7259
Epoch 94/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1097 - accuracy: 0.9833 - val_loss: 1.0360 - val_accuracy: 0.7259
Epoch 95/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0952 - accuracy: 0.9970 - val_loss: 1.0346 - val_accuracy: 0.7259
Epoch 96/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0897 - accuracy: 0.9924 - val_loss: 1.0332 - val_accuracy: 0.7259
Epoch 97/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1007 - accuracy: 0.9924 - val_loss: 1.0316 - val_accuracy: 0.7296
Epoch 98/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0941 - accuracy: 0.9924 - val_loss: 1.0298 - val_accuracy: 0.7296
Epoch 99/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0933 - accuracy: 0.9954 - val_loss: 1.0283 - val_accuracy: 0.7296
Epoch 100/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0922 - accuracy: 0.9939 - val_loss: 1.0268 - val_accuracy: 0.7296
Epoch 101/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0971 - accuracy: 0.9909 - val_loss: 1.0250 - val_accuracy: 0.7296
Epoch 102/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0914 - accuracy: 0.9924 - val_loss: 1.0232 - val_accuracy: 0.7296
Epoch 103/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0807 - accuracy: 0.9954 - val_loss: 1.0216 - val_accuracy: 0.7296
Epoch 104/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1010 - accuracy: 0.9924 - val_loss: 1.0202 - val_accuracy: 0.7296
Epoch 105/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0916 - accuracy: 0.9924 - val_loss: 1.0190 - val_accuracy: 0.7296
Epoch 106/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1085 - accuracy: 0.9863 - val_loss: 1.0176 - val_accuracy: 0.7296
Epoch 107/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0999 - accuracy: 0.9939 - val_loss: 1.0164 - val_accuracy: 0.7296
Epoch 108/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0907 - accuracy: 0.9985 - val_loss: 1.0153 - val_accuracy: 0.7296
Epoch 109/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1059 - accuracy: 0.9894 - val_loss: 1.0140 - val_accuracy: 0.7296
Epoch 110/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0979 - accuracy: 0.9985 - val_loss: 1.0129 - val_accuracy: 0.7296
Epoch 111/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0979 - accuracy: 0.9924 - val_loss: 1.0120 - val_accuracy: 0.7296
Epoch 112/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9970 - val_loss: 1.0109 - val_accuracy: 0.7296
Epoch 113/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0920 - accuracy: 0.9924 - val_loss: 1.0099 - val_accuracy: 0.7333
Epoch 114/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0923 - accuracy: 0.9970 - val_loss: 1.0089 - val_accuracy: 0.7333
Epoch 115/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0986 - accuracy: 0.9924 - val_loss: 1.0077 - val_accuracy: 0.7333
Epoch 116/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1029 - accuracy: 0.9894 - val_loss: 1.0067 - val_accuracy: 0.7333
Epoch 117/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1019 - accuracy: 0.9939 - val_loss: 1.0058 - val_accuracy: 0.7333
Epoch 118/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0906 - accuracy: 0.9924 - val_loss: 1.0045 - val_accuracy: 0.7333
Epoch 119/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0970 - accuracy: 0.9909 - val_loss: 1.0035 - val_accuracy: 0.7333
Epoch 120/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0875 - accuracy: 0.9939 - val_loss: 1.0023 - val_accuracy: 0.7333
Epoch 121/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0934 - accuracy: 0.9924 - val_loss: 1.0012 - val_accuracy: 0.7296
Epoch 122/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9909 - val_loss: 1.0003 - val_accuracy: 0.7296
Epoch 123/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9970 - val_loss: 0.9994 - val_accuracy: 0.7259
Epoch 124/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0903 - accuracy: 0.9939 - val_loss: 0.9985 - val_accuracy: 0.7259
Epoch 125/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0988 - accuracy: 0.9939 - val_loss: 0.9978 - val_accuracy: 0.7259
Epoch 126/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0875 - accuracy: 0.9954 - val_loss: 0.9972 - val_accuracy: 0.7259
Epoch 127/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9939 - val_loss: 0.9965 - val_accuracy: 0.7259
Epoch 128/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0960 - accuracy: 0.9939 - val_loss: 0.9958 - val_accuracy: 0.7259
Epoch 129/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0958 - accuracy: 0.9924 - val_loss: 0.9950 - val_accuracy: 0.7259
Epoch 130/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0930 - accuracy: 0.9954 - val_loss: 0.9943 - val_accuracy: 0.7259
Epoch 131/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0939 - accuracy: 0.9954 - val_loss: 0.9934 - val_accuracy: 0.7259
Epoch 132/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9954 - val_loss: 0.9928 - val_accuracy: 0.7259
Epoch 133/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0891 - accuracy: 0.9970 - val_loss: 0.9919 - val_accuracy: 0.7259
Epoch 134/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9939 - val_loss: 0.9910 - val_accuracy: 0.7259
Epoch 135/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1022 - accuracy: 0.9878 - val_loss: 0.9899 - val_accuracy: 0.7259
Epoch 136/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9894 - val_loss: 0.9887 - val_accuracy: 0.7259
Epoch 137/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0872 - accuracy: 0.9954 - val_loss: 0.9876 - val_accuracy: 0.7259
Epoch 138/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0917 - accuracy: 0.9954 - val_loss: 0.9866 - val_accuracy: 0.7259
Epoch 139/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0917 - accuracy: 0.9909 - val_loss: 0.9856 - val_accuracy: 0.7259
Epoch 140/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0950 - accuracy: 0.9954 - val_loss: 0.9845 - val_accuracy: 0.7259
Epoch 141/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0934 - accuracy: 0.9909 - val_loss: 0.9834 - val_accuracy: 0.7259
Epoch 142/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0876 - accuracy: 1.0000 - val_loss: 0.9824 - val_accuracy: 0.7296
Epoch 143/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1049 - accuracy: 0.9939 - val_loss: 0.9814 - val_accuracy: 0.7296
Epoch 144/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0899 - accuracy: 0.9939 - val_loss: 0.9806 - val_accuracy: 0.7296
Epoch 145/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1050 - accuracy: 0.9848 - val_loss: 0.9796 - val_accuracy: 0.7296
Epoch 146/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0893 - accuracy: 0.9939 - val_loss: 0.9789 - val_accuracy: 0.7296
Epoch 147/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0877 - accuracy: 0.9985 - val_loss: 0.9780 - val_accuracy: 0.7296
Epoch 148/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0921 - accuracy: 0.9924 - val_loss: 0.9771 - val_accuracy: 0.7296
Epoch 149/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1006 - accuracy: 0.9939 - val_loss: 0.9762 - val_accuracy: 0.7296
Epoch 150/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0830 - accuracy: 0.9970 - val_loss: 0.9752 - val_accuracy: 0.7296
Epoch 151/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9939 - val_loss: 0.9743 - val_accuracy: 0.7296
Epoch 152/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1073 - accuracy: 0.9909 - val_loss: 0.9732 - val_accuracy: 0.7296
Epoch 153/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0923 - accuracy: 0.9939 - val_loss: 0.9724 - val_accuracy: 0.7296
Epoch 154/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1013 - accuracy: 0.9894 - val_loss: 0.9715 - val_accuracy: 0.7296
Epoch 155/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1008 - accuracy: 0.9924 - val_loss: 0.9706 - val_accuracy: 0.7333
Epoch 156/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9970 - val_loss: 0.9699 - val_accuracy: 0.7333
Epoch 157/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9954 - val_loss: 0.9691 - val_accuracy: 0.7333
