In [138]:

    

%load_ext autoreload
%autoreload 2


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [139]:

    

from pearce.emulator import OriginalRecipe, ExtraCrispy
from pearce.mocks import cat_dict
import numpy as np
from os import path


    

In [140]:

    

import tensorflow as tf


    

In [141]:

    

import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()


    

In [142]:

    

training_file = '/home/sean/PearceRedMagicXiCosmo.hdf5'
test_file = '/home/sean/PearceRedMagicXiCosmoTest.hdf5'


    

In [143]:

    

em_method = 'nn'
split_method = 'random'


    

In [144]:

    

a = 1.0
z = 1.0/a - 1.0


    

In [145]:

    

fixed_params = {'z':z}#, 'r':17.0389993 }


    

In [146]:

    

emu = OriginalRecipe(training_file, method = em_method, fixed_params=fixed_params,
                    hyperparams = {'hidden_layer_sizes': (10),
                                 'activation': 'relu', 'verbose': True, 
                                    'tol': 1e-8, 'learning_rate_init':1e-4,\
                                   'max_iter':10, 'alpha':0, 'early_stopping':False, 'validation_fraction':0.3})


    

    




Iteration 1, loss = 0.32825790
Iteration 2, loss = 0.10845689
Iteration 3, loss = 0.08724234
Iteration 4, loss = 0.08313204
Iteration 5, loss = 0.08168116
Iteration 6, loss = 0.08096126
Iteration 7, loss = 0.08054406
Iteration 8, loss = 0.08027376
Iteration 9, loss = 0.08007939
Iteration 10, loss = 0.07993874

In [147]:

    

#idxs = np.random.choice(emu.x.shape[0], size = int(emu.x.shape[0]*1.0), replace=False)

#x_train, y_train,yerr_train = emu.x[idxs, :],emu.y[idxs],emu.yerr[idxs]
x_train, y_train,yerr_train = emu.x,emu.y,emu.yerr

y_train = y_train*(emu._y_std + 1e-5) + emu._y_mean
yerr_train = yerr_train*(emu._y_std+1e-5)


    

In [148]:

    

x_train = x_train[0:-1:emu.n_bins, :-1]

y_train = y_train.reshape([-1, emu.n_bins])
yerr_train = yerr_train.reshape([-1, emu.n_bins])


    

In [149]:

    

emu.get_param_names()[:7]


    

    
Out[149]:





['ombh2', 'omch2', 'w0', 'ns', 'ln10As', 'H0', 'Neff']

In [150]:

    

unique_cosmos = np.unique(x_train[:, :7], axis =0)#*(emu._x_std[:7]+1e-5) + emu._x_mean[:7]


    

In [151]:

    

unique_cosmos.shape


    

    
Out[151]:





(40, 7)

In [152]:

    

x_test, y_test, ycov_test, _ = emu.get_data(test_file, fixed_params, None, False)
x_test = (x_test - emu._x_mean)/(emu._x_std+1e-5)

#split_ycov = np.dsplit(ycov_test, ycov_test.shape[-1])
        #fullcov = block_diag(*[yc[:,:,0] for yc in split_ycov])
#yerr_test = np.sqrt(np.hstack(np.diag(syc[:,:,0]) for syc in split_ycov))


    

In [153]:

    

x_test = x_test[0:-1:emu.n_bins, :-1]

y_test = y_test.reshape([-1, emu.n_bins])
#yerr_test = yerr_test.reshape([-1, emu.n_bins])


    

In [154]:

    

def n_layer_fc(x, hidden_sizes, training=False, l = 1e-8):
    initializer = tf.variance_scaling_initializer(scale=2.0)
    regularizer = tf.contrib.layers.l2_regularizer(l)
    fc_output = tf.layers.dense(x, hidden_sizes[0], activation=tf.nn.relu,
                                 kernel_initializer = initializer, kernel_regularizer = regularizer)
                                 #kernel_regularizer = tf.nn.l2_loss)
    #fc2_output = tf.layers.dense(fc1_output, hidden_sizes[1], activation=tf.nn.relu,
    #                             kernel_initializer = initializer, kernel_regularizer = regularizer)
    for size in hidden_sizes[1:]:
        fc_output = tf.layers.dense(fc_output, size, activation=tf.nn.relu, kernel_initializer=initializer,
                                 kernel_regularizer = regularizer)
    pred = tf.layers.dense(fc_output, 1, kernel_initializer=initializer, 
                              kernel_regularizer = regularizer)[:,0]#,
    return pred


    

In [175]:

    

def n_layer_fc_do_bn(x, hidden_sizes, training=False, l = 1e2):
    initializer = tf.variance_scaling_initializer(scale=2.0)
    regularizer = tf.contrib.layers.l1_regularizer(l)
    nl_out = x

    for size in hidden_sizes:
        fc_output = tf.layers.dense(nl_out, size,
                                 kernel_initializer = initializer, kernel_regularizer = regularizer)
        bd_out = tf.layers.dropout(fc_output, 0.333, training = training)
        bn_out = tf.layers.batch_normalization(bd_out, axis = -1, training=training)
        nl_out = tf.nn.relu(bn_out)#tf.nn.leaky_relu(bn_out, alpha=0.01)
    
    pred = tf.layers.dense(nl_out, emu.n_bins, kernel_initializer=initializer, 
                              kernel_regularizer = regularizer)#[:,0]#,
    return pred


    

In [176]:

    

def optimizer_init_fn(learning_rate = 1e-7):
    return tf.train.AdamOptimizer(learning_rate)#, beta1=0.9, beta2=0.999, epsilon=1e-6)


    

In [177]:

    

from sklearn.metrics import r2_score, mean_squared_error


    

In [178]:

    

def check_accuracy(sess, val_data,batch_size, x, weights, preds, is_training=None):
    val_x, val_y = val_data
    perc_acc, scores = [],[]
    for idx in xrange(0, val_x.shape[0], batch_size):
        feed_dict = {x: val_x[idx:idx+batch_size],
                     is_training: 0}
        y_pred = sess.run(preds, feed_dict=feed_dict)
        #print y_pred.shape, val_y[idx:idx+batch_size].shape
        score = r2_score(val_y[idx:idx+batch_size], y_pred)
        scores.append(score)
        
        perc_acc = np.mean(emu._y_std*np.abs(val_y[idx:idx+batch_size]-y_pred)/np.abs(emu._y_std*val_y[idx:idx+batch_size] + emu._y_mean) )

        
    print 'Val score: %.6f, %.2f %% Loss'%(np.mean(np.array(scores)), 100*np.mean(np.array(perc_acc)))


    

In [179]:

    

device = '/device:GPU:0'
#device = '/cpu:0'
def train(model_init_fn, optimizer_init_fn,num_params, train_data, val_data, hidden_sizes,\
               num_epochs=1, batch_size = 200, print_every=10):
    tf.reset_default_graph()    
    with tf.device(device):
        # Construct the computational graph we will use to train the model. We
        # use the model_init_fn to construct the model, declare placeholders for
        # the data and labels
        x = tf.placeholder(tf.float32, [None,num_params], name = 'x')
        y = tf.placeholder(tf.float32, [None, emu.n_bins], name = 'y')
        weights = tf.placeholder(tf.float32, [None, emu.n_bins], name = 'weights')
        
        is_training = tf.placeholder(tf.bool, name='is_training')
        
        preds = model_init_fn(x, hidden_sizes, is_training)

        # Compute the loss like we did in Part II
        #loss = tf.reduce_mean(loss)
        
    with tf.device('/cpu:0'):
        loss = tf.losses.mean_squared_error(labels=y,\
                    predictions=preds, weights = weights)#weights?
        #loss = tf.losses.absolute_difference(labels=y, predictions=preds, weights = tf.abs(1.0/y))#weights?

    with tf.device(device):
        optimizer = optimizer_init_fn()
        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        with tf.control_dependencies(update_ops):
            train_op = optimizer.minimize(loss)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        #t = 0
        train_x, train_y, train_yerr = train_data
        rand_idxs = range(train_x.shape[0])
        for epoch in range(num_epochs):
            #print('Starting epoch %d' % epoch)
            np.random.shuffle(rand_idxs)
            losses = []
            for idx in xrange(0, train_x.shape[0], batch_size):
                _bs = train_x[rand_idxs[idx:idx+batch_size]].shape[0]
                feed_dict = {x: train_x[rand_idxs[idx:idx+batch_size]],\
                             y: train_y[rand_idxs[idx:idx+batch_size]] + np.random.randn(_bs, emu.n_bins)*train_yerr[rand_idxs[idx:idx+batch_size]],\
                             weights: 1/train_yerr[rand_idxs[idx:idx+batch_size]],\
                             is_training:1}
                loss_np, _, preds_np = sess.run([loss, train_op, preds], feed_dict=feed_dict)
                #preds_np = sess.run([preds], feed_dict=feed_dict)
                #print preds_np[0].shape
                losses.append(loss_np)
                
                
            if epoch % print_every == 0:
                loss_avg = np.mean(np.array(losses))
                print('Epoch %d, loss = %e' % (epoch, loss_avg))
                check_accuracy(sess, val_data, batch_size, x, weights, preds, is_training=is_training)
            #t += 1


    

In [180]:

    

train(n_layer_fc_do_bn, optimizer_init_fn, x_train.shape[1],\
           (x_train, y_train, yerr_train), (x_test, y_test),\
           [200, 500, 500,1000,500, 500, 200], num_epochs= int(1e4), batch_size = 100, \
           print_every = 10)


    

    




