深度殘差網路+自適應引數化ReLU啟用函式（調參記錄9）
本文在調參記錄6的基礎上，繼續調整超引數，測試Adaptively Parametric ReLU（APReLU）啟用函式在Cifar10影像集上的效果。
深度殘差網路+自適應引數化ReLU啟用函式（調參記錄6）
https://blog.csdn.net/dangqing1988/article/details/105628681
自適應引數化ReLU啟用函式的基本原理見下圖：
在Keras裡，Batch Normalization的momentum預設為0.99，現在設定為0.9，這是因為momentum=0.9似乎更常見。原先Batch Normalization預設沒有正則化，現在加上L2正則化，來減小過擬合。
Keras程式如下：
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 14 04:17:45 2020
Implemented using TensorFlow 1.10.0 and Keras 2.2.1
Minghang Zhao, Shisheng Zhong, Xuyun Fu, Baoping Tang, Shaojiang Dong, Michael Pecht,
Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis,
IEEE Transactions on Industrial Electronics, 2020, DOI: 10.1109/TIE.2020.2972458
@author: Minghang Zhao
"""
from __future__ import print_function
import keras
import numpy as np
from keras.datasets import cifar10
from keras.layers import Dense, Conv2D, BatchNormalization, Activation, Minimum
from keras.layers import AveragePooling2D, Input, GlobalAveragePooling2D, Concatenate, Reshape
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
K.set_learning_phase(1)
# The data, split between train and test sets
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Noised data
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_test = x_test-np.mean(x_train)
x_train = x_train-np.mean(x_train)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# Schedule the learning rate, multiply 0.1 every 300 epoches
def scheduler(epoch):
if epoch % 300 == 0 and epoch != 0:
lr = K.get_value(model.optimizer.lr)
K.set_value(model.optimizer.lr, lr * 0.1)
print("lr changed to {}".format(lr * 0.1))
return K.get_value(model.optimizer.lr)
# An adaptively parametric rectifier linear unit (APReLU)
def aprelu(inputs):
# get the number of channels
channels = inputs.get_shape().as_list()[-1]
# get a zero feature map
zeros_input = keras.layers.subtract([inputs, inputs])
# get a feature map with only positive features
pos_input = Activation('relu')(inputs)
# get a feature map with only negative features
neg_input = Minimum()([inputs,zeros_input])
# define a network to obtain the scaling coefficients
scales_p = GlobalAveragePooling2D()(pos_input)
scales_n = GlobalAveragePooling2D()(neg_input)
scales = Concatenate()([scales_n, scales_p])
scales = Dense(channels, activation='linear', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(scales)
scales = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(scales)
scales = Activation('relu')(scales)
scales = Dense(channels, activation='linear', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(scales)
scales = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(scales)
scales = Activation('sigmoid')(scales)
scales = Reshape((1,1,channels))(scales)
# apply a paramtetric relu
neg_part = keras.layers.multiply([scales, neg_input])
return keras.layers.add([pos_input, neg_part])
# Residual Block
def residual_block(incoming, nb_blocks, out_channels, downsample=False,
downsample_strides=2):
residual = incoming
in_channels = incoming.get_shape().as_list()[-1]
for i in range(nb_blocks):
identity = residual
if not downsample:
downsample_strides = 1
residual = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(residual)
residual = aprelu(residual)
residual = Conv2D(out_channels, 3, strides=(downsample_strides, downsample_strides),
padding='same', kernel_initializer='he_normal',
kernel_regularizer=l2(1e-4))(residual)
residual = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(residual)
residual = aprelu(residual)
residual = Conv2D(out_channels, 3, padding='same', kernel_initializer='he_normal',
kernel_regularizer=l2(1e-4))(residual)
# Downsampling
if downsample_strides > 1:
identity = AveragePooling2D(pool_size=(1,1), strides=(2,2))(identity)
# Zero_padding to match channels
if in_channels != out_channels:
zeros_identity = keras.layers.subtract([identity, identity])
identity = keras.layers.concatenate([identity, zeros_identity])
in_channels = out_channels
residual = keras.layers.add([residual, identity])
return residual
# define and train a model
inputs = Input(shape=(32, 32, 3))
net = Conv2D(16, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(inputs)
net = residual_block(net, 9, 16, downsample=False)
net = residual_block(net, 1, 32, downsample=True)
net = residual_block(net, 8, 32, downsample=False)
net = residual_block(net, 1, 64, downsample=True)
net = residual_block(net, 8, 64, downsample=False)
net = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(net)
net = Activation('relu')(net)
net = GlobalAveragePooling2D()(net)
outputs = Dense(10, activation='softmax', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(net)
model = Model(inputs=inputs, outputs=outputs)
sgd = optimizers.SGD(lr=0.1, decay=0., momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# data augmentation
datagen = ImageDataGenerator(
# randomly rotate images in the range (deg 0 to 180)
rotation_range=30,
# randomly flip images
horizontal_flip=True,
# randomly shift images horizontally
width_shift_range=0.125,
# randomly shift images vertically
height_shift_range=0.125)
reduce_lr = LearningRateScheduler(scheduler)
# fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size=100),
validation_data=(x_test, y_test), epochs=1000,
verbose=1, callbacks=[reduce_lr], workers=4)
# get results
K.set_learning_phase(0)
DRSN_train_score = model.evaluate(x_train, y_train, batch_size=100, verbose=0)
print('Train loss:', DRSN_train_score[0])
print('Train accuracy:', DRSN_train_score[1])
DRSN_test_score = model.evaluate(x_test, y_test, batch_size=100, verbose=0)
print('Test loss:', DRSN_test_score[0])
print('Test accuracy:', DRSN_test_score[1])
實驗結果如下：
x_train shape: (50000, 32, 32, 3) 50000 train samples 10000 test samples Epoch 1/1000 500/500 [==============================] - 97s 195ms/step - loss: 3.2344 - acc: 0.4133 - val_loss: 2.7840 - val_acc: 0.5398 Epoch 2/1000 500/500 [==============================] - 65s 131ms/step - loss: 2.6095 - acc: 0.5574 - val_loss: 2.3084 - val_acc: 0.6296 Epoch 3/1000 500/500 [==============================] - 65s 131ms/step - loss: 2.2160 - acc: 0.6249 - val_loss: 1.9625 - val_acc: 0.6837 Epoch 4/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.9251 - acc: 0.6702 - val_loss: 1.7395 - val_acc: 0.7116 Epoch 5/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.7015 - acc: 0.7016 - val_loss: 1.5316 - val_acc: 0.7429 Epoch 6/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.5268 - acc: 0.7228 - val_loss: 1.3858 - val_acc: 0.7608 Epoch 7/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.3979 - acc: 0.7372 - val_loss: 1.2604 - val_acc: 0.7761 Epoch 8/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.2921 - acc: 0.7483 - val_loss: 1.1713 - val_acc: 0.7798 Epoch 9/1000 500/500 [==============================] - 66s 131ms/step - loss: 1.2057 - acc: 0.7627 - val_loss: 1.1200 - val_acc: 0.7846 Epoch 10/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.1358 - acc: 0.7690 - val_loss: 1.0900 - val_acc: 0.7811 Epoch 11/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.0823 - acc: 0.7741 - val_loss: 0.9822 - val_acc: 0.8058 Epoch 12/1000 500/500 [==============================] - 65s 131ms/step - loss: 1.0365 - acc: 0.7802 - val_loss: 0.9840 - val_acc: 0.7976 Epoch 13/1000 500/500 [==============================] - 65s 130ms/step - loss: 1.0040 - acc: 0.7847 - val_loss: 0.9539 - val_acc: 0.7995 Epoch 14/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.9737 - acc: 0.7870 - val_loss: 0.9181 - val_acc: 0.8093 Epoch 15/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.9468 - acc: 0.7933 - val_loss: 0.8972 - val_acc: 0.8071 Epoch 16/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.9210 - acc: 0.7964 - val_loss: 0.9039 - val_acc: 0.8077 Epoch 17/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.9084 - acc: 0.8008 - val_loss: 0.8491 - val_acc: 0.8200 Epoch 18/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8879 - acc: 0.8027 - val_loss: 0.8565 - val_acc: 0.8161 Epoch 19/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8770 - acc: 0.8044 - val_loss: 0.8640 - val_acc: 0.8116 Epoch 20/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8695 - acc: 0.8066 - val_loss: 0.8369 - val_acc: 0.8187 Epoch 21/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8565 - acc: 0.8097 - val_loss: 0.8403 - val_acc: 0.8221 Epoch 22/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8516 - acc: 0.8119 - val_loss: 0.8131 - val_acc: 0.8315 Epoch 23/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8402 - acc: 0.8156 - val_loss: 0.7879 - val_acc: 0.8397 Epoch 24/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8271 - acc: 0.8179 - val_loss: 0.7942 - val_acc: 0.8379 Epoch 25/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8282 - acc: 0.8196 - val_loss: 0.8132 - val_acc: 0.8270 Epoch 26/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.8203 - acc: 0.8203 - val_loss: 0.7870 - val_acc: 0.8354 Epoch 27/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8141 - acc: 0.8231 - val_loss: 0.7780 - val_acc: 0.8405 Epoch 28/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8075 - acc: 0.8270 - val_loss: 0.7806 - val_acc: 0.8386 Epoch 29/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8051 - acc: 0.8260 - val_loss: 0.7865 - val_acc: 0.8309 Epoch 30/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.8015 - acc: 0.8262 - val_loss: 0.7600 - val_acc: 0.8458 Epoch 31/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7948 - acc: 0.8295 - val_loss: 0.7560 - val_acc: 0.8458 Epoch 32/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7890 - acc: 0.8323 - val_loss: 0.7760 - val_acc: 0.8407 Epoch 33/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7868 - acc: 0.8335 - val_loss: 0.7845 - val_acc: 0.8348 Epoch 34/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.7845 - acc: 0.8346 - val_loss: 0.7517 - val_acc: 0.8460 Epoch 35/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7764 - acc: 0.8377 - val_loss: 0.7683 - val_acc: 0.8432 Epoch 36/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7720 - acc: 0.8370 - val_loss: 0.7383 - val_acc: 0.8518 Epoch 37/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7738 - acc: 0.8374 - val_loss: 0.7491 - val_acc: 0.8469 Epoch 38/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7666 - acc: 0.8405 - val_loss: 0.7591 - val_acc: 0.8437 Epoch 39/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7656 - acc: 0.8421 - val_loss: 0.7389 - val_acc: 0.8533 Epoch 40/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7619 - acc: 0.8431 - val_loss: 0.7583 - val_acc: 0.8461 Epoch 41/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7594 - acc: 0.8433 - val_loss: 0.7199 - val_acc: 0.8576 Epoch 42/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7594 - acc: 0.8428 - val_loss: 0.7272 - val_acc: 0.8558 Epoch 43/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7559 - acc: 0.8451 - val_loss: 0.7353 - val_acc: 0.8535 Epoch 44/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7528 - acc: 0.8454 - val_loss: 0.7492 - val_acc: 0.8487 Epoch 45/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7564 - acc: 0.8465 - val_loss: 0.7510 - val_acc: 0.8505 Epoch 46/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7494 - acc: 0.8487 - val_loss: 0.7626 - val_acc: 0.8462 Epoch 47/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7505 - acc: 0.8491 - val_loss: 0.7417 - val_acc: 0.8561 Epoch 48/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7434 - acc: 0.8509 - val_loss: 0.7247 - val_acc: 0.8580 Epoch 49/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7426 - acc: 0.8502 - val_loss: 0.7203 - val_acc: 0.8612 Epoch 50/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7436 - acc: 0.8503 - val_loss: 0.7190 - val_acc: 0.8621 Epoch 51/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7415 - acc: 0.8509 - val_loss: 0.7315 - val_acc: 0.8590 Epoch 52/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7342 - acc: 0.8549 - val_loss: 0.7141 - val_acc: 0.8627 Epoch 53/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7341 - acc: 0.8525 - val_loss: 0.7209 - val_acc: 0.8582 Epoch 54/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7326 - acc: 0.8546 - val_loss: 0.7114 - val_acc: 0.8640 Epoch 55/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7338 - acc: 0.8546 - val_loss: 0.7166 - val_acc: 0.8587 Epoch 56/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7291 - acc: 0.8564 - val_loss: 0.7109 - val_acc: 0.8642 Epoch 57/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7261 - acc: 0.8563 - val_loss: 0.7116 - val_acc: 0.8638 Epoch 58/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7270 - acc: 0.8567 - val_loss: 0.7272 - val_acc: 0.8591 Epoch 59/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7240 - acc: 0.8577 - val_loss: 0.6949 - val_acc: 0.8730 Epoch 60/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.7268 - acc: 0.8575 - val_loss: 0.7129 - val_acc: 0.8645 Epoch 61/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7222 - acc: 0.8599 - val_loss: 0.7174 - val_acc: 0.8642 Epoch 62/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7195 - acc: 0.8611 - val_loss: 0.7178 - val_acc: 0.8608 Epoch 63/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7177 - acc: 0.8619 - val_loss: 0.7142 - val_acc: 0.8586 Epoch 64/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7146 - acc: 0.8632 - val_loss: 0.7119 - val_acc: 0.8619 Epoch 65/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7174 - acc: 0.8599 - val_loss: 0.7174 - val_acc: 0.8640 Epoch 66/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7145 - acc: 0.8619 - val_loss: 0.7075 - val_acc: 0.8647 Epoch 67/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7116 - acc: 0.8635 - val_loss: 0.7449 - val_acc: 0.8534 Epoch 68/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7058 - acc: 0.8632 - val_loss: 0.6978 - val_acc: 0.8713 Epoch 69/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7111 - acc: 0.8632 - val_loss: 0.7132 - val_acc: 0.8641 Epoch 70/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.7046 - acc: 0.8655 - val_loss: 0.6695 - val_acc: 0.8764 Epoch 71/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.7062 - acc: 0.8640 - val_loss: 0.6967 - val_acc: 0.8704 Epoch 72/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.7044 - acc: 0.8655 - val_loss: 0.6786 - val_acc: 0.8771 Epoch 73/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.7018 - acc: 0.8667 - val_loss: 0.7139 - val_acc: 0.8639 Epoch 74/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.7029 - acc: 0.8667 - val_loss: 0.7264 - val_acc: 0.8565 Epoch 75/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6981 - acc: 0.8661 - val_loss: 0.6919 - val_acc: 0.8738 Epoch 76/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6997 - acc: 0.8667 - val_loss: 0.7023 - val_acc: 0.8700 Epoch 77/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6967 - acc: 0.8685 - val_loss: 0.6810 - val_acc: 0.8769 Epoch 78/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6982 - acc: 0.8673 - val_loss: 0.7090 - val_acc: 0.8648 Epoch 79/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6989 - acc: 0.8670 - val_loss: 0.7114 - val_acc: 0.8691 Epoch 80/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6900 - acc: 0.8704 - val_loss: 0.7039 - val_acc: 0.8707 Epoch 81/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6920 - acc: 0.8703 - val_loss: 0.6878 - val_acc: 0.8742 Epoch 82/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6904 - acc: 0.8705 - val_loss: 0.6966 - val_acc: 0.8724 Epoch 83/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6907 - acc: 0.8694 - val_loss: 0.6880 - val_acc: 0.8725 Epoch 84/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6933 - acc: 0.8692 - val_loss: 0.7006 - val_acc: 0.8697 Epoch 85/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6934 - acc: 0.8709 - val_loss: 0.7079 - val_acc: 0.8679 Epoch 86/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6899 - acc: 0.8710 - val_loss: 0.7029 - val_acc: 0.8661 Epoch 87/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6946 - acc: 0.8696 - val_loss: 0.6892 - val_acc: 0.8746 Epoch 88/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6925 - acc: 0.8709 - val_loss: 0.6920 - val_acc: 0.8698 Epoch 89/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6861 - acc: 0.8703 - val_loss: 0.6857 - val_acc: 0.8762 Epoch 90/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6878 - acc: 0.8721 - val_loss: 0.6827 - val_acc: 0.8740 Epoch 91/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6845 - acc: 0.8728 - val_loss: 0.6995 - val_acc: 0.8702 Epoch 92/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6890 - acc: 0.8719 - val_loss: 0.6769 - val_acc: 0.8767 Epoch 93/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6836 - acc: 0.8734 - val_loss: 0.6992 - val_acc: 0.8689 Epoch 94/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6809 - acc: 0.8737 - val_loss: 0.7046 - val_acc: 0.8682 Epoch 95/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6803 - acc: 0.8727 - val_loss: 0.6755 - val_acc: 0.8793 Epoch 96/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6833 - acc: 0.8742 - val_loss: 0.6857 - val_acc: 0.8741 Epoch 97/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6837 - acc: 0.8732 - val_loss: 0.6969 - val_acc: 0.8715 Epoch 98/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6836 - acc: 0.8738 - val_loss: 0.6762 - val_acc: 0.8763 Epoch 99/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6837 - acc: 0.8727 - val_loss: 0.6817 - val_acc: 0.8759 Epoch 100/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6809 - acc: 0.8755 - val_loss: 0.6859 - val_acc: 0.8736 Epoch 101/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6814 - acc: 0.8745 - val_loss: 0.6695 - val_acc: 0.8816 Epoch 102/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6813 - acc: 0.8735 - val_loss: 0.6878 - val_acc: 0.8732 Epoch 103/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6852 - acc: 0.8744 - val_loss: 0.6906 - val_acc: 0.8719 Epoch 104/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6804 - acc: 0.8753 - val_loss: 0.6803 - val_acc: 0.8779 Epoch 105/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6771 - acc: 0.8748 - val_loss: 0.6838 - val_acc: 0.8754 Epoch 106/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6741 - acc: 0.8768 - val_loss: 0.7191 - val_acc: 0.8606 Epoch 107/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6774 - acc: 0.8751 - val_loss: 0.6901 - val_acc: 0.8725 Epoch 108/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6752 - acc: 0.8768 - val_loss: 0.7003 - val_acc: 0.8711 Epoch 109/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6772 - acc: 0.8752 - val_loss: 0.6926 - val_acc: 0.8756 Epoch 110/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6729 - acc: 0.8775 - val_loss: 0.7088 - val_acc: 0.8647 Epoch 111/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6670 - acc: 0.8793 - val_loss: 0.6932 - val_acc: 0.8725 Epoch 