Epoch 158/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0967 - accuracy: 0.9894 - val_loss: 0.9681 - val_accuracy: 0.7333
Epoch 159/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1165 - accuracy: 0.9878 - val_loss: 0.9670 - val_accuracy: 0.7333
Epoch 160/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0902 - accuracy: 0.9924 - val_loss: 0.9659 - val_accuracy: 0.7333
Epoch 161/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0956 - accuracy: 0.9970 - val_loss: 0.9651 - val_accuracy: 0.7333
Epoch 162/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0994 - accuracy: 0.9909 - val_loss: 0.9642 - val_accuracy: 0.7333
Epoch 163/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0999 - accuracy: 0.9894 - val_loss: 0.9632 - val_accuracy: 0.7370
Epoch 164/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1110 - accuracy: 0.9924 - val_loss: 0.9622 - val_accuracy: 0.7370
Epoch 165/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1022 - accuracy: 0.9924 - val_loss: 0.9612 - val_accuracy: 0.7370
Epoch 166/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0871 - accuracy: 0.9970 - val_loss: 0.9601 - val_accuracy: 0.7370
Epoch 167/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1034 - accuracy: 0.9909 - val_loss: 0.9590 - val_accuracy: 0.7370
Epoch 168/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0854 - accuracy: 1.0000 - val_loss: 0.9578 - val_accuracy: 0.7370
Epoch 169/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1009 - accuracy: 0.9954 - val_loss: 0.9566 - val_accuracy: 0.7370
Epoch 170/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1076 - accuracy: 0.9894 - val_loss: 0.9556 - val_accuracy: 0.7370
Epoch 171/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9954 - val_loss: 0.9546 - val_accuracy: 0.7370
Epoch 172/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0909 - accuracy: 0.9939 - val_loss: 0.9537 - val_accuracy: 0.7370
Epoch 173/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0897 - accuracy: 0.9970 - val_loss: 0.9528 - val_accuracy: 0.7370
Epoch 174/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0870 - accuracy: 0.9939 - val_loss: 0.9520 - val_accuracy: 0.7370
Epoch 175/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0797 - accuracy: 0.9985 - val_loss: 0.9512 - val_accuracy: 0.7370
Epoch 176/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1028 - accuracy: 1.0000 - val_loss: 0.9505 - val_accuracy: 0.7370
Epoch 177/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0896 - accuracy: 0.9954 - val_loss: 0.9497 - val_accuracy: 0.7407
Epoch 178/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9954 - val_loss: 0.9491 - val_accuracy: 0.7407
Epoch 179/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1179 - accuracy: 0.9909 - val_loss: 0.9485 - val_accuracy: 0.7407
Epoch 180/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0970 - accuracy: 0.9954 - val_loss: 0.9478 - val_accuracy: 0.7407
Epoch 181/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1073 - accuracy: 0.9894 - val_loss: 0.9472 - val_accuracy: 0.7370
Epoch 182/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0997 - accuracy: 0.9909 - val_loss: 0.9465 - val_accuracy: 0.7370
Epoch 183/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0934 - accuracy: 0.9939 - val_loss: 0.9459 - val_accuracy: 0.7407
Epoch 184/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0944 - accuracy: 0.9878 - val_loss: 0.9452 - val_accuracy: 0.7407
Epoch 185/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0971 - accuracy: 0.9909 - val_loss: 0.9446 - val_accuracy: 0.7407
Epoch 186/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0969 - accuracy: 0.9939 - val_loss: 0.9440 - val_accuracy: 0.7407
Epoch 187/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1016 - accuracy: 0.9924 - val_loss: 0.9435 - val_accuracy: 0.7407
Epoch 188/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9970 - val_loss: 0.9427 - val_accuracy: 0.7407
Epoch 189/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0962 - accuracy: 0.9909 - val_loss: 0.9420 - val_accuracy: 0.7407
Epoch 190/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0947 - accuracy: 0.9878 - val_loss: 0.9412 - val_accuracy: 0.7407
Epoch 191/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0934 - accuracy: 0.9939 - val_loss: 0.9405 - val_accuracy: 0.7407
Epoch 192/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1036 - accuracy: 0.9909 - val_loss: 0.9399 - val_accuracy: 0.7407
Epoch 193/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0790 - accuracy: 0.9985 - val_loss: 0.9392 - val_accuracy: 0.7407
Epoch 194/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0905 - accuracy: 0.9924 - val_loss: 0.9388 - val_accuracy: 0.7407
Epoch 195/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0956 - accuracy: 0.9954 - val_loss: 0.9383 - val_accuracy: 0.7407
Epoch 196/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0978 - accuracy: 0.9939 - val_loss: 0.9378 - val_accuracy: 0.7407
Epoch 197/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0914 - accuracy: 0.9939 - val_loss: 0.9373 - val_accuracy: 0.7407
Epoch 198/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0962 - accuracy: 0.9954 - val_loss: 0.9365 - val_accuracy: 0.7407
Epoch 199/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0897 - accuracy: 0.9924 - val_loss: 0.9359 - val_accuracy: 0.7407
Epoch 200/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0894 - accuracy: 0.9954 - val_loss: 0.9353 - val_accuracy: 0.7407
Epoch 201/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0916 - accuracy: 0.9863 - val_loss: 0.9348 - val_accuracy: 0.7407
Epoch 202/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0894 - accuracy: 0.9909 - val_loss: 0.9342 - val_accuracy: 0.7407
Epoch 203/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0998 - accuracy: 0.9924 - val_loss: 0.9336 - val_accuracy: 0.7407
Epoch 204/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0888 - accuracy: 0.9939 - val_loss: 0.9329 - val_accuracy: 0.7407
Epoch 205/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1050 - accuracy: 0.9894 - val_loss: 0.9324 - val_accuracy: 0.7407
Epoch 206/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0838 - accuracy: 0.9985 - val_loss: 0.9317 - val_accuracy: 0.7407
Epoch 207/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0885 - accuracy: 0.9954 - val_loss: 0.9312 - val_accuracy: 0.7407
Epoch 208/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0845 - accuracy: 0.9985 - val_loss: 0.9306 - val_accuracy: 0.7407
Epoch 209/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0923 - accuracy: 0.9970 - val_loss: 0.9299 - val_accuracy: 0.7407
Epoch 210/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0840 - accuracy: 0.9985 - val_loss: 0.9293 - val_accuracy: 0.7407
Epoch 211/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0978 - accuracy: 0.9954 - val_loss: 0.9289 - val_accuracy: 0.7407
Epoch 212/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0866 - accuracy: 0.9985 - val_loss: 0.9285 - val_accuracy: 0.7407
Epoch 213/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0963 - accuracy: 0.9939 - val_loss: 0.9280 - val_accuracy: 0.7407
Epoch 214/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0829 - accuracy: 0.9939 - val_loss: 0.9276 - val_accuracy: 0.7407
Epoch 215/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0843 - accuracy: 0.9970 - val_loss: 0.9273 - val_accuracy: 0.7407
Epoch 216/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0823 - accuracy: 0.9985 - val_loss: 0.9270 - val_accuracy: 0.7407
Epoch 217/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0884 - accuracy: 0.9924 - val_loss: 0.9267 - val_accuracy: 0.7407
Epoch 218/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0948 - accuracy: 0.9985 - val_loss: 0.9265 - val_accuracy: 0.7407
Epoch 219/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0914 - accuracy: 0.9939 - val_loss: 0.9263 - val_accuracy: 0.7444
Epoch 220/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0970 - accuracy: 0.9924 - val_loss: 0.9261 - val_accuracy: 0.7444
Epoch 221/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0879 - accuracy: 0.9985 - val_loss: 0.9259 - val_accuracy: 0.7444