Epoch 0, loss = 1.396695e+03
Val score: -12.715815, 62.54 % Loss
Epoch 10, loss = 1.370375e+03
Val score: -12.722258, 62.38 % Loss
Epoch 20, loss = 1.348620e+03
Val score: -12.609831, 62.05 % Loss
Epoch 30, loss = 1.321280e+03
Val score: -12.493435, 61.80 % Loss
Epoch 40, loss = 1.296086e+03
Val score: -12.387121, 61.49 % Loss
Epoch 50, loss = 1.275644e+03
Val score: -12.246988, 61.21 % Loss
Epoch 60, loss = 1.252707e+03
Val score: -12.136255, 60.89 % Loss
Epoch 70, loss = 1.235530e+03
Val score: -11.936174, 60.30 % Loss
Epoch 80, loss = 1.213781e+03
Val score: -11.819302, 60.05 % Loss
Epoch 90, loss = 1.192854e+03
Val score: -11.664311, 59.67 % Loss
Epoch 100, loss = 1.173524e+03
Val score: -11.454376, 59.05 % Loss
Epoch 110, loss = 1.155185e+03
Val score: -11.354989, 58.74 % Loss
Epoch 120, loss = 1.137976e+03
Val score: -11.130107, 58.11 % Loss
Epoch 130, loss = 1.121424e+03
Val score: -10.926523, 57.49 % Loss
Epoch 140, loss = 1.101845e+03
Val score: -10.771099, 57.01 % Loss
Epoch 150, loss = 1.085455e+03
Val score: -10.627053, 56.54 % Loss
Epoch 160, loss = 1.066781e+03
Val score: -10.492824, 56.25 % Loss
Epoch 170, loss = 1.055770e+03
Val score: -10.326376, 55.67 % Loss
Epoch 180, loss = 1.036394e+03
Val score: -10.196875, 55.29 % Loss
Epoch 190, loss = 1.023768e+03
Val score: -10.074885, 54.90 % Loss
Epoch 200, loss = 1.005816e+03
Val score: -9.927440, 54.38 % Loss
Epoch 210, loss = 9.929311e+02
Val score: -9.777783, 53.90 % Loss
Epoch 220, loss = 9.799485e+02
Val score: -9.615787, 53.39 % Loss
Epoch 230, loss = 9.647162e+02
Val score: -9.527869, 53.04 % Loss
Epoch 240, loss = 9.506934e+02
Val score: -9.436911, 52.77 % Loss
Epoch 250, loss = 9.355485e+02
Val score: -9.282436, 52.27 % Loss
Epoch 260, loss = 9.208045e+02
Val score: -9.136605, 51.71 % Loss
Epoch 270, loss = 9.107366e+02
Val score: -9.059946, 51.51 % Loss
Epoch 280, loss = 8.959863e+02
Val score: -8.913797, 51.00 % Loss
Epoch 290, loss = 8.828681e+02
Val score: -8.839971, 50.74 % Loss
Epoch 300, loss = 8.722394e+02
Val score: -8.722478, 50.23 % Loss
Epoch 310, loss = 8.586633e+02
Val score: -8.608355, 49.89 % Loss
Epoch 320, loss = 8.455894e+02
Val score: -8.538518, 49.67 % Loss
Epoch 330, loss = 8.336525e+02
Val score: -8.421952, 49.24 % Loss
Epoch 340, loss = 8.206378e+02
Val score: -8.357695, 49.06 % Loss
Epoch 350, loss = 8.108140e+02
Val score: -8.261620, 48.63 % Loss
Epoch 360, loss = 7.991937e+02
Val score: -8.101357, 48.09 % Loss
Epoch 370, loss = 7.858992e+02
Val score: -8.015712, 47.76 % Loss
Epoch 380, loss = 7.748350e+02
Val score: -7.910311, 47.38 % Loss
Epoch 390, loss = 7.630208e+02
Val score: -7.860373, 47.17 % Loss
Epoch 400, loss = 7.554628e+02
Val score: -7.748424, 46.76 % Loss
Epoch 410, loss = 7.449933e+02
Val score: -7.679460, 46.53 % Loss
Epoch 420, loss = 7.297604e+02
Val score: -7.545845, 45.94 % Loss
Epoch 430, loss = 7.193609e+02
Val score: -7.497258, 45.86 % Loss
Epoch 440, loss = 7.095253e+02
Val score: -7.437127, 45.57 % Loss
Epoch 450, loss = 6.986450e+02
Val score: -7.314292, 45.14 % Loss
Epoch 460, loss = 6.894048e+02
Val score: -7.248599, 44.94 % Loss
Epoch 470, loss = 6.790557e+02
Val score: -7.152797, 44.57 % Loss
Epoch 480, loss = 6.703985e+02
Val score: -7.080434, 44.26 % Loss
Epoch 490, loss = 6.596353e+02
Val score: -6.982467, 43.88 % Loss
Epoch 500, loss = 6.489151e+02
Val score: -6.938665, 43.73 % Loss
Epoch 510, loss = 6.404108e+02
Val score: -6.871518, 43.45 % Loss
Epoch 520, loss = 6.310102e+02
Val score: -6.817040, 43.25 % Loss
Epoch 530, loss = 6.211272e+02
Val score: -6.730032, 42.98 % Loss
Epoch 540, loss = 6.138525e+02
Val score: -6.702587, 42.87 % Loss
Epoch 550, loss = 6.036790e+02
Val score: -6.580913, 42.41 % Loss
Epoch 560, loss = 5.965213e+02
Val score: -6.535045, 42.18 % Loss
Epoch 570, loss = 5.854652e+02
Val score: -6.455138, 41.91 % Loss
Epoch 580, loss = 5.778593e+02
Val score: -6.404805, 41.76 % Loss
Epoch 590, loss = 5.684307e+02
Val score: -6.336162, 41.40 % Loss
Epoch 600, loss = 5.600781e+02
Val score: -6.251955, 41.10 % Loss
Epoch 610, loss = 5.531262e+02
Val score: -6.208263, 40.98 % Loss
Epoch 620, loss = 5.443940e+02
Val score: -6.162805, 40.80 % Loss
Epoch 630, loss = 5.360303e+02
Val score: -6.060410, 40.41 % Loss
Epoch 640, loss = 5.277578e+02
Val score: -6.073433, 40.42 % Loss
Epoch 650, loss = 5.193170e+02
Val score: -5.991687, 40.20 % Loss
Epoch 660, loss = 5.132900e+02
Val score: -5.934887, 39.97 % Loss
Epoch 670, loss = 5.049596e+02
Val score: -5.899403, 39.86 % Loss
Epoch 680, loss = 4.969875e+02
Val score: -5.861780, 39.73 % Loss
Epoch 690, loss = 4.902868e+02
Val score: -5.800671, 39.41 % Loss
Epoch 700, loss = 4.838077e+02
Val score: -5.743599, 39.23 % Loss
Epoch 710, loss = 4.749805e+02
Val score: -5.706707, 39.13 % Loss
Epoch 720, loss = 4.693218e+02
Val score: -5.666101, 38.95 % Loss
Epoch 730, loss = 4.617874e+02
Val score: -5.620975, 38.84 % Loss
Epoch 740, loss = 4.538631e+02
Val score: -5.577375, 38.66 % Loss
Epoch 750, loss = 4.464920e+02
Val score: -5.536211, 38.56 % Loss
Epoch 760, loss = 4.400079e+02
Val score: -5.525903, 38.48 % Loss
Epoch 770, loss = 4.331817e+02
Val score: -5.456353, 38.21 % Loss
Epoch 780, loss = 4.262562e+02
Val score: -5.388563, 38.15 % Loss
Epoch 790, loss = 4.195246e+02
Val score: -5.409807, 38.10 % Loss
Epoch 800, loss = 4.160828e+02
Val score: -5.301074, 37.76 % Loss
Epoch 810, loss = 4.081619e+02
Val score: -5.313445, 37.81 % Loss
Epoch 820, loss = 4.023738e+02
Val score: -5.266389, 37.60 % Loss
Epoch 830, loss = 3.950007e+02
Val score: -5.241892, 37.55 % Loss
Epoch 840, loss = 3.897103e+02
Val score: -5.202107, 37.40 % Loss
Epoch 850, loss = 3.838001e+02
Val score: -5.204065, 37.47 % Loss
Epoch 860, loss = 3.775473e+02
Val score: -5.123686, 37.09 % Loss
Epoch 870, loss = 3.727884e+02
Val score: -5.085824, 37.09 % Loss
Epoch 880, loss = 3.657935e+02
Val score: -5.054607, 36.95 % Loss
Epoch 890, loss = 3.599409e+02
Val score: -5.046117, 37.01 % Loss
Epoch 900, loss = 3.557124e+02
Val score: -4.975295, 36.72 % Loss
Epoch 910, loss = 3.498872e+02
Val score: -4.968002, 36.59 % Loss
Epoch 920, loss = 3.439537e+02
Val score: -4.951104, 36.66 % Loss
Epoch 930, loss = 3.398123e+02
Val score: -4.906460, 36.55 % Loss
Epoch 940, loss = 3.336962e+02
Val score: -4.873492, 36.35 % Loss
Epoch 950, loss = 3.282013e+02
Val score: -4.833649, 36.31 % Loss
Epoch 960, loss = 3.231322e+02
Val score: -4.830673, 36.27 % Loss
Epoch 970, loss = 3.176427e+02
Val score: -4.779759, 36.06 % Loss
Epoch 980, loss = 3.134378e+02
Val score: -4.729104, 35.94 % Loss
Epoch 990, loss = 3.091466e+02
Val score: -4.738312, 35.99 % Loss
Epoch 1000, loss = 3.024035e+02
Val score: -4.716375, 35.96 % Loss
Epoch 1010, loss = 2.988683e+02
Val score: -4.671447, 35.71 % Loss
Epoch 1020, loss = 2.944299e+02
Val score: -4.615485, 35.61 % Loss
Epoch 1030, loss = 2.891487e+02
Val score: -4.577154, 35.45 % Loss
Epoch 1040, loss = 2.840279e+02
Val score: -4.594942, 35.61 % Loss
Epoch 1050, loss = 2.797281e+02
Val score: -4.482403, 35.04 % Loss
Epoch 1060, loss = 2.756430e+02
Val score: -4.485107, 35.12 % Loss
Epoch 1070, loss = 2.699856e+02
Val score: -4.483145, 35.12 % Loss
Epoch 1080, loss = 2.664143e+02
Val score: -4.459398, 35.08 % Loss
Epoch 1090, loss = 2.632115e+02
Val score: -4.451974, 35.09 % Loss
Epoch 1100, loss = 2.581927e+02
Val score: -4.388453, 34.86 % Loss
Epoch 1110, loss = 2.540489e+02
Val score: -4.368767, 34.82 % Loss
Epoch 1120, loss = 2.503095e+02
Val score: -4.329369, 34.77 % Loss
Epoch 1130, loss = 2.477134e+02
Val score: -4.310349, 34.60 % Loss
Epoch 1140, loss = 2.433776e+02
Val score: -4.292425, 34.60 % Loss
Epoch 1150, loss = 2.388003e+02
Val score: -4.223860, 34.32 % Loss
Epoch 1160, loss = 2.350155e+02
Val score: -4.235833, 34.35 % Loss
Epoch 1170, loss = 2.323134e+02
Val score: -4.157962, 34.07 % Loss
Epoch 1180, loss = 2.273119e+02
Val score: -4.178806, 34.17 % Loss
Epoch 1190, loss = 2.246806e+02
Val score: -4.131541, 33.98 % Loss
Epoch 1200, loss = 2.218918e+02
Val score: -4.112838, 33.96 % Loss
Epoch 1210, loss = 2.173309e+02
Val score: -4.096595, 33.97 % Loss
Epoch 1220, loss = 2.139834e+02
Val score: -4.060366, 33.81 % Loss
Epoch 1230, loss = 2.115509e+02
Val score: -4.016286, 33.66 % Loss
Epoch 1240, loss = 2.083735e+02
Val score: -4.042878, 33.78 % Loss
Epoch 1250, loss = 2.060578e+02
Val score: -3.986312, 33.58 % Loss
Epoch 1260, loss = 2.018887e+02
Val score: -3.952912, 33.48 % Loss
Epoch 1270, loss = 1.987358e+02
Val score: -3.902921, 33.23 % Loss
Epoch 1280, loss = 1.964800e+02
Val score: -3.942853, 33.43 % Loss
Epoch 1290, loss = 1.935793e+02
Val score: -3.902930, 33.33 % Loss
Epoch 1300, loss = 1.914431e+02
Val score: -3.858679, 33.19 % Loss
Epoch 1310, loss = 1.883045e+02
Val score: -3.824035, 33.06 % Loss
Epoch 1320, loss = 1.855563e+02
Val score: -3.846383, 33.16 % Loss
Epoch 1330, loss = 1.829454e+02
Val score: -3.792781, 33.00 % Loss
Epoch 1340, loss = 1.814371e+02
Val score: -3.769845, 32.88 % Loss
Epoch 1350, loss = 1.771391e+02
Val score: -3.729591, 32.73 % Loss
Epoch 1360, loss = 1.753880e+02
Val score: -3.752314, 32.73 % Loss
Epoch 1370, loss = 1.743495e+02
Val score: -3.723012, 32.74 % Loss
Epoch 1380, loss = 1.710054e+02
Val score: -3.633008, 32.29 % Loss
Epoch 1390, loss = 1.686367e+02
Val score: -3.630678, 32.49 % Loss
Epoch 1400, loss = 1.672433e+02
Val score: -3.611035, 32.32 % Loss
Epoch 1410, loss = 1.651394e+02
Val score: -3.584611, 32.23 % Loss
Epoch 1420, loss = 1.633531e+02
Val score: -3.575239, 32.22 % Loss
Epoch 1430, loss = 1.605292e+02
Val score: -3.536231, 32.07 % Loss
Epoch 1440, loss = 1.589091e+02
Val score: -3.531270, 32.03 % Loss
Epoch 1450, loss = 1.568990e+02
Val score: -3.490296, 31.92 % Loss
Epoch 1460, loss = 1.553503e+02
Val score: -3.514995, 32.05 % Loss
Epoch 1470, loss = 1.542238e+02
Val score: -3.470147, 31.92 % Loss
Epoch 1480, loss = 1.516452e+02
Val score: -3.406290, 31.67 % Loss
Epoch 1490, loss = 1.504289e+02
Val score: -3.392471, 31.51 % Loss
Epoch 1500, loss = 1.482383e+02
Val score: -3.386778, 31.54 % Loss
Epoch 1510, loss = 1.467948e+02
Val score: -3.356183, 31.47 % Loss
Epoch 1520, loss = 1.455685e+02
Val score: -3.308516, 31.30 % Loss
Epoch 1530, loss = 1.448332e+02
Val score: -3.326703, 31.23 % Loss
Epoch 1540, loss = 1.425630e+02
Val score: -3.281400, 31.27 % Loss
Epoch 1550, loss = 1.411440e+02
Val score: -3.294910, 31.19 % Loss
Epoch 1560, loss = 1.400164e+02
Val score: -3.232248, 30.99 % Loss
Epoch 1570, loss = 1.396433e+02
Val score: -3.203453, 30.79 % Loss
Epoch 1580, loss = 1.376197e+02
Val score: -3.221381, 30.82 % Loss
Epoch 1590, loss = 1.373833e+02
Val score: -3.149678, 30.63 % Loss
Epoch 1600, loss = 1.355736e+02
Val score: -3.138412, 30.70 % Loss
Epoch 1610, loss = 1.343087e+02
Val score: -3.106933, 30.56 % Loss
Epoch 1620, loss = 1.332023e+02
Val score: -3.114376, 30.59 % Loss
Epoch 1630, loss = 1.322430e+02
Val score: -3.081917, 30.47 % Loss
Epoch 1640, loss = 1.315971e+02
Val score: -3.085326, 30.45 % Loss
Epoch 1650, loss = 1.307201e+02
Val score: -3.052963, 30.37 % Loss
Epoch 1660, loss = 1.294012e+02
Val score: -3.025085, 30.22 % Loss
Epoch 1670, loss = 1.289443e+02
Val score: -2.984117, 30.13 % Loss