112/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6724 - acc: 0.8775 - val_loss: 0.6781 - val_acc: 0.8779 Epoch 113/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6753 - acc: 0.8771 - val_loss: 0.6676 - val_acc: 0.8815 Epoch 114/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6720 - acc: 0.8775 - val_loss: 0.6813 - val_acc: 0.8763 Epoch 115/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6754 - acc: 0.8746 - val_loss: 0.6662 - val_acc: 0.8761 Epoch 116/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6763 - acc: 0.8758 - val_loss: 0.6668 - val_acc: 0.8798 Epoch 117/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6680 - acc: 0.8788 - val_loss: 0.6860 - val_acc: 0.8791 Epoch 118/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6737 - acc: 0.8781 - val_loss: 0.6630 - val_acc: 0.8794 Epoch 119/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6621 - acc: 0.8812 - val_loss: 0.6847 - val_acc: 0.8772 Epoch 120/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6638 - acc: 0.8794 - val_loss: 0.6777 - val_acc: 0.8768 Epoch 121/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6682 - acc: 0.8793 - val_loss: 0.7159 - val_acc: 0.8659 Epoch 122/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6726 - acc: 0.8762 - val_loss: 0.6771 - val_acc: 0.8803 Epoch 123/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6660 - acc: 0.8800 - val_loss: 0.6986 - val_acc: 0.8730 Epoch 124/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6697 - acc: 0.8780 - val_loss: 0.6978 - val_acc: 0.8741 Epoch 125/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6680 - acc: 0.8803 - val_loss: 0.6767 - val_acc: 0.8787 Epoch 126/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6604 - acc: 0.8827 - val_loss: 0.6827 - val_acc: 0.8751 Epoch 127/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6647 - acc: 0.8816 - val_loss: 0.7081 - val_acc: 0.8681 Epoch 128/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6668 - acc: 0.8808 - val_loss: 0.6697 - val_acc: 0.8780 Epoch 129/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6629 - acc: 0.8808 - val_loss: 0.6848 - val_acc: 0.8725 Epoch 130/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6634 - acc: 0.8802 - val_loss: 0.6862 - val_acc: 0.8730 Epoch 131/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6637 - acc: 0.8797 - val_loss: 0.7044 - val_acc: 0.8704 Epoch 132/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6647 - acc: 0.8817 - val_loss: 0.6798 - val_acc: 0.8779 Epoch 133/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6604 - acc: 0.8830 - val_loss: 0.6790 - val_acc: 0.8770 Epoch 134/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6638 - acc: 0.8821 - val_loss: 0.6786 - val_acc: 0.8777 Epoch 135/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6621 - acc: 0.8829 - val_loss: 0.6990 - val_acc: 0.8676 Epoch 136/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6650 - acc: 0.8803 - val_loss: 0.6916 - val_acc: 0.8742 Epoch 137/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6600 - acc: 0.8814 - val_loss: 0.6645 - val_acc: 0.8822 Epoch 138/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6606 - acc: 0.8827 - val_loss: 0.6554 - val_acc: 0.8902 Epoch 139/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6575 - acc: 0.8849 - val_loss: 0.6895 - val_acc: 0.8782 Epoch 140/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6590 - acc: 0.8824 - val_loss: 0.6689 - val_acc: 0.8830 Epoch 141/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6589 - acc: 0.8827 - val_loss: 0.6620 - val_acc: 0.8816 Epoch 142/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6580 - acc: 0.8833 - val_loss: 0.6765 - val_acc: 0.8787 Epoch 143/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6559 - acc: 0.8830 - val_loss: 0.7018 - val_acc: 0.8691 Epoch 144/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6579 - acc: 0.8818 - val_loss: 0.6733 - val_acc: 0.8819 Epoch 145/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6559 - acc: 0.8843 - val_loss: 0.6702 - val_acc: 0.8809 Epoch 146/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6557 - acc: 0.8826 - val_loss: 0.6474 - val_acc: 0.8871 Epoch 147/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6552 - acc: 0.8844 - val_loss: 0.6815 - val_acc: 0.8769 Epoch 148/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6565 - acc: 0.8830 - val_loss: 0.6770 - val_acc: 0.8818 Epoch 149/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6501 - acc: 0.8852 - val_loss: 0.6885 - val_acc: 0.8764 Epoch 150/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6566 - acc: 0.8832 - val_loss: 0.6701 - val_acc: 0.8815 Epoch 151/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6521 - acc: 0.8861 - val_loss: 0.6785 - val_acc: 0.8785 Epoch 152/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6539 - acc: 0.8851 - val_loss: 0.6681 - val_acc: 0.8841 Epoch 153/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6514 - acc: 0.8849 - val_loss: 0.6773 - val_acc: 0.8785 Epoch 154/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6561 - acc: 0.8836 - val_loss: 0.6747 - val_acc: 0.8803 Epoch 155/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6524 - acc: 0.8852 - val_loss: 0.6545 - val_acc: 0.8854 Epoch 156/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6587 - acc: 0.8828 - val_loss: 0.7070 - val_acc: 0.8692 Epoch 157/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6558 - acc: 0.8838 - val_loss: 0.6618 - val_acc: 0.8843 Epoch 158/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6514 - acc: 0.8873 - val_loss: 0.6874 - val_acc: 0.8763 Epoch 159/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6564 - acc: 0.8848 - val_loss: 0.6804 - val_acc: 0.8805 Epoch 160/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6450 - acc: 0.8868 - val_loss: 0.6752 - val_acc: 0.8800 Epoch 161/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6555 - acc: 0.8847 - val_loss: 0.6589 - val_acc: 0.8857 Epoch 162/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6492 - acc: 0.8860 - val_loss: 0.6544 - val_acc: 0.8862 Epoch 163/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6544 - acc: 0.8844 - val_loss: 0.6807 - val_acc: 0.8775 Epoch 164/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6504 - acc: 0.8850 - val_loss: 0.6861 - val_acc: 0.8761 Epoch 165/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6538 - acc: 0.8832 - val_loss: 0.6653 - val_acc: 0.8842 Epoch 166/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6520 - acc: 0.8866 - val_loss: 0.6685 - val_acc: 0.8823 Epoch 167/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6483 - acc: 0.8869 - val_loss: 0.6916 - val_acc: 0.8719 Epoch 168/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6501 - acc: 0.8855 - val_loss: 0.6789 - val_acc: 0.8785 Epoch 169/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6484 - acc: 0.8863 - val_loss: 0.6853 - val_acc: 0.8740 Epoch 170/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6485 - acc: 0.8863 - val_loss: 0.6654 - val_acc: 0.8808 Epoch 171/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6474 - acc: 0.8863 - val_loss: 0.6636 - val_acc: 0.8858 Epoch 172/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6469 - acc: 0.8863 - val_loss: 0.6752 - val_acc: 0.8793 Epoch 173/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6411 - acc: 0.8886 - val_loss: 0.6869 - val_acc: 0.8769 Epoch 174/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6456 - acc: 0.8873 - val_loss: 0.6714 - val_acc: 0.8808 Epoch 175/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6536 - acc: 0.8853 - val_loss: 0.6580 - val_acc: 0.8885 Epoch 176/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6491 - acc: 0.8857 - val_loss: 0.6743 - val_acc: 0.8816 Epoch 177/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6492 - acc: 0.8851 - val_loss: 0.6625 - val_acc: 0.8897 Epoch 178/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6481 - acc: 0.8845 - val_loss: 0.6671 - val_acc: 0.8826 Epoch 179/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6495 - acc: 0.8854 - val_loss: 0.6968 - val_acc: 0.8724 Epoch 180/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6474 - acc: 0.8879 - val_loss: 0.6602 - val_acc: 0.8860 Epoch 181/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6449 - acc: 0.8869 - val_loss: 0.6648 - val_acc: 0.8849 Epoch 182/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6515 - acc: 0.8849 - val_loss: 0.6675 - val_acc: 0.8812 Epoch 183/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6489 - acc: 0.8861 - val_loss: 0.6561 - val_acc: 0.8863 Epoch 184/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6435 - acc: 0.8892 - val_loss: 0.6526 - val_acc: 0.8894 Epoch 185/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6471 - acc: 0.8868 - val_loss: 0.6856 - val_acc: 0.8758 Epoch 186/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6525 - acc: 0.8854 - val_loss: 0.6785 - val_acc: 0.8781 Epoch 187/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6489 - acc: 0.8850 - val_loss: 0.6638 - val_acc: 0.8832 Epoch 188/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6454 - acc: 0.8872 - val_loss: 0.6673 - val_acc: 0.8841 Epoch 189/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6491 - acc: 0.8868 - val_loss: 0.6410 - val_acc: 0.8893 Epoch 190/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6428 - acc: 0.8884 - val_loss: 0.6678 - val_acc: 0.8835 Epoch 191/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6463 - acc: 0.8871 - val_loss: 0.6676 - val_acc: 0.8854 Epoch 192/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6435 - acc: 0.8892 - val_loss: 0.6869 - val_acc: 0.8764 Epoch 193/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6465 - acc: 0.8877 - val_loss: 0.6578 - val_acc: 0.8849 Epoch 194/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6446 - acc: 0.8879 - val_loss: 0.6819 - val_acc: 0.8825 Epoch 195/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6464 - acc: 0.8868 - val_loss: 0.6682 - val_acc: 0.8831 Epoch 196/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6455 - acc: 0.8888 - val_loss: 0.6580 - val_acc: 0.8863 Epoch 197/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6408 - acc: 0.8883 - val_loss: 0.6818 - val_acc: 0.8778 Epoch 198/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6415 - acc: 0.8887 - val_loss: 0.6616 - val_acc: 0.8856 Epoch 199/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6429 - acc: 0.8897 - val_loss: 0.6876 - val_acc: 0.8769 Epoch 200/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6490 - acc: 0.8857 - val_loss: 0.6679 - val_acc: 0.8827 Epoch 201/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6403 - acc: 0.8905 - val_loss: 0.6663 - val_acc: 0.8818 Epoch 202/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6407 - acc: 0.8900 - val_loss: 0.6714 - val_acc: 0.8789 Epoch 203/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6380 - acc: 0.8906 - val_loss: 0.6718 - val_acc: 0.8799 Epoch 204/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6422 - acc: 0.8882 - val_loss: 0.6778 - val_acc: 0.8770 Epoch 205/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.6392 - acc: 0.8894 - val_loss: 0.6697 - val_acc: 0.8805 Epoch 206/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.6467 - acc: 0.8882 - val_loss: 0.6956 - val_acc: 0.8737 Epoch 207/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6391 - acc: 0.8902 - val_loss: 0.6641 - val_acc: 0.8849 Epoch 208/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6378 - acc: 0.8900 - val_loss: 0.6890 - val_acc: 0.8733 Epoch 209/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6421 - acc: 0.8897 - val_loss: 0.6654 - val_acc: 0.8824 Epoch 210/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6405 - acc: 0.8892 - val_loss: 0.6685 - val_acc: 0.8793 Epoch 211/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6381 - acc: 0.8893 - val_loss: 0.6581 - val_acc: 0.8855 Epoch 212/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6379 - acc: 0.8915 - val_loss: 0.6626 - val_acc: 0.8893 Epoch 213/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6405 - acc: 0.8892 - val_loss: 0.6688 - val_acc: 0.8803 Epoch 214/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6369 - acc: 0.8896 - val_loss: 0.6827 - val_acc: 0.8770 Epoch 215/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6412 - acc: 0.8892 - val_loss: 0.6545 - val_acc: 0.8849 Epoch 216/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6383 - acc: 0.8901 - val_loss: 0.6683 - val_acc: 0.8836 Epoch 217/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6369 - acc: 0.8901 - val_loss: 0.6657 - val_acc: 0.8854 Epoch 218/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6408 - acc: 0.8896 - val_loss: 0.6496 - val_acc: 0.8864 Epoch 219/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6391 - acc: 0.8900 - val_loss: 0.6728 - val_acc: 0.8818 Epoch 220/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6352 - acc: 0.8905 - val_loss: 0.6821 - val_acc: 0.8817 Epoch 221/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6365 - acc: 0.8919 - val_loss: 0.6650 - val_acc: 0.8845 Epoch 222/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6389 - acc: 0.8907 - val_loss: 0.6509 - val_acc: 0.8870 Epoch 223/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6364 - acc: 0.8911 - val_loss: 0.6672 - val_acc: 0.8853 Epoch 224/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6329 - acc: 0.8909 - val_loss: 0.6668 - val_acc: 0.8819 Epoch 225/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6349 - acc: 0.8918 - val_loss: 0.6517 - val_acc: 0.8890 Epoch 226/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6383 - acc: 0.8901 - val_loss: 0.6778 - val_acc: 0.8791 Epoch 227/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6375 - acc: 0.8907 - val_loss: 0.6692 - val_acc: 0.8836 Epoch 228/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6354 - acc: 0.8914 - val_loss: 0.6800 - val_acc: 0.8805 Epoch 229/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6373 - acc: 0.8915 - val_loss: 0.6575 - val_acc: 0.8852 Epoch 230/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6388 - acc: 0.8894 - val_loss: 0.6676 - val_acc: 0.8846 Epoch 231/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6374 - acc: 0.8916 - val_loss: 0.6638 - val_acc: 0.8841 Epoch 232/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.6367 - acc: 0.8925 - val_loss: 0.6715 - val_acc: 0.8851 Epoch 233/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6407 - acc: 0.8894 - val_loss: 0.6633 - val_acc: 0.8862 Epoch 234/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6320 - acc: 0.8936 - val_loss: 0.6821 - val_acc: 0.8789 Epoch 235/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6376 - acc: 0.8914 - val_loss: 0.6735 - val_acc: 0.8812 Epoch 236/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6353 - acc: 0.8904 - val_loss: 0.6680 - val_acc: 0.8871 Epoch 237/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6357 - acc: 0.8913 - val_loss: 0.6624 - val_acc: 0.8864 Epoch 238/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6310 - acc: 0.8936 - val_loss: 0.6616 - val_acc: 0.8832 Epoch 239/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6383 - acc: 0.8902 - val_loss: 0.6663 - val_acc: 0.8842 Epoch 240/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6337 - acc: 0.8932 - val_loss: 0.6471 - val_acc: 0.8892 Epoch 241/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6311 - acc: 0.8921 - val_loss: 0.6608 - val_acc: 0.8853 Epoch 242/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6373 - acc: 0.8899 - val_loss: 0.6988 - val_acc: 0.8710 Epoch 243/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6369 - acc: 0.8905 - val_loss: 0.6644 - val_acc: 0.8843 Epoch 244/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6317 - acc: 0.8927 - val_loss: 0.6922 - val_acc: 0.8721 Epoch 245/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6304 - acc: 0.8929 - val_loss: 0.6733 - val_acc: 0.8798 Epoch 246/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6328 - acc: 0.8912 - val_loss: 0.6564 - val_acc: 0.8860 Epoch 247/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6400 - acc: 0.8896 - val_loss: 0.6664 - val_acc: 0.8794 Epoch 248/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6361 - acc: 0.8898 - val_loss: 0.6896 - val_acc: 0.8777 Epoch 249/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6332 - acc: 0.8914 - val_loss: 0.6707 - val_acc: 0.8829 Epoch 250/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6348 - acc: 0.8901 - val_loss: 0.6581 - val_acc: 0.8850 Epoch 251/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6325 - acc: 0.8918 - val_loss: 0.6623 - val_acc: 0.8870 Epoch 252/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6337 - acc: 0.8915 - val_loss: 0.6795 - val_acc: 0.8806 Epoch 253/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6339 - acc: 0.8909 - val_loss: 0.6760 - val_acc: 0.8788 Epoch 254/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6350 - acc: 0.8907 - val_loss: 0.6667 - val_acc: 0.8863 Epoch 255/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6312 - acc: 0.8927 - val_loss: 0.6825 - val_acc: 0.8775 Epoch 256/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6304 - acc: 0.8920 - val_loss: 0.6648 - val_acc: 0.8839 Epoch 257/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6317 - acc: 0.8917 - val_loss: 0.6624 - val_acc: 0.8830 Epoch 258/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6335 - acc: 0.8914 - val_loss: 0.6547 - val_acc: 0.8877 Epoch 259/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6346 - acc: 0.8903 - val_loss: 0.6671 - val_acc: 0.8863 Epoch 260/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6303 - acc: 0.8909 - val_loss: 0.6491 - val_acc: 0.8862 Epoch 261/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6348 - acc: 0.8902 - val_loss: 0.6778 - val_acc: 0.8781 Epoch 262/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6325 - acc: 0.8928 - val_loss: 0.6651 - val_acc: 0.8800 Epoch 263/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6377 - acc: 0.8895 - val_loss: 0.6474 - val_acc: 0.8908 Epoch 264/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6293 - acc: 0.8927 - val_loss: 0.6707 - val_acc: 0.8821 Epoch 265/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6321 - acc: 0.8915 - val_loss: 0.6679 - val_acc: 0.8820 Epoch 266/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6323 - acc: 0.8936 - val_loss: 0.6647 - val_acc: 0.8851 Epoch 267/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6311 - acc: 0.8926 - val_loss: 0.6748 - val_acc: 0.8786 Epoch 268/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6344 - acc: 0.8920 - val_loss: 0.6851 - val_acc: 0.8826 Epoch 269/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6296 - acc: 0.8943 - val_loss: 0.6626 - val_acc: 0.8854 Epoch 270/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6323 - acc: 0.8931 - val_loss: 0.6555 - val_acc: 0.8864 Epoch 271/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6285 - acc: 0.8933 - val_loss: 0.6781 - val_acc: 0.8817 Epoch 272/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6316 - acc: 0.8921 - val_loss: 0.6630 - val_acc: 0.8870 Epoch 273/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6296 - acc: 0.8943 - val_loss: 0.6682 - val_acc: 0.8824 Epoch 274/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6221 - acc: 0.8957 - val_loss: 0.6788 - val_acc: 0.8791 Epoch 275/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6317 - acc: 0.8918 - val_loss: 0.6434 - val_acc: 0.8917 Epoch 276/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.6290 - acc: 0.8927 - val_loss: 0.6572 - val_acc: 0.8868 Epoch 277/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6279 - acc: 0.8931 - val_loss: 0.6877 - val_acc: 0.8757 Epoch 278/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6301 - acc: 0.8923 - val_loss: 0.6746 - val_acc: 0.8770 Epoch 279/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6334 - acc: 0.8919 - val_loss: 0.6553 - val_acc: 0.8863 Epoch 280/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6320 - acc: 0.8927 - val_loss: 0.6727 - val_acc: 0.8812 Epoch 281/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6290 - acc: 0.8944 - val_loss: 0.6784 - val_acc: 0.8765 Epoch 282/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6290 - acc: 0.8937 - val_loss: 0.6466 - val_acc: 0.8924 Epoch 283/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6297 - acc: 0.8940 - val_loss: 0.6622 - val_acc: 0.8853 Epoch 284/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6267 - acc: 0.8940 - val_loss: 0.6592 - val_acc: 0.8860 Epoch 285/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6319 - acc: 0.8926 - val_loss: 0.6628 - val_acc: 0.8849 Epoch 286/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6314 - acc: 0.8935 - val_loss: 0.6617 - val_acc: 0.8855 Epoch 287/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6251 - acc: 0.8949 - val_loss: 0.6846 - val_acc: 0.8761 Epoch 288/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6311 - acc: 0.8923 - val_loss: 0.6675 - val_acc: 0.8826 Epoch 289/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6282 - acc: 0.8938 - val_loss: 0.6756 - val_acc: 0.8799 Epoch 290/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6289 - acc: 0.8938 - val_loss: 0.6717 - val_acc: 0.8831 Epoch 291/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6288 - acc: 0.8926 - val_loss: 0.6444 - val_acc: 0.8908 Epoch 292/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6257 - acc: 0.8943 - val_loss: 0.6434 - val_acc: 0.8882 Epoch 293/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6269 - acc: 0.8926 - val_loss: 0.6450 - val_acc: 0.8896 Epoch 294/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6284 - acc: 0.8929 - val_loss: 0.6520 - val_acc: 0.8855 Epoch 295/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6234 - acc: 0.8941 - val_loss: 0.6519 - val_acc: 0.8899 Epoch 296/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.6284 - acc: 0.8935 - val_loss: 0.6571 - val_acc: 0.8827 Epoch 297/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6265 - acc: 0.8940 - val_loss: 0.6566 - val_acc: 0.8857 Epoch 298/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6287 - acc: 0.8936 - val_loss: 0.6573 - val_acc: 0.8841 Epoch 299/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6237 - acc: 0.8954 - val_loss: 0.6371 - val_acc: 0.8937 Epoch 300/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.6263 - acc: 0.8943 - val_loss: 0.6537 - val_acc: 0.8884 Epoch 301/1000 lr changed to 0.010000000149011612 500/500 [==============================] - 65s 131ms/step - loss: 0.5256 - acc: 0.9298 - val_loss: 0.5518 - val_acc: 0.9215 Epoch 302/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.4681 - acc: 0.9470 - val_loss: 0.5407 - val_acc: 0.9233 Epoch 303/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.4455 - acc: 0.9532 - val_loss: 0.5319 - val_acc: 0.9258 Epoch 304/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.4308 - acc: 0.9559 - val_loss: 0.5251 - val_acc: 0.9277 Epoch 305/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.4180 - acc: 0.9595 - val_loss: 0.5182 - val_acc: 0.9290 Epoch 306/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.4088 - acc: 0.9609 - val_loss: 0.5124 - val_acc: 0.9300 Epoch 307/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3970 - acc: 0.9628 - val_loss: 0.5158 - val_acc: 0.9277 Epoch 308/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3877 - acc: 0.9653 - val_loss: 0.5093 - val_acc: 0.9298 Epoch 309/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3794 - acc: 0.9664 - val_loss: 0.5062 - val_acc: 0.9281 Epoch 310/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3736 - acc: 0.9666 - val_loss: 0.5056 - val_acc: 0.9267 Epoch 311/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3675 - acc: 0.9669 - val_loss: 0.4959 - val_acc: 0.9295 Epoch 312/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3631 - acc: 0.9670 - val_loss: 0.4913 - val_acc: 0.9313 Epoch 313/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3538 - acc: 0.9686 - val_loss: 0.4924 - val_acc: 0.9299 Epoch 314/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3432 - acc: 0.9716 - val_loss: 0.4920 - val_acc: 0.9296 Epoch 315/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3434 - acc: 0.9701 - val_loss: 0.4838 - val_acc: 0.9277 Epoch 316/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3325 - acc: 0.9719 - val_loss: 0.4822 - val_acc: 0.9301 Epoch 317/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.3283 - acc: 0.9724 - val_loss: 0.4882 - val_acc: 0.9270 Epoch 318/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.3259 - acc: 0.9727 - val_loss: 0.4866 - val_acc: 0.9263 Epoch 319/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.3200 - acc: 0.9728 - val_loss: 0.4780 - val_acc: 0.9279 Epoch 320/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3156 - acc: 0.9733 - val_loss: 0.4768 - val_acc: 0.9256 Epoch 321/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3109 - acc: 0.9738 - val_loss: 0.4662 - val_acc: 0.9274 Epoch 322/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3070 - acc: 0.9743 - val_loss: 0.4666 - val_acc: 0.9266 Epoch 323/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3008 - acc: 0.9754 - val_loss: 0.4734 - val_acc: 0.9244 Epoch 324/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.3005 - acc: 0.9739 - val_loss: 0.4770 - val_acc: 0.9276 Epoch 325/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2967 - acc: 0.9736 - val_loss: 0.4575 - val_acc: 0.9289 Epoch 326/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2945 - acc: 0.9742 - val_loss: 0.4677 - val_acc: 0.9247 Epoch 327/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2862 - acc: 0.9760 - val_loss: 0.4682 - val_acc: 0.9263 Epoch 328/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2850 - acc: 0.9762 - val_loss: 0.4657 - val_acc: 0.9247 Epoch 329/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2816 - acc: 0.9757 - val_loss: 0.4617 - val_acc: 0.9265 Epoch 330/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2812 - acc: 0.9744 - val_loss: 0.4649 - val_acc: 0.9226 Epoch 331/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2791 - acc: 0.9744 - val_loss: 0.4484 - val_acc: 0.9282 Epoch 332/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2743 - acc: 0.9757 - val_loss: 0.4503 - val_acc: 0.9242 Epoch 333/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2706 - acc: 0.9767 - val_loss: 0.4464 - val_acc: 0.9295 Epoch 334/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2690 - acc: 0.9757 - val_loss: 0.4507 - val_acc: 0.9272 Epoch 335/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2649 - acc: 0.9762 - val_loss: 0.4510 - val_acc: 0.9246 Epoch 336/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.2626 - acc: 0.9776 - val_loss: 0.4529 - val_acc: 0.9226 Epoch 337/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.2615 - acc: 0.9772 - val_loss: 0.4453 - val_acc: 0.9270 Epoch 338/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.2597 - acc: 0.9763 - val_loss: 0.4571 - val_acc: 0.9232 Epoch 339/1000 500/500 [==============================] - 65s 131ms/step - loss: 0.2555 - acc: 0.9776 - val_loss: 0.4449 - val_acc: 0.9247 ... Epoch 755/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1093 - acc: 0.9992 - val_loss: 0.3584 - val_acc: 0.9337 Epoch 756/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1093 - acc: 0.9990 - val_loss: 0.3583 - val_acc: 0.9346 Epoch 757/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1095 - acc: 0.9991 - val_loss: 0.3560 - val_acc: 0.9346 Epoch 758/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1090 - acc: 0.9991 - val_loss: 0.3587 - val_acc: 0.9346 Epoch 759/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1092 - acc: 0.9989 - val_loss: 0.3594 - val_acc: 0.9346 Epoch 760/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1086 - acc: 0.9992 - val_loss: 0.3560 - val_acc: 0.9345 Epoch 761/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1081 - acc: 0.9993 - val_loss: 0.3573 - val_acc: 0.9346 Epoch 762/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1083 - acc: 0.9992 - val_loss: 0.3598 - val_acc: 0.9343 Epoch 763/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1080 - acc: 0.9991 - val_loss: 0.3590 - val_acc: 0.9341 Epoch 764/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1076 - acc: 0.9993 - val_loss: 0.3567 - val_acc: 0.9336 Epoch 