Epoch 222/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0866 - accuracy: 0.9970 - val_loss: 0.9259 - val_accuracy: 0.7444
Epoch 223/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1087 - accuracy: 0.9878 - val_loss: 0.9255 - val_accuracy: 0.7444
Epoch 224/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0876 - accuracy: 0.9894 - val_loss: 0.9251 - val_accuracy: 0.7444
Epoch 225/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0948 - accuracy: 0.9970 - val_loss: 0.9248 - val_accuracy: 0.7407
Epoch 226/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0883 - accuracy: 0.9970 - val_loss: 0.9245 - val_accuracy: 0.7407
Epoch 227/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0821 - accuracy: 0.9954 - val_loss: 0.9241 - val_accuracy: 0.7407
Epoch 228/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0881 - accuracy: 0.9985 - val_loss: 0.9238 - val_accuracy: 0.7407
Epoch 229/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0873 - accuracy: 0.9954 - val_loss: 0.9234 - val_accuracy: 0.7407
Epoch 230/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0934 - accuracy: 0.9939 - val_loss: 0.9230 - val_accuracy: 0.7407
Epoch 231/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0941 - accuracy: 0.9954 - val_loss: 0.9226 - val_accuracy: 0.7407
Epoch 232/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0860 - accuracy: 0.9970 - val_loss: 0.9224 - val_accuracy: 0.7407
Epoch 233/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1060 - accuracy: 0.9894 - val_loss: 0.9219 - val_accuracy: 0.7407
Epoch 234/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0809 - accuracy: 0.9970 - val_loss: 0.9213 - val_accuracy: 0.7407
Epoch 235/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0996 - accuracy: 0.9939 - val_loss: 0.9208 - val_accuracy: 0.7407
Epoch 236/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0937 - accuracy: 0.9878 - val_loss: 0.9201 - val_accuracy: 0.7407
Epoch 237/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1004 - accuracy: 0.9924 - val_loss: 0.9197 - val_accuracy: 0.7407
Epoch 238/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0814 - accuracy: 0.9970 - val_loss: 0.9194 - val_accuracy: 0.7407
Epoch 239/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0991 - accuracy: 0.9970 - val_loss: 0.9190 - val_accuracy: 0.7407
Epoch 240/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9924 - val_loss: 0.9186 - val_accuracy: 0.7407
Epoch 241/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0838 - accuracy: 0.9970 - val_loss: 0.9183 - val_accuracy: 0.7407
Epoch 242/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9985 - val_loss: 0.9180 - val_accuracy: 0.7407
Epoch 243/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0928 - accuracy: 0.9970 - val_loss: 0.9177 - val_accuracy: 0.7407
Epoch 244/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0909 - accuracy: 0.9954 - val_loss: 0.9174 - val_accuracy: 0.7444
Epoch 245/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9894 - val_loss: 0.9172 - val_accuracy: 0.7444
Epoch 246/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0856 - accuracy: 0.9939 - val_loss: 0.9169 - val_accuracy: 0.7444
Epoch 247/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0889 - accuracy: 0.9970 - val_loss: 0.9167 - val_accuracy: 0.7444
Epoch 248/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0875 - accuracy: 0.9924 - val_loss: 0.9164 - val_accuracy: 0.7444
Epoch 249/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1064 - accuracy: 0.9878 - val_loss: 0.9163 - val_accuracy: 0.7444
Epoch 250/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0987 - accuracy: 0.9939 - val_loss: 0.9161 - val_accuracy: 0.7444
Epoch 251/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0921 - accuracy: 0.9939 - val_loss: 0.9159 - val_accuracy: 0.7444
Epoch 252/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0903 - accuracy: 0.9894 - val_loss: 0.9156 - val_accuracy: 0.7444
Epoch 253/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0990 - accuracy: 0.9939 - val_loss: 0.9153 - val_accuracy: 0.7444
Epoch 254/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0873 - accuracy: 0.9939 - val_loss: 0.9150 - val_accuracy: 0.7444
Epoch 255/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0874 - accuracy: 0.9985 - val_loss: 0.9147 - val_accuracy: 0.7444
Epoch 256/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9970 - val_loss: 0.9143 - val_accuracy: 0.7444
Epoch 257/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0876 - accuracy: 0.9970 - val_loss: 0.9139 - val_accuracy: 0.7481
Epoch 258/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0928 - accuracy: 0.9954 - val_loss: 0.9134 - val_accuracy: 0.7481
Epoch 259/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0895 - accuracy: 0.9924 - val_loss: 0.9131 - val_accuracy: 0.7481
Epoch 260/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0895 - accuracy: 0.9939 - val_loss: 0.9129 - val_accuracy: 0.7481
Epoch 261/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0794 - accuracy: 0.9970 - val_loss: 0.9128 - val_accuracy: 0.7481
Epoch 262/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0908 - accuracy: 0.9924 - val_loss: 0.9125 - val_accuracy: 0.7481
Epoch 263/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9894 - val_loss: 0.9123 - val_accuracy: 0.7481
Epoch 264/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0939 - accuracy: 0.9924 - val_loss: 0.9120 - val_accuracy: 0.7481
Epoch 265/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0910 - accuracy: 0.9924 - val_loss: 0.9117 - val_accuracy: 0.7481
Epoch 266/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0968 - accuracy: 0.9924 - val_loss: 0.9116 - val_accuracy: 0.7481
Epoch 267/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0936 - accuracy: 0.9924 - val_loss: 0.9116 - val_accuracy: 0.7481
Epoch 268/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0917 - accuracy: 0.9939 - val_loss: 0.9114 - val_accuracy: 0.7481
Epoch 269/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0782 - accuracy: 0.9970 - val_loss: 0.9112 - val_accuracy: 0.7481
Epoch 270/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1001 - accuracy: 0.9909 - val_loss: 0.9110 - val_accuracy: 0.7481
Epoch 271/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0802 - accuracy: 0.9939 - val_loss: 0.9107 - val_accuracy: 0.7481
Epoch 272/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0753 - accuracy: 0.9985 - val_loss: 0.9105 - val_accuracy: 0.7481
Epoch 273/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0932 - accuracy: 0.9970 - val_loss: 0.9103 - val_accuracy: 0.7481
Epoch 274/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9954 - val_loss: 0.9099 - val_accuracy: 0.7481
Epoch 275/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0888 - accuracy: 0.9954 - val_loss: 0.9097 - val_accuracy: 0.7481
Epoch 276/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9954 - val_loss: 0.9094 - val_accuracy: 0.7481
Epoch 277/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9970 - val_loss: 0.9092 - val_accuracy: 0.7481
Epoch 278/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0838 - accuracy: 0.9970 - val_loss: 0.9089 - val_accuracy: 0.7481
Epoch 279/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0932 - accuracy: 0.9970 - val_loss: 0.9087 - val_accuracy: 0.7481
Epoch 280/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0892 - accuracy: 0.9954 - val_loss: 0.9085 - val_accuracy: 0.7481
Epoch 281/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0807 - accuracy: 0.9985 - val_loss: 0.9082 - val_accuracy: 0.7481
Epoch 282/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0910 - accuracy: 0.9939 - val_loss: 0.9079 - val_accuracy: 0.7481
Epoch 283/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0919 - accuracy: 0.9939 - val_loss: 0.9077 - val_accuracy: 0.7481
Epoch 284/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0758 - accuracy: 0.9970 - val_loss: 0.9074 - val_accuracy: 0.7481
Epoch 285/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0829 - accuracy: 0.9970 - val_loss: 0.9071 - val_accuracy: 0.7481