Epoch 1680, loss = 1.282149e+02
Val score: -3.016555, 30.30 % Loss
Epoch 1690, loss = 1.266455e+02
Val score: -3.007354, 30.16 % Loss
Epoch 1700, loss = 1.264162e+02
Val score: -2.971070, 30.10 % Loss
Epoch 1710, loss = 1.252648e+02
Val score: -2.952973, 30.03 % Loss
Epoch 1720, loss = 1.242680e+02
Val score: -2.931739, 29.90 % Loss
Epoch 1730, loss = 1.240738e+02
Val score: -2.907347, 29.82 % Loss
Epoch 1740, loss = 1.230236e+02
Val score: -2.922610, 29.91 % Loss
Epoch 1750, loss = 1.214795e+02
Val score: -2.895377, 29.82 % Loss
Epoch 1760, loss = 1.213890e+02
Val score: -2.840135, 29.54 % Loss
Epoch 1770, loss = 1.207496e+02
Val score: -2.848124, 29.65 % Loss
Epoch 1780, loss = 1.207498e+02
Val score: -2.823091, 29.56 % Loss
Epoch 1790, loss = 1.190502e+02
Val score: -2.829780, 29.55 % Loss
Epoch 1800, loss = 1.188946e+02
Val score: -2.800860, 29.43 % Loss
Epoch 1810, loss = 1.187648e+02
Val score: -2.769208, 29.36 % Loss
Epoch 1820, loss = 1.181914e+02
Val score: -2.820318, 29.65 % Loss
Epoch 1830, loss = 1.180994e+02
Val score: -2.735696, 29.17 % Loss
Epoch 1840, loss = 1.168420e+02
Val score: -2.749977, 29.29 % Loss
Epoch 1850, loss = 1.173641e+02
Val score: -2.758301, 29.36 % Loss
Epoch 1860, loss = 1.162554e+02
Val score: -2.728314, 29.24 % Loss
Epoch 1870, loss = 1.155277e+02
Val score: -2.700580, 29.04 % Loss
Epoch 1880, loss = 1.151188e+02
Val score: -2.730238, 29.33 % Loss
Epoch 1890, loss = 1.147345e+02
Val score: -2.666100, 29.11 % Loss
Epoch 1900, loss = 1.144288e+02
Val score: -2.701971, 29.17 % Loss
Epoch 1910, loss = 1.147703e+02
Val score: -2.695506, 29.13 % Loss
Epoch 1920, loss = 1.134699e+02
Val score: -2.620971, 28.82 % Loss
Epoch 1930, loss = 1.131098e+02
Val score: -2.666064, 29.06 % Loss
Epoch 1940, loss = 1.135329e+02
Val score: -2.653487, 29.04 % Loss
Epoch 1950, loss = 1.125646e+02
Val score: -2.632747, 28.93 % Loss
Epoch 1960, loss = 1.125414e+02
Val score: -2.619052, 28.95 % Loss
Epoch 1970, loss = 1.120641e+02
Val score: -2.574475, 28.75 % Loss
Epoch 1980, loss = 1.107545e+02
Val score: -2.597799, 28.84 % Loss
Epoch 1990, loss = 1.115581e+02
Val score: -2.562083, 28.69 % Loss
Epoch 2000, loss = 1.108626e+02
Val score: -2.536848, 28.56 % Loss
Epoch 2010, loss = 1.105098e+02
Val score: -2.571025, 28.73 % Loss
Epoch 2020, loss = 1.102346e+02
Val score: -2.539477, 28.60 % Loss
Epoch 2030, loss = 1.098118e+02
Val score: -2.520311, 28.61 % Loss
Epoch 2040, loss = 1.093281e+02
Val score: -2.532596, 28.61 % Loss
Epoch 2050, loss = 1.088615e+02
Val score: -2.527466, 28.56 % Loss
Epoch 2060, loss = 1.086421e+02
Val score: -2.512746, 28.51 % Loss
Epoch 2070, loss = 1.084049e+02
Val score: -2.492624, 28.49 % Loss
Epoch 2080, loss = 1.081208e+02
Val score: -2.472478, 28.33 % Loss
Epoch 2090, loss = 1.080616e+02
Val score: -2.436218, 28.24 % Loss
Epoch 2100, loss = 1.077740e+02
Val score: -2.504629, 28.50 % Loss
Epoch 2110, loss = 1.071397e+02
Val score: -2.447919, 28.25 % Loss
Epoch 2120, loss = 1.072941e+02
Val score: -2.487330, 28.48 % Loss
Epoch 2130, loss = 1.069155e+02
Val score: -2.445337, 28.33 % Loss
Epoch 2140, loss = 1.069784e+02
Val score: -2.454615, 28.34 % Loss
Epoch 2150, loss = 1.068392e+02
Val score: -2.412060, 28.08 % Loss
Epoch 2160, loss = 1.062729e+02
Val score: -2.444015, 28.28 % Loss
Epoch 2170, loss = 1.066661e+02
Val score: -2.382679, 28.04 % Loss
Epoch 2180, loss = 1.061060e+02
Val score: -2.423254, 28.14 % Loss
Epoch 2190, loss = 1.051894e+02
Val score: -2.388915, 28.09 % Loss
Epoch 2200, loss = 1.051370e+02
Val score: -2.396588, 28.05 % Loss
Epoch 2210, loss = 1.054087e+02
Val score: -2.384234, 28.06 % Loss
Epoch 2220, loss = 1.046907e+02
Val score: -2.339221, 27.87 % Loss
Epoch 2230, loss = 1.047381e+02
Val score: -2.328809, 27.89 % Loss
Epoch 2240, loss = 1.042416e+02
Val score: -2.367702, 28.06 % Loss
Epoch 2250, loss = 1.040797e+02
Val score: -2.332325, 27.88 % Loss
Epoch 2260, loss = 1.039211e+02
Val score: -2.304323, 27.81 % Loss
Epoch 2270, loss = 1.032650e+02
Val score: -2.330493, 27.79 % Loss
Epoch 2280, loss = 1.037058e+02
Val score: -2.345833, 27.96 % Loss
Epoch 2290, loss = 1.031569e+02
Val score: -2.300166, 27.67 % Loss
Epoch 2300, loss = 1.025255e+02
Val score: -2.328537, 27.88 % Loss
Epoch 2310, loss = 1.030111e+02
Val score: -2.252594, 27.53 % Loss
Epoch 2320, loss = 1.024980e+02
Val score: -2.298378, 27.74 % Loss
Epoch 2330, loss = 1.025028e+02
Val score: -2.312267, 27.84 % Loss
Epoch 2340, loss = 1.027738e+02
Val score: -2.285172, 27.65 % Loss
Epoch 2350, loss = 1.017389e+02
Val score: -2.299112, 27.76 % Loss
Epoch 2360, loss = 1.016108e+02
Val score: -2.271334, 27.68 % Loss
Epoch 2370, loss = 1.016980e+02
Val score: -2.250088, 27.50 % Loss
Epoch 2380, loss = 1.007342e+02
Val score: -2.262422, 27.61 % Loss
Epoch 2390, loss = 1.014420e+02
Val score: -2.228891, 27.46 % Loss
Epoch 2400, loss = 1.009463e+02
Val score: -2.226898, 27.35 % Loss
Epoch 2410, loss = 1.007933e+02
Val score: -2.236293, 27.51 % Loss
Epoch 2420, loss = 1.004027e+02
Val score: -2.212935, 27.40 % Loss
Epoch 2430, loss = 1.004306e+02
Val score: -2.201764, 27.47 % Loss
Epoch 2440, loss = 1.002515e+02
Val score: -2.209196, 27.36 % Loss
Epoch 2450, loss = 9.996782e+01
Val score: -2.195081, 27.33 % Loss
Epoch 2460, loss = 1.001973e+02
Val score: -2.185265, 27.27 % Loss
Epoch 2470, loss = 9.965119e+01
Val score: -2.171038, 27.25 % Loss
Epoch 2480, loss = 9.917828e+01
Val score: -2.179177, 27.24 % Loss
Epoch 2490, loss = 9.960882e+01
Val score: -2.209034, 27.45 % Loss
Epoch 2500, loss = 9.909964e+01
Val score: -2.165418, 27.25 % Loss
Epoch 2510, loss = 9.903596e+01
Val score: -2.162885, 27.26 % Loss
Epoch 2520, loss = 9.917923e+01
Val score: -2.160438, 27.09 % Loss
Epoch 2530, loss = 9.898936e+01
Val score: -2.166319, 27.27 % Loss
Epoch 2540, loss = 9.836063e+01
Val score: -2.130549, 27.10 % Loss
Epoch 2550, loss = 9.839883e+01
Val score: -2.110451, 26.93 % Loss
Epoch 2560, loss = 9.794351e+01
Val score: -2.088506, 26.91 % Loss
Epoch 2570, loss = 9.772655e+01
Val score: -2.128097, 27.06 % Loss
Epoch 2580, loss = 9.796035e+01
Val score: -2.100055, 26.95 % Loss
Epoch 2590, loss = 9.751897e+01
Val score: -2.070937, 26.84 % Loss
Epoch 2600, loss = 9.776514e+01
Val score: -2.082129, 26.83 % Loss
Epoch 2610, loss = 9.743335e+01
Val score: -2.100771, 26.94 % Loss
Epoch 2620, loss = 9.723962e+01
Val score: -2.057293, 26.74 % Loss
Epoch 2630, loss = 9.715646e+01
Val score: -2.064905, 26.73 % Loss
Epoch 2640, loss = 9.654023e+01
Val score: -2.071030, 26.85 % Loss
Epoch 2650, loss = 9.633844e+01
Val score: -2.070323, 26.74 % Loss
Epoch 2660, loss = 9.599423e+01
Val score: -2.036431, 26.63 % Loss
Epoch 2670, loss = 9.634474e+01
Val score: -2.040293, 26.71 % Loss
Epoch 2680, loss = 9.622235e+01
Val score: -2.035139, 26.59 % Loss
Epoch 2690, loss = 9.599014e+01
Val score: -2.025597, 26.57 % Loss
Epoch 2700, loss = 9.566050e+01
Val score: -2.029904, 26.60 % Loss
Epoch 2710, loss = 9.545512e+01
Val score: -2.024200, 26.68 % Loss
Epoch 2720, loss = 9.528504e+01
Val score: -2.001830, 26.51 % Loss
Epoch 2730, loss = 9.489790e+01
Val score: -1.978206, 26.44 % Loss
Epoch 2740, loss = 9.503160e+01
Val score: -1.985517, 26.40 % Loss
Epoch 2750, loss = 9.462374e+01
Val score: -2.005724, 26.62 % Loss
Epoch 2760, loss = 9.505080e+01
Val score: -1.986958, 26.44 % Loss
Epoch 2770, loss = 9.546724e+01
Val score: -1.992130, 26.46 % Loss
Epoch 2780, loss = 9.420038e+01
Val score: -1.994584, 26.45 % Loss
Epoch 2790, loss = 9.438010e+01
Val score: -1.938795, 26.19 % Loss
Epoch 2800, loss = 9.436678e+01
Val score: -1.950957, 26.32 % Loss
Epoch 2810, loss = 9.359521e+01
Val score: -1.967776, 26.36 % Loss
Epoch 2820, loss = 9.348549e+01
Val score: -1.915852, 26.08 % Loss
Epoch 2830, loss = 9.378568e+01
Val score: -1.915921, 26.22 % Loss
Epoch 2840, loss = 9.303117e+01
Val score: -1.922007, 26.14 % Loss
Epoch 2850, loss = 9.266698e+01
Val score: -1.939668, 26.25 % Loss
Epoch 2860, loss = 9.309799e+01
Val score: -1.925871, 26.13 % Loss
Epoch 2870, loss = 9.339220e+01
Val score: -1.919070, 26.18 % Loss
Epoch 2880, loss = 9.252976e+01
Val score: -1.908007, 26.10 % Loss
Epoch 2890, loss = 9.299758e+01
Val score: -1.899796, 26.01 % Loss
Epoch 2900, loss = 9.277282e+01
Val score: -1.896678, 26.05 % Loss
Epoch 2910, loss = 9.288770e+01
Val score: -1.879032, 25.95 % Loss
Epoch 2920, loss = 9.212437e+01
Val score: -1.881754, 25.98 % Loss
Epoch 2930, loss = 9.249641e+01
Val score: -1.854802, 25.86 % Loss
Epoch 2940, loss = 9.228114e+01
Val score: -1.898659, 26.05 % Loss
Epoch 2950, loss = 9.211734e+01
Val score: -1.851790, 25.88 % Loss
Epoch 2960, loss = 9.149431e+01
Val score: -1.823702, 25.71 % Loss
Epoch 2970, loss = 9.198950e+01
Val score: -1.861835, 25.82 % Loss
Epoch 2980, loss = 9.169418e+01
Val score: -1.845387, 25.83 % Loss
Epoch 2990, loss = 9.112376e+01
Val score: -1.826759, 25.72 % Loss
Epoch 3000, loss = 9.125747e+01
Val score: -1.821064, 25.73 % Loss
Epoch 3010, loss = 9.148502e+01
Val score: -1.815869, 25.74 % Loss
Epoch 3020, loss = 9.085550e+01
Val score: -1.798620, 25.55 % Loss
Epoch 3030, loss = 9.048315e+01
Val score: -1.797110, 25.64 % Loss
Epoch 3040, loss = 9.062988e+01
Val score: -1.805488, 25.69 % Loss
Epoch 3050, loss = 9.059814e+01
Val score: -1.777417, 25.53 % Loss
Epoch 3060, loss = 9.014315e+01
Val score: -1.787420, 25.53 % Loss
Epoch 3070, loss = 8.994548e+01
Val score: -1.775412, 25.47 % Loss
Epoch 3080, loss = 9.049339e+01
Val score: -1.783580, 25.54 % Loss
Epoch 3090, loss = 8.998299e+01
Val score: -1.761698, 25.46 % Loss
Epoch 3100, loss = 8.951574e+01
Val score: -1.774139, 25.49 % Loss
Epoch 3110, loss = 8.987463e+01
Val score: -1.749793, 25.35 % Loss
Epoch 3120, loss = 9.017957e+01
Val score: -1.724269, 25.28 % Loss
Epoch 3130, loss = 8.949751e+01
Val score: -1.748875, 25.40 % Loss
Epoch 3140, loss = 8.972816e+01
Val score: -1.746286, 25.33 % Loss
Epoch 3150, loss = 8.923042e+01
Val score: -1.746105, 25.34 % Loss
Epoch 3160, loss = 8.910303e+01
Val score: -1.731740, 25.33 % Loss
Epoch 3170, loss = 8.920857e+01
Val score: -1.727018, 25.27 % Loss
Epoch 3180, loss = 8.947161e+01
Val score: -1.722153, 25.27 % Loss
Epoch 3190, loss = 8.878246e+01
Val score: -1.722371, 25.19 % Loss
Epoch 3200, loss = 8.875024e+01
Val score: -1.728995, 25.23 % Loss
Epoch 3210, loss = 8.801901e+01
Val score: -1.694719, 25.15 % Loss
Epoch 3220, loss = 8.801228e+01
Val score: -1.723605, 25.32 % Loss
Epoch 3230, loss = 8.770090e+01
Val score: -1.698853, 25.24 % Loss
Epoch 3240, loss = 8.758587e+01
Val score: -1.698637, 25.19 % Loss
Epoch 3250, loss = 8.790375e+01
Val score: -1.680315, 25.03 % Loss
Epoch 3260, loss = 8.784006e+01
Val score: -1.668777, 24.97 % Loss
Epoch 3270, loss = 8.735526e+01
Val score: -1.692461, 25.15 % Loss
Epoch 3280, loss = 8.717262e+01
Val score: -1.672916, 25.08 % Loss
Epoch 3290, loss = 8.729331e+01
Val score: -1.650453, 24.95 % Loss
Epoch 3300, loss = 8.743495e+01
Val score: -1.648418, 24.91 % Loss
Epoch 3310, loss = 8.682108e+01
Val score: -1.640877, 24.85 % Loss
Epoch 3320, loss = 8.717510e+01
Val score: -1.617998, 24.77 % Loss
Epoch 3330, loss = 8.693353e+01
Val score: -1.605086, 24.71 % Loss
Epoch 3340, loss = 8.660320e+01