765/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1077 - acc: 0.9991 - val_loss: 0.3556 - val_acc: 0.9375 Epoch 766/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1072 - acc: 0.9993 - val_loss: 0.3562 - val_acc: 0.9349 Epoch 767/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1075 - acc: 0.9992 - val_loss: 0.3538 - val_acc: 0.9364 Epoch 768/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1071 - acc: 0.9991 - val_loss: 0.3607 - val_acc: 0.9347 Epoch 769/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1067 - acc: 0.9994 - val_loss: 0.3626 - val_acc: 0.9348 Epoch 770/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1070 - acc: 0.9991 - val_loss: 0.3595 - val_acc: 0.9364 Epoch 771/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1067 - acc: 0.9991 - val_loss: 0.3578 - val_acc: 0.9353 Epoch 772/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1066 - acc: 0.9991 - val_loss: 0.3561 - val_acc: 0.9357 Epoch 773/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1062 - acc: 0.9992 - val_loss: 0.3555 - val_acc: 0.9357 Epoch 774/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1062 - acc: 0.9992 - val_loss: 0.3546 - val_acc: 0.9367 Epoch 775/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1059 - acc: 0.9992 - val_loss: 0.3570 - val_acc: 0.9367 Epoch 776/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1061 - acc: 0.9990 - val_loss: 0.3570 - val_acc: 0.9355 Epoch 777/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1065 - acc: 0.9988 - val_loss: 0.3569 - val_acc: 0.9361 Epoch 778/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1055 - acc: 0.9991 - val_loss: 0.3592 - val_acc: 0.9347 Epoch 779/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1053 - acc: 0.9991 - val_loss: 0.3578 - val_acc: 0.9345 Epoch 780/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1057 - acc: 0.9990 - val_loss: 0.3550 - val_acc: 0.9361 Epoch 781/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1054 - acc: 0.9988 - val_loss: 0.3598 - val_acc: 0.9359 Epoch 782/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1053 - acc: 0.9988 - val_loss: 0.3548 - val_acc: 0.9349 Epoch 783/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1047 - acc: 0.9992 - val_loss: 0.3541 - val_acc: 0.9366 Epoch 784/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1048 - acc: 0.9990 - val_loss: 0.3540 - val_acc: 0.9346 Epoch 785/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1046 - acc: 0.9991 - val_loss: 0.3534 - val_acc: 0.9350 Epoch 786/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1041 - acc: 0.9992 - val_loss: 0.3559 - val_acc: 0.9349 Epoch 787/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1042 - acc: 0.9992 - val_loss: 0.3547 - val_acc: 0.9336 Epoch 788/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1039 - acc: 0.9992 - val_loss: 0.3523 - val_acc: 0.9347 Epoch 789/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1037 - acc: 0.9991 - val_loss: 0.3487 - val_acc: 0.9375 Epoch 790/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1034 - acc: 0.9992 - val_loss: 0.3481 - val_acc: 0.9365 Epoch 791/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1034 - acc: 0.9992 - val_loss: 0.3514 - val_acc: 0.9370 Epoch 792/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1034 - acc: 0.9991 - val_loss: 0.3507 - val_acc: 0.9363 Epoch 793/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1029 - acc: 0.9992 - val_loss: 0.3531 - val_acc: 0.9358 Epoch 794/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1032 - acc: 0.9990 - val_loss: 0.3563 - val_acc: 0.9351 Epoch 795/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1026 - acc: 0.9992 - val_loss: 0.3529 - val_acc: 0.9362 Epoch 796/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1024 - acc: 0.9992 - val_loss: 0.3511 - val_acc: 0.9360 Epoch 797/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1023 - acc: 0.9990 - val_loss: 0.3520 - val_acc: 0.9358 Epoch 798/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1023 - acc: 0.9990 - val_loss: 0.3524 - val_acc: 0.9354 Epoch 799/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1022 - acc: 0.9991 - val_loss: 0.3547 - val_acc: 0.9349 Epoch 800/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1020 - acc: 0.9991 - val_loss: 0.3548 - val_acc: 0.9356 Epoch 801/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1016 - acc: 0.9993 - val_loss: 0.3524 - val_acc: 0.9356 Epoch 802/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1016 - acc: 0.9992 - val_loss: 0.3516 - val_acc: 0.9360 Epoch 803/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1015 - acc: 0.9991 - val_loss: 0.3497 - val_acc: 0.9353 Epoch 804/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1012 - acc: 0.9992 - val_loss: 0.3520 - val_acc: 0.9355 Epoch 805/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1014 - acc: 0.9991 - val_loss: 0.3539 - val_acc: 0.9354 Epoch 806/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1010 - acc: 0.9990 - val_loss: 0.3580 - val_acc: 0.9352 Epoch 807/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1011 - acc: 0.9990 - val_loss: 0.3513 - val_acc: 0.9349 Epoch 808/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1006 - acc: 0.9992 - val_loss: 0.3521 - val_acc: 0.9367 Epoch 809/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.1005 - acc: 0.9991 - val_loss: 0.3495 - val_acc: 0.9368 Epoch 810/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1008 - acc: 0.9988 - val_loss: 0.3529 - val_acc: 0.9350 Epoch 811/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.1001 - acc: 0.9992 - val_loss: 0.3569 - val_acc: 0.9358 Epoch 812/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0998 - acc: 0.9991 - val_loss: 0.3532 - val_acc: 0.9355 Epoch 813/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0996 - acc: 0.9992 - val_loss: 0.3559 - val_acc: 0.9347 Epoch 814/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0997 - acc: 0.9992 - val_loss: 0.3532 - val_acc: 0.9345 Epoch 815/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0996 - acc: 0.9991 - val_loss: 0.3544 - val_acc: 0.9340 Epoch 816/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0991 - acc: 0.9991 - val_loss: 0.3529 - val_acc: 0.9358 Epoch 817/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0984 - acc: 0.9995 - val_loss: 0.3508 - val_acc: 0.9365 Epoch 818/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0994 - acc: 0.9989 - val_loss: 0.3533 - val_acc: 0.9362 Epoch 819/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0987 - acc: 0.9993 - val_loss: 0.3519 - val_acc: 0.9351 Epoch 820/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0988 - acc: 0.9991 - val_loss: 0.3528 - val_acc: 0.9352 Epoch 821/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0983 - acc: 0.9992 - val_loss: 0.3479 - val_acc: 0.9354 Epoch 822/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0984 - acc: 0.9991 - val_loss: 0.3485 - val_acc: 0.9367 Epoch 823/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0985 - acc: 0.9990 - val_loss: 0.3530 - val_acc: 0.9358 Epoch 824/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0981 - acc: 0.9992 - val_loss: 0.3464 - val_acc: 0.9377 Epoch 825/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0978 - acc: 0.9993 - val_loss: 0.3477 - val_acc: 0.9358 Epoch 826/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0973 - acc: 0.9992 - val_loss: 0.3468 - val_acc: 0.9364 Epoch 827/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0979 - acc: 0.9991 - val_loss: 0.3502 - val_acc: 0.9358 Epoch 828/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0974 - acc: 0.9991 - val_loss: 0.3470 - val_acc: 0.9356 Epoch 829/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0969 - acc: 0.9994 - val_loss: 0.3459 - val_acc: 0.9351 Epoch 830/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0970 - acc: 0.9990 - val_loss: 0.3528 - val_acc: 0.9347 Epoch 831/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0969 - acc: 0.9992 - val_loss: 0.3484 - val_acc: 0.9360 Epoch 832/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0970 - acc: 0.9992 - val_loss: 0.3542 - val_acc: 0.9353 Epoch 833/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0969 - acc: 0.9990 - val_loss: 0.3496 - val_acc: 0.9345 Epoch 834/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0970 - acc: 0.9990 - val_loss: 0.3460 - val_acc: 0.9372 Epoch 835/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0960 - acc: 0.9993 - val_loss: 0.3514 - val_acc: 0.9349 Epoch 836/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0962 - acc: 0.9994 - val_loss: 0.3420 - val_acc: 0.9376 Epoch 837/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0960 - acc: 0.9992 - val_loss: 0.3441 - val_acc: 0.9358 Epoch 838/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0957 - acc: 0.9993 - val_loss: 0.3474 - val_acc: 0.9368 Epoch 839/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0955 - acc: 0.9993 - val_loss: 0.3447 - val_acc: 0.9355 Epoch 840/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0951 - acc: 0.9995 - val_loss: 0.3508 - val_acc: 0.9355 Epoch 841/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0951 - acc: 0.9993 - val_loss: 0.3488 - val_acc: 0.9366 Epoch 842/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0952 - acc: 0.9992 - val_loss: 0.3500 - val_acc: 0.9368 Epoch 843/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0952 - acc: 0.9991 - val_loss: 0.3464 - val_acc: 0.9359 Epoch 844/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0947 - acc: 0.9993 - val_loss: 0.3470 - val_acc: 