Epoch 286/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 1.0000 - val_loss: 0.9069 - val_accuracy: 0.7481
Epoch 287/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0932 - accuracy: 0.9954 - val_loss: 0.9068 - val_accuracy: 0.7481
Epoch 288/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0916 - accuracy: 0.9954 - val_loss: 0.9066 - val_accuracy: 0.7481
Epoch 289/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0981 - accuracy: 0.9924 - val_loss: 0.9064 - val_accuracy: 0.7481
Epoch 290/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0879 - accuracy: 0.9939 - val_loss: 0.9061 - val_accuracy: 0.7481
Epoch 291/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0845 - accuracy: 0.9954 - val_loss: 0.9058 - val_accuracy: 0.7481
Epoch 292/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0990 - accuracy: 0.9939 - val_loss: 0.9059 - val_accuracy: 0.7481
Epoch 293/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0847 - accuracy: 0.9924 - val_loss: 0.9058 - val_accuracy: 0.7481
Epoch 294/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0919 - accuracy: 0.9954 - val_loss: 0.9056 - val_accuracy: 0.7481
Epoch 295/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0917 - accuracy: 0.9939 - val_loss: 0.9056 - val_accuracy: 0.7481
Epoch 296/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0825 - accuracy: 0.9939 - val_loss: 0.9054 - val_accuracy: 0.7481
Epoch 297/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0874 - accuracy: 0.9970 - val_loss: 0.9053 - val_accuracy: 0.7481
Epoch 298/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0939 - accuracy: 0.9954 - val_loss: 0.9051 - val_accuracy: 0.7481
Epoch 299/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0911 - accuracy: 0.9924 - val_loss: 0.9050 - val_accuracy: 0.7481
Epoch 300/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0789 - accuracy: 1.0000 - val_loss: 0.9051 - val_accuracy: 0.7481
Epoch 301/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0838 - accuracy: 0.9970 - val_loss: 0.9050 - val_accuracy: 0.7481
Epoch 302/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0736 - accuracy: 0.9970 - val_loss: 0.9049 - val_accuracy: 0.7481
Epoch 303/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0948 - accuracy: 0.9939 - val_loss: 0.9048 - val_accuracy: 0.7481
Epoch 304/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0837 - accuracy: 0.9954 - val_loss: 0.9047 - val_accuracy: 0.7481
Epoch 305/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0904 - accuracy: 0.9970 - val_loss: 0.9046 - val_accuracy: 0.7481
Epoch 306/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0901 - accuracy: 0.9924 - val_loss: 0.9045 - val_accuracy: 0.7481
Epoch 307/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0828 - accuracy: 1.0000 - val_loss: 0.9044 - val_accuracy: 0.7481
Epoch 308/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0940 - accuracy: 0.9924 - val_loss: 0.9042 - val_accuracy: 0.7481
Epoch 309/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0819 - accuracy: 0.9954 - val_loss: 0.9040 - val_accuracy: 0.7481
Epoch 310/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0920 - accuracy: 0.9939 - val_loss: 0.9037 - val_accuracy: 0.7481
Epoch 311/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0899 - accuracy: 0.9924 - val_loss: 0.9034 - val_accuracy: 0.7481
Epoch 312/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0970 - accuracy: 0.9909 - val_loss: 0.9033 - val_accuracy: 0.7481
Epoch 313/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0863 - accuracy: 0.9939 - val_loss: 0.9032 - val_accuracy: 0.7481
Epoch 314/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0935 - accuracy: 0.9894 - val_loss: 0.9030 - val_accuracy: 0.7481
Epoch 315/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1053 - accuracy: 0.9954 - val_loss: 0.9027 - val_accuracy: 0.7481
Epoch 316/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9924 - val_loss: 0.9025 - val_accuracy: 0.7481
Epoch 317/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0955 - accuracy: 0.9954 - val_loss: 0.9022 - val_accuracy: 0.7481
Epoch 318/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0820 - accuracy: 0.9939 - val_loss: 0.9018 - val_accuracy: 0.7481
Epoch 319/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0944 - accuracy: 0.9909 - val_loss: 0.9015 - val_accuracy: 0.7481
Epoch 320/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1023 - accuracy: 0.9878 - val_loss: 0.9013 - val_accuracy: 0.7481
Epoch 321/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0915 - accuracy: 0.9924 - val_loss: 0.9010 - val_accuracy: 0.7481
Epoch 322/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0855 - accuracy: 0.9970 - val_loss: 0.9008 - val_accuracy: 0.7481
Epoch 323/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0920 - accuracy: 0.9939 - val_loss: 0.9006 - val_accuracy: 0.7481
Epoch 324/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0919 - accuracy: 0.9985 - val_loss: 0.9005 - val_accuracy: 0.7481
Epoch 325/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9924 - val_loss: 0.9002 - val_accuracy: 0.7481
Epoch 326/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0858 - accuracy: 0.9939 - val_loss: 0.9000 - val_accuracy: 0.7481
Epoch 327/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0937 - accuracy: 0.9924 - val_loss: 0.8998 - val_accuracy: 0.7481
Epoch 328/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0866 - accuracy: 0.9939 - val_loss: 0.8996 - val_accuracy: 0.7481
Epoch 329/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0886 - accuracy: 0.9939 - val_loss: 0.8995 - val_accuracy: 0.7481
Epoch 330/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1003 - accuracy: 0.9939 - val_loss: 0.8992 - val_accuracy: 0.7481
Epoch 331/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0860 - accuracy: 0.9939 - val_loss: 0.8989 - val_accuracy: 0.7481
Epoch 332/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0828 - accuracy: 0.9939 - val_loss: 0.8987 - val_accuracy: 0.7481
Epoch 333/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0958 - accuracy: 0.9924 - val_loss: 0.8985 - val_accuracy: 0.7481
Epoch 334/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0893 - accuracy: 0.9970 - val_loss: 0.8983 - val_accuracy: 0.7481
Epoch 335/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0913 - accuracy: 0.9939 - val_loss: 0.8981 - val_accuracy: 0.7481
Epoch 336/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0786 - accuracy: 0.9954 - val_loss: 0.8979 - val_accuracy: 0.7481
Epoch 337/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9939 - val_loss: 0.8978 - val_accuracy: 0.7481
Epoch 338/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0920 - accuracy: 0.9954 - val_loss: 0.8976 - val_accuracy: 0.7481
Epoch 339/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0949 - accuracy: 0.9894 - val_loss: 0.8975 - val_accuracy: 0.7481
Epoch 340/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1030 - accuracy: 0.9924 - val_loss: 0.8975 - val_accuracy: 0.7481
Epoch 341/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1038 - accuracy: 0.9939 - val_loss: 0.8976 - val_accuracy: 0.7481
Epoch 342/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0855 - accuracy: 0.9909 - val_loss: 0.8977 - val_accuracy: 0.7481
Epoch 343/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0916 - accuracy: 0.9878 - val_loss: 0.8977 - val_accuracy: 0.7481
Epoch 344/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0985 - accuracy: 0.9954 - val_loss: 0.8977 - val_accuracy: 0.7481
Epoch 345/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0895 - accuracy: 0.9939 - val_loss: 0.8977 - val_accuracy: 0.7519
Epoch 346/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0889 - accuracy: 0.9954 - val_loss: 0.8974 - val_accuracy: 0.7519
Epoch 347/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9954 - val_loss: 0.8972 - val_accuracy: 0.7519
Epoch 348/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0868 - accuracy: 0.9970 - val_loss: 0.8971 - val_accuracy: 0.7519