Val score: -1.617841, 24.79 % Loss
Epoch 3350, loss = 8.654809e+01
Val score: -1.617825, 24.77 % Loss
Epoch 3360, loss = 8.640225e+01
Val score: -1.639728, 24.84 % Loss
Epoch 3370, loss = 8.626653e+01
Val score: -1.615415, 24.79 % Loss
Epoch 3380, loss = 8.652577e+01
Val score: -1.596826, 24.65 % Loss
Epoch 3390, loss = 8.645753e+01
Val score: -1.594198, 24.64 % Loss
Epoch 3400, loss = 8.634910e+01
Val score: -1.604478, 24.64 % Loss
Epoch 3410, loss = 8.590334e+01
Val score: -1.563632, 24.53 % Loss
Epoch 3420, loss = 8.639730e+01
Val score: -1.591661, 24.62 % Loss
Epoch 3430, loss = 8.532090e+01
Val score: -1.581491, 24.58 % Loss
Epoch 3440, loss = 8.567282e+01
Val score: -1.559534, 24.52 % Loss
Epoch 3450, loss = 8.545015e+01
Val score: -1.546824, 24.41 % Loss
Epoch 3460, loss = 8.525850e+01
Val score: -1.567765, 24.53 % Loss
Epoch 3470, loss = 8.544419e+01
Val score: -1.573401, 24.49 % Loss
Epoch 3480, loss = 8.509579e+01
Val score: -1.578016, 24.58 % Loss
Epoch 3490, loss = 8.522684e+01
Val score: -1.523104, 24.31 % Loss
Epoch 3500, loss = 8.425309e+01
Val score: -1.529819, 24.29 % Loss
Epoch 3510, loss = 8.460883e+01
Val score: -1.550677, 24.45 % Loss
Epoch 3520, loss = 8.497235e+01
Val score: -1.522256, 24.28 % Loss
Epoch 3530, loss = 8.416190e+01
Val score: -1.527886, 24.33 % Loss
Epoch 3540, loss = 8.407588e+01
Val score: -1.492364, 24.19 % Loss
Epoch 3550, loss = 8.493201e+01
Val score: -1.510756, 24.27 % Loss
Epoch 3560, loss = 8.384769e+01
Val score: -1.508985, 24.31 % Loss
Epoch 3570, loss = 8.489032e+01
Val score: -1.513917, 24.25 % Loss
Epoch 3580, loss = 8.439744e+01
Val score: -1.487683, 24.11 % Loss
Epoch 3590, loss = 8.391051e+01
Val score: -1.491919, 24.09 % Loss
Epoch 3600, loss = 8.383900e+01
Val score: -1.491204, 24.14 % Loss
Epoch 3610, loss = 8.407285e+01
Val score: -1.477550, 24.04 % Loss
Epoch 3620, loss = 8.342655e+01
Val score: -1.459560, 24.10 % Loss
Epoch 3630, loss = 8.377379e+01
Val score: -1.466446, 24.00 % Loss
Epoch 3640, loss = 8.277325e+01
Val score: -1.463154, 23.99 % Loss
Epoch 3650, loss = 8.321098e+01
Val score: -1.456127, 23.95 % Loss
Epoch 3660, loss = 8.360218e+01
Val score: -1.460837, 23.96 % Loss
Epoch 3670, loss = 8.343013e+01
Val score: -1.438258, 23.87 % Loss
Epoch 3680, loss = 8.319492e+01
Val score: -1.453914, 23.94 % Loss
Epoch 3690, loss = 8.322269e+01
Val score: -1.437725, 23.92 % Loss
Epoch 3700, loss = 8.241844e+01
Val score: -1.428732, 23.87 % Loss
Epoch 3710, loss = 8.306236e+01
Val score: -1.450036, 23.94 % Loss
Epoch 3720, loss = 8.252190e+01
Val score: -1.432713, 23.86 % Loss
Epoch 3730, loss = 8.190011e+01
Val score: -1.410175, 23.73 % Loss
Epoch 3740, loss = 8.237660e+01
Val score: -1.399827, 23.70 % Loss
Epoch 3750, loss = 8.206761e+01
Val score: -1.429891, 23.81 % Loss
Epoch 3760, loss = 8.231115e+01
Val score: -1.407094, 23.76 % Loss
Epoch 3770, loss = 8.187096e+01
Val score: -1.399386, 23.70 % Loss
Epoch 3780, loss = 8.226395e+01
Val score: -1.398778, 23.65 % Loss
Epoch 3790, loss = 8.183837e+01
Val score: -1.363282, 23.54 % Loss
Epoch 3800, loss = 8.153862e+01
Val score: -1.392632, 23.64 % Loss
Epoch 3810, loss = 8.189048e+01
Val score: -1.396420, 23.69 % Loss
Epoch 3820, loss = 8.146529e+01
Val score: -1.393151, 23.65 % Loss
Epoch 3830, loss = 8.092895e+01
Val score: -1.376071, 23.52 % Loss
Epoch 3840, loss = 8.110699e+01
Val score: -1.357316, 23.44 % Loss
Epoch 3850, loss = 8.188045e+01
Val score: -1.364816, 23.51 % Loss
Epoch 3860, loss = 8.130652e+01
Val score: -1.369136, 23.47 % Loss
Epoch 3870, loss = 8.090435e+01
Val score: -1.362317, 23.50 % Loss
Epoch 3880, loss = 8.074787e+01
Val score: -1.359343, 23.47 % Loss
Epoch 3890, loss = 8.049292e+01
Val score: -1.345145, 23.40 % Loss
Epoch 3900, loss = 8.037652e+01
Val score: -1.346531, 23.36 % Loss
Epoch 3910, loss = 8.081808e+01
Val score: -1.340771, 23.43 % Loss
Epoch 3920, loss = 8.039416e+01
Val score: -1.333916, 23.29 % Loss
Epoch 3930, loss = 8.054531e+01
Val score: -1.322159, 23.28 % Loss
Epoch 3940, loss = 8.027528e+01
Val score: -1.332038, 23.35 % Loss
Epoch 3950, loss = 8.055847e+01
Val score: -1.316745, 23.27 % Loss
Epoch 3960, loss = 8.023204e+01
Val score: -1.325597, 23.33 % Loss
Epoch 3970, loss = 8.042554e+01
Val score: -1.300075, 23.18 % Loss
Epoch 3980, loss = 7.976310e+01
Val score: -1.320056, 23.28 % Loss
Epoch 3990, loss = 8.007828e+01
Val score: -1.304849, 23.14 % Loss
Epoch 4000, loss = 7.934077e+01
Val score: -1.292625, 23.07 % Loss
Epoch 4010, loss = 8.036355e+01
Val score: -1.298021, 23.16 % Loss
Epoch 4020, loss = 7.975358e+01
Val score: -1.276727, 23.01 % Loss
Epoch 4030, loss = 7.952716e+01
Val score: -1.301938, 23.12 % Loss
Epoch 4040, loss = 7.975562e+01
Val score: -1.292797, 23.13 % Loss
Epoch 4050, loss = 7.939077e+01
Val score: -1.273477, 23.03 % Loss
Epoch 4060, loss = 7.948519e+01
Val score: -1.271821, 23.03 % Loss
Epoch 4070, loss = 7.896845e+01
Val score: -1.265097, 22.98 % Loss
Epoch 4080, loss = 7.911568e+01
Val score: -1.248284, 22.87 % Loss
Epoch 4090, loss = 7.894657e+01
Val score: -1.265312, 22.95 % Loss
Epoch 4100, loss = 7.858781e+01
Val score: -1.267529, 22.93 % Loss
Epoch 4110, loss = 7.835651e+01
Val score: -1.265603, 22.93 % Loss
Epoch 4120, loss = 7.917680e+01
Val score: -1.243907, 22.86 % Loss
Epoch 4130, loss = 7.885072e+01
Val score: -1.251643, 22.88 % Loss
Epoch 4140, loss = 7.861745e+01
Val score: -1.232519, 22.84 % Loss
Epoch 4150, loss = 7.860346e+01
Val score: -1.234922, 22.81 % Loss
Epoch 4160, loss = 7.857874e+01
Val score: -1.237558, 22.77 % Loss
Epoch 4170, loss = 7.802122e+01
Val score: -1.234117, 22.77 % Loss
Epoch 4180, loss = 7.862309e+01
Val score: -1.219855, 22.77 % Loss
Epoch 4190, loss = 7.822439e+01
Val score: -1.252318, 22.86 % Loss
Epoch 4200, loss = 7.765124e+01
Val score: -1.232313, 22.79 % Loss
Epoch 4210, loss = 7.858684e+01
Val score: -1.229435, 22.75 % Loss
Epoch 4220, loss = 7.767501e+01
Val score: -1.198610, 22.62 % Loss
Epoch 4230, loss = 7.757095e+01
Val score: -1.222070, 22.70 % Loss
Epoch 4240, loss = 7.783369e+01
Val score: -1.219877, 22.72 % Loss
Epoch 4250, loss = 7.771298e+01
Val score: -1.191040, 22.55 % Loss
Epoch 4260, loss = 7.723917e+01
Val score: -1.194623, 22.63 % Loss
Epoch 4270, loss = 7.732416e+01
Val score: -1.206949, 22.65 % Loss
Epoch 4280, loss = 7.737871e+01
Val score: -1.191057, 22.56 % Loss
Epoch 4290, loss = 7.743748e+01
Val score: -1.186629, 22.50 % Loss
Epoch 4300, loss = 7.749210e+01
Val score: -1.194833, 22.52 % Loss
Epoch 4310, loss = 7.675397e+01
Val score: -1.179296, 22.50 % Loss
Epoch 4320, loss = 7.725714e+01
Val score: -1.187608, 22.51 % Loss
Epoch 4330, loss = 7.668305e+01
Val score: -1.184155, 22.50 % Loss
Epoch 4340, loss = 7.733094e+01
Val score: -1.165620, 22.38 % Loss
Epoch 4350, loss = 7.693639e+01
Val score: -1.169322, 22.49 % Loss
Epoch 4360, loss = 7.651212e+01
Val score: -1.157237, 22.38 % Loss
Epoch 4370, loss = 7.641026e+01
Val score: -1.165797, 22.38 % Loss
Epoch 4380, loss = 7.652712e+01
Val score: -1.150591, 22.35 % Loss
Epoch 4390, loss = 7.700625e+01
Val score: -1.150743, 22.32 % Loss
Epoch 4400, loss = 7.657177e+01
Val score: -1.150729, 22.32 % Loss
Epoch 4410, loss = 7.683944e+01
Val score: -1.154060, 22.30 % Loss
Epoch 4420, loss = 7.611033e+01
Val score: -1.148296, 22.30 % Loss
Epoch 4430, loss = 7.648394e+01
Val score: -1.129636, 22.25 % Loss
Epoch 4440, loss = 7.578316e+01
Val score: -1.130360, 22.22 % Loss
Epoch 4450, loss = 7.602080e+01
Val score: -1.122128, 22.15 % Loss
Epoch 4460, loss = 7.598547e+01
Val score: -1.128213, 22.19 % Loss
Epoch 4470, loss = 7.565623e+01
Val score: -1.128590, 22.20 % Loss
Epoch 4480, loss = 7.560555e+01
Val score: -1.124943, 22.18 % Loss
Epoch 4490, loss = 7.608803e+01
Val score: -1.124030, 22.23 % Loss
Epoch 4500, loss = 7.555865e+01
Val score: -1.085882, 22.03 % Loss
Epoch 4510, loss = 7.560670e+01
Val score: -1.102598, 22.09 % Loss
Epoch 4520, loss = 7.588208e+01
Val score: -1.096561, 22.01 % Loss
Epoch 4530, loss = 7.540952e+01
Val score: -1.091766, 21.98 % Loss
Epoch 4540, loss = 7.541101e+01
Val score: -1.115729, 22.13 % Loss
Epoch 4550, loss = 7.539388e+01
Val score: -1.103611, 22.06 % Loss
Epoch 4560, loss = 7.524373e+01
Val score: -1.087028, 21.98 % Loss
Epoch 4570, loss = 7.501928e+01
Val score: -1.079500, 21.98 % Loss
Epoch 4580, loss = 7.454662e+01
Val score: -1.084538, 21.94 % Loss
Epoch 4590, loss = 7.470612e+01
Val score: -1.077693, 21.98 % Loss
Epoch 4600, loss = 7.481991e+01
Val score: -1.071970, 21.89 % Loss
Epoch 4610, loss = 7.485300e+01
Val score: -1.070551, 21.92 % Loss
Epoch 4620, loss = 7.463386e+01
Val score: -1.080129, 21.94 % Loss
Epoch 4630, loss = 7.504373e+01
Val score: -1.078528, 21.91 % Loss
Epoch 4640, loss = 7.524170e+01
Val score: -1.065419, 21.82 % Loss
Epoch 4650, loss = 7.461322e+01
Val score: -1.062042, 21.79 % Loss
Epoch 4660, loss = 7.493524e+01
Val score: -1.066874, 21.84 % Loss
Epoch 4670, loss = 7.481067e+01
Val score: -1.064486, 21.85 % Loss
Epoch 4680, loss = 7.440849e+01
Val score: -1.050915, 21.75 % Loss
Epoch 4690, loss = 7.412859e+01
Val score: -1.065479, 21.83 % Loss
Epoch 4700, loss = 7.457347e+01
Val score: -1.043316, 21.73 % Loss
Epoch 4710, loss = 7.384691e+01
Val score: -1.039673, 21.72 % Loss
Epoch 4720, loss = 7.396766e+01
Val score: -1.049501, 21.73 % Loss
Epoch 4730, loss = 7.420478e+01
Val score: -1.038387, 21.69 % Loss
Epoch 4740, loss = 7.389151e+01
Val score: -1.035987, 21.66 % Loss
Epoch 4750, loss = 7.440099e+01
Val score: -1.054738, 21.75 % Loss
Epoch 4760, loss = 7.393752e+01
Val score: -1.037860, 21.73 % Loss
Epoch 4770, loss = 7.403574e+01
Val score: -1.044111, 21.73 % Loss
Epoch 4780, loss = 7.336671e+01
Val score: -1.041196, 21.67 % Loss
Epoch 4790, loss = 7.354460e+01
Val score: -1.038656, 21.66 % Loss
Epoch 4800, loss = 7.357454e+01
Val score: -1.012525, 21.58 % Loss
Epoch 4810, loss = 7.392107e+01
Val score: -1.023591, 21.56 % Loss
Epoch 4820, loss = 7.332887e+01
Val score: -1.009305, 21.49 % Loss
Epoch 4830, loss = 7.323559e+01
Val score: -1.017726, 21.56 % Loss
Epoch 4840, loss = 7.347012e+01
Val score: -1.004584, 21.52 % Loss
Epoch 4850, loss = 7.313071e+01
Val score: -1.011525, 21.56 % Loss
Epoch 4860, loss = 7.323811e+01
Val score: -1.016688, 21.57 % Loss
Epoch 4870, loss = 7.323230e+01
Val score: -1.012637, 21.53 % Loss
Epoch 4880, loss = 7.299526e+01
Val score: -1.002067, 21.47 % Loss
Epoch 4890, loss = 7.320778e+01
Val score: -0.998109, 21.43 % Loss
Epoch 4900, loss = 7.303589e+01
Val score: -0.989696, 21.45 % Loss
Epoch 4910, loss = 7.259716e+01
Val score: -0.994327, 21.45 % Loss
Epoch 4920, loss = 7.290592e+01
Val score: -0.986034, 21.41 % Loss
Epoch 4930, loss = 7.293774e+01
Val score: -0.996176, 21.41 % Loss
Epoch 4940, loss = 7.268549e+01
Val score: -0.992165, 21.44 % Loss
Epoch 4950, loss = 7.250778e+01
Val score: -0.986528, 21.40 % Loss
Epoch 4960, loss = 7.238806e+01
Val score: -0.985690, 21.35 % Loss
Epoch 4970, loss = 7.220260e+01
Val score: -0.982698, 21.35 % Loss
Epoch 4980, loss = 7.234345e+01
Val score: -0.958193, 21.31 % Loss
Epoch 4990, loss = 7.247191e+01
Val score: -0.979360, 21.34 % Loss
Epoch 5000, loss = 7.224237e+01