0.9365 Epoch 845/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0947 - acc: 0.9993 - val_loss: 0.3478 - val_acc: 0.9353 Epoch 846/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0952 - acc: 0.9990 - val_loss: 0.3501 - val_acc: 0.9355 Epoch 847/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0944 - acc: 0.9993 - val_loss: 0.3463 - val_acc: 0.9354 Epoch 848/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0944 - acc: 0.9993 - val_loss: 0.3504 - val_acc: 0.9351 Epoch 849/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0941 - acc: 0.9993 - val_loss: 0.3468 - val_acc: 0.9373 Epoch 850/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0947 - acc: 0.9988 - val_loss: 0.3432 - val_acc: 0.9378 Epoch 851/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0943 - acc: 0.9989 - val_loss: 0.3456 - val_acc: 0.9369 Epoch 852/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0943 - acc: 0.9990 - val_loss: 0.3471 - val_acc: 0.9365 Epoch 853/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0940 - acc: 0.9990 - val_loss: 0.3506 - val_acc: 0.9356 Epoch 854/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0936 - acc: 0.9992 - val_loss: 0.3498 - val_acc: 0.9358 Epoch 855/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0934 - acc: 0.9992 - val_loss: 0.3469 - val_acc: 0.9361 Epoch 856/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0931 - acc: 0.9993 - val_loss: 0.3483 - val_acc: 0.9361 Epoch 857/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0930 - acc: 0.9993 - val_loss: 0.3440 - val_acc: 0.9350 Epoch 858/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0930 - acc: 0.9991 - val_loss: 0.3445 - val_acc: 0.9365 Epoch 859/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0928 - acc: 0.9992 - val_loss: 0.3465 - val_acc: 0.9366 Epoch 860/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0928 - acc: 0.9990 - val_loss: 0.3527 - val_acc: 0.9345 Epoch 861/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0924 - acc: 0.9992 - val_loss: 0.3465 - val_acc: 0.9369 Epoch 862/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0923 - acc: 0.9992 - val_loss: 0.3445 - val_acc: 0.9366 Epoch 863/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0923 - acc: 0.9992 - val_loss: 0.3476 - val_acc: 0.9362 Epoch 864/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0920 - acc: 0.9993 - val_loss: 0.3454 - val_acc: 0.9369 Epoch 865/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0922 - acc: 0.9990 - val_loss: 0.3486 - val_acc: 0.9337 Epoch 866/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0914 - acc: 0.9994 - val_loss: 0.3489 - val_acc: 0.9355 Epoch 867/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0918 - acc: 0.9991 - val_loss: 0.3467 - val_acc: 0.9359 Epoch 868/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0918 - acc: 0.9992 - val_loss: 0.3486 - val_acc: 0.9348 Epoch 869/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0913 - acc: 0.9992 - val_loss: 0.3437 - val_acc: 0.9364 Epoch 870/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0914 - acc: 0.9992 - val_loss: 0.3488 - val_acc: 0.9350 Epoch 871/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0913 - acc: 0.9991 - val_loss: 0.3473 - val_acc: 0.9367 Epoch 872/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0911 - acc: 0.9992 - val_loss: 0.3448 - val_acc: 0.9380 Epoch 873/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0907 - acc: 0.9993 - val_loss: 0.3439 - val_acc: 0.9373 Epoch 874/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0911 - acc: 0.9988 - val_loss: 0.3421 - val_acc: 0.9384 Epoch 875/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0904 - acc: 0.9992 - val_loss: 0.3430 - val_acc: 0.9365 Epoch 876/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0908 - acc: 0.9990 - val_loss: 0.3471 - val_acc: 0.9355 Epoch 877/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0905 - acc: 0.9991 - val_loss: 0.3452 - val_acc: 0.9359 Epoch 878/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0905 - acc: 0.9990 - val_loss: 0.3379 - val_acc: 0.9372 Epoch 879/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0906 - acc: 0.9989 - val_loss: 0.3442 - val_acc: 0.9369 Epoch 880/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0903 - acc: 0.9990 - val_loss: 0.3413 - val_acc: 0.9363 Epoch 881/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0898 - acc: 0.9992 - val_loss: 0.3437 - val_acc: 0.9354 Epoch 882/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0898 - acc: 0.9992 - val_loss: 0.3421 - val_acc: 0.9371 Epoch 883/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0897 - acc: 0.9991 - val_loss: 0.3442 - val_acc: 0.9363 Epoch 884/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0900 - acc: 0.9990 - val_loss: 0.3471 - val_acc: 0.9366 Epoch 885/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0897 - acc: 0.9991 - val_loss: 0.3443 - val_acc: 0.9361 Epoch 886/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0892 - acc: 0.9990 - val_loss: 0.3434 - val_acc: 0.9355 Epoch 887/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0890 - acc: 0.9991 - val_loss: 0.3411 - val_acc: 0.9367 Epoch 888/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0889 - acc: 0.9992 - val_loss: 0.3478 - val_acc: 0.9338 Epoch 889/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0889 - acc: 0.9991 - val_loss: 0.3404 - val_acc: 0.9366 Epoch 890/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0889 - acc: 0.9991 - val_loss: 0.3356 - val_acc: 0.9373 Epoch 891/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0886 - acc: 0.9992 - val_loss: 0.3358 - val_acc: 0.9362 Epoch 892/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0883 - acc: 0.9992 - val_loss: 0.3380 - val_acc: 0.9368 Epoch 893/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0886 - acc: 0.9991 - val_loss: 0.3369 - val_acc: 0.9374 Epoch 894/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0881 - acc: 0.9993 - val_loss: 0.3397 - val_acc: 0.9386 Epoch 895/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0885 - acc: 0.9991 - val_loss: 0.3400 - val_acc: 0.9365 Epoch 896/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0883 - acc: 0.9989 - val_loss: 0.3367 - val_acc: 0.9355 Epoch 897/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0886 - acc: 0.9986 - val_loss: 0.3375 - val_acc: 0.9361 Epoch 898/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0878 - acc: 0.9989 - val_loss: 0.3444 - val_acc: 0.9354 Epoch 899/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0875 - acc: 0.9992 - val_loss: 0.3444 - val_acc: 0.9367 Epoch 900/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0877 - acc: 0.9990 - val_loss: 0.3457 - val_acc: 0.9353 Epoch 901/1000 lr changed to 9.999999310821295e-05 500/500 [==============================] - 66s 132ms/step - loss: 0.0873 - acc: 0.9992 - val_loss: 0.3442 - val_acc: 0.9350 Epoch 902/1000 500/500 [==============================] - 66s 133ms/step - loss: 0.0867 - acc: 0.9994 - val_loss: 0.3425 - val_acc: 0.9361 Epoch 903/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0874 - acc: 0.9991 - val_loss: 0.3432 - val_acc: 0.9358 Epoch 904/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.0872 - acc: 0.9992 - val_loss: 0.3431 - val_acc: 0.9360 Epoch 905/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.0871 - acc: 0.9991 - val_loss: 0.3426 - val_acc: 0.9371 Epoch 906/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0868 - acc: 0.9991 - val_loss: 0.3422 - val_acc: 0.9371 Epoch 907/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0869 - acc: 0.9993 - val_loss: 0.3418 - val_acc: 0.9368 Epoch 908/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0870 - acc: 0.9991 - val_loss: 0.3415 - val_acc: 0.9366 Epoch 909/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.0863 - acc: 0.9995 - val_loss: 0.3410 - val_acc: 0.9371 Epoch 910/1000 500/500 [==============================] - 66s 131ms/step - loss: 0.0870 - acc: 0.9991 - val_loss: 0.3405 - val_acc: 0.9363 Epoch 911/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0864 - acc: 0.9995 - val_loss: 0.3412 - val_acc: 0.9367 Epoch 912/1000 500/500 [==============================] - 66s 132ms/step - loss: 0.0862 - acc: 0.9995 - val_loss: 0.3403 - val_acc: 0.9370 Epoch 913/1000 500/500 [==============================] - 78s 155ms/step - loss: 0.0862 - acc: 0.9995 - val_loss: 0.3399 - val_acc: 0.9368 Epoch 914/1000 500/500 [==============================] - 84s 168ms/step - loss: 0.0860 - acc: 0.9997 - val_loss: 0.3402 - val_acc: 0.9373 Epoch 915/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0865 - acc: 0.9994 - val_loss: 0.3403 - val_acc: 0.9371 Epoch 916/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0866 - acc: 0.9993 - val_loss: 0.3399 - val_acc: 0.9369 Epoch 917/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0868 - acc: 0.9992 - val_loss: 0.3385 - val_acc: 0.9378 Epoch 918/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0865 - acc: 0.9993 - val_loss: 0.3374 - val_acc: 0.9376 Epoch 919/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3378 - val_acc: 0.9373 Epoch 920/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0864 - acc: 0.9993 - val_loss: 0.3373 - val_acc: 0.9380 Epoch 921/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0863 - acc: 0.9995 - val_loss: 0.3374 - val_acc: 0.9375 Epoch 922/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0863 - acc: 0.9993 - val_loss: 0.3371 - val_acc: 0.9376 Epoch 923/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3372 - val_acc: 0.9370 Epoch 924/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0860 - acc: 0.9994 - val_loss: 0.3374 - val_acc: 0.9369 Epoch 925/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0860 - acc: 0.9996 - val_loss: 0.3375 - val_acc: 0.9368 Epoch 926/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0862 - acc: 0.9994 - val_loss: 0.3378 - val_acc: 0.9373 Epoch 927/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0864 - acc: 0.9992 - val_loss: 0.3384 - val_acc: 0.9371 Epoch 928/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0863 - acc: 0.9993 - val_loss: 0.3386 - val_acc: 0.9367 Epoch 929/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3392 - val_acc: 0.9365 Epoch 930/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3386 - val_acc: 0.9368 Epoch 931/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3384 - val_acc: 0.9375 Epoch 932/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9996 - val_loss: 0.3388 - val_acc: 0.9376 Epoch 933/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9995 - val_loss: 0.3390 - val_acc: 0.9376 Epoch 934/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0861 - acc: 0.9995 - val_loss: 0.3389 - val_acc: 0.9375 Epoch 935/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9994 - val_loss: 0.3390 - val_acc: 0.9376 Epoch 936/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9994 - val_loss: 0.3397 - val_acc: 0.9373 Epoch 937/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0858 - acc: 0.9995 - val_loss: 0.3396 - val_acc: 0.9371 Epoch 938/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0860 - acc: 0.9994 - val_loss: 0.3390 - val_acc: 0.9379 Epoch 939/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9993 - val_loss: 0.3393 - val_acc: 0.9382 Epoch 940/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0858 - acc: 0.9994 - val_loss: 0.3391 - val_acc: 0.9379 Epoch 941/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0858 - acc: 0.9995 - val_loss: 0.3392 - val_acc: 0.9378 Epoch 942/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0857 - acc: 0.9995 - val_loss: 0.3396 - val_acc: 0.9382 Epoch 943/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0858 - acc: 0.9995 - val_loss: 0.3403 - val_acc: 0.9376 Epoch 944/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9993 - val_loss: 0.3405 - val_acc: 0.9374 Epoch 945/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9996 - val_loss: 0.3402 - val_acc: 0.9371 Epoch 946/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0859 - acc: 0.9994 - val_loss: 0.3398 - val_acc: 0.9376 Epoch 947/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0857 - acc: 0.9994 - val_loss: 0.3397 - val_acc: 0.9371 Epoch 948/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0855 - acc: 0.9996 - val_loss: 0.3396 - val_acc: 0.9375 Epoch 949/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9996 - val_loss: 0.3398 - val_acc: 0.9376 Epoch 950/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9996 - val_loss: 0.3397 - val_acc: 0.9378 Epoch 951/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9994 - val_loss: 0.3393 - val_acc: 0.9375 Epoch 952/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0857 - acc: 0.9996 - val_loss: 0.3397 - val_acc: 0.9374 Epoch 953/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9995 - val_loss: 0.3400 - val_acc: 0.9378 Epoch 954/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9995 - val_loss: 0.3401 - val_acc: 0.9368 Epoch 955/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0855 - acc: 0.9994 - val_loss: 0.3403 - val_acc: 0.9370 Epoch 956/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9994 - val_loss: 0.3405 - val_acc: 0.9371 Epoch 957/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0857 - acc: 0.9994 - val_loss: 0.3408 - val_acc: 0.9375 Epoch 958/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9994 - val_loss: 0.3405 - val_acc: 0.9374 Epoch 959/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9993 - val_loss: 0.3408 - val_acc: 0.9375 Epoch 960/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0856 - acc: 0.9995 - val_loss: 0.3407 - val_acc: 0.9369 Epoch 961/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9996 - val_loss: 0.3402 - val_acc: 0.9371 Epoch 962/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0855 - acc: 0.9994 - val_loss: 0.3399 - val_acc: 0.9371 Epoch 963/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0852 - acc: 0.9996 - val_loss: 0.3400 - val_acc: 0.9378 Epoch 964/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0856 - acc: 0.9994 - val_loss: 0.3399 - val_acc: 0.9375 Epoch 965/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9994 - val_loss: 0.3396 - val_acc: 0.9375 Epoch 966/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0852 - acc: 0.9995 - val_loss: 0.3391 - val_acc: 0.9368 Epoch 967/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9994 - val_loss: 0.3383 - val_acc: 0.9374 Epoch 968/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9994 - val_loss: 0.3384 - val_acc: 0.9375 Epoch 969/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9997 - val_loss: 0.3383 - val_acc: 0.9375 Epoch 970/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0853 - acc: 0.9995 - val_loss: 0.3388 - val_acc: 0.9365 Epoch 971/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9996 - val_loss: 0.3381 - val_acc: 0.9356 Epoch 972/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0855 - acc: 0.9994 - val_loss: 0.3387 - val_acc: 0.9362 Epoch 973/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0857 - acc: 0.9994 - val_loss: 0.3385 - val_acc: 0.9372 Epoch 974/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9995 - val_loss: 0.3385 - val_acc: 0.9373 Epoch 975/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9996 - val_loss: 0.3380 - val_acc: 0.9375 Epoch 976/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9994 - val_loss: 0.3380 - val_acc: 0.9379 Epoch 977/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9994 - val_loss: 0.3374 - val_acc: 0.9376 Epoch 978/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9996 - val_loss: 0.3376 - val_acc: 0.9379 Epoch 979/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9995 - val_loss: 0.3380 - val_acc: 0.9378 Epoch 980/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0852 - acc: 0.9995 - val_loss: 0.3376 - val_acc: 0.9381 Epoch 981/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0854 - acc: 0.9995 - val_loss: 0.3377 - val_acc: 0.9381 Epoch 982/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0849 - acc: 0.9996 - val_loss: 0.3373 - val_acc: 0.9384 Epoch 983/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0852 - acc: 0.9993 - val_loss: 0.3372 - val_acc: 0.9379 Epoch 984/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0848 - acc: 0.9997 - val_loss: 0.3368 - val_acc: 0.9381 Epoch 985/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0852 - acc: 0.9994 - val_loss: 0.3373 - val_acc: 0.9382 Epoch 986/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0847 - acc: 0.9997 - val_loss: 0.3372 - val_acc: 0.9380 Epoch 987/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0848 - acc: 0.9996 - val_loss: 0.3371 - val_acc: 0.9387 Epoch 988/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9993 - val_loss: 0.3377 - val_acc: 0.9380 Epoch 989/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9995 - val_loss: 0.3371 - val_acc: 0.9385 Epoch 990/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0848 - acc: 0.9996 - val_loss: 0.3379 - val_acc: 0.9384 Epoch 991/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0849 - acc: 0.9994 - val_loss: 0.3377 - val_acc: 0.9380 Epoch 992/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0852 - acc: 0.9993 - val_loss: 0.3370 - val_acc: 0.9381 Epoch 993/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0851 - acc: 0.9994 - val_loss: 0.3371 - val_acc: 0.9380 Epoch 994/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0848 - acc: 0.9996 - val_loss: 0.3371 - val_acc: 0.9381 Epoch 995/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0849 - acc: 0.9994 - val_loss: 0.3381 - val_acc: 0.9381 Epoch 996/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0849 - acc: 0.9996 - val_loss: 0.3379 - val_acc: 0.9379 Epoch 997/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0853 - acc: 0.9993 - val_loss: 0.3384 - val_acc: 0.9377 Epoch 998/1000 500/500 [==============================] - 65s 129ms/step - loss: 0.0849 - acc: 0.9995 - val_loss: 0.3393 - val_acc: 0.9369 Epoch 999/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0849 - acc: 0.9994 - val_loss: 0.3395 - val_acc: 0.9369 Epoch 1000/1000 500/500 [==============================] - 65s 130ms/step - loss: 0.0847 - acc: 0.9996 - val_loss: 0.3389 - val_acc: 0.9371 Train loss: 0.08910960255563259 Train accuracy: 0.9977200021743774 Test loss: 0.3388938118517399 Test accuracy: 0.9371000009775162
測試準確率到了93.71%，比之前的都高一點。
Minghang Zhao, Shisheng Zhong, Xuyun Fu, Baoping Tang, Shaojiang Dong, Michael Pecht, Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis, IEEE Transactions on Industrial Electronics, 2020, DOI: 10.1109/TIE.2020.2972458
https://ieeexplore.ieee.org/document/8998530
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版權宣告：本文為CSDN博主「dangqing1988」的原創文章，遵循 CC 4.0 BY-SA 版權協議，轉載請附上原文出處連結及本宣告。
原文連結：https://blog.csdn.net/dangqing1988/article/details/105688144
來自 “ ITPUB部落格 ” ，連結：http://blog.itpub.net/69972329/viewspace-2687948/，如需轉載，請註明出處，否則將追究法律責任。
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