Epoch 349/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0807 - accuracy: 0.9939 - val_loss: 0.8967 - val_accuracy: 0.7519
Epoch 350/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0964 - accuracy: 0.9954 - val_loss: 0.8964 - val_accuracy: 0.7519
Epoch 351/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0966 - accuracy: 0.9939 - val_loss: 0.8960 - val_accuracy: 0.7519
Epoch 352/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0897 - accuracy: 0.9970 - val_loss: 0.8957 - val_accuracy: 0.7519
Epoch 353/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0904 - accuracy: 0.9924 - val_loss: 0.8955 - val_accuracy: 0.7519
Epoch 354/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0933 - accuracy: 0.9894 - val_loss: 0.8951 - val_accuracy: 0.7519
Epoch 355/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0843 - accuracy: 0.9939 - val_loss: 0.8950 - val_accuracy: 0.7519
Epoch 356/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9954 - val_loss: 0.8948 - val_accuracy: 0.7519
Epoch 357/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0977 - accuracy: 0.9939 - val_loss: 0.8946 - val_accuracy: 0.7556
Epoch 358/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0936 - accuracy: 0.9924 - val_loss: 0.8943 - val_accuracy: 0.7556
Epoch 359/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0886 - accuracy: 0.9924 - val_loss: 0.8939 - val_accuracy: 0.7556
Epoch 360/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0964 - accuracy: 0.9878 - val_loss: 0.8935 - val_accuracy: 0.7556
Epoch 361/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0905 - accuracy: 0.9939 - val_loss: 0.8931 - val_accuracy: 0.7556
Epoch 362/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 1.0000 - val_loss: 0.8927 - val_accuracy: 0.7556
Epoch 363/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1056 - accuracy: 0.9878 - val_loss: 0.8925 - val_accuracy: 0.7556
Epoch 364/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0734 - accuracy: 0.9970 - val_loss: 0.8923 - val_accuracy: 0.7556
Epoch 365/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0825 - accuracy: 0.9954 - val_loss: 0.8920 - val_accuracy: 0.7556
Epoch 366/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0872 - accuracy: 0.9939 - val_loss: 0.8917 - val_accuracy: 0.7556
Epoch 367/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0886 - accuracy: 0.9954 - val_loss: 0.8915 - val_accuracy: 0.7556
Epoch 368/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0795 - accuracy: 0.9954 - val_loss: 0.8912 - val_accuracy: 0.7556
Epoch 369/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0846 - accuracy: 0.9939 - val_loss: 0.8911 - val_accuracy: 0.7556
Epoch 370/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0834 - accuracy: 0.9939 - val_loss: 0.8910 - val_accuracy: 0.7556
Epoch 371/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0869 - accuracy: 0.9939 - val_loss: 0.8910 - val_accuracy: 0.7556
Epoch 372/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0786 - accuracy: 0.9970 - val_loss: 0.8909 - val_accuracy: 0.7556
Epoch 373/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9939 - val_loss: 0.8909 - val_accuracy: 0.7556
Epoch 374/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0829 - accuracy: 0.9954 - val_loss: 0.8908 - val_accuracy: 0.7556
Epoch 375/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0958 - accuracy: 0.9894 - val_loss: 0.8908 - val_accuracy: 0.7556
Epoch 376/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9970 - val_loss: 0.8906 - val_accuracy: 0.7556
Epoch 377/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0867 - accuracy: 0.9939 - val_loss: 0.8906 - val_accuracy: 0.7556
Epoch 378/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9970 - val_loss: 0.8905 - val_accuracy: 0.7556
Epoch 379/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0844 - accuracy: 0.9939 - val_loss: 0.8906 - val_accuracy: 0.7556
Epoch 380/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0863 - accuracy: 0.9954 - val_loss: 0.8906 - val_accuracy: 0.7556
Epoch 381/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0808 - accuracy: 0.9970 - val_loss: 0.8905 - val_accuracy: 0.7556
Epoch 382/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9939 - val_loss: 0.8904 - val_accuracy: 0.7556
Epoch 383/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9939 - val_loss: 0.8903 - val_accuracy: 0.7556
Epoch 384/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0952 - accuracy: 0.9954 - val_loss: 0.8902 - val_accuracy: 0.7519
Epoch 385/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0758 - accuracy: 0.9954 - val_loss: 0.8902 - val_accuracy: 0.7519
Epoch 386/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0888 - accuracy: 0.9954 - val_loss: 0.8903 - val_accuracy: 0.7519
Epoch 387/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0966 - accuracy: 0.9909 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 388/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0836 - accuracy: 1.0000 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 389/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0872 - accuracy: 0.9954 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 390/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0945 - accuracy: 0.9894 - val_loss: 0.8906 - val_accuracy: 0.7519
Epoch 391/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0842 - accuracy: 0.9954 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 392/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0702 - accuracy: 0.9970 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 393/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0851 - accuracy: 0.9939 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 394/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0779 - accuracy: 1.0000 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 395/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0853 - accuracy: 0.9970 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 396/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0875 - accuracy: 0.9909 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 397/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0965 - accuracy: 0.9970 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 398/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0835 - accuracy: 0.9954 - val_loss: 0.8904 - val_accuracy: 0.7519
Epoch 399/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0911 - accuracy: 0.9954 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 400/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1000 - accuracy: 0.9894 - val_loss: 0.8905 - val_accuracy: 0.7519
Epoch 401/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0945 - accuracy: 0.9924 - val_loss: 0.8906 - val_accuracy: 0.7519
Epoch 402/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9939 - val_loss: 0.8907 - val_accuracy: 0.7519
Epoch 403/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1001 - accuracy: 0.9894 - val_loss: 0.8908 - val_accuracy: 0.7519
Epoch 404/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0812 - accuracy: 0.9909 - val_loss: 0.8909 - val_accuracy: 0.7519
Epoch 405/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0881 - accuracy: 0.9985 - val_loss: 0.8910 - val_accuracy: 0.7519
Epoch 406/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0825 - accuracy: 0.9954 - val_loss: 0.8910 - val_accuracy: 0.7519
Epoch 407/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0824 - accuracy: 0.9970 - val_loss: 0.8911 - val_accuracy: 0.7519
Epoch 408/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0945 - accuracy: 0.9909 - val_loss: 0.8912 - val_accuracy: 0.7519
Epoch 409/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9954 - val_loss: 0.8912 - val_accuracy: 0.7519
Epoch 410/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0941 - accuracy: 0.9939 - val_loss: 0.8912 - val_accuracy: 0.7481
Epoch 411/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0894 - accuracy: 0.9924 - val_loss: 0.8913 - val_accuracy: 0.7481
Epoch 412/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0841 - accuracy: 0.9954 - val_loss: 0.8914 - val_accuracy: 0.7481