Val score: -0.972040, 21.31 % Loss
Epoch 5010, loss = 7.252645e+01
Val score: -0.964318, 21.24 % Loss
Epoch 5020, loss = 7.223064e+01
Val score: -0.974421, 21.29 % Loss
Epoch 5030, loss = 7.219926e+01
Val score: -0.961472, 21.25 % Loss
Epoch 5040, loss = 7.209634e+01
Val score: -0.969698, 21.28 % Loss
Epoch 5050, loss = 7.197521e+01
Val score: -0.956387, 21.27 % Loss
Epoch 5060, loss = 7.164151e+01
Val score: -0.939389, 21.15 % Loss
Epoch 5070, loss = 7.158199e+01
Val score: -0.944525, 21.15 % Loss
Epoch 5080, loss = 7.165360e+01
Val score: -0.948005, 21.17 % Loss
Epoch 5090, loss = 7.152509e+01
Val score: -0.942501, 21.16 % Loss
Epoch 5100, loss = 7.149208e+01
Val score: -0.948389, 21.21 % Loss
Epoch 5110, loss = 7.129099e+01
Val score: -0.944456, 21.19 % Loss
Epoch 5120, loss = 7.162277e+01
Val score: -0.945345, 21.12 % Loss
Epoch 5130, loss = 7.165803e+01
Val score: -0.947350, 21.13 % Loss
Epoch 5140, loss = 7.113144e+01
Val score: -0.931994, 21.10 % Loss
Epoch 5150, loss = 7.149436e+01
Val score: -0.930269, 21.07 % Loss
Epoch 5160, loss = 7.113300e+01
Val score: -0.942299, 21.12 % Loss
Epoch 5170, loss = 7.115275e+01
Val score: -0.940014, 21.13 % Loss
Epoch 5180, loss = 7.108391e+01
Val score: -0.917881, 21.00 % Loss
Epoch 5190, loss = 7.098728e+01
Val score: -0.924006, 21.04 % Loss
Epoch 5200, loss = 7.090575e+01
Val score: -0.914517, 21.00 % Loss
Epoch 5210, loss = 7.083916e+01
Val score: -0.928616, 21.03 % Loss
Epoch 5220, loss = 7.061874e+01
Val score: -0.909283, 20.99 % Loss
Epoch 5230, loss = 7.062470e+01
Val score: -0.921291, 20.98 % Loss
Epoch 5240, loss = 7.081012e+01
Val score: -0.920462, 21.03 % Loss
Epoch 5250, loss = 7.081303e+01
Val score: -0.904950, 20.88 % Loss
Epoch 5260, loss = 7.078822e+01
Val score: -0.921158, 20.99 % Loss
Epoch 5270, loss = 7.062331e+01
Val score: -0.901352, 20.89 % Loss
Epoch 5280, loss = 7.046740e+01
Val score: -0.895715, 20.85 % Loss
Epoch 5290, loss = 7.003619e+01
Val score: -0.900193, 20.86 % Loss
Epoch 5300, loss = 7.070466e+01
Val score: -0.898684, 20.88 % Loss
Epoch 5310, loss = 7.012537e+01
Val score: -0.901593, 20.90 % Loss
Epoch 5320, loss = 7.048516e+01
Val score: -0.895847, 20.84 % Loss
Epoch 5330, loss = 6.990751e+01
Val score: -0.887178, 20.83 % Loss
Epoch 5340, loss = 7.019209e+01
Val score: -0.898852, 20.90 % Loss
Epoch 5350, loss = 7.024263e+01
Val score: -0.889117, 20.82 % Loss
Epoch 5360, loss = 6.963821e+01
Val score: -0.894050, 20.86 % Loss
Epoch 5370, loss = 6.976179e+01
Val score: -0.887303, 20.80 % Loss
Epoch 5380, loss = 6.955674e+01
Val score: -0.885439, 20.79 % Loss
Epoch 5390, loss = 6.967567e+01
Val score: -0.861946, 20.69 % Loss
Epoch 5400, loss = 6.958836e+01
Val score: -0.867920, 20.72 % Loss
Epoch 5410, loss = 6.993639e+01
Val score: -0.875656, 20.75 % Loss
Epoch 5420, loss = 6.945581e+01
Val score: -0.862664, 20.69 % Loss
Epoch 5430, loss = 6.957434e+01
Val score: -0.866609, 20.67 % Loss
Epoch 5440, loss = 6.953712e+01
Val score: -0.868095, 20.70 % Loss
Epoch 5450, loss = 6.931785e+01
Val score: -0.868958, 20.70 % Loss
Epoch 5460, loss = 6.922482e+01
Val score: -0.871947, 20.69 % Loss
Epoch 5470, loss = 6.958953e+01
Val score: -0.850806, 20.59 % Loss
Epoch 5480, loss = 6.935057e+01
Val score: -0.869424, 20.70 % Loss
Epoch 5490, loss = 6.964986e+01
Val score: -0.855985, 20.61 % Loss
Epoch 5500, loss = 6.950745e+01
Val score: -0.855042, 20.64 % Loss
Epoch 5510, loss = 6.903584e+01
Val score: -0.865777, 20.71 % Loss
Epoch 5520, loss = 6.939318e+01
Val score: -0.854165, 20.59 % Loss
Epoch 5530, loss = 6.904831e+01
Val score: -0.861409, 20.65 % Loss
Epoch 5540, loss = 6.898359e+01
Val score: -0.843021, 20.55 % Loss
Epoch 5550, loss = 6.883952e+01
Val score: -0.851423, 20.59 % Loss
Epoch 5560, loss = 6.862095e+01
Val score: -0.847224, 20.57 % Loss
Epoch 5570, loss = 6.881691e+01
Val score: -0.848236, 20.55 % Loss
Epoch 5580, loss = 6.900276e+01
Val score: -0.842674, 20.53 % Loss
Epoch 5590, loss = 6.859751e+01
Val score: -0.843265, 20.55 % Loss
Epoch 5600, loss = 6.858651e+01
Val score: -0.843558, 20.56 % Loss
Epoch 5610, loss = 6.832621e+01
Val score: -0.836317, 20.55 % Loss
Epoch 5620, loss = 6.851038e+01
Val score: -0.841032, 20.55 % Loss
Epoch 5630, loss = 6.848654e+01
Val score: -0.836729, 20.51 % Loss
Epoch 5640, loss = 6.882124e+01
Val score: -0.815252, 20.44 % Loss
Epoch 5650, loss = 6.823808e+01
Val score: -0.830341, 20.47 % Loss
Epoch 5660, loss = 6.847212e+01
Val score: -0.844337, 20.54 % Loss
Epoch 5670, loss = 6.826556e+01
Val score: -0.830436, 20.45 % Loss
Epoch 5680, loss = 6.816217e+01
Val score: -0.830944, 20.45 % Loss
Epoch 5690, loss = 6.822025e+01
Val score: -0.836884, 20.48 % Loss
Epoch 5700, loss = 6.845010e+01
Val score: -0.816295, 20.42 % Loss
Epoch 5710, loss = 6.873203e+01
Val score: -0.824550, 20.42 % Loss
Epoch 5720, loss = 6.817230e+01
Val score: -0.812137, 20.43 % Loss
Epoch 5730, loss = 6.839768e+01
Val score: -0.812063, 20.34 % Loss
Epoch 5740, loss = 6.766991e+01
Val score: -0.816231, 20.38 % Loss
Epoch 5750, loss = 6.786514e+01
Val score: -0.812376, 20.38 % Loss
Epoch 5760, loss = 6.791435e+01
Val score: -0.822174, 20.44 % Loss
Epoch 5770, loss = 6.755039e+01
Val score: -0.803668, 20.33 % Loss
Epoch 5780, loss = 6.780672e+01
Val score: -0.825357, 20.44 % Loss
Epoch 5790, loss = 6.792429e+01
Val score: -0.809713, 20.33 % Loss
Epoch 5800, loss = 6.781615e+01
Val score: -0.800904, 20.28 % Loss
Epoch 5810, loss = 6.753580e+01
Val score: -0.812154, 20.32 % Loss
Epoch 5820, loss = 6.747301e+01
Val score: -0.820097, 20.39 % Loss
Epoch 5830, loss = 6.721845e+01
Val score: -0.810133, 20.38 % Loss
Epoch 5840, loss = 6.757332e+01
Val score: -0.798448, 20.28 % Loss
Epoch 5850, loss = 6.722839e+01
Val score: -0.795294, 20.26 % Loss
Epoch 5860, loss = 6.741950e+01
Val score: -0.791089, 20.25 % Loss
Epoch 5870, loss = 6.724426e+01
Val score: -0.796104, 20.25 % Loss
Epoch 5880, loss = 6.694543e+01
Val score: -0.786875, 20.19 % Loss
Epoch 5890, loss = 6.735486e+01
Val score: -0.795486, 20.26 % Loss
Epoch 5900, loss = 6.703338e+01
Val score: -0.796172, 20.25 % Loss
Epoch 5910, loss = 6.702602e+01
Val score: -0.789779, 20.20 % Loss
Epoch 5920, loss = 6.715900e+01
Val score: -0.780675, 20.16 % Loss
Epoch 5930, loss = 6.672705e+01
Val score: -0.791447, 20.23 % Loss
Epoch 5940, loss = 6.658733e+01
Val score: -0.782662, 20.19 % Loss
Epoch 5950, loss = 6.681603e+01
Val score: -0.789947, 20.21 % Loss
Epoch 5960, loss = 6.648647e+01
Val score: -0.778345, 20.14 % Loss
Epoch 5970, loss = 6.683720e+01
Val score: -0.784533, 20.22 % Loss
Epoch 5980, loss = 6.683090e+01
Val score: -0.794144, 20.25 % Loss
Epoch 5990, loss = 6.668090e+01
Val score: -0.777333, 20.17 % Loss
Epoch 6000, loss = 6.735136e+01
Val score: -0.789851, 20.20 % Loss
Epoch 6010, loss = 6.721457e+01
Val score: -0.769136, 20.13 % Loss
Epoch 6020, loss = 6.689829e+01
Val score: -0.778368, 20.17 % Loss
Epoch 6030, loss = 6.666183e+01
Val score: -0.779120, 20.16 % Loss
Epoch 6040, loss = 6.597859e+01
Val score: -0.783811, 20.18 % Loss
Epoch 6050, loss = 6.659574e+01
Val score: -0.782560, 20.20 % Loss
Epoch 6060, loss = 6.646192e+01
Val score: -0.776918, 20.18 % Loss
Epoch 6070, loss = 6.595976e+01
Val score: -0.776506, 20.13 % Loss
Epoch 6080, loss = 6.647042e+01
Val score: -0.782934, 20.17 % Loss
Epoch 6090, loss = 6.601914e+01
Val score: -0.772620, 20.15 % Loss
Epoch 6100, loss = 6.639190e+01
Val score: -0.771012, 20.12 % Loss
Epoch 6110, loss = 6.598906e+01
Val score: -0.761034, 20.05 % Loss
Epoch 6120, loss = 6.570895e+01
Val score: -0.774334, 20.11 % Loss
Epoch 6130, loss = 6.639645e+01
Val score: -0.770890, 20.12 % Loss
Epoch 6140, loss = 6.621303e+01
Val score: -0.763015, 20.10 % Loss
Epoch 6150, loss = 6.611273e+01
Val score: -0.775637, 20.13 % Loss
Epoch 6160, loss = 6.584721e+01
Val score: -0.760224, 20.09 % Loss
Epoch 6170, loss = 6.574802e+01
Val score: -0.767871, 20.13 % Loss
Epoch 6180, loss = 6.612467e+01
Val score: -0.757858, 20.02 % Loss
Epoch 6190, loss = 6.580331e+01
Val score: -0.754238, 20.03 % Loss
Epoch 6200, loss = 6.591731e+01
Val score: -0.758423, 20.06 % Loss
Epoch 6210, loss = 6.582111e+01
Val score: -0.754976, 20.00 % Loss
Epoch 6220, loss = 6.587987e+01
Val score: -0.756409, 20.01 % Loss
Epoch 6230, loss = 6.583056e+01
Val score: -0.747381, 19.97 % Loss
Epoch 6240, loss = 6.574294e+01
Val score: -0.753395, 20.00 % Loss
Epoch 6250, loss = 6.558680e+01
Val score: -0.746568, 20.00 % Loss
Epoch 6260, loss = 6.595879e+01
Val score: -0.760088, 20.04 % Loss
Epoch 6270, loss = 6.582130e+01
Val score: -0.752487, 19.98 % Loss
Epoch 6280, loss = 6.552458e+01
Val score: -0.745759, 20.01 % Loss
Epoch 6290, loss = 6.555289e+01
Val score: -0.745410, 19.97 % Loss
Epoch 6300, loss = 6.535790e+01
Val score: -0.752313, 20.02 % Loss
Epoch 6310, loss = 6.536575e+01
Val score: -0.753055, 20.04 % Loss
Epoch 6320, loss = 6.559390e+01
Val score: -0.741405, 19.95 % Loss
Epoch 6330, loss = 6.519392e+01
Val score: -0.752566, 19.97 % Loss
Epoch 6340, loss = 6.528835e+01
Val score: -0.753603, 20.03 % Loss
Epoch 6350, loss = 6.465169e+01
Val score: -0.748965, 19.99 % Loss
Epoch 6360, loss = 6.488669e+01
Val score: -0.743829, 19.97 % Loss
Epoch 6370, loss = 6.509798e+01
Val score: -0.744960, 19.97 % Loss
Epoch 6380, loss = 6.522144e+01
Val score: -0.743891, 20.00 % Loss
Epoch 6390, loss = 6.504265e+01
Val score: -0.735888, 19.93 % Loss
Epoch 6400, loss = 6.476616e+01
Val score: -0.741758, 19.94 % Loss
Epoch 6410, loss = 6.525378e+01
Val score: -0.735775, 19.90 % Loss
Epoch 6420, loss = 6.472646e+01
Val score: -0.734225, 19.88 % Loss
Epoch 6430, loss = 6.523225e+01
Val score: -0.727450, 19.88 % Loss
Epoch 6440, loss = 6.477625e+01
Val score: -0.734207, 19.90 % Loss
Epoch 6450, loss = 6.447545e+01
Val score: -0.726599, 19.88 % Loss
Epoch 6460, loss = 6.492140e+01
Val score: -0.738340, 19.91 % Loss
Epoch 6470, loss = 6.517126e+01
Val score: -0.722368, 19.87 % Loss
Epoch 6480, loss = 6.509841e+01
Val score: -0.729333, 19.89 % Loss
Epoch 6490, loss = 6.470391e+01
Val score: -0.718670, 19.86 % Loss
Epoch 6500, loss = 6.432578e+01
Val score: -0.723297, 19.83 % Loss
Epoch 6510, loss = 6.464614e+01
Val score: -0.736912, 19.91 % Loss
Epoch 6520, loss = 6.460484e+01
Val score: -0.734597, 19.90 % Loss
Epoch 6530, loss = 6.451626e+01
Val score: -0.736165, 19.90 % Loss
Epoch 6540, loss = 6.472211e+01
Val score: -0.744779, 19.95 % Loss
Epoch 6550, loss = 6.460258e+01
Val score: -0.722500, 19.86 % Loss
Epoch 6560, loss = 6.477090e+01
Val score: -0.721663, 19.83 % Loss
Epoch 6570, loss = 6.417838e+01
Val score: -0.710854, 19.78 % Loss
Epoch 6580, loss = 6.439877e+01
Val score: -0.715766, 19.79 % Loss
Epoch 6590, loss = 6.411702e+01
Val score: -0.720890, 19.85 % Loss
Epoch 6600, loss = 6.445799e+01
Val score: -0.729147, 19.87 % Loss
Epoch 6610, loss = 6.394807e+01
Val score: -0.721112, 19.84 % Loss
Epoch 6620, loss = 6.381238e+01
Val score: -0.729913, 19.87 % Loss
Epoch 6630, loss = 6.417941e+01
Val score: -0.703383, 19.74 % Loss
Epoch 6640, loss = 6.428614e+01
Val score: -0.719530, 19.84 % Loss
Epoch 6650, loss = 6.417181e+01
Val score: -0.726846, 19.85 % Loss
Epoch 6660, loss = 6.368817e+01
Val score: -0.720946, 19.84 % Loss