Epoch 413/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0894 - accuracy: 0.9924 - val_loss: 0.8916 - val_accuracy: 0.7481
Epoch 414/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0798 - accuracy: 0.9985 - val_loss: 0.8916 - val_accuracy: 0.7481
Epoch 415/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0779 - accuracy: 0.9985 - val_loss: 0.8916 - val_accuracy: 0.7481
Epoch 416/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0785 - accuracy: 0.9985 - val_loss: 0.8915 - val_accuracy: 0.7481
Epoch 417/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0980 - accuracy: 0.9924 - val_loss: 0.8914 - val_accuracy: 0.7481
Epoch 418/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0764 - accuracy: 0.9909 - val_loss: 0.8913 - val_accuracy: 0.7481
Epoch 419/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0907 - accuracy: 0.9924 - val_loss: 0.8914 - val_accuracy: 0.7481
Epoch 420/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1000 - accuracy: 0.9894 - val_loss: 0.8915 - val_accuracy: 0.7481
Epoch 421/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0908 - accuracy: 0.9954 - val_loss: 0.8915 - val_accuracy: 0.7481
Epoch 422/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0798 - accuracy: 0.9985 - val_loss: 0.8914 - val_accuracy: 0.7481
Epoch 423/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0938 - accuracy: 0.9954 - val_loss: 0.8913 - val_accuracy: 0.7481
Epoch 424/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0952 - accuracy: 0.9939 - val_loss: 0.8912 - val_accuracy: 0.7481
Epoch 425/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0854 - accuracy: 0.9970 - val_loss: 0.8912 - val_accuracy: 0.7481
Epoch 426/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0801 - accuracy: 0.9985 - val_loss: 0.8910 - val_accuracy: 0.7481
Epoch 427/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0828 - accuracy: 0.9954 - val_loss: 0.8908 - val_accuracy: 0.7481
Epoch 428/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0923 - accuracy: 0.9939 - val_loss: 0.8905 - val_accuracy: 0.7481
Epoch 429/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0932 - accuracy: 0.9954 - val_loss: 0.8903 - val_accuracy: 0.7481
Epoch 430/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0812 - accuracy: 0.9985 - val_loss: 0.8901 - val_accuracy: 0.7481
Epoch 431/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0796 - accuracy: 0.9924 - val_loss: 0.8898 - val_accuracy: 0.7481
Epoch 432/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0884 - accuracy: 0.9924 - val_loss: 0.8894 - val_accuracy: 0.7481
Epoch 433/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0929 - accuracy: 0.9909 - val_loss: 0.8889 - val_accuracy: 0.7481
Epoch 434/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0837 - accuracy: 0.9954 - val_loss: 0.8885 - val_accuracy: 0.7481
Epoch 435/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0913 - accuracy: 0.9985 - val_loss: 0.8881 - val_accuracy: 0.7481
Epoch 436/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0873 - accuracy: 0.9924 - val_loss: 0.8877 - val_accuracy: 0.7481
Epoch 437/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0812 - accuracy: 0.9954 - val_loss: 0.8874 - val_accuracy: 0.7481
Epoch 438/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0816 - accuracy: 0.9970 - val_loss: 0.8872 - val_accuracy: 0.7481
Epoch 439/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0906 - accuracy: 0.9954 - val_loss: 0.8870 - val_accuracy: 0.7481
Epoch 440/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0893 - accuracy: 0.9939 - val_loss: 0.8869 - val_accuracy: 0.7519
Epoch 441/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0877 - accuracy: 0.9970 - val_loss: 0.8866 - val_accuracy: 0.7519
Epoch 442/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0753 - accuracy: 0.9970 - val_loss: 0.8865 - val_accuracy: 0.7519
Epoch 443/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0842 - accuracy: 1.0000 - val_loss: 0.8865 - val_accuracy: 0.7519
Epoch 444/600
658/658 [==============================] - 6s 9ms/step - loss: 0.0889 - accuracy: 0.9954 - val_loss: 0.8865 - val_accuracy: 0.7519
Epoch 445/600
658/658 [==============================] - 6s 9ms/step - loss: 0.0964 - accuracy: 0.9954 - val_loss: 0.8866 - val_accuracy: 0.7519
Epoch 446/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0860 - accuracy: 0.9909 - val_loss: 0.8868 - val_accuracy: 0.7519
Epoch 447/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0910 - accuracy: 0.9970 - val_loss: 0.8869 - val_accuracy: 0.7519
Epoch 448/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0863 - accuracy: 0.9939 - val_loss: 0.8870 - val_accuracy: 0.7519
Epoch 449/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0842 - accuracy: 0.9954 - val_loss: 0.8872 - val_accuracy: 0.7519
Epoch 450/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0918 - accuracy: 0.9924 - val_loss: 0.8873 - val_accuracy: 0.7481
Epoch 451/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0951 - accuracy: 0.9939 - val_loss: 0.8872 - val_accuracy: 0.7481
Epoch 452/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0915 - accuracy: 0.9970 - val_loss: 0.8870 - val_accuracy: 0.7481
Epoch 453/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9924 - val_loss: 0.8868 - val_accuracy: 0.7481
Epoch 454/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0922 - accuracy: 0.9924 - val_loss: 0.8866 - val_accuracy: 0.7481
Epoch 455/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0887 - accuracy: 0.9970 - val_loss: 0.8864 - val_accuracy: 0.7481
Epoch 456/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9924 - val_loss: 0.8861 - val_accuracy: 0.7519
Epoch 457/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0779 - accuracy: 0.9985 - val_loss: 0.8858 - val_accuracy: 0.7519
Epoch 458/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9939 - val_loss: 0.8855 - val_accuracy: 0.7519
Epoch 459/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9924 - val_loss: 0.8853 - val_accuracy: 0.7519
Epoch 460/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0867 - accuracy: 0.9924 - val_loss: 0.8852 - val_accuracy: 0.7519
Epoch 461/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9939 - val_loss: 0.8851 - val_accuracy: 0.7519
Epoch 462/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0822 - accuracy: 0.9924 - val_loss: 0.8852 - val_accuracy: 0.7519
Epoch 463/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0855 - accuracy: 0.9939 - val_loss: 0.8853 - val_accuracy: 0.7519
Epoch 464/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0762 - accuracy: 0.9970 - val_loss: 0.8852 - val_accuracy: 0.7519
Epoch 465/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0905 - accuracy: 0.9894 - val_loss: 0.8853 - val_accuracy: 0.7481
Epoch 466/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0847 - accuracy: 0.9970 - val_loss: 0.8855 - val_accuracy: 0.7481
Epoch 467/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0710 - accuracy: 0.9985 - val_loss: 0.8854 - val_accuracy: 0.7481
Epoch 468/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0755 - accuracy: 0.9970 - val_loss: 0.8856 - val_accuracy: 0.7481
Epoch 469/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0813 - accuracy: 0.9970 - val_loss: 0.8856 - val_accuracy: 0.7481
Epoch 470/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0797 - accuracy: 0.9939 - val_loss: 0.8858 - val_accuracy: 0.7481
Epoch 471/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0838 - accuracy: 0.9970 - val_loss: 0.8858 - val_accuracy: 0.7481
Epoch 472/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0779 - accuracy: 0.9970 - val_loss: 0.8859 - val_accuracy: 0.7481
Epoch 473/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1005 - accuracy: 0.9939 - val_loss: 0.8859 - val_accuracy: 0.7481
Epoch 474/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0892 - accuracy: 0.9954 - val_loss: 0.8859 - val_accuracy: 0.7481
Epoch 475/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0806 - accuracy: 0.9954 - val_loss: 0.8858 - val_accuracy: 0.7481
Epoch 476/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0911 - accuracy: 0.9939 - val_loss: 0.8858 - val_accuracy: 0.7481