Epoch 6670, loss = 6.384788e+01
Val score: -0.712706, 19.76 % Loss
Epoch 6680, loss = 6.381014e+01
Val score: -0.717874, 19.76 % Loss
Epoch 6690, loss = 6.410208e+01
Val score: -0.720123, 19.80 % Loss
Epoch 6700, loss = 6.395401e+01
Val score: -0.714404, 19.80 % Loss
Epoch 6710, loss = 6.373789e+01
Val score: -0.715912, 19.77 % Loss
Epoch 6720, loss = 6.347847e+01
Val score: -0.708193, 19.75 % Loss
Epoch 6730, loss = 6.391945e+01
Val score: -0.699774, 19.73 % Loss
Epoch 6740, loss = 6.399825e+01
Val score: -0.719003, 19.81 % Loss
Epoch 6750, loss = 6.359200e+01
Val score: -0.705038, 19.72 % Loss
Epoch 6760, loss = 6.319445e+01
Val score: -0.712978, 19.78 % Loss
Epoch 6770, loss = 6.363141e+01
Val score: -0.706190, 19.75 % Loss
Epoch 6780, loss = 6.349977e+01
Val score: -0.703777, 19.72 % Loss
Epoch 6790, loss = 6.310729e+01
Val score: -0.726431, 19.82 % Loss
Epoch 6800, loss = 6.369207e+01
Val score: -0.715031, 19.75 % Loss
Epoch 6810, loss = 6.382350e+01
Val score: -0.704545, 19.77 % Loss
Epoch 6820, loss = 6.356957e+01
Val score: -0.704801, 19.71 % Loss
Epoch 6830, loss = 6.341454e+01
Val score: -0.709660, 19.75 % Loss
Epoch 6840, loss = 6.358646e+01
Val score: -0.691833, 19.66 % Loss
Epoch 6850, loss = 6.325313e+01
Val score: -0.702287, 19.72 % Loss
Epoch 6860, loss = 6.340593e+01
Val score: -0.708264, 19.74 % Loss
Epoch 6870, loss = 6.352378e+01
Val score: -0.705803, 19.72 % Loss
Epoch 6880, loss = 6.275776e+01
Val score: -0.694495, 19.68 % Loss
Epoch 6890, loss = 6.298875e+01
Val score: -0.699213, 19.70 % Loss
Epoch 6900, loss = 6.336538e+01
Val score: -0.695466, 19.68 % Loss
Epoch 6910, loss = 6.294935e+01
Val score: -0.697574, 19.71 % Loss
Epoch 6920, loss = 6.342043e+01
Val score: -0.697905, 19.68 % Loss
Epoch 6930, loss = 6.314592e+01
Val score: -0.699495, 19.69 % Loss
Epoch 6940, loss = 6.311332e+01
Val score: -0.708092, 19.74 % Loss
Epoch 6950, loss = 6.284718e+01
Val score: -0.695079, 19.66 % Loss
Epoch 6960, loss = 6.276693e+01
Val score: -0.700187, 19.70 % Loss
Epoch 6970, loss = 6.263319e+01
Val score: -0.699592, 19.66 % Loss
Epoch 6980, loss = 6.291587e+01
Val score: -0.697627, 19.67 % Loss
Epoch 6990, loss = 6.313544e+01
Val score: -0.700223, 19.70 % Loss
Epoch 7000, loss = 6.261324e+01
Val score: -0.690576, 19.66 % Loss
Epoch 7010, loss = 6.266778e+01
Val score: -0.699707, 19.68 % Loss
Epoch 7020, loss = 6.318299e+01
Val score: -0.696825, 19.66 % Loss
Epoch 7030, loss = 6.286063e+01
Val score: -0.702615, 19.71 % Loss
Epoch 7040, loss = 6.280796e+01
Val score: -0.690442, 19.66 % Loss
Epoch 7050, loss = 6.246656e+01
Val score: -0.689798, 19.67 % Loss
Epoch 7060, loss = 6.271430e+01
Val score: -0.700196, 19.68 % Loss
Epoch 7070, loss = 6.247466e+01
Val score: -0.687404, 19.62 % Loss
Epoch 7080, loss = 6.282721e+01
Val score: -0.689159, 19.61 % Loss
Epoch 7090, loss = 6.230653e+01
Val score: -0.691722, 19.66 % Loss
Epoch 7100, loss = 6.210285e+01
Val score: -0.691354, 19.65 % Loss
Epoch 7110, loss = 6.236148e+01
Val score: -0.702899, 19.70 % Loss
Epoch 7120, loss = 6.268736e+01
Val score: -0.695913, 19.67 % Loss
Epoch 7130, loss = 6.234177e+01
Val score: -0.688644, 19.66 % Loss
Epoch 7140, loss = 6.216270e+01
Val score: -0.688439, 19.63 % Loss
Epoch 7150, loss = 6.214576e+01
Val score: -0.682781, 19.60 % Loss
Epoch 7160, loss = 6.226074e+01
Val score: -0.690476, 19.60 % Loss
Epoch 7170, loss = 6.222019e+01
Val score: -0.696485, 19.66 % Loss
Epoch 7180, loss = 6.245636e+01
Val score: -0.691178, 19.64 % Loss
Epoch 7190, loss = 6.202065e+01
Val score: -0.683648, 19.58 % Loss
Epoch 7200, loss = 6.252700e+01
Val score: -0.692459, 19.61 % Loss
Epoch 7210, loss = 6.201507e+01
Val score: -0.688211, 19.60 % Loss
Epoch 7220, loss = 6.194118e+01
Val score: -0.689614, 19.60 % Loss
Epoch 7230, loss = 6.205875e+01
Val score: -0.693952, 19.67 % Loss
Epoch 7240, loss = 6.214891e+01
Val score: -0.688744, 19.63 % Loss
Epoch 7250, loss = 6.200813e+01
Val score: -0.686280, 19.58 % Loss
Epoch 7260, loss = 6.216060e+01
Val score: -0.683188, 19.59 % Loss
Epoch 7270, loss = 6.203445e+01
Val score: -0.688012, 19.61 % Loss
Epoch 7280, loss = 6.242037e+01
Val score: -0.679684, 19.59 % Loss
Epoch 7290, loss = 6.169744e+01
Val score: -0.681630, 19.58 % Loss
Epoch 7300, loss = 6.182244e+01
Val score: -0.678746, 19.58 % Loss
Epoch 7310, loss = 6.180360e+01
Val score: -0.692771, 19.61 % Loss
Epoch 7320, loss = 6.146965e+01
Val score: -0.695929, 19.64 % Loss
Epoch 7330, loss = 6.214045e+01
Val score: -0.687729, 19.61 % Loss
Epoch 7340, loss = 6.179718e+01
Val score: -0.680465, 19.58 % Loss
Epoch 7350, loss = 6.133166e+01
Val score: -0.680982, 19.60 % Loss
Epoch 7360, loss = 6.153944e+01
Val score: -0.686698, 19.59 % Loss
Epoch 7370, loss = 6.153180e+01
Val score: -0.683838, 19.58 % Loss
Epoch 7380, loss = 6.184425e+01
Val score: -0.686787, 19.60 % Loss
Epoch 7390, loss = 6.188707e+01
Val score: -0.677686, 19.54 % Loss
Epoch 7400, loss = 6.080898e+01
Val score: -0.684586, 19.60 % Loss
Epoch 7410, loss = 6.125956e+01
Val score: -0.668446, 19.53 % Loss
Epoch 7420, loss = 6.164423e+01
Val score: -0.683546, 19.58 % Loss
Epoch 7430, loss = 6.132212e+01
Val score: -0.678453, 19.54 % Loss
Epoch 7440, loss = 6.177175e+01
Val score: -0.670163, 19.51 % Loss
Epoch 7450, loss = 6.125412e+01
Val score: -0.680928, 19.57 % Loss
Epoch 7460, loss = 6.130932e+01
Val score: -0.675801, 19.54 % Loss
Epoch 7470, loss = 6.146381e+01
Val score: -0.671973, 19.52 % Loss
Epoch 7480, loss = 6.098122e+01
Val score: -0.676892, 19.55 % Loss
Epoch 7490, loss = 6.160057e+01
Val score: -0.673029, 19.51 % Loss
Epoch 7500, loss = 6.118596e+01
Val score: -0.694134, 19.60 % Loss
Epoch 7510, loss = 6.176707e+01
Val score: -0.672821, 19.54 % Loss
Epoch 7520, loss = 6.097066e+01
Val score: -0.678141, 19.54 % Loss
Epoch 7530, loss = 6.137647e+01
Val score: -0.674203, 19.54 % Loss
Epoch 7540, loss = 6.082292e+01
Val score: -0.672506, 19.49 % Loss
Epoch 7550, loss = 6.129293e+01
Val score: -0.673929, 19.52 % Loss
Epoch 7560, loss = 6.158079e+01
Val score: -0.674346, 19.53 % Loss
Epoch 7570, loss = 6.111036e+01
Val score: -0.681395, 19.56 % Loss
Epoch 7580, loss = 6.101421e+01
Val score: -0.673908, 19.51 % Loss
Epoch 7590, loss = 6.138713e+01
Val score: -0.672806, 19.55 % Loss
Epoch 7600, loss = 6.085365e+01
Val score: -0.675300, 19.51 % Loss
Epoch 7610, loss = 6.090511e+01
Val score: -0.671813, 19.51 % Loss
Epoch 7620, loss = 6.076468e+01
Val score: -0.675658, 19.51 % Loss
Epoch 7630, loss = 6.112134e+01
Val score: -0.673200, 19.53 % Loss
Epoch 7640, loss = 6.123507e+01
Val score: -0.673783, 19.52 % Loss
Epoch 7650, loss = 6.087331e+01
Val score: -0.673784, 19.50 % Loss
Epoch 7660, loss = 6.091951e+01
Val score: -0.677953, 19.53 % Loss
Epoch 7670, loss = 6.114415e+01
Val score: -0.682012, 19.56 % Loss
Epoch 7680, loss = 6.068085e+01
Val score: -0.671667, 19.48 % Loss
Epoch 7690, loss = 6.100217e+01
Val score: -0.671444, 19.48 % Loss
Epoch 7700, loss = 6.084414e+01
Val score: -0.665295, 19.47 % Loss
Epoch 7710, loss = 6.057090e+01
Val score: -0.667096, 19.46 % Loss
Epoch 7720, loss = 6.079557e+01
Val score: -0.671183, 19.50 % Loss
Epoch 7730, loss = 6.071536e+01
Val score: -0.664368, 19.47 % Loss
Epoch 7740, loss = 6.063676e+01
Val score: -0.672446, 19.49 % Loss
Epoch 7750, loss = 6.042054e+01
Val score: -0.669081, 19.49 % Loss
Epoch 7760, loss = 6.084235e+01
Val score: -0.670127, 19.45 % Loss
Epoch 7770, loss = 6.031443e+01
Val score: -0.673874, 19.50 % Loss
Epoch 7780, loss = 6.035620e+01
Val score: -0.662862, 19.46 % Loss
Epoch 7790, loss = 6.040779e+01
Val score: -0.673856, 19.50 % Loss
Epoch 7800, loss = 6.022025e+01
Val score: -0.663271, 19.45 % Loss
Epoch 7810, loss = 6.066874e+01
Val score: -0.675131, 19.49 % Loss
Epoch 7820, loss = 6.054182e+01
Val score: -0.663223, 19.43 % Loss
Epoch 7830, loss = 6.009216e+01
Val score: -0.667663, 19.45 % Loss
Epoch 7840, loss = 6.065091e+01
Val score: -0.675898, 19.50 % Loss
Epoch 7850, loss = 6.086457e+01
Val score: -0.667349, 19.47 % Loss
Epoch 7860, loss = 6.069387e+01
Val score: -0.691052, 19.58 % Loss
Epoch 7870, loss = 6.036654e+01
Val score: -0.669392, 19.48 % Loss
Epoch 7880, loss = 6.008580e+01
Val score: -0.664585, 19.47 % Loss
Epoch 7890, loss = 6.013453e+01
Val score: -0.676043, 19.52 % Loss
Epoch 7900, loss = 6.029380e+01
Val score: -0.669866, 19.49 % Loss
Epoch 7910, loss = 6.003480e+01
Val score: -0.674643, 19.51 % Loss
Epoch 7920, loss = 6.041119e+01
Val score: -0.675803, 19.52 % Loss
Epoch 7930, loss = 6.029032e+01
Val score: -0.657965, 19.43 % Loss
Epoch 7940, loss = 6.011192e+01
Val score: -0.678337, 19.51 % Loss
Epoch 7950, loss = 5.995006e+01
Val score: -0.674817, 19.52 % Loss
Epoch 7960, loss = 6.068149e+01
Val score: -0.670551, 19.45 % Loss
Epoch 7970, loss = 6.010012e+01
Val score: -0.671297, 19.49 % Loss
Epoch 7980, loss = 5.997108e+01
Val score: -0.676519, 19.52 % Loss
Epoch 7990, loss = 5.971500e+01
Val score: -0.663667, 19.44 % Loss
Epoch 8000, loss = 5.997350e+01
Val score: -0.667241, 19.43 % Loss
Epoch 8010, loss = 6.025647e+01
Val score: -0.677803, 19.50 % Loss
Epoch 8020, loss = 5.992962e+01
Val score: -0.677522, 19.49 % Loss
Epoch 8030, loss = 5.957079e+01
Val score: -0.677778, 19.52 % Loss
Epoch 8040, loss = 6.005635e+01
Val score: -0.666280, 19.46 % Loss
Epoch 8050, loss = 5.948372e+01
Val score: -0.674880, 19.48 % Loss
Epoch 8060, loss = 5.966697e+01
Val score: -0.676215, 19.51 % Loss
Epoch 8070, loss = 5.967202e+01
Val score: -0.666646, 19.45 % Loss
Epoch 8080, loss = 5.965155e+01
Val score: -0.665860, 19.45 % Loss
Epoch 8090, loss = 6.002782e+01
Val score: -0.670490, 19.49 % Loss
Epoch 8100, loss = 5.993519e+01
Val score: -0.668726, 19.47 % Loss
Epoch 8110, loss = 5.981028e+01
Val score: -0.665150, 19.42 % Loss
Epoch 8120, loss = 5.975926e+01
Val score: -0.663808, 19.43 % Loss
Epoch 8130, loss = 5.981056e+01
Val score: -0.665832, 19.43 % Loss
Epoch 8140, loss = 5.981734e+01
Val score: -0.661332, 19.44 % Loss
Epoch 8150, loss = 5.996127e+01
Val score: -0.678400, 19.47 % Loss
Epoch 8160, loss = 5.971338e+01
Val score: -0.677923, 19.51 % Loss
Epoch 8170, loss = 5.965150e+01
Val score: -0.672319, 19.46 % Loss
Epoch 8180, loss = 5.974865e+01
Val score: -0.662926, 19.43 % Loss
Epoch 8190, loss = 5.958762e+01
Val score: -0.673370, 19.46 % Loss
Epoch 8200, loss = 5.948555e+01
Val score: -0.671057, 19.47 % Loss
Epoch 8210, loss = 5.921408e+01
Val score: -0.666580, 19.43 % Loss
Epoch 8220, loss = 5.957809e+01
Val score: -0.664878, 19.42 % Loss
Epoch 8230, loss = 5.954530e+01
Val score: -0.680024, 19.52 % Loss
Epoch 8240, loss = 5.968613e+01
Val score: -0.664290, 19.44 % Loss
Epoch 8250, loss = 5.913847e+01
Val score: -0.659912, 19.40 % Loss
Epoch 8260, loss = 5.925164e+01
Val score: -0.675485, 19.46 % Loss
Epoch 8270, loss = 5.907370e+01
Val score: -0.669380, 19.46 % Loss
Epoch 8280, loss = 5.940377e+01
Val score: -0.673283, 19.45 % Loss
Epoch 8290, loss = 5.941398e+01
Val score: -0.669964, 19.46 % Loss
Epoch 8300, loss = 5.912668e+01
Val score: -0.664990, 19.45 % Loss
Epoch 8310, loss = 5.930399e+01
Val score: -0.672652, 19.48 % Loss
Epoch 8320, loss = 5.883572e+01
Val score: -0.665058, 19.43 % Loss
Epoch 8330, loss = 5.904406e+01
Val score: -0.671462, 19.44 % Loss
Epoch 8340, loss = 5.942471e+01
Val score: -0.670325, 19.45 % Loss
Epoch 8350, loss = 5.929345e+01
Val score: -0.678230, 19.48 % Loss
Epoch 8360, loss = 5.921229e+01
Val score: -0.669605, 19.42 % Loss
Epoch 8370, loss = 5.931038e+01
Val score: -0.679120, 19.50 % Loss
Epoch 8380, loss = 5.891576e+01
Val score: -0.675882, 19.46 % Loss
Epoch 8390, loss = 5.951698e+01
Val score: -0.664707, 19.42 % Loss
Epoch 8400, loss = 5.916932e+01
Val score: -0.669799, 19.44 % Loss
Epoch 8410, loss = 5.883062e+01
Val score: -0.668481, 19.42 % Loss
Epoch 8420, loss = 5.898448e+01
Val score: -0.673262, 19.43 % Loss
Epoch 8430, loss = 5.876442e+01
Val score: -0.669985, 19.41 % Loss
Epoch 8440, loss = 5.863298e+01
Val score: -0.667577, 19.40 % Loss
Epoch 8450, loss = 5.945317e+01
Val score: -0.676987, 19.47 % Loss
Epoch 8460, loss = 5.891917e+01
Val score: -0.663298, 19.35 % Loss
Epoch 8470, loss = 5.924192e+01
Val score: -0.662998, 19.42 % Loss
Epoch 8480, loss = 5.902496e+01
Val score: -0.665606, 19.43 % Loss
Epoch 8490, loss = 5.905937e+01
Val score: -0.669663, 19.43 % Loss
Epoch 8500, loss = 5.886369e+01
Val score: -0.671177, 19.43 % Loss
Epoch 8510, loss = 5.894883e+01
Val score: -0.660138, 19.37 % Loss
Epoch 8520, loss = 5.909237e+01
Val score: -0.683311, 19.52 % Loss
Epoch 8530, loss = 5.849179e+01
Val score: -0.669012, 19.45 % Loss
Epoch 8540, loss = 5.886721e+01
Val score: -0.670245, 19.42 % Loss
Epoch 8550, loss = 5.814772e+01
Val score: -0.676640, 19.48 % Loss
Epoch 8560, loss = 5.906112e+01
Val score: -0.673244, 19.45 % Loss
Epoch 8570, loss = 5.859086e+01
Val score: -0.674954, 19.47 % Loss
Epoch 8580, loss = 5.845842e+01
Val score: -0.656978, 19.34 % Loss
Epoch 8590, loss = 5.890038e+01
Val score: -0.675672, 19.46 % Loss
Epoch 8600, loss = 5.881251e+01
Val score: -0.665709, 19.39 % Loss
Epoch 8610, loss = 5.893949e+01
Val score: -0.678140, 19.47 % Loss
Epoch 8620, loss = 5.880954e+01
Val score: -0.674538, 19.43 % Loss
Epoch 8630, loss = 5.836072e+01
Val score: -0.683201, 19.47 % Loss
Epoch 8640, loss = 5.892368e+01
Val score: -0.671686, 19.43 % Loss
Epoch 8650, loss = 5.838038e+01
Val score: -0.676864, 19.44 % Loss
Epoch 8660, loss = 5.835272e+01
Val score: -0.660669, 19.37 % Loss
Epoch 8670, loss = 5.840124e+01
Val score: -0.673719, 19.41 % Loss
Epoch 8680, loss = 5.823202e+01
Val score: -0.663591, 19.38 % Loss
Epoch 8690, loss = 5.848689e+01
Val score: -0.677642, 19.43 % Loss
Epoch 8700, loss = 5.847242e+01
Val score: -0.673946, 19.42 % Loss
Epoch 8710, loss = 5.823428e+01
Val score: -0.670725, 19.42 % Loss
Epoch 8720, loss = 5.836524e+01
Val score: -0.673792, 19.44 % Loss
Epoch 8730, loss = 5.847680e+01
Val score: -0.668225, 19.40 % Loss
Epoch 8740, loss = 5.851144e+01
Val score: -0.663054, 19.38 % Loss
Epoch 8750, loss = 5.828814e+01
Val score: -0.672811, 19.42 % Loss
Epoch 8760, loss = 5.820416e+01
Val score: -0.672218, 19.42 % Loss
Epoch 8770, loss = 5.850966e+01
Val score: -0.668627, 19.40 % Loss
Epoch 8780, loss = 5.832585e+01
Val score: -0.673850, 19.43 % Loss
Epoch 8790, loss = 5.855996e+01
Val score: -0.674454, 19.44 % Loss
Epoch 8800, loss = 5.843046e+01
Val score: -0.678138, 19.43 % Loss
Epoch 8810, loss = 5.848151e+01
Val score: -0.681428, 19.48 % Loss
Epoch 8820, loss = 5.834695e+01
Val score: -0.665596, 19.37 % Loss
Epoch 8830, loss = 5.821759e+01
Val score: -0.671002, 19.41 % Loss
Epoch 8840, loss = 5.814821e+01
Val score: -0.672676, 19.42 % Loss
Epoch 8850, loss = 5.795890e+01
Val score: -0.672657, 19.39 % Loss
Epoch 8860, loss = 5.788960e+01
Val score: -0.673314, 19.43 % Loss
Epoch 8870, loss = 5.850646e+01
Val score: -0.678365, 19.45 % Loss
Epoch 8880, loss = 5.801618e+01
Val score: -0.667368, 19.39 % Loss
Epoch 8890, loss = 5.805737e+01
Val score: -0.657075, 19.35 % Loss
Epoch 8900, loss = 5.800782e+01
Val score: -0.670582, 19.43 % Loss
Epoch 8910, loss = 5.851399e+01
Val score: -0.672217, 19.41 % Loss
Epoch 8920, loss = 5.775510e+01
Val score: -0.679866, 19.45 % Loss
Epoch 8930, loss = 5.795470e+01
Val score: -0.678298, 19.44 % Loss
Epoch 8940, loss = 5.780952e+01
Val score: -0.664020, 19.36 % Loss
Epoch 8950, loss = 5.797835e+01
Val score: -0.675668, 19.45 % Loss
Epoch 8960, loss = 5.787606e+01
Val score: -0.676288, 19.43 % Loss
Epoch 8970, loss = 5.759392e+01
Val score: -0.669400, 19.38 % Loss
Epoch 8980, loss = 5.791908e+01
Val score: -0.681090, 19.45 % Loss
Epoch 8990, loss = 5.783466e+01
Val score: -0.682969, 19.45 % Loss
Epoch 9000, loss = 5.798174e+01
Val score: -0.676321, 19.39 % Loss
Epoch 9010, loss = 5.738425e+01
Val score: -0.667842, 19.37 % Loss
Epoch 9020, loss = 5.790824e+01
Val score: -0.677479, 19.43 % Loss
Epoch 9030, loss = 5.784143e+01
Val score: -0.680826, 19.47 % Loss
Epoch 9040, loss = 5.788340e+01
Val score: -0.677522, 19.42 % Loss
Epoch 9050, loss = 5.770205e+01
Val score: -0.680548, 19.45 % Loss
Epoch 9060, loss = 5.705964e+01
Val score: -0.668007, 19.38 % Loss
Epoch 9070, loss = 5.817008e+01
Val score: -0.668964, 19.37 % Loss
Epoch 9080, loss = 5.763229e+01
Val score: -0.683301, 19.46 % Loss
Epoch 9090, loss = 5.740450e+01
Val score: -0.681365, 19.45 % Loss
Epoch 9100, loss = 5.757099e+01
Val score: -0.673039, 19.40 % Loss
Epoch 9110, loss = 5.821884e+01
Val score: -0.679181, 19.42 % Loss
Epoch 9120, loss = 5.759447e+01
Val score: -0.676897, 19.41 % Loss
Epoch 9130, loss = 5.799232e+01
Val score: -0.669788, 19.38 % Loss
Epoch 9140, loss = 5.760716e+01
Val score: -0.678515, 19.44 % Loss
Epoch 9150, loss = 5.755650e+01
Val score: -0.672810, 19.39 % Loss
Epoch 9160, loss = 5.771980e+01
Val score: -0.670126, 19.37 % Loss
Epoch 9170, loss = 5.740450e+01
Val score: -0.682332, 19.46 % Loss
Epoch 9180, loss = 5.767505e+01
Val score: -0.677682, 19.41 % Loss
Epoch 9190, loss = 5.778453e+01
Val score: -0.681602, 19.43 % Loss
Epoch 9200, loss = 5.758291e+01
Val score: -0.669808, 19.38 % Loss
Epoch 9210, loss = 5.753016e+01
Val score: -0.677102, 19.43 % Loss
Epoch 9220, loss = 5.781709e+01
Val score: -0.683438, 19.45 % Loss
Epoch 9230, loss = 5.753685e+01
Val score: -0.674900, 19.40 % Loss
Epoch 9240, loss = 5.730212e+01
Val score: -0.680810, 19.45 % Loss
Epoch 9250, loss = 5.756005e+01
Val score: -0.671947, 19.41 % Loss
Epoch 9260, loss = 5.775009e+01
Val score: -0.675211, 19.37 % Loss
Epoch 9270, loss = 5.750023e+01
Val score: -0.670021, 19.37 % Loss
Epoch 9280, loss = 5.740005e+01
Val score: -0.673347, 19.39 % Loss
Epoch 9290, loss = 5.733993e+01
Val score: -0.679293, 19.41 % Loss
Epoch 9300, loss = 5.735462e+01
Val score: -0.680534, 19.46 % Loss
Epoch 9310, loss = 5.732624e+01
Val score: -0.680449, 19.44 % Loss
Epoch 9320, loss = 5.717604e+01
Val score: -0.683831, 19.42 % Loss
Epoch 9330, loss = 5.748838e+01
Val score: -0.674072, 19.40 % Loss
Epoch 9340, loss = 5.723652e+01
Val score: -0.679938, 19.41 % Loss
Epoch 9350, loss = 5.740718e+01
Val score: -0.671170, 19.38 % Loss
Epoch 9360, loss = 5.733052e+01
Val score: -0.678102, 19.42 % Loss
Epoch 9370, loss = 5.712220e+01
Val score: -0.683097, 19.43 % Loss
Epoch 9380, loss = 5.751854e+01
Val score: -0.684814, 19.44 % Loss
Epoch 9390, loss = 5.714858e+01
Val score: -0.678240, 19.42 % Loss
Epoch 9400, loss = 5.709167e+01
Val score: -0.684576, 19.43 % Loss
Epoch 9410, loss = 5.698269e+01
Val score: -0.678499, 19.39 % Loss
Epoch 9420, loss = 5.690039e+01
Val score: -0.684148, 19.41 % Loss
Epoch 9430, loss = 5.679152e+01
Val score: -0.681589, 19.42 % Loss
Epoch 9440, loss = 5.722615e+01
Val score: -0.684749, 19.43 % Loss
Epoch 9450, loss = 5.753084e+01
Val score: -0.689305, 19.44 % Loss
Epoch 9460, loss = 5.709787e+01
Val score: -0.687960, 19.43 % Loss
Epoch 9470, loss = 5.700498e+01
Val score: -0.680304, 19.43 % Loss
Epoch 9480, loss = 5.670747e+01
Val score: -0.678424, 19.39 % Loss
Epoch 9490, loss = 5.689940e+01
Val score: -0.671371, 19.41 % Loss
Epoch 9500, loss = 5.711086e+01
Val score: -0.680893, 19.40 % Loss
Epoch 9510, loss = 5.720503e+01
Val score: -0.673488, 19.39 % Loss
Epoch 9520, loss = 5.681502e+01
Val score: -0.684852, 19.44 % Loss
Epoch 9530, loss = 5.725047e+01
Val score: -0.679592, 19.39 % Loss
Epoch 9540, loss = 5.693608e+01
Val score: -0.679906, 19.41 % Loss
Epoch 9550, loss = 5.723088e+01
Val score: -0.691302, 19.45 % Loss
Epoch 9560, loss = 5.696611e+01
Val score: -0.694037, 19.47 % Loss
Epoch 9570, loss = 5.707171e+01
Val score: -0.684002, 19.45 % Loss
Epoch 9580, loss = 5.708721e+01
Val score: -0.679384, 19.41 % Loss
Epoch 9590, loss = 5.758678e+01
Val score: -0.693713, 19.48 % Loss
Epoch 9600, loss = 5.674108e+01
Val score: -0.683204, 19.43 % Loss
Epoch 9610, loss = 5.676634e+01
Val score: -0.692885, 19.47 % Loss
Epoch 9620, loss = 5.667816e+01
Val score: -0.685277, 19.41 % Loss
Epoch 9630, loss = 5.692311e+01
Val score: -0.681076, 19.40 % Loss
Epoch 9640, loss = 5.714715e+01
Val score: -0.684603, 19.42 % Loss
Epoch 9650, loss = 5.680701e+01
Val score: -0.688489, 19.42 % Loss
Epoch 9660, loss = 5.667834e+01
Val score: -0.671626, 19.35 % Loss
Epoch 9670, loss = 5.653851e+01
Val score: -0.695261, 19.46 % Loss
Epoch 9680, loss = 5.674909e+01
Val score: -0.685636, 19.43 % Loss
Epoch 9690, loss = 5.668309e+01
Val score: -0.691187, 19.47 % Loss
Epoch 9700, loss = 5.682217e+01
Val score: -0.689833, 19.43 % Loss
Epoch 9710, loss = 5.689998e+01
Val score: -0.696590, 19.47 % Loss
Epoch 9720, loss = 5.652053e+01
Val score: -0.676734, 19.38 % Loss
Epoch 9730, loss = 5.678841e+01
Val score: -0.689319, 19.44 % Loss
Epoch 9740, loss = 5.667107e+01
Val score: -0.684229, 19.42 % Loss
Epoch 9750, loss = 5.722211e+01
Val score: -0.690434, 19.40 % Loss
Epoch 9760, loss = 5.640524e+01
Val score: -0.684147, 19.41 % Loss
Epoch 9770, loss = 5.687386e+01
Val score: -0.691992, 19.45 % Loss
Epoch 9780, loss = 5.634221e+01
Val score: -0.693101, 19.48 % Loss
Epoch 9790, loss = 5.679113e+01
Val score: -0.682731, 19.42 % Loss
Epoch 9800, loss = 5.652845e+01
Val score: -0.707068, 19.52 % Loss
Epoch 9810, loss = 5.676057e+01
Val score: -0.692035, 19.43 % Loss
Epoch 9820, loss = 5.650859e+01
Val score: -0.690011, 19.42 % Loss
Epoch 9830, loss = 5.619950e+01
Val score: -0.682815, 19.38 % Loss
Epoch 9840, loss = 5.599682e+01
Val score: -0.689279, 19.44 % Loss
Epoch 9850, loss = 5.665324e+01
Val score: -0.688519, 19.42 % Loss
Epoch 9860, loss = 5.623232e+01
Val score: -0.689466, 19.43 % Loss
Epoch 9870, loss = 5.677708e+01
Val score: -0.687880, 19.42 % Loss
Epoch 9880, loss = 5.615081e+01
Val score: -0.679071, 19.38 % Loss
Epoch 9890, loss = 5.659758e+01
Val score: -0.699400, 19.46 % Loss
Epoch 9900, loss = 5.653627e+01
Val score: -0.695067, 19.46 % Loss
Epoch 9910, loss = 5.639317e+01
Val score: -0.685550, 19.38 % Loss
Epoch 9920, loss = 5.627105e+01
Val score: -0.697463, 19.45 % Loss
Epoch 9930, loss = 5.626694e+01
Val score: -0.680098, 19.38 % Loss
Epoch 9940, loss = 5.596341e+01
Val score: -0.691442, 19.44 % Loss
Epoch 9950, loss = 5.630378e+01
Val score: -0.684863, 19.40 % Loss
Epoch 9960, loss = 5.640816e+01
Val score: -0.692069, 19.45 % Loss
Epoch 9970, loss = 5.626254e+01
Val score: -0.687647, 19.40 % Loss
Epoch 9980, loss = 5.659062e+01
Val score: -0.689532, 19.42 % Loss
Epoch 9990, loss = 5.652359e+01
Val score: -0.693296, 19.45 % Loss