Epoch 477/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0943 - accuracy: 0.9909 - val_loss: 0.8859 - val_accuracy: 0.7481
Epoch 478/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0853 - accuracy: 0.9939 - val_loss: 0.8857 - val_accuracy: 0.7481
Epoch 479/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9954 - val_loss: 0.8857 - val_accuracy: 0.7481
Epoch 480/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0874 - accuracy: 0.9954 - val_loss: 0.8856 - val_accuracy: 0.7481
Epoch 481/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0752 - accuracy: 0.9985 - val_loss: 0.8854 - val_accuracy: 0.7481
Epoch 482/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0782 - accuracy: 0.9970 - val_loss: 0.8852 - val_accuracy: 0.7481
Epoch 483/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0813 - accuracy: 0.9954 - val_loss: 0.8849 - val_accuracy: 0.7481
Epoch 484/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0819 - accuracy: 0.9939 - val_loss: 0.8848 - val_accuracy: 0.7481
Epoch 485/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0933 - accuracy: 0.9878 - val_loss: 0.8847 - val_accuracy: 0.7481
Epoch 486/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1005 - accuracy: 0.9878 - val_loss: 0.8844 - val_accuracy: 0.7481
Epoch 487/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9909 - val_loss: 0.8842 - val_accuracy: 0.7481
Epoch 488/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0819 - accuracy: 0.9939 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 489/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0827 - accuracy: 0.9939 - val_loss: 0.8840 - val_accuracy: 0.7481
Epoch 490/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0765 - accuracy: 0.9970 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 491/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0950 - accuracy: 0.9909 - val_loss: 0.8840 - val_accuracy: 0.7481
Epoch 492/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0888 - accuracy: 0.9924 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 493/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0865 - accuracy: 0.9924 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 494/600
658/658 [==============================] - 4s 6ms/step - loss: 0.1009 - accuracy: 0.9909 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 495/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0901 - accuracy: 0.9909 - val_loss: 0.8839 - val_accuracy: 0.7481
Epoch 496/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0838 - accuracy: 0.9985 - val_loss: 0.8840 - val_accuracy: 0.7481
Epoch 497/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0946 - accuracy: 0.9909 - val_loss: 0.8842 - val_accuracy: 0.7481
Epoch 498/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0830 - accuracy: 0.9939 - val_loss: 0.8842 - val_accuracy: 0.7481
Epoch 499/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0920 - accuracy: 0.9939 - val_loss: 0.8843 - val_accuracy: 0.7481
Epoch 500/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0831 - accuracy: 0.9985 - val_loss: 0.8844 - val_accuracy: 0.7481
Epoch 501/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0772 - accuracy: 0.9954 - val_loss: 0.8845 - val_accuracy: 0.7481
Epoch 502/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0797 - accuracy: 0.9970 - val_loss: 0.8847 - val_accuracy: 0.7481
Epoch 503/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0787 - accuracy: 0.9954 - val_loss: 0.8849 - val_accuracy: 0.7481
Epoch 504/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0855 - accuracy: 0.9939 - val_loss: 0.8848 - val_accuracy: 0.7481
Epoch 505/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0768 - accuracy: 0.9939 - val_loss: 0.8846 - val_accuracy: 0.7481
Epoch 506/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9924 - val_loss: 0.8846 - val_accuracy: 0.7481
Epoch 507/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0757 - accuracy: 0.9985 - val_loss: 0.8845 - val_accuracy: 0.7481
Epoch 508/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0784 - accuracy: 1.0000 - val_loss: 0.8842 - val_accuracy: 0.7481
Epoch 509/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0883 - accuracy: 0.9970 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 510/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0777 - accuracy: 0.9954 - val_loss: 0.8841 - val_accuracy: 0.7481
Epoch 511/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0808 - accuracy: 0.9954 - val_loss: 0.8840 - val_accuracy: 0.7481
Epoch 512/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0805 - accuracy: 0.9954 - val_loss: 0.8839 - val_accuracy: 0.7481
Epoch 513/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0866 - accuracy: 0.9954 - val_loss: 0.8837 - val_accuracy: 0.7481
Epoch 514/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0829 - accuracy: 0.9985 - val_loss: 0.8836 - val_accuracy: 0.7481
Epoch 515/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0808 - accuracy: 0.9970 - val_loss: 0.8836 - val_accuracy: 0.7481
Epoch 516/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0944 - accuracy: 0.9924 - val_loss: 0.8835 - val_accuracy: 0.7481
Epoch 517/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0994 - accuracy: 0.9924 - val_loss: 0.8835 - val_accuracy: 0.7481
Epoch 518/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0763 - accuracy: 0.9939 - val_loss: 0.8833 - val_accuracy: 0.7481
Epoch 519/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0881 - accuracy: 0.9939 - val_loss: 0.8832 - val_accuracy: 0.7481
Epoch 520/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0785 - accuracy: 1.0000 - val_loss: 0.8831 - val_accuracy: 0.7481
Epoch 521/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0902 - accuracy: 0.9985 - val_loss: 0.8830 - val_accuracy: 0.7481
Epoch 522/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0961 - accuracy: 0.9924 - val_loss: 0.8827 - val_accuracy: 0.7481
Epoch 523/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0894 - accuracy: 0.9970 - val_loss: 0.8827 - val_accuracy: 0.7481
Epoch 524/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0750 - accuracy: 0.9970 - val_loss: 0.8826 - val_accuracy: 0.7481
Epoch 525/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9909 - val_loss: 0.8826 - val_accuracy: 0.7481
Epoch 526/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0777 - accuracy: 0.9970 - val_loss: 0.8825 - val_accuracy: 0.7481
Epoch 527/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0865 - accuracy: 0.9939 - val_loss: 0.8825 - val_accuracy: 0.7481
Epoch 528/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0930 - accuracy: 0.9924 - val_loss: 0.8826 - val_accuracy: 0.7481
Epoch 529/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0754 - accuracy: 0.9954 - val_loss: 0.8826 - val_accuracy: 0.7481
Epoch 530/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0809 - accuracy: 0.9970 - val_loss: 0.8826 - val_accuracy: 0.7481
Epoch 531/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0919 - accuracy: 0.9954 - val_loss: 0.8825 - val_accuracy: 0.7481
Epoch 532/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0840 - accuracy: 0.9954 - val_loss: 0.8823 - val_accuracy: 0.7481
Epoch 533/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0799 - accuracy: 1.0000 - val_loss: 0.8821 - val_accuracy: 0.7481
Epoch 534/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0833 - accuracy: 0.9954 - val_loss: 0.8820 - val_accuracy: 0.7481
Epoch 535/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0953 - accuracy: 0.9939 - val_loss: 0.8819 - val_accuracy: 0.7481
Epoch 536/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0857 - accuracy: 0.9939 - val_loss: 0.8817 - val_accuracy: 0.7481
Epoch 537/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0861 - accuracy: 0.9954 - val_loss: 0.8814 - val_accuracy: 0.7481
Epoch 538/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0839 - accuracy: 0.9939 - val_loss: 0.8813 - val_accuracy: 0.7481
Epoch 539/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0737 - accuracy: 1.0000 - val_loss: 0.8812 - val_accuracy: 0.7481
Epoch 540/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0874 - accuracy: 0.9924 - val_loss: 0.8811 - val_accuracy: 0.7481
Epoch 541/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0816 - accuracy: 0.9970 - val_loss: 0.8810 - val_accuracy: 0.7481
Epoch 542/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0879 - accuracy: 0.9970 - val_loss: 0.8808 - val_accuracy: 0.7481
Epoch 543/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0942 - accuracy: 0.9939 - val_loss: 0.8805 - val_accuracy: 0.7481
Epoch 544/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0891 - accuracy: 0.9909 - val_loss: 0.8802 - val_accuracy: 0.7481
Epoch 545/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0890 - accuracy: 0.9939 - val_loss: 0.8798 - val_accuracy: 0.7481
Epoch 546/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0891 - accuracy: 0.9970 - val_loss: 0.8795 - val_accuracy: 0.7481
Epoch 547/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0951 - accuracy: 0.9878 - val_loss: 0.8794 - val_accuracy: 0.7519
Epoch 548/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0779 - accuracy: 0.9954 - val_loss: 0.8792 - val_accuracy: 0.7519
Epoch 549/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9985 - val_loss: 0.8792 - val_accuracy: 0.7519
Epoch 550/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0897 - accuracy: 0.9909 - val_loss: 0.8792 - val_accuracy: 0.7519
Epoch 551/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9939 - val_loss: 0.8791 - val_accuracy: 0.7519
Epoch 552/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0868 - accuracy: 0.9954 - val_loss: 0.8789 - val_accuracy: 0.7519
Epoch 553/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0783 - accuracy: 0.9939 - val_loss: 0.8789 - val_accuracy: 0.7519
Epoch 554/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0756 - accuracy: 0.9970 - val_loss: 0.8788 - val_accuracy: 0.7519
Epoch 555/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0822 - accuracy: 0.9954 - val_loss: 0.8788 - val_accuracy: 0.7556
Epoch 556/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0905 - accuracy: 0.9924 - val_loss: 0.8787 - val_accuracy: 0.7556
Epoch 557/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9924 - val_loss: 0.8786 - val_accuracy: 0.7556
Epoch 558/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0835 - accuracy: 0.9954 - val_loss: 0.8783 - val_accuracy: 0.7556
Epoch 559/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0789 - accuracy: 0.9970 - val_loss: 0.8781 - val_accuracy: 0.7556
Epoch 560/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0886 - accuracy: 0.9954 - val_loss: 0.8779 - val_accuracy: 0.7556
Epoch 561/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0900 - accuracy: 0.9924 - val_loss: 0.8775 - val_accuracy: 0.7556
Epoch 562/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0882 - accuracy: 0.9970 - val_loss: 0.8773 - val_accuracy: 0.7556
Epoch 563/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0799 - accuracy: 0.9939 - val_loss: 0.8772 - val_accuracy: 0.7556
Epoch 564/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0832 - accuracy: 0.9954 - val_loss: 0.8771 - val_accuracy: 0.7556
Epoch 565/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0840 - accuracy: 0.9985 - val_loss: 0.8770 - val_accuracy: 0.7556
Epoch 566/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0872 - accuracy: 0.9954 - val_loss: 0.8770 - val_accuracy: 0.7556
Epoch 567/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0870 - accuracy: 0.9970 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 568/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0791 - accuracy: 0.9970 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 569/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0826 - accuracy: 0.9954 - val_loss: 0.8772 - val_accuracy: 0.7519
Epoch 570/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0904 - accuracy: 0.9924 - val_loss: 0.8773 - val_accuracy: 0.7519
Epoch 571/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0743 - accuracy: 0.9970 - val_loss: 0.8772 - val_accuracy: 0.7519
Epoch 572/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0904 - accuracy: 0.9985 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 573/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0772 - accuracy: 0.9939 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 574/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0814 - accuracy: 0.9985 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 575/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0741 - accuracy: 0.9954 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 576/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0834 - accuracy: 1.0000 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 577/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0738 - accuracy: 0.9939 - val_loss: 0.8769 - val_accuracy: 0.7519
Epoch 578/600
658/658 [==============================] - 4s 5ms/step - loss: 0.0845 - accuracy: 1.0000 - val_loss: 0.8769 - val_accuracy: 0.7519
Epoch 579/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0846 - accuracy: 0.9970 - val_loss: 0.8769 - val_accuracy: 0.7519
Epoch 580/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0786 - accuracy: 0.9939 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 581/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0846 - accuracy: 0.9970 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 582/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0931 - accuracy: 0.9939 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 583/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0946 - accuracy: 0.9924 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 584/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0901 - accuracy: 0.9894 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 585/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0828 - accuracy: 0.9954 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 586/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0866 - accuracy: 0.9924 - val_loss: 0.8770 - val_accuracy: 0.7519
Epoch 587/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0800 - accuracy: 0.9954 - val_loss: 0.8771 - val_accuracy: 0.7519
Epoch 588/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0804 - accuracy: 0.9970 - val_loss: 0.8773 - val_accuracy: 0.7519
Epoch 589/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0872 - accuracy: 0.9924 - val_loss: 0.8773 - val_accuracy: 0.7519
Epoch 590/600
658/658 [==============================] - 4s 5ms/step - loss: 0.1051 - accuracy: 0.9878 - val_loss: 0.8774 - val_accuracy: 0.7519
Epoch 591/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0857 - accuracy: 0.9954 - val_loss: 0.8774 - val_accuracy: 0.7519
Epoch 592/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0840 - accuracy: 0.9924 - val_loss: 0.8776 - val_accuracy: 0.7519
Epoch 593/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0789 - accuracy: 0.9954 - val_loss: 0.8778 - val_accuracy: 0.7519
Epoch 594/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0958 - accuracy: 0.9909 - val_loss: 0.8778 - val_accuracy: 0.7519
Epoch 595/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0728 - accuracy: 0.9954 - val_loss: 0.8779 - val_accuracy: 0.7519
Epoch 596/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9909 - val_loss: 0.8778 - val_accuracy: 0.7519
Epoch 597/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0878 - accuracy: 0.9954 - val_loss: 0.8779 - val_accuracy: 0.7519
Epoch 598/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0764 - accuracy: 0.9970 - val_loss: 0.8781 - val_accuracy: 0.7519
Epoch 599/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0727 - accuracy: 0.9954 - val_loss: 0.8783 - val_accuracy: 0.7519
Epoch 600/600
658/658 [==============================] - 4s 6ms/step - loss: 0.0890 - accuracy: 0.9939 - val_loss: 0.8784 - val_accuracy: 0.7519
In [8]:
# Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
In [0]:
Content source: is-cs/druljs
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