In [ ]:

    

np.abs(emu.goodness_of_fit(training_file, statistic = 'log_frac')).mean()


    

In [ ]:

    

np.abs(emu.goodness_of_fit(training_file, statistic = 'frac')).mean()


    

In [ ]:

    

fit_idxs = np.argsort(gof.mean(axis = 1))


    

In [ ]:

    

emu.goodness_of_fit(training_file).mean()#, statistic = 'log_frac')).mean()


    

In [ ]:

    

model = emu._emulator


    

In [ ]:

    

ypred = model.predict(emu.x)


    

In [ ]:

    

plt.hist( np.log10( (emu._y_std+1e-5)*np.abs(ypred-emu.y)/np.abs((emu._y_std+1e-5)*emu.y+emu._y_mean) ))


    

In [ ]:

    

( (emu._y_std+1e-5)*np.abs(ypred-emu.y)/np.abs((emu._y_std+1e-5)*emu.y+emu._y_mean) ).mean()


    

In [ ]:

    

emu._y_mean, emu._y_std


    

In [ ]:

    

for idx in fit_idxs[:10]:
    print gof[idx].mean()
    print (ypred[idx*emu.n_bins:(idx+1)*emu.n_bins]-emu.y[idx*emu.n_bins:(idx+1)*emu.n_bins])/emu.y[idx*emu.n_bins:(idx+1)*emu.n_bins]
    plt.plot(emu.scale_bin_centers, ypred[idx*emu.n_bins:(idx+1)*emu.n_bins], label = 'Emu')
    plt.plot(emu.scale_bin_centers, emu.y[idx*emu.n_bins:(idx+1)*emu.n_bins], label = 'True')
    plt.legend(loc='best')
    plt.xscale('log')
    plt.show()


    

In [ ]:

    

print dict(zip(emu.get_param_names(), emu.x[8*emu.n_bins, :]*emu._x_std+emu._x_mean))


    

In [ ]:

    

emu.get_param_names()


    

In [ ]:

    

emu._ordered_params


    

In [ ]:

    

gof = emu.goodness_of_fit(training_file, statistic = 'frac')
print gof.mean()


    

In [ ]:

    

for row in gof:
    print row


    

In [ ]:

    

gof = emu.goodness_of_fit(training_file, statistic = 'frac')
print gof.mean()


    

In [ ]:

    

model = emu._emulator


    

In [ ]:

    

model.score(emu.x, emu.y)


    

In [ ]:

    

ypred = model.predict(emu.x)

np.mean(np.abs(ypred-emu.y)/emu.y)


    

In [ ]:

    

plt.plot(emu.scale_bin_centers, np.abs(gof.mean(axis = 0)) )
plt.plot(emu.scale_bin_centers, np.ones_like(emu.scale_bin_centers)*0.01)
plt.plot(emu.scale_bin_centers, np.ones_like(emu.scale_bin_centers)*0.05)
plt.plot(emu.scale_bin_centers, np.ones_like(emu.scale_bin_centers)*0.1)


plt.loglog();


    

In [ ]:

    

plt.plot(emu.scale_bin_centers, np.abs(gof.T),alpha = 0.1, color = 'b')
plt.plot(emu.scale_bin_centers, np.ones_like(emu.scale_bin_centers)*0.01, lw = 2, color = 'k')
plt.loglog();


    

In [ ]:

    

gof[:,i].shape


    

In [ ]:

    

for i in xrange(gof.shape[1]):
    plt.hist(np.log10(gof[:, i]), label = str(i), alpha = 0.2);
    
plt.legend(loc='best')
plt.show()


    

In [ ]: