深度殘差網路+自適應引數化ReLU啟用函式（調參記錄10）
本文在調參記錄9的基礎上，在資料增強部分新增了shear_range = 30，測試Adaptively Parametric ReLU(APReLU)啟用函式在Cifar10影像集上的效果。
Keras裡ImageDataGenerator的用法見如下網址：
深度殘差網路+自適應引數化ReLU啟用函式(調參記錄9)
https://blog.csdn.net/dangqing1988/article/details/105688144
自適應引數化ReLU啟用函式的基本原理見下圖：
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,
# shear angle in counter-clockwise direction in degrees
shear_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 [==============================] - 113s 225ms/step - loss: 3.2549 - acc: 0.4158 - val_loss: 2.7729 - val_acc: 0.5394 Epoch 2/1000 500/500 [==============================] - 68s 137ms/step - loss: 2.6403 - acc: 0.5484 - val_loss: 2.3416 - val_acc: 0.6117 Epoch 3/1000 500/500 [==============================] - 69s 138ms/step - loss: 2.2763 - acc: 0.6049 - val_loss: 2.0151 - val_acc: 0.6705 Epoch 4/1000 500/500 [==============================] - 69s 137ms/step - loss: 2.0062 - acc: 0.6393 - val_loss: 1.8055 - val_acc: 0.6907 Epoch 5/1000 500/500 [==============================] - 69s 137ms/step - loss: 1.7997 - acc: 0.6673 - val_loss: 1.6339 - val_acc: 0.7058 Epoch 6/1000 500/500 [==============================] - 69s 138ms/step - loss: 1.6338 - acc: 0.6849 - val_loss: 1.4391 - val_acc: 0.7345 Epoch 7/1000 500/500 [==============================] - 69s 138ms/step - loss: 1.4911 - acc: 0.7032 - val_loss: 1.3495 - val_acc: 0.7435 Epoch 8/1000 500/500 [==============================] - 69s 138ms/step - loss: 1.3733 - acc: 0.7196 - val_loss: 1.2311 - val_acc: 0.7668 Epoch 9/1000 500/500 [==============================] - 68s 137ms/step - loss: 1.2893 - acc: 0.7308 - val_loss: 1.1543 - val_acc: 0.7741 Epoch 10/1000 500/500 [==============================] - 68s 137ms/step - loss: 1.2164 - acc: 0.7402 - val_loss: 1.0974 - val_acc: 0.7761 Epoch 11/1000 500/500 [==============================] - 69s 137ms/step - loss: 1.1580 - acc: 0.7470 - val_loss: 1.0477 - val_acc: 0.7835 Epoch 12/1000 500/500 [==============================] - 69s 137ms/step - loss: 1.1127 - acc: 0.7519 - val_loss: 1.0269 - val_acc: 0.7813 Epoch 13/1000 500/500 [==============================] - 69s 138ms/step - loss: 1.0713 - acc: 0.7598 - val_loss: 0.9656 - val_acc: 0.7996 Epoch 14/1000 500/500 [==============================] - 68s 136ms/step - loss: 1.0369 - acc: 0.7664 - val_loss: 0.9576 - val_acc: 0.7929 Epoch 15/1000 500/500 [==============================] - 68s 135ms/step - loss: 1.0158 - acc: 0.7677 - val_loss: 0.9189 - val_acc: 0.8064 Epoch 16/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9948 - acc: 0.7733 - val_loss: 0.9198 - val_acc: 0.8022 Epoch 17/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.9720 - acc: 0.7775 - val_loss: 0.9267 - val_acc: 0.7954 Epoch 18/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9548 - acc: 0.7813 - val_loss: 0.8897 - val_acc: 0.8043 Epoch 19/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9446 - acc: 0.7847 - val_loss: 0.8642 - val_acc: 0.8104 Epoch 20/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9290 - acc: 0.7873 - val_loss: 0.8666 - val_acc: 0.8119 Epoch 21/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9131 - acc: 0.7913 - val_loss: 0.8433 - val_acc: 0.8202 Epoch 22/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.9099 - acc: 0.7912 - val_loss: 0.8735 - val_acc: 0.8077 Epoch 23/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.9000 - acc: 0.7956 - val_loss: 0.8418 - val_acc: 0.8150 Epoch 24/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8962 - acc: 0.7966 - val_loss: 0.8452 - val_acc: 0.8181 Epoch 25/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8874 - acc: 0.7994 - val_loss: 0.8209 - val_acc: 0.8242 Epoch 26/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.8810 - acc: 0.8021 - val_loss: 0.8378 - val_acc: 0.8202 Epoch 27/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8764 - acc: 0.8026 - val_loss: 0.8474 - val_acc: 0.8173 Epoch 28/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.8706 - acc: 0.8040 - val_loss: 0.8239 - val_acc: 0.8230 Epoch 29/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8655 - acc: 0.8075 - val_loss: 0.8163 - val_acc: 0.8244 Epoch 30/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8600 - acc: 0.8074 - val_loss: 0.8065 - val_acc: 0.8288 Epoch 31/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8544 - acc: 0.8113 - val_loss: 0.8080 - val_acc: 0.8306 Epoch 32/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8510 - acc: 0.8121 - val_loss: 0.8152 - val_acc: 0.8304 Epoch 33/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8464 - acc: 0.8142 - val_loss: 0.7827 - val_acc: 0.8387 Epoch 34/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8429 - acc: 0.8166 - val_loss: 0.7738 - val_acc: 0.8453 Epoch 35/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8366 - acc: 0.8160 - val_loss: 0.7855 - val_acc: 0.8388 Epoch 36/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8352 - acc: 0.8191 - val_loss: 0.7651 - val_acc: 0.8468 Epoch 37/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8292 - acc: 0.8212 - val_loss: 0.7620 - val_acc: 0.8470 Epoch 38/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8319 - acc: 0.8208 - val_loss: 0.7890 - val_acc: 0.8376 Epoch 39/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.8239 - acc: 0.8256 - val_loss: 0.7870 - val_acc: 0.8370 Epoch 40/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8266 - acc: 0.8216 - val_loss: 0.7975 - val_acc: 0.8331 Epoch 41/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8209 - acc: 0.8239 - val_loss: 0.7982 - val_acc: 0.8334 Epoch 42/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8135 - acc: 0.8276 - val_loss: 0.7722 - val_acc: 0.8427 Epoch 43/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8115 - acc: 0.8280 - val_loss: 0.7658 - val_acc: 0.8430 Epoch 44/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.8166 - acc: 0.8259 - val_loss: 0.7388 - val_acc: 0.8559 Epoch 45/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.8108 - acc: 0.8293 - val_loss: 0.7728 - val_acc: 0.8436 Epoch 46/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8046 - acc: 0.8303 - val_loss: 0.7684 - val_acc: 0.8434 Epoch 47/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.8055 - acc: 0.8322 - val_loss: 0.7478 - val_acc: 0.8511 Epoch 48/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8100 - acc: 0.8290 - val_loss: 0.7644 - val_acc: 0.8445 Epoch 49/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.8027 - acc: 0.8325 - val_loss: 0.7449 - val_acc: 0.8545 Epoch 50/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.8052 - acc: 0.8299 - val_loss: 0.7941 - val_acc: 0.8377 Epoch 51/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7969 - acc: 0.8339 - val_loss: 0.7617 - val_acc: 0.8481 Epoch 52/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7989 - acc: 0.8335 - val_loss: 0.7559 - val_acc: 0.8550 Epoch 53/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7927 - acc: 0.8353 - val_loss: 0.7482 - val_acc: 0.8536 Epoch 54/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7931 - acc: 0.8365 - val_loss: 0.7405 - val_acc: 0.8570 Epoch 55/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7933 - acc: 0.8372 - val_loss: 0.7541 - val_acc: 0.8535 Epoch 56/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7887 - acc: 0.8389 - val_loss: 0.7805 - val_acc: 0.8436 Epoch 57/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7877 - acc: 0.8385 - val_loss: 0.7304 - val_acc: 0.8617 Epoch 58/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7836 - acc: 0.8404 - val_loss: 0.7630 - val_acc: 0.8480 Epoch 59/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7859 - acc: 0.8394 - val_loss: 0.7369 - val_acc: 0.8568 Epoch 60/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7864 - acc: 0.8376 - val_loss: 0.7606 - val_acc: 0.8492 Epoch 61/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7827 - acc: 0.8401 - val_loss: 0.7497 - val_acc: 0.8524 Epoch 62/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7804 - acc: 0.8427 - val_loss: 0.7526 - val_acc: 0.8559 Epoch 63/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7766 - acc: 0.8435 - val_loss: 0.7448 - val_acc: 0.8586 Epoch 64/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7792 - acc: 0.8419 - val_loss: 0.7605 - val_acc: 0.8511 Epoch 65/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7790 - acc: 0.8435 - val_loss: 0.7330 - val_acc: 0.8551 Epoch 66/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7748 - acc: 0.8435 - val_loss: 0.7528 - val_acc: 0.8543 Epoch 67/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7733 - acc: 0.8452 - val_loss: 0.7330 - val_acc: 0.8585 Epoch 68/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7759 - acc: 0.8438 - val_loss: 0.7497 - val_acc: 0.8520 Epoch 69/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7680 - acc: 0.8466 - val_loss: 0.7422 - val_acc: 0.8606 Epoch 70/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7662 - acc: 0.8473 - val_loss: 0.7185 - val_acc: 0.8633 Epoch 71/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7658 - acc: 0.8467 - val_loss: 0.7170 - val_acc: 0.8657 Epoch 72/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7681 - acc: 0.8464 - val_loss: 0.7325 - val_acc: 0.8600 Epoch 73/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7658 - acc: 0.8477 - val_loss: 0.7109 - val_acc: 0.8662 Epoch 74/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7616 - acc: 0.8499 - val_loss: 0.7028 - val_acc: 0.8733 Epoch 75/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7621 - acc: 0.8482 - val_loss: 0.7178 - val_acc: 0.8639 Epoch 76/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7606 - acc: 0.8496 - val_loss: 0.7096 - val_acc: 0.8674 Epoch 77/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7590 - acc: 0.8500 - val_loss: 0.7340 - val_acc: 0.8598 Epoch 78/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7639 - acc: 0.8475 - val_loss: 0.7212 - val_acc: 0.8655 Epoch 79/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7613 - acc: 0.8477 - val_loss: 0.7171 - val_acc: 0.8702 Epoch 80/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7562 - acc: 0.8518 - val_loss: 0.7336 - val_acc: 0.8594 Epoch 81/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7532 - acc: 0.8515 - val_loss: 0.7229 - val_acc: 0.8607 Epoch 82/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7511 - acc: 0.8541 - val_loss: 0.7062 - val_acc: 0.8688 Epoch 83/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7510 - acc: 0.8530 - val_loss: 0.6977 - val_acc: 0.8746 Epoch 84/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7562 - acc: 0.8524 - val_loss: 0.7319 - val_acc: 0.8595 Epoch 85/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7527 - acc: 0.8530 - val_loss: 0.7161 - val_acc: 0.8660 Epoch 86/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7523 - acc: 0.8524 - val_loss: 0.7244 - val_acc: 0.8654 Epoch 87/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7505 - acc: 0.8532 - val_loss: 0.7192 - val_acc: 0.8636 Epoch 88/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7528 - acc: 0.8516 - val_loss: 0.7316 - val_acc: 0.8645 Epoch 89/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7480 - acc: 0.8557 - val_loss: 0.7289 - val_acc: 0.8638 Epoch 90/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7435 - acc: 0.8550 - val_loss: 0.7020 - val_acc: 0.8763 Epoch 91/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7466 - acc: 0.8563 - val_loss: 0.6977 - val_acc: 0.8750 Epoch 92/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7438 - acc: 0.8561 - val_loss: 0.7171 - val_acc: 0.8643 Epoch 93/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7438 - acc: 0.8564 - val_loss: 0.7189 - val_acc: 0.8687 Epoch 94/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7442 - acc: 0.8566 - val_loss: 0.7072 - val_acc: 0.8685 Epoch 95/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7468 - acc: 0.8569 - val_loss: 0.7547 - val_acc: 0.8560 Epoch 96/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7468 - acc: 0.8547 - val_loss: 0.7080 - val_acc: 0.8699 Epoch 97/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7455 - acc: 0.8559 - val_loss: 0.7020 - val_acc: 0.8711 Epoch 98/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7427 - acc: 0.8544 - val_loss: 0.7352 - val_acc: 0.8610 Epoch 99/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7424 - acc: 0.8567 - val_loss: 0.7480 - val_acc: 0.8583 Epoch 100/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7397 - acc: 0.8579 - val_loss: 0.7151 - val_acc: 0.8650 Epoch 101/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7447 - acc: 0.8568 - val_loss: 0.7235 - val_acc: 0.8659 Epoch 102/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7367 - acc: 0.8598 - val_loss: 0.7229 - val_acc: 0.8623 Epoch 103/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7371 - acc: 0.8586 - val_loss: 0.6899 - val_acc: 0.8769 Epoch 104/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7401 - acc: 0.8567 - val_loss: 0.7273 - val_acc: 0.8616 Epoch 105/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7382 - acc: 0.8578 - val_loss: 0.7089 - val_acc: 0.8682 Epoch 106/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7386 - acc: 0.8580 - val_loss: 0.7158 - val_acc: 0.8659 Epoch 107/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7361 - acc: 0.8584 - val_loss: 0.7147 - val_acc: 0.8701 Epoch 108/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7408 - acc: 0.8580 - val_loss: 0.7083 - val_acc: 0.8686 Epoch 109/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7362 - acc: 0.8599 - val_loss: 0.7096 - val_acc: 0.8703 Epoch 110/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7335 - acc: 0.8600 - val_loss: 0.7148 - val_acc: 0.8683 Epoch 111/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7334 - acc: 0.8626 - val_loss: 0.7050 - val_acc: 0.8741 Epoch 112/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7360 - acc: 0.8586 - val_loss: 0.7150 - val_acc: 0.8682 Epoch 113/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7371 - acc: 0.8583 - val_loss: 0.7447 - val_acc: 0.8583 Epoch 114/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7352 - acc: 0.8599 - val_loss: 0.6937 - val_acc: 0.8755 Epoch 115/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7314 - acc: 0.8604 - val_loss: 0.7140 - val_acc: 0.8684 Epoch 116/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7333 - acc: 0.8607 - val_loss: 0.7305 - val_acc: 0.8686 Epoch 117/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7277 - acc: 0.8617 - val_loss: 0.7002 - val_acc: 0.8719 Epoch 118/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7356 - acc: 0.8580 - val_loss: 0.6926 - val_acc: 0.8763 Epoch 119/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7244 - acc: 0.8642 - val_loss: 0.7079 - val_acc: 0.8669 Epoch 120/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7302 - acc: 0.8613 - val_loss: 0.7113 - val_acc: 0.8695 Epoch 121/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7340 - acc: 0.8608 - val_loss: 0.7415 - val_acc: 0.8554 Epoch 122/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7304 - acc: 0.8608 - val_loss: 0.6978 - val_acc: 0.8760 Epoch 123/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7263 - acc: 0.8630 - val_loss: 0.6974 - val_acc: 0.8734 Epoch 124/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7261 - acc: 0.8625 - val_loss: 0.7109 - val_acc: 0.8715 Epoch 125/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7313 - acc: 0.8623 - val_loss: 0.6946 - val_acc: 0.8745 Epoch 126/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7277 - acc: 0.8620 - val_loss: 0.7178 - val_acc: 0.8685 Epoch 127/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7231 - acc: 0.8653 - val_loss: 0.6999 - val_acc: 0.8762 Epoch 128/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7252 - acc: 0.8635 - val_loss: 0.7009 - val_acc: 0.8718 Epoch 129/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7284 - acc: 0.8626 - val_loss: 0.7148 - val_acc: 0.8682 Epoch 130/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7236 - acc: 0.8646 - val_loss: 0.6945 - val_acc: 0.8746 Epoch 131/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7203 - acc: 0.8653 - val_loss: 0.7002 - val_acc: 0.8705 Epoch 132/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7248 - acc: 0.8626 - val_loss: 0.7097 - val_acc: 0.8718 Epoch 133/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7190 - acc: 0.8660 - val_loss: 0.6993 - val_acc: 0.8722 Epoch 134/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7206 - acc: 0.8645 - val_loss: 0.7042 - val_acc: 0.8763 Epoch 135/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7248 - acc: 0.8637 - val_loss: 0.6742 - val_acc: 0.8844 Epoch 136/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7181 - acc: 0.8650 - val_loss: 0.6972 - val_acc: 0.8721 Epoch 137/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7170 - acc: 0.8667 - val_loss: 0.7270 - val_acc: 0.8642 Epoch 138/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7209 - acc: 0.8649 - val_loss: 0.7107 - val_acc: 0.8687 Epoch 139/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7195 - acc: 0.8652 - val_loss: 0.6993 - val_acc: 0.8752 Epoch 140/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7229 - acc: 0.8647 - val_loss: 0.6949 - val_acc: 0.8800 Epoch 141/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7154 - acc: 0.8674 - val_loss: 0.6828 - val_acc: 0.8780 Epoch 142/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7146 - acc: 0.8675 - val_loss: 0.6799 - val_acc: 0.8818 Epoch 143/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7131 - acc: 0.8679 - val_loss: 0.7237 - val_acc: 0.8655 Epoch 144/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7167 - acc: 0.8662 - val_loss: 0.7140 - val_acc: 0.8696 Epoch 145/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7131 - acc: 0.8677 - val_loss: 0.7086 - val_acc: 0.8696 Epoch 146/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7184 - acc: 0.8665 - val_loss: 0.7058 - val_acc: 0.8729 Epoch 147/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7179 - acc: 0.8654 - val_loss: 0.7021 - val_acc: 0.8741 Epoch 148/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7176 - acc: 0.8671 - val_loss: 0.6892 - val_acc: 0.8795 Epoch 149/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7123 - acc: 0.8685 - val_loss: 0.7027 - val_acc: 0.8700 Epoch 150/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7146 - acc: 0.8671 - val_loss: 0.6926 - val_acc: 0.8755 Epoch 151/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7122 - acc: 0.8651 - val_loss: 0.7179 - val_acc: 0.8685 Epoch 152/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7149 - acc: 0.8675 - val_loss: 0.7136 - val_acc: 0.8690 Epoch 153/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7141 - acc: 0.8669 - val_loss: 0.7193 - val_acc: 0.8672 Epoch 154/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7084 - acc: 0.8684 - val_loss: 0.6779 - val_acc: 0.8826 Epoch 155/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7143 - acc: 0.8671 - val_loss: 0.7092 - val_acc: 0.8685 Epoch 156/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7118 - acc: 0.8674 - val_loss: 0.7010 - val_acc: 0.8732 Epoch 157/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.7126 - acc: 0.8677 - val_loss: 0.6918 - val_acc: 0.8766 Epoch 158/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7064 - acc: 0.8701 - val_loss: 0.7253 - val_acc: 0.8636 Epoch 159/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7107 - acc: 0.8674 - val_loss: 0.7008 - val_acc: 0.8745 Epoch 160/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7097 - acc: 0.8698 - val_loss: 0.6922 - val_acc: 0.8771 Epoch 161/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7091 - acc: 0.8675 - val_loss: 0.6786 - val_acc: 0.8813 Epoch 162/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.7117 - acc: 0.8680 - val_loss: 0.7017 - val_acc: 0.8740 Epoch 163/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.7110 - acc: 0.8681 - val_loss: 0.6862 - val_acc: 0.8800 Epoch 164/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7099 - acc: 0.8693 - val_loss: 0.7053 - val_acc: 0.8709 Epoch 165/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.7104 - acc: 0.8694 - val_loss: 0.6846 - val_acc: 0.8828 Epoch 166/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7078 - acc: 0.8715 - val_loss: 0.6968 - val_acc: 0.8749 Epoch 167/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7076 - acc: 0.8719 - val_loss: 0.6872 - val_acc: 0.8782 Epoch 168/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7099 - acc: 0.8679 - val_loss: 0.6928 - val_acc: 0.8755 Epoch 169/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7101 - acc: 0.8678 - val_loss: 0.6947 - val_acc: 0.8786 Epoch 170/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7097 - acc: 0.8717 - val_loss: 0.6886 - val_acc: 0.8789 Epoch 171/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.7070 - acc: 0.8702 - val_loss: 0.6878 - val_acc: 0.8793 Epoch 172/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.7117 - acc: 0.8679 - val_loss: 0.6783 - val_acc: 0.8836 Epoch 173/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7102 - acc: 0.8687 - val_loss: 0.6709 - val_acc: 0.8865 Epoch 174/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.7038 - acc: 0.8717 - val_loss: 0.6839 - val_acc: 0.8804 Epoch 175/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7062 - acc: 0.8713 - val_loss: 0.6934 - val_acc: 0.8780 Epoch 176/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7092 - acc: 0.8684 - val_loss: 0.7045 - val_acc: 0.8737 Epoch 177/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7048 - acc: 0.8703 - val_loss: 0.6935 - val_acc: 0.8764 Epoch 178/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.7056 - acc: 0.8713 - val_loss: 0.6825 - val_acc: 0.8800 Epoch 179/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.7027 - acc: 0.8722 - val_loss: 0.6860 - val_acc: 0.8812 Epoch 180/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7056 - acc: 0.8699 - val_loss: 0.6882 - val_acc: 0.8762 Epoch 181/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6974 - acc: 0.8745 - val_loss: 0.7030 - val_acc: 0.8704 Epoch 182/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7028 - acc: 0.8714 - val_loss: 0.6754 - val_acc: 0.8860 Epoch 183/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7022 - acc: 0.8715 - val_loss: 0.6635 - val_acc: 0.8842 Epoch 184/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7034 - acc: 0.8704 - val_loss: 0.6905 - val_acc: 0.8762 Epoch 185/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7058 - acc: 0.8709 - val_loss: 0.7066 - val_acc: 0.8740 Epoch 186/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7016 - acc: 0.8726 - val_loss: 0.6842 - val_acc: 0.8784 Epoch 187/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6999 - acc: 0.8719 - val_loss: 0.7051 - val_acc: 0.8731 Epoch 188/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7026 - acc: 0.8710 - val_loss: 0.6811 - val_acc: 0.8811 Epoch 189/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7040 - acc: 0.8711 - val_loss: 0.6794 - val_acc: 0.8786 Epoch 190/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7004 - acc: 0.8728 - val_loss: 0.6594 - val_acc: 0.8916 Epoch 191/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6982 - acc: 0.8747 - val_loss: 0.6616 - val_acc: 0.8850 Epoch 192/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7036 - acc: 0.8718 - val_loss: 0.6959 - val_acc: 0.8730 Epoch 193/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7017 - acc: 0.8708 - val_loss: 0.6671 - val_acc: 0.8862 Epoch 194/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6982 - acc: 0.8738 - val_loss: 0.6885 - val_acc: 0.8790 Epoch 195/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6996 - acc: 0.8714 - val_loss: 0.6892 - val_acc: 0.8770 Epoch 196/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7026 - acc: 0.8706 - val_loss: 0.6824 - val_acc: 0.8792 Epoch 197/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7061 - acc: 0.8695 - val_loss: 0.6893 - val_acc: 0.8793 Epoch 198/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7023 - acc: 0.8714 - val_loss: 0.6797 - val_acc: 0.8819 Epoch 199/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.7021 - acc: 0.8726 - val_loss: 0.6969 - val_acc: 0.8754 Epoch 200/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7023 - acc: 0.8711 - val_loss: 0.6922 - val_acc: 0.8758 Epoch 201/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7050 - acc: 0.8705 - val_loss: 0.6879 - val_acc: 0.8792 Epoch 202/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7012 - acc: 0.8713 - val_loss: 0.6756 - val_acc: 0.8845 Epoch 203/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7021 - acc: 0.8726 - val_loss: 0.6542 - val_acc: 0.8904 Epoch 204/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6981 - acc: 0.8741 - val_loss: 0.7060 - val_acc: 0.8739 Epoch 205/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7008 - acc: 0.8718 - val_loss: 0.6938 - val_acc: 0.8741 Epoch 206/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6974 - acc: 0.8725 - val_loss: 0.6786 - val_acc: 0.8833 Epoch 207/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6938 - acc: 0.8739 - val_loss: 0.6928 - val_acc: 0.8750 Epoch 208/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7075 - acc: 0.8690 - val_loss: 0.6770 - val_acc: 0.8806 Epoch 209/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6978 - acc: 0.8723 - val_loss: 0.6913 - val_acc: 0.8812 Epoch 210/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6974 - acc: 0.8727 - val_loss: 0.6764 - val_acc: 0.8827 Epoch 211/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6998 - acc: 0.8724 - val_loss: 0.7139 - val_acc: 0.8700 Epoch 212/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6975 - acc: 0.8740 - val_loss: 0.6851 - val_acc: 0.8805 Epoch 213/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7032 - acc: 0.8704 - val_loss: 0.7101 - val_acc: 0.8712 Epoch 214/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6979 - acc: 0.8732 - val_loss: 0.7108 - val_acc: 0.8756 Epoch 215/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6986 - acc: 0.8749 - val_loss: 0.7092 - val_acc: 0.8701 Epoch 216/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6921 - acc: 0.8757 - val_loss: 0.6868 - val_acc: 0.8792 Epoch 217/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6930 - acc: 0.8755 - val_loss: 0.7097 - val_acc: 0.8721 Epoch 218/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7039 - acc: 0.8713 - val_loss: 0.6901 - val_acc: 0.8789 Epoch 219/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6931 - acc: 0.8757 - val_loss: 0.6927 - val_acc: 0.8793 Epoch 220/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6991 - acc: 0.8734 - val_loss: 0.6946 - val_acc: 0.8777 Epoch 221/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6950 - acc: 0.8739 - val_loss: 0.6740 - val_acc: 0.8819 Epoch 222/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6970 - acc: 0.8739 - val_loss: 0.6847 - val_acc: 0.8787 Epoch 223/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.7006 - acc: 0.8719 - val_loss: 0.7139 - val_acc: 0.8707 Epoch 224/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.7014 - acc: 0.8720 - val_loss: 0.6901 - val_acc: 0.8768 Epoch 225/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6981 - acc: 0.8739 - val_loss: 0.6758 - val_acc: 0.8853 Epoch 226/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6921 - acc: 0.8739 - val_loss: 0.6743 - val_acc: 0.8844 Epoch 227/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6983 - acc: 0.8734 - val_loss: 0.7031 - val_acc: 0.8736 Epoch 228/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6954 - acc: 0.8734 - val_loss: 0.7181 - val_acc: 0.8663 Epoch 229/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6893 - acc: 0.8759 - val_loss: 0.6982 - val_acc: 0.8740 Epoch 230/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6964 - acc: 0.8736 - val_loss: 0.6927 - val_acc: 0.8748 Epoch 231/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6987 - acc: 0.8742 - val_loss: 0.6898 - val_acc: 0.8772 Epoch 232/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6980 - acc: 0.8731 - val_loss: 0.6862 - val_acc: 0.8810 Epoch 233/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6975 - acc: 0.8749 - val_loss: 0.6987 - val_acc: 0.8783 Epoch 234/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6892 - acc: 0.8778 - val_loss: 0.6902 - val_acc: 0.8773 Epoch 235/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.6925 - acc: 0.8762 - val_loss: 0.6787 - val_acc: 0.8799 Epoch 236/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6954 - acc: 0.8735 - val_loss: 0.6910 - val_acc: 0.8797 Epoch 237/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6963 - acc: 0.8746 - val_loss: 0.6886 - val_acc: 0.8785 Epoch 238/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6950 - acc: 0.8764 - val_loss: 0.7008 - val_acc: 0.8766 Epoch 239/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.6969 - acc: 0.8749 - val_loss: 0.7100 - val_acc: 0.8736 Epoch 240/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6905 - acc: 0.8757 - val_loss: 0.6971 - val_acc: 0.8733 Epoch 241/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6912 - acc: 0.8740 - val_loss: 0.6809 - val_acc: 0.8805 Epoch 242/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6949 - acc: 0.8729 - val_loss: 0.6903 - val_acc: 0.8760 Epoch 243/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6938 - acc: 0.8753 - val_loss: 0.6809 - val_acc: 0.8823 Epoch 244/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.6912 - acc: 0.8754 - val_loss: 0.6700 - val_acc: 0.8829 Epoch 245/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6939 - acc: 0.8766 - val_loss: 0.6691 - val_acc: 0.8847 Epoch 246/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6885 - acc: 0.8756 - val_loss: 0.7018 - val_acc: 0.8782 Epoch 247/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6916 - acc: 0.8766 - val_loss: 0.6896 - val_acc: 0.8789 Epoch 248/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6918 - acc: 0.8751 - val_loss: 0.7025 - val_acc: 0.8735 Epoch 249/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6944 - acc: 0.8756 - val_loss: 0.6754 - val_acc: 0.8811 Epoch 250/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6845 - acc: 0.8766 - val_loss: 0.6937 - val_acc: 0.8776 Epoch 251/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6915 - acc: 0.8753 - val_loss: 0.6944 - val_acc: 0.8773 Epoch 252/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6923 - acc: 0.8751 - val_loss: 0.6830 - val_acc: 0.8790 Epoch 253/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6889 - acc: 0.8756 - val_loss: 0.7251 - val_acc: 0.8658 Epoch 254/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6963 - acc: 0.8741 - val_loss: 0.6919 - val_acc: 0.8777 Epoch 255/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6920 - acc: 0.8759 - val_loss: 0.7098 - val_acc: 0.8706 Epoch 256/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6896 - acc: 0.8750 - val_loss: 0.6964 - val_acc: 0.8772 Epoch 257/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6891 - acc: 0.8757 - val_loss: 0.6604 - val_acc: 0.8871 Epoch 258/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6932 - acc: 0.8753 - val_loss: 0.6820 - val_acc: 0.8803 Epoch 259/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6863 - acc: 0.8767 - val_loss: 0.7197 - val_acc: 0.8710 Epoch 260/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6900 - acc: 0.8762 - val_loss: 0.6588 - val_acc: 0.8907 Epoch 261/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6912 - acc: 0.8750 - val_loss: 0.6815 - val_acc: 0.8833 Epoch 262/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6893 - acc: 0.8750 - val_loss: 0.6795 - val_acc: 0.8831 Epoch 263/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6920 - acc: 0.8755 - val_loss: 0.6830 - val_acc: 0.8822 Epoch 264/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6924 - acc: 0.8743 - val_loss: 0.6989 - val_acc: 0.8762 Epoch 265/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6905 - acc: 0.8763 - val_loss: 0.6836 - val_acc: 0.8806 Epoch 266/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6883 - acc: 0.8759 - val_loss: 0.6731 - val_acc: 0.8855 Epoch 267/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6852 - acc: 0.8768 - val_loss: 0.6898 - val_acc: 0.8794 Epoch 268/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6901 - acc: 0.8754 - val_loss: 0.6825 - val_acc: 0.8804 Epoch 269/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6929 - acc: 0.8742 - val_loss: 0.6916 - val_acc: 0.8745 Epoch 270/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6870 - acc: 0.8772 - val_loss: 0.6772 - val_acc: 0.8804 Epoch 271/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6891 - acc: 0.8761 - val_loss: 0.6891 - val_acc: 0.8756 Epoch 272/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6863 - acc: 0.8768 - val_loss: 0.6851 - val_acc: 0.8813 Epoch 273/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6891 - acc: 0.8765 - val_loss: 0.6921 - val_acc: 0.8776 Epoch 274/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6853 - acc: 0.8779 - val_loss: 0.6785 - val_acc: 0.8820 Epoch 275/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6879 - acc: 0.8767 - val_loss: 0.6994 - val_acc: 0.8728 Epoch 276/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6869 - acc: 0.8767 - val_loss: 0.6949 - val_acc: 0.8732 Epoch 277/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6796 - acc: 0.8793 - val_loss: 0.6813 - val_acc: 0.8820 Epoch 278/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6909 - acc: 0.8746 - val_loss: 0.6745 - val_acc: 0.8841 Epoch 279/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6837 - acc: 0.8777 - val_loss: 0.6951 - val_acc: 0.8761 Epoch 280/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6882 - acc: 0.8769 - val_loss: 0.6828 - val_acc: 0.8805 Epoch 281/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6909 - acc: 0.8767 - val_loss: 0.6801 - val_acc: 0.8836 Epoch 282/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6890 - acc: 0.8743 - val_loss: 0.6931 - val_acc: 0.8757 Epoch 283/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6871 - acc: 0.8772 - val_loss: 0.6791 - val_acc: 0.8837 Epoch 284/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6846 - acc: 0.8796 - val_loss: 0.7228 - val_acc: 0.8674 Epoch 285/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6857 - acc: 0.8792 - val_loss: 0.7068 - val_acc: 0.8735 Epoch 286/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6891 - acc: 0.8759 - val_loss: 0.7089 - val_acc: 0.8735 Epoch 287/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6927 - acc: 0.8770 - val_loss: 0.6755 - val_acc: 0.8823 Epoch 288/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6878 - acc: 0.8770 - val_loss: 0.6939 - val_acc: 0.8761 Epoch 289/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6858 - acc: 0.8795 - val_loss: 0.6844 - val_acc: 0.8829 Epoch 290/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6901 - acc: 0.8774 - val_loss: 0.6603 - val_acc: 0.8877 Epoch 291/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6827 - acc: 0.8805 - val_loss: 0.6700 - val_acc: 0.8877 Epoch 292/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6875 - acc: 0.8770 - val_loss: 0.6843 - val_acc: 0.8802 Epoch 293/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6861 - acc: 0.8795 - val_loss: 0.6889 - val_acc: 0.8812 Epoch 294/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.6896 - acc: 0.8759 - val_loss: 0.6688 - val_acc: 0.8874 Epoch 295/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6792 - acc: 0.8805 - val_loss: 0.6813 - val_acc: 0.8802 Epoch 296/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6946 - acc: 0.8733 - val_loss: 0.6697 - val_acc: 0.8858 Epoch 297/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6887 - acc: 0.8755 - val_loss: 0.6707 - val_acc: 0.8848 Epoch 298/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6875 - acc: 0.8765 - val_loss: 0.7025 - val_acc: 0.8718 Epoch 299/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.6853 - acc: 0.8789 - val_loss: 0.6842 - val_acc: 0.8805 Epoch 300/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.6806 - acc: 0.8809 - val_loss: 0.6948 - val_acc: 0.8809 Epoch 301/1000 lr changed to 0.010000000149011612 500/500 [==============================] - 69s 138ms/step - loss: 0.5763 - acc: 0.9142 - val_loss: 0.5780 - val_acc: 0.9169 Epoch 302/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.5127 - acc: 0.9355 - val_loss: 0.5618 - val_acc: 0.9209 Epoch 303/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.4950 - acc: 0.9401 - val_loss: 0.5561 - val_acc: 0.9223 Epoch 304/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.4744 - acc: 0.9449 - val_loss: 0.5485 - val_acc: 0.9229 Epoch 305/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.4602 - acc: 0.9489 - val_loss: 0.5469 - val_acc: 0.9206 Epoch 306/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.4533 - acc: 0.9479 - val_loss: 0.5368 - val_acc: 0.9209 Epoch 307/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.4463 - acc: 0.9498 - val_loss: 0.5294 - val_acc: 0.9230 Epoch 308/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.4371 - acc: 0.9508 - val_loss: 0.5304 - val_acc: 0.9228 Epoch 309/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.4276 - acc: 0.9515 - val_loss: 0.5217 - val_acc: 0.9236 Epoch 310/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.4185 - acc: 0.9542 - val_loss: 0.5202 - val_acc: 0.9235 Epoch 311/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.4079 - acc: 0.9563 - val_loss: 0.5213 - val_acc: 0.9224 Epoch 312/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.4028 - acc: 0.9559 - val_loss: 0.5149 - val_acc: 0.9241 Epoch 313/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3940 - acc: 0.9582 - val_loss: 0.5182 - val_acc: 0.9229 Epoch 314/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3913 - acc: 0.9584 - val_loss: 0.5063 - val_acc: 0.9222 Epoch 315/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3815 - acc: 0.9599 - val_loss: 0.5065 - val_acc: 0.9242 Epoch 316/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3779 - acc: 0.9596 - val_loss: 0.5105 - val_acc: 0.9197 Epoch 317/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3734 - acc: 0.9607 - val_loss: 0.4951 - val_acc: 0.9242 Epoch 318/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3668 - acc: 0.9608 - val_loss: 0.4984 - val_acc: 0.9226 Epoch 319/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.3600 - acc: 0.9628 - val_loss: 0.5003 - val_acc: 0.9195 Epoch 320/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.3562 - acc: 0.9622 - val_loss: 0.4927 - val_acc: 0.9206 Epoch 321/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3551 - acc: 0.9619 - val_loss: 0.4883 - val_acc: 0.9233 Epoch 322/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3467 - acc: 0.9635 - val_loss: 0.4820 - val_acc: 0.9247 Epoch 323/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3468 - acc: 0.9621 - val_loss: 0.4795 - val_acc: 0.9225 Epoch 324/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3386 - acc: 0.9651 - val_loss: 0.4927 - val_acc: 0.9205 Epoch 325/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.3368 - acc: 0.9644 - val_loss: 0.4823 - val_acc: 0.9205 Epoch 326/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3284 - acc: 0.9667 - val_loss: 0.4691 - val_acc: 0.9236 Epoch 327/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3255 - acc: 0.9658 - val_loss: 0.4734 - val_acc: 0.9252 Epoch 328/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3255 - acc: 0.9648 - val_loss: 0.4795 - val_acc: 0.9230 Epoch 329/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3257 - acc: 0.9638 - val_loss: 0.4681 - val_acc: 0.9223 Epoch 330/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3181 - acc: 0.9648 - val_loss: 0.4670 - val_acc: 0.9215 Epoch 331/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3138 - acc: 0.9660 - val_loss: 0.4821 - val_acc: 0.9185 Epoch 332/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.3140 - acc: 0.9648 - val_loss: 0.4727 - val_acc: 0.9202 Epoch 333/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.3102 - acc: 0.9663 - val_loss: 0.4632 - val_acc: 0.9231 Epoch 334/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.3085 - acc: 0.9663 - val_loss: 0.4611 - val_acc: 0.9240 Epoch 335/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.3019 - acc: 0.9679 - val_loss: 0.4614 - val_acc: 0.9238 Epoch 336/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.3046 - acc: 0.9654 - val_loss: 0.4635 - val_acc: 0.9202 Epoch 337/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.3015 - acc: 0.9660 - val_loss: 0.4599 - val_acc: 0.9228 Epoch 338/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.2992 - acc: 0.9662 - val_loss: 0.4577 - val_acc: 0.9207 Epoch 339/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.2942 - acc: 0.9669 - val_loss: 0.4702 - val_acc: 0.9172 Epoch 340/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.2924 - acc: 0.9675 - val_loss: 0.4545 - val_acc: 0.9211 ... Epoch 597/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.2366 - acc: 0.9703 - val_loss: 0.4557 - val_acc: 0.9103 Epoch 598/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.2399 - acc: 0.9697 - val_loss: 0.4449 - val_acc: 0.9117 Epoch 599/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.2397 - acc: 0.9689 - val_loss: 0.4359 - val_acc: 0.9147 Epoch 600/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.2341 - acc: 0.9717 - val_loss: 0.4224 - val_acc: 0.9169 Epoch 601/1000 lr changed to 0.0009999999776482583 500/500 [==============================] - 68s 136ms/step - loss: 0.2082 - acc: 0.9813 - val_loss: 0.3916 - val_acc: 0.9268 Epoch 602/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1952 - acc: 0.9865 - val_loss: 0.3854 - val_acc: 0.9281 Epoch 603/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1878 - acc: 0.9881 - val_loss: 0.3852 - val_acc: 0.9299 Epoch 604/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1846 - acc: 0.9899 - val_loss: 0.3842 - val_acc: 0.9298 Epoch 605/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1826 - acc: 0.9909 - val_loss: 0.3829 - val_acc: 0.9326 Epoch 606/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1808 - acc: 0.9912 - val_loss: 0.3838 - val_acc: 0.9305 Epoch 607/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1771 - acc: 0.9927 - val_loss: 0.3851 - val_acc: 0.9303 Epoch 608/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1768 - acc: 0.9922 - val_loss: 0.3898 - val_acc: 0.9304 Epoch 609/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1758 - acc: 0.9926 - val_loss: 0.3878 - val_acc: 0.9309 Epoch 610/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1739 - acc: 0.9931 - val_loss: 0.3887 - val_acc: 0.9294 Epoch 611/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1731 - acc: 0.9934 - val_loss: 0.3874 - val_acc: 0.9311 Epoch 612/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1725 - acc: 0.9935 - val_loss: 0.3898 - val_acc: 0.9297 Epoch 613/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1717 - acc: 0.9937 - val_loss: 0.3900 - val_acc: 0.9298 Epoch 614/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1705 - acc: 0.9937 - val_loss: 0.3912 - val_acc: 0.9299 Epoch 615/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1709 - acc: 0.9934 - val_loss: 0.3898 - val_acc: 0.9307 Epoch 616/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1686 - acc: 0.9948 - val_loss: 0.3905 - val_acc: 0.9311 Epoch 617/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1695 - acc: 0.9942 - val_loss: 0.3948 - val_acc: 0.9303 Epoch 618/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1688 - acc: 0.9941 - val_loss: 0.3936 - val_acc: 0.9298 Epoch 619/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1679 - acc: 0.9945 - val_loss: 0.3950 - val_acc: 0.9290 Epoch 620/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1675 - acc: 0.9941 - val_loss: 0.3940 - val_acc: 0.9300 Epoch 621/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1651 - acc: 0.9949 - val_loss: 0.3956 - val_acc: 0.9309 Epoch 622/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1653 - acc: 0.9951 - val_loss: 0.3950 - val_acc: 0.9306 Epoch 623/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1656 - acc: 0.9946 - val_loss: 0.3947 - val_acc: 0.9306 Epoch 624/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1644 - acc: 0.9949 - val_loss: 0.3946 - val_acc: 0.9304 Epoch 625/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1636 - acc: 0.9951 - val_loss: 0.3944 - val_acc: 0.9296 Epoch 626/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1630 - acc: 0.9951 - val_loss: 0.3937 - val_acc: 0.9295 Epoch 627/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1630 - acc: 0.9953 - val_loss: 0.3959 - val_acc: 0.9296 Epoch 628/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1627 - acc: 0.9954 - val_loss: 0.3939 - val_acc: 0.9289 Epoch 629/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1630 - acc: 0.9947 - val_loss: 0.3937 - val_acc: 0.9303 Epoch 630/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1614 - acc: 0.9958 - val_loss: 0.3909 - val_acc: 0.9316 Epoch 631/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1624 - acc: 0.9950 - val_loss: 0.3922 - val_acc: 0.9310 Epoch 632/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1611 - acc: 0.9954 - val_loss: 0.3907 - val_acc: 0.9313 Epoch 633/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1599 - acc: 0.9955 - val_loss: 0.3893 - val_acc: 0.9295 Epoch 634/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1600 - acc: 0.9954 - val_loss: 0.3886 - val_acc: 0.9308 Epoch 635/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1593 - acc: 0.9953 - val_loss: 0.3926 - val_acc: 0.9297 Epoch 636/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1594 - acc: 0.9950 - val_loss: 0.3945 - val_acc: 0.9289 Epoch 637/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1595 - acc: 0.9955 - val_loss: 0.3937 - val_acc: 0.9306 Epoch 638/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1591 - acc: 0.9958 - val_loss: 0.3882 - val_acc: 0.9306 Epoch 639/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1586 - acc: 0.9959 - val_loss: 0.3893 - val_acc: 0.9309 Epoch 640/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1588 - acc: 0.9956 - val_loss: 0.3935 - val_acc: 0.9300 Epoch 641/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1571 - acc: 0.9960 - val_loss: 0.3917 - val_acc: 0.9298 Epoch 642/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1576 - acc: 0.9956 - val_loss: 0.3945 - val_acc: 0.9284 Epoch 643/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1570 - acc: 0.9961 - val_loss: 0.3899 - val_acc: 0.9309 Epoch 644/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1565 - acc: 0.9962 - val_loss: 0.3918 - val_acc: 0.9307 Epoch 645/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1563 - acc: 0.9956 - val_loss: 0.3940 - val_acc: 0.9307 Epoch 646/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1563 - acc: 0.9956 - val_loss: 0.3895 - val_acc: 0.9322 Epoch 647/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1555 - acc: 0.9963 - val_loss: 0.3903 - val_acc: 0.9302 Epoch 648/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1556 - acc: 0.9958 - val_loss: 0.3926 - val_acc: 0.9307 Epoch 649/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1542 - acc: 0.9962 - val_loss: 0.3904 - val_acc: 0.9308 Epoch 650/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1552 - acc: 0.9959 - val_loss: 0.3934 - val_acc: 0.9295 Epoch 651/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1548 - acc: 0.9959 - val_loss: 0.3921 - val_acc: 0.9307 Epoch 652/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1537 - acc: 0.9964 - val_loss: 0.3973 - val_acc: 0.9293 Epoch 653/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1540 - acc: 0.9958 - val_loss: 0.3950 - val_acc: 0.9287 Epoch 654/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1523 - acc: 0.9965 - val_loss: 0.3956 - val_acc: 0.9296 Epoch 655/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1532 - acc: 0.9964 - val_loss: 0.3991 - val_acc: 0.9292 Epoch 656/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1538 - acc: 0.9957 - val_loss: 0.3995 - val_acc: 0.9296 Epoch 657/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1520 - acc: 0.9966 - val_loss: 0.3988 - val_acc: 0.9310 Epoch 658/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1532 - acc: 0.9959 - val_loss: 0.3961 - val_acc: 0.9307 Epoch 659/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1526 - acc: 0.9958 - val_loss: 0.3948 - val_acc: 0.9306 Epoch 660/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1512 - acc: 0.9965 - val_loss: 0.3947 - val_acc: 0.9309 Epoch 661/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1519 - acc: 0.9962 - val_loss: 0.3959 - val_acc: 0.9315 Epoch 662/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1510 - acc: 0.9963 - val_loss: 0.3962 - val_acc: 0.9312 Epoch 663/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1517 - acc: 0.9960 - val_loss: 0.3939 - val_acc: 0.9304 Epoch 664/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1494 - acc: 0.9964 - val_loss: 0.3928 - val_acc: 0.9309 Epoch 665/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1492 - acc: 0.9966 - val_loss: 0.3900 - val_acc: 0.9320 Epoch 666/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1493 - acc: 0.9963 - val_loss: 0.3907 - val_acc: 0.9312 Epoch 667/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1491 - acc: 0.9967 - val_loss: 0.3930 - val_acc: 0.9309 Epoch 668/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1494 - acc: 0.9960 - val_loss: 0.3923 - val_acc: 0.9301 Epoch 669/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1485 - acc: 0.9966 - val_loss: 0.3941 - val_acc: 0.9308 Epoch 670/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1486 - acc: 0.9963 - val_loss: 0.3927 - val_acc: 0.9314 Epoch 671/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1481 - acc: 0.9965 - val_loss: 0.3939 - val_acc: 0.9322 Epoch 672/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1474 - acc: 0.9968 - val_loss: 0.3950 - val_acc: 0.9309 Epoch 673/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1471 - acc: 0.9967 - val_loss: 0.3931 - val_acc: 0.9322 Epoch 674/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1470 - acc: 0.9968 - val_loss: 0.3934 - val_acc: 0.9319 Epoch 675/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1469 - acc: 0.9965 - val_loss: 0.3920 - val_acc: 0.9319 Epoch 676/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1469 - acc: 0.9967 - val_loss: 0.3923 - val_acc: 0.9309 Epoch 677/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1461 - acc: 0.9968 - val_loss: 0.3940 - val_acc: 0.9297 Epoch 678/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1462 - acc: 0.9969 - val_loss: 0.3924 - val_acc: 0.9309 Epoch 679/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1443 - acc: 0.9971 - val_loss: 0.3930 - val_acc: 0.9317 Epoch 680/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1458 - acc: 0.9966 - val_loss: 0.3978 - val_acc: 0.9296 Epoch 681/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1453 - acc: 0.9970 - val_loss: 0.3978 - val_acc: 0.9286 Epoch 682/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1444 - acc: 0.9972 - val_loss: 0.3968 - val_acc: 0.9285 Epoch 683/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1444 - acc: 0.9969 - val_loss: 0.3922 - val_acc: 0.9299 Epoch 684/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1447 - acc: 0.9970 - val_loss: 0.3907 - val_acc: 0.9297 Epoch 685/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1441 - acc: 0.9966 - val_loss: 0.3925 - val_acc: 0.9285 Epoch 686/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1454 - acc: 0.9964 - val_loss: 0.3939 - val_acc: 0.9296 Epoch 687/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1433 - acc: 0.9968 - val_loss: 0.3955 - val_acc: 0.9293 Epoch 688/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1435 - acc: 0.9969 - val_loss: 0.3958 - val_acc: 0.9295 Epoch 689/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1423 - acc: 0.9972 - val_loss: 0.3981 - val_acc: 0.9305 Epoch 690/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1438 - acc: 0.9965 - val_loss: 0.3986 - val_acc: 0.9299 Epoch 691/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1422 - acc: 0.9972 - val_loss: 0.3956 - val_acc: 0.9302 Epoch 692/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1425 - acc: 0.9968 - val_loss: 0.3962 - val_acc: 0.9309 Epoch 693/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1420 - acc: 0.9968 - val_loss: 0.3972 - val_acc: 0.9300 Epoch 694/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1422 - acc: 0.9967 - val_loss: 0.3947 - val_acc: 0.9301 Epoch 695/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1420 - acc: 0.9970 - val_loss: 0.3945 - val_acc: 0.9306 Epoch 696/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1412 - acc: 0.9970 - val_loss: 0.3942 - val_acc: 0.9313 Epoch 697/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1402 - acc: 0.9972 - val_loss: 0.3950 - val_acc: 0.9309 Epoch 698/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1408 - acc: 0.9969 - val_loss: 0.3931 - val_acc: 0.9307 Epoch 699/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1409 - acc: 0.9970 - val_loss: 0.3936 - val_acc: 0.9297 Epoch 700/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1404 - acc: 0.9970 - val_loss: 0.3930 - val_acc: 0.9289 Epoch 701/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1403 - acc: 0.9972 - val_loss: 0.3905 - val_acc: 0.9308 Epoch 702/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1387 - acc: 0.9976 - val_loss: 0.3957 - val_acc: 0.9295 Epoch 703/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1402 - acc: 0.9967 - val_loss: 0.3950 - val_acc: 0.9294 Epoch 704/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1393 - acc: 0.9971 - val_loss: 0.3950 - val_acc: 0.9298 Epoch 705/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1386 - acc: 0.9969 - val_loss: 0.3950 - val_acc: 0.9302 Epoch 706/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1384 - acc: 0.9973 - val_loss: 0.3936 - val_acc: 0.9303 Epoch 707/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1386 - acc: 0.9970 - val_loss: 0.3974 - val_acc: 0.9290 Epoch 708/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1392 - acc: 0.9968 - val_loss: 0.3938 - val_acc: 0.9295 Epoch 709/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1383 - acc: 0.9970 - val_loss: 0.3931 - val_acc: 0.9288 Epoch 710/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1383 - acc: 0.9970 - val_loss: 0.3905 - val_acc: 0.9305 Epoch 711/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1381 - acc: 0.9970 - val_loss: 0.3904 - val_acc: 0.9286 Epoch 712/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1375 - acc: 0.9971 - val_loss: 0.3923 - val_acc: 0.9302 Epoch 713/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1370 - acc: 0.9972 - val_loss: 0.3931 - val_acc: 0.9308 Epoch 714/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1364 - acc: 0.9974 - val_loss: 0.3883 - val_acc: 0.9322 Epoch 715/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1364 - acc: 0.9974 - val_loss: 0.3894 - val_acc: 0.9306 Epoch 716/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1365 - acc: 0.9972 - val_loss: 0.3894 - val_acc: 0.9290 Epoch 717/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1358 - acc: 0.9973 - val_loss: 0.3908 - val_acc: 0.9294 Epoch 718/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1360 - acc: 0.9971 - val_loss: 0.3899 - val_acc: 0.9297 Epoch 719/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1370 - acc: 0.9969 - val_loss: 0.3880 - val_acc: 0.9311 Epoch 720/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1348 - acc: 0.9971 - val_loss: 0.3884 - val_acc: 0.9308 Epoch 721/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1354 - acc: 0.9973 - val_loss: 0.3946 - val_acc: 0.9299 Epoch 722/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1346 - acc: 0.9973 - val_loss: 0.3890 - val_acc: 0.9313 Epoch 723/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1355 - acc: 0.9972 - val_loss: 0.3914 - val_acc: 0.9313 Epoch 724/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1353 - acc: 0.9970 - val_loss: 0.3956 - val_acc: 0.9308 Epoch 725/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1349 - acc: 0.9972 - val_loss: 0.3914 - val_acc: 0.9303 Epoch 726/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1338 - acc: 0.9975 - val_loss: 0.3917 - val_acc: 0.9297 Epoch 727/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1335 - acc: 0.9977 - val_loss: 0.3877 - val_acc: 0.9318 Epoch 728/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1329 - acc: 0.9977 - val_loss: 0.3830 - val_acc: 0.9324 Epoch 729/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1332 - acc: 0.9973 - val_loss: 0.3870 - val_acc: 0.9314 Epoch 730/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1330 - acc: 0.9976 - val_loss: 0.3870 - val_acc: 0.9321 Epoch 731/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1324 - acc: 0.9978 - val_loss: 0.3841 - val_acc: 0.9308 Epoch 732/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1329 - acc: 0.9971 - val_loss: 0.3853 - val_acc: 0.9316 Epoch 733/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1323 - acc: 0.9975 - val_loss: 0.3868 - val_acc: 0.9310 Epoch 734/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1322 - acc: 0.9975 - val_loss: 0.3882 - val_acc: 0.9301 Epoch 735/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1314 - acc: 0.9975 - val_loss: 0.3880 - val_acc: 0.9289 Epoch 736/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1327 - acc: 0.9971 - val_loss: 0.3891 - val_acc: 0.9295 Epoch 737/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1308 - acc: 0.9978 - val_loss: 0.3862 - val_acc: 0.9303 Epoch 738/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1314 - acc: 0.9975 - val_loss: 0.3872 - val_acc: 0.9294 Epoch 739/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1305 - acc: 0.9979 - val_loss: 0.3864 - val_acc: 0.9309 Epoch 740/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1310 - acc: 0.9973 - val_loss: 0.3896 - val_acc: 0.9307 Epoch 741/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1311 - acc: 0.9974 - val_loss: 0.3883 - val_acc: 0.9312 Epoch 742/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1315 - acc: 0.9968 - val_loss: 0.3888 - val_acc: 0.9304 Epoch 743/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1297 - acc: 0.9977 - val_loss: 0.3892 - val_acc: 0.9307 Epoch 744/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1298 - acc: 0.9976 - val_loss: 0.3864 - val_acc: 0.9297 Epoch 745/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1299 - acc: 0.9974 - val_loss: 0.3883 - val_acc: 0.9306 Epoch 746/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1301 - acc: 0.9972 - val_loss: 0.3892 - val_acc: 0.9290 Epoch 747/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1290 - acc: 0.9977 - val_loss: 0.3860 - val_acc: 0.9301 Epoch 748/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1299 - acc: 0.9972 - val_loss: 0.3846 - val_acc: 0.9308 Epoch 749/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1292 - acc: 0.9973 - val_loss: 0.3888 - val_acc: 0.9293 Epoch 750/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1297 - acc: 0.9973 - val_loss: 0.3864 - val_acc: 0.9289 Epoch 751/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1296 - acc: 0.9969 - val_loss: 0.3886 - val_acc: 0.9305 Epoch 752/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1288 - acc: 0.9972 - val_loss: 0.3893 - val_acc: 0.9285 Epoch 753/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1281 - acc: 0.9977 - val_loss: 0.3824 - val_acc: 0.9308 Epoch 754/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1288 - acc: 0.9973 - val_loss: 0.3817 - val_acc: 0.9300 Epoch 755/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1274 - acc: 0.9978 - val_loss: 0.3818 - val_acc: 0.9290 Epoch 756/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1282 - acc: 0.9971 - val_loss: 0.3843 - val_acc: 0.9277 Epoch 757/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1276 - acc: 0.9975 - val_loss: 0.3822 - val_acc: 0.9285 Epoch 758/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1274 - acc: 0.9974 - val_loss: 0.3837 - val_acc: 0.9301 Epoch 759/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1274 - acc: 0.9971 - val_loss: 0.3819 - val_acc: 0.9290 Epoch 760/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1261 - acc: 0.9977 - val_loss: 0.3803 - val_acc: 0.9308 Epoch 761/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1274 - acc: 0.9970 - val_loss: 0.3834 - val_acc: 0.9297 Epoch 762/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1264 - acc: 0.9977 - val_loss: 0.3845 - val_acc: 0.9300 Epoch 763/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1271 - acc: 0.9969 - val_loss: 0.3827 - val_acc: 0.9296 Epoch 764/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1264 - acc: 0.9974 - val_loss: 0.3772 - val_acc: 0.9316 Epoch 765/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1255 - acc: 0.9976 - val_loss: 0.3735 - val_acc: 0.9323 Epoch 766/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1253 - acc: 0.9977 - val_loss: 0.3743 - val_acc: 0.9325 Epoch 767/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1252 - acc: 0.9977 - val_loss: 0.3774 - val_acc: 0.9319 Epoch 768/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1251 - acc: 0.9975 - val_loss: 0.3778 - val_acc: 0.9324 Epoch 769/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1261 - acc: 0.9971 - val_loss: 0.3811 - val_acc: 0.9310 Epoch 770/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1242 - acc: 0.9979 - val_loss: 0.3808 - val_acc: 0.9295 Epoch 771/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1249 - acc: 0.9975 - val_loss: 0.3780 - val_acc: 0.9304 Epoch 772/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1247 - acc: 0.9974 - val_loss: 0.3779 - val_acc: 0.9312 Epoch 773/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1246 - acc: 0.9974 - val_loss: 0.3811 - val_acc: 0.9314 Epoch 774/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1244 - acc: 0.9976 - val_loss: 0.3798 - val_acc: 0.9303 Epoch 775/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1243 - acc: 0.9974 - val_loss: 0.3804 - val_acc: 0.9307 Epoch 776/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1235 - acc: 0.9975 - val_loss: 0.3800 - val_acc: 0.9310 Epoch 777/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1240 - acc: 0.9973 - val_loss: 0.3795 - val_acc: 0.9304 Epoch 778/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1234 - acc: 0.9975 - val_loss: 0.3760 - val_acc: 0.9320 Epoch 779/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1235 - acc: 0.9976 - val_loss: 0.3750 - val_acc: 0.9312 Epoch 780/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1226 - acc: 0.9976 - val_loss: 0.3721 - val_acc: 0.9332 Epoch 781/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1227 - acc: 0.9976 - val_loss: 0.3753 - val_acc: 0.9322 Epoch 782/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1226 - acc: 0.9975 - val_loss: 0.3756 - val_acc: 0.9316 Epoch 783/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1228 - acc: 0.9975 - val_loss: 0.3761 - val_acc: 0.9302 Epoch 784/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1216 - acc: 0.9978 - val_loss: 0.3711 - val_acc: 0.9329 Epoch 785/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1221 - acc: 0.9975 - val_loss: 0.3750 - val_acc: 0.9300 Epoch 786/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1213 - acc: 0.9978 - val_loss: 0.3739 - val_acc: 0.9305 Epoch 787/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1211 - acc: 0.9978 - val_loss: 0.3744 - val_acc: 0.9315 Epoch 788/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1209 - acc: 0.9978 - val_loss: 0.3730 - val_acc: 0.9321 Epoch 789/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1219 - acc: 0.9975 - val_loss: 0.3719 - val_acc: 0.9329 Epoch 790/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1212 - acc: 0.9975 - val_loss: 0.3753 - val_acc: 0.9318 Epoch 791/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1208 - acc: 0.9974 - val_loss: 0.3744 - val_acc: 0.9310 Epoch 792/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1222 - acc: 0.9969 - val_loss: 0.3804 - val_acc: 0.9291 Epoch 793/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1209 - acc: 0.9977 - val_loss: 0.3806 - val_acc: 0.9295 Epoch 794/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1203 - acc: 0.9977 - val_loss: 0.3809 - val_acc: 0.9282 Epoch 795/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1203 - acc: 0.9975 - val_loss: 0.3785 - val_acc: 0.9286 Epoch 796/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1199 - acc: 0.9978 - val_loss: 0.3783 - val_acc: 0.9274 Epoch 797/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1196 - acc: 0.9977 - val_loss: 0.3780 - val_acc: 0.9281 Epoch 798/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1195 - acc: 0.9976 - val_loss: 0.3763 - val_acc: 0.9311 Epoch 799/1000 500/500 [==============================] - 67s 135ms/step - loss: 0.1193 - acc: 0.9977 - val_loss: 0.3820 - val_acc: 0.9303 Epoch 800/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1200 - acc: 0.9972 - val_loss: 0.3833 - val_acc: 0.9296 Epoch 801/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1185 - acc: 0.9978 - val_loss: 0.3774 - val_acc: 0.9300 Epoch 802/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1195 - acc: 0.9974 - val_loss: 0.3775 - val_acc: 0.9305 Epoch 803/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1183 - acc: 0.9976 - val_loss: 0.3759 - val_acc: 0.9308 Epoch 804/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1182 - acc: 0.9979 - val_loss: 0.3728 - val_acc: 0.9316 Epoch 805/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1191 - acc: 0.9974 - val_loss: 0.3771 - val_acc: 0.9311 Epoch 806/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1179 - acc: 0.9977 - val_loss: 0.3768 - val_acc: 0.9299 Epoch 807/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1185 - acc: 0.9972 - val_loss: 0.3765 - val_acc: 0.9302 Epoch 808/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1173 - acc: 0.9978 - val_loss: 0.3794 - val_acc: 0.9291 Epoch 809/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1172 - acc: 0.9978 - val_loss: 0.3773 - val_acc: 0.9297 Epoch 810/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1181 - acc: 0.9975 - val_loss: 0.3811 - val_acc: 0.9306 Epoch 811/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1173 - acc: 0.9975 - val_loss: 0.3753 - val_acc: 0.9302 Epoch 812/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1171 - acc: 0.9975 - val_loss: 0.3812 - val_acc: 0.9285 Epoch 813/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1171 - acc: 0.9976 - val_loss: 0.3845 - val_acc: 0.9297 Epoch 814/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1163 - acc: 0.9978 - val_loss: 0.3829 - val_acc: 0.9295 Epoch 815/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1166 - acc: 0.9979 - val_loss: 0.3807 - val_acc: 0.9284 Epoch 816/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1165 - acc: 0.9976 - val_loss: 0.3813 - val_acc: 0.9286 Epoch 817/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.1170 - acc: 0.9972 - val_loss: 0.3840 - val_acc: 0.9283 Epoch 818/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1160 - acc: 0.9973 - val_loss: 0.3826 - val_acc: 0.9274 Epoch 819/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1157 - acc: 0.9977 - val_loss: 0.3755 - val_acc: 0.9312 Epoch 820/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1155 - acc: 0.9978 - val_loss: 0.3794 - val_acc: 0.9291 Epoch 821/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1163 - acc: 0.9973 - val_loss: 0.3751 - val_acc: 0.9293 Epoch 822/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1154 - acc: 0.9977 - val_loss: 0.3764 - val_acc: 0.9298 Epoch 823/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1143 - acc: 0.9980 - val_loss: 0.3754 - val_acc: 0.9293 Epoch 824/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1142 - acc: 0.9979 - val_loss: 0.3743 - val_acc: 0.9304 Epoch 825/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1153 - acc: 0.9974 - val_loss: 0.3772 - val_acc: 0.9307 Epoch 826/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1149 - acc: 0.9976 - val_loss: 0.3718 - val_acc: 0.9312 Epoch 827/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1148 - acc: 0.9976 - val_loss: 0.3777 - val_acc: 0.9317 Epoch 828/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1147 - acc: 0.9976 - val_loss: 0.3769 - val_acc: 0.9303 Epoch 829/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1137 - acc: 0.9978 - val_loss: 0.3748 - val_acc: 0.9309 Epoch 830/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1139 - acc: 0.9978 - val_loss: 0.3728 - val_acc: 0.9308 Epoch 831/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1138 - acc: 0.9976 - val_loss: 0.3724 - val_acc: 0.9296 Epoch 832/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1132 - acc: 0.9978 - val_loss: 0.3807 - val_acc: 0.9288 Epoch 833/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1140 - acc: 0.9975 - val_loss: 0.3810 - val_acc: 0.9290 Epoch 834/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1135 - acc: 0.9975 - val_loss: 0.3816 - val_acc: 0.9291 Epoch 835/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1130 - acc: 0.9979 - val_loss: 0.3830 - val_acc: 0.9284 Epoch 836/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1131 - acc: 0.9976 - val_loss: 0.3792 - val_acc: 0.9278 Epoch 837/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1126 - acc: 0.9978 - val_loss: 0.3712 - val_acc: 0.9306 Epoch 838/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1126 - acc: 0.9979 - val_loss: 0.3771 - val_acc: 0.9293 Epoch 839/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1119 - acc: 0.9981 - val_loss: 0.3768 - val_acc: 0.9288 Epoch 840/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1120 - acc: 0.9980 - val_loss: 0.3769 - val_acc: 0.9289 Epoch 841/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1120 - acc: 0.9977 - val_loss: 0.3774 - val_acc: 0.9285 Epoch 842/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1120 - acc: 0.9975 - val_loss: 0.3718 - val_acc: 0.9312 Epoch 843/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1115 - acc: 0.9976 - val_loss: 0.3707 - val_acc: 0.9312 Epoch 844/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1120 - acc: 0.9978 - val_loss: 0.3777 - val_acc: 0.9285 Epoch 845/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1115 - acc: 0.9978 - val_loss: 0.3777 - val_acc: 0.9284 Epoch 846/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1115 - acc: 0.9978 - val_loss: 0.3742 - val_acc: 0.9303 Epoch 847/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1113 - acc: 0.9974 - val_loss: 0.3749 - val_acc: 0.9300 Epoch 848/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1114 - acc: 0.9976 - val_loss: 0.3795 - val_acc: 0.9286 Epoch 849/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1115 - acc: 0.9975 - val_loss: 0.3754 - val_acc: 0.9284 Epoch 850/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1105 - acc: 0.9978 - val_loss: 0.3705 - val_acc: 0.9305 Epoch 851/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1098 - acc: 0.9978 - val_loss: 0.3752 - val_acc: 0.9290 Epoch 852/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1118 - acc: 0.9971 - val_loss: 0.3773 - val_acc: 0.9280 Epoch 853/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1103 - acc: 0.9978 - val_loss: 0.3732 - val_acc: 0.9303 Epoch 854/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1109 - acc: 0.9977 - val_loss: 0.3715 - val_acc: 0.9302 Epoch 855/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1096 - acc: 0.9977 - val_loss: 0.3780 - val_acc: 0.9306 Epoch 856/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1100 - acc: 0.9977 - val_loss: 0.3764 - val_acc: 0.9290 Epoch 857/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1093 - acc: 0.9981 - val_loss: 0.3750 - val_acc: 0.9291 Epoch 858/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1088 - acc: 0.9980 - val_loss: 0.3738 - val_acc: 0.9287 Epoch 859/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1098 - acc: 0.9975 - val_loss: 0.3711 - val_acc: 0.9291 Epoch 860/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1091 - acc: 0.9979 - val_loss: 0.3636 - val_acc: 0.9302 Epoch 861/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1094 - acc: 0.9976 - val_loss: 0.3689 - val_acc: 0.9303 Epoch 862/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1088 - acc: 0.9978 - val_loss: 0.3687 - val_acc: 0.9306 Epoch 863/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1083 - acc: 0.9978 - val_loss: 0.3720 - val_acc: 0.9318 Epoch 864/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1080 - acc: 0.9978 - val_loss: 0.3695 - val_acc: 0.9302 Epoch 865/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1093 - acc: 0.9973 - val_loss: 0.3733 - val_acc: 0.9297 Epoch 866/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1092 - acc: 0.9974 - val_loss: 0.3713 - val_acc: 0.9296 Epoch 867/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1082 - acc: 0.9978 - val_loss: 0.3674 - val_acc: 0.9306 Epoch 868/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1087 - acc: 0.9974 - val_loss: 0.3684 - val_acc: 0.9296 Epoch 869/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1072 - acc: 0.9982 - val_loss: 0.3684 - val_acc: 0.9307 Epoch 870/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1080 - acc: 0.9976 - val_loss: 0.3695 - val_acc: 0.9294 Epoch 871/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1075 - acc: 0.9977 - val_loss: 0.3655 - val_acc: 0.9306 Epoch 872/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1073 - acc: 0.9979 - val_loss: 0.3667 - val_acc: 0.9303 Epoch 873/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1079 - acc: 0.9977 - val_loss: 0.3717 - val_acc: 0.9278 Epoch 874/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1081 - acc: 0.9973 - val_loss: 0.3722 - val_acc: 0.9292 Epoch 875/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1072 - acc: 0.9975 - val_loss: 0.3716 - val_acc: 0.9298 Epoch 876/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1070 - acc: 0.9977 - val_loss: 0.3721 - val_acc: 0.9311 Epoch 877/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1066 - acc: 0.9978 - val_loss: 0.3722 - val_acc: 0.9289 Epoch 878/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1068 - acc: 0.9977 - val_loss: 0.3736 - val_acc: 0.9296 Epoch 879/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1065 - acc: 0.9977 - val_loss: 0.3767 - val_acc: 0.9280 Epoch 880/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1055 - acc: 0.9979 - val_loss: 0.3741 - val_acc: 0.9285 Epoch 881/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1056 - acc: 0.9979 - val_loss: 0.3716 - val_acc: 0.9290 Epoch 882/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1061 - acc: 0.9977 - val_loss: 0.3736 - val_acc: 0.9295 Epoch 883/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1066 - acc: 0.9976 - val_loss: 0.3745 - val_acc: 0.9307 Epoch 884/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1059 - acc: 0.9975 - val_loss: 0.3702 - val_acc: 0.9302 Epoch 885/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1051 - acc: 0.9979 - val_loss: 0.3656 - val_acc: 0.9311 Epoch 886/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1051 - acc: 0.9978 - val_loss: 0.3677 - val_acc: 0.9305 Epoch 887/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1062 - acc: 0.9974 - val_loss: 0.3636 - val_acc: 0.9315 Epoch 888/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1052 - acc: 0.9977 - val_loss: 0.3710 - val_acc: 0.9295 Epoch 889/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1046 - acc: 0.9979 - val_loss: 0.3642 - val_acc: 0.9318 Epoch 890/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1051 - acc: 0.9975 - val_loss: 0.3673 - val_acc: 0.9306 Epoch 891/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1045 - acc: 0.9978 - val_loss: 0.3681 - val_acc: 0.9299 Epoch 892/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1043 - acc: 0.9979 - val_loss: 0.3659 - val_acc: 0.9320 Epoch 893/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1040 - acc: 0.9979 - val_loss: 0.3627 - val_acc: 0.9326 Epoch 894/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1041 - acc: 0.9976 - val_loss: 0.3698 - val_acc: 0.9301 Epoch 895/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1039 - acc: 0.9978 - val_loss: 0.3659 - val_acc: 0.9321 Epoch 896/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1040 - acc: 0.9978 - val_loss: 0.3718 - val_acc: 0.9300 Epoch 897/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1039 - acc: 0.9977 - val_loss: 0.3728 - val_acc: 0.9311 Epoch 898/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1044 - acc: 0.9973 - val_loss: 0.3743 - val_acc: 0.9313 Epoch 899/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1036 - acc: 0.9976 - val_loss: 0.3675 - val_acc: 0.9312 Epoch 900/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1030 - acc: 0.9979 - val_loss: 0.3730 - val_acc: 0.9313 Epoch 901/1000 lr changed to 9.999999310821295e-05 500/500 [==============================] - 69s 138ms/step - loss: 0.1023 - acc: 0.9982 - val_loss: 0.3709 - val_acc: 0.9310 Epoch 902/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1025 - acc: 0.9979 - val_loss: 0.3690 - val_acc: 0.9311 Epoch 903/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1024 - acc: 0.9980 - val_loss: 0.3679 - val_acc: 0.9311 Epoch 904/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1020 - acc: 0.9982 - val_loss: 0.3673 - val_acc: 0.9315 Epoch 905/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1027 - acc: 0.9979 - val_loss: 0.3672 - val_acc: 0.9310 Epoch 906/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1015 - acc: 0.9984 - val_loss: 0.3678 - val_acc: 0.9304 Epoch 907/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1016 - acc: 0.9984 - val_loss: 0.3673 - val_acc: 0.9302 Epoch 908/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1031 - acc: 0.9977 - val_loss: 0.3667 - val_acc: 0.9307 Epoch 909/1000 500/500 [==============================] - 69s 139ms/step - loss: 0.1019 - acc: 0.9983 - val_loss: 0.3672 - val_acc: 0.9317 Epoch 910/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1018 - acc: 0.9983 - val_loss: 0.3671 - val_acc: 0.9313 Epoch 911/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1018 - acc: 0.9982 - val_loss: 0.3669 - val_acc: 0.9309 Epoch 912/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1014 - acc: 0.9986 - val_loss: 0.3677 - val_acc: 0.9303 Epoch 913/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1015 - acc: 0.9982 - val_loss: 0.3666 - val_acc: 0.9303 Epoch 914/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1015 - acc: 0.9984 - val_loss: 0.3659 - val_acc: 0.9309 Epoch 915/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1013 - acc: 0.9983 - val_loss: 0.3651 - val_acc: 0.9318 Epoch 916/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1014 - acc: 0.9983 - val_loss: 0.3652 - val_acc: 0.9322 Epoch 917/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1010 - acc: 0.9984 - val_loss: 0.3648 - val_acc: 0.9322 Epoch 918/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1016 - acc: 0.9981 - val_loss: 0.3644 - val_acc: 0.9324 Epoch 919/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1013 - acc: 0.9983 - val_loss: 0.3635 - val_acc: 0.9319 Epoch 920/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1008 - acc: 0.9984 - val_loss: 0.3629 - val_acc: 0.9318 Epoch 921/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1006 - acc: 0.9986 - val_loss: 0.3627 - val_acc: 0.9319 Epoch 922/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1007 - acc: 0.9985 - val_loss: 0.3632 - val_acc: 0.9314 Epoch 923/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1004 - acc: 0.9987 - val_loss: 0.3626 - val_acc: 0.9319 Epoch 924/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1012 - acc: 0.9985 - val_loss: 0.3629 - val_acc: 0.9319 Epoch 925/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1011 - acc: 0.9983 - val_loss: 0.3620 - val_acc: 0.9318 Epoch 926/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1005 - acc: 0.9987 - val_loss: 0.3617 - val_acc: 0.9322 Epoch 927/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1013 - acc: 0.9983 - val_loss: 0.3618 - val_acc: 0.9330 Epoch 928/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1005 - acc: 0.9985 - val_loss: 0.3614 - val_acc: 0.9321 Epoch 929/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1006 - acc: 0.9985 - val_loss: 0.3616 - val_acc: 0.9319 Epoch 930/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1006 - acc: 0.9985 - val_loss: 0.3613 - val_acc: 0.9321 Epoch 931/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1009 - acc: 0.9985 - val_loss: 0.3612 - val_acc: 0.9328 Epoch 932/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1004 - acc: 0.9985 - val_loss: 0.3612 - val_acc: 0.9319 Epoch 933/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1004 - acc: 0.9987 - val_loss: 0.3618 - val_acc: 0.9314 Epoch 934/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1008 - acc: 0.9983 - val_loss: 0.3615 - val_acc: 0.9316 Epoch 935/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1011 - acc: 0.9983 - val_loss: 0.3621 - val_acc: 0.9317 Epoch 936/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1008 - acc: 0.9985 - val_loss: 0.3617 - val_acc: 0.9320 Epoch 937/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1006 - acc: 0.9984 - val_loss: 0.3613 - val_acc: 0.9322 Epoch 938/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1008 - acc: 0.9985 - val_loss: 0.3613 - val_acc: 0.9325 Epoch 939/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1006 - acc: 0.9984 - val_loss: 0.3614 - val_acc: 0.9326 Epoch 940/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1006 - acc: 0.9983 - val_loss: 0.3612 - val_acc: 0.9320 Epoch 941/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1005 - acc: 0.9984 - val_loss: 0.3612 - val_acc: 0.9322 Epoch 942/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1001 - acc: 0.9986 - val_loss: 0.3615 - val_acc: 0.9318 Epoch 943/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0998 - acc: 0.9987 - val_loss: 0.3613 - val_acc: 0.9320 Epoch 944/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1006 - acc: 0.9985 - val_loss: 0.3613 - val_acc: 0.9323 Epoch 945/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1000 - acc: 0.9985 - val_loss: 0.3608 - val_acc: 0.9319 Epoch 946/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1001 - acc: 0.9987 - val_loss: 0.3608 - val_acc: 0.9313 Epoch 947/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0998 - acc: 0.9987 - val_loss: 0.3606 - val_acc: 0.9314 Epoch 948/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1000 - acc: 0.9986 - val_loss: 0.3609 - val_acc: 0.9311 Epoch 949/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0995 - acc: 0.9988 - val_loss: 0.3610 - val_acc: 0.9316 Epoch 950/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0999 - acc: 0.9986 - val_loss: 0.3609 - val_acc: 0.9317 Epoch 951/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1002 - acc: 0.9986 - val_loss: 0.3612 - val_acc: 0.9314 Epoch 952/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0992 - acc: 0.9989 - val_loss: 0.3618 - val_acc: 0.9312 Epoch 953/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0996 - acc: 0.9988 - val_loss: 0.3617 - val_acc: 0.9317 Epoch 954/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0994 - acc: 0.9987 - val_loss: 0.3617 - val_acc: 0.9323 Epoch 955/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1004 - acc: 0.9984 - val_loss: 0.3610 - val_acc: 0.9320 Epoch 956/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.1000 - acc: 0.9986 - val_loss: 0.3616 - val_acc: 0.9318 Epoch 957/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1000 - acc: 0.9985 - val_loss: 0.3617 - val_acc: 0.9319 Epoch 958/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0996 - acc: 0.9985 - val_loss: 0.3627 - val_acc: 0.9323 Epoch 959/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0995 - acc: 0.9987 - val_loss: 0.3625 - val_acc: 0.9316 Epoch 960/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0995 - acc: 0.9987 - val_loss: 0.3634 - val_acc: 0.9317 Epoch 961/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0998 - acc: 0.9985 - val_loss: 0.3636 - val_acc: 0.9318 Epoch 962/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0997 - acc: 0.9986 - val_loss: 0.3645 - val_acc: 0.9319 Epoch 963/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1001 - acc: 0.9984 - val_loss: 0.3637 - val_acc: 0.9316 Epoch 964/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0998 - acc: 0.9985 - val_loss: 0.3631 - val_acc: 0.9317 Epoch 965/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0995 - acc: 0.9988 - val_loss: 0.3625 - val_acc: 0.9316 Epoch 966/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0998 - acc: 0.9986 - val_loss: 0.3622 - val_acc: 0.9324 Epoch 967/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.1002 - acc: 0.9985 - val_loss: 0.3623 - val_acc: 0.9327 Epoch 968/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0993 - acc: 0.9987 - val_loss: 0.3627 - val_acc: 0.9324 Epoch 969/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0996 - acc: 0.9985 - val_loss: 0.3624 - val_acc: 0.9327 Epoch 970/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0999 - acc: 0.9985 - val_loss: 0.3618 - val_acc: 0.9323 Epoch 971/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.1001 - acc: 0.9983 - val_loss: 0.3616 - val_acc: 0.9324 Epoch 972/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0994 - acc: 0.9986 - val_loss: 0.3620 - val_acc: 0.9320 Epoch 973/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0997 - acc: 0.9985 - val_loss: 0.3627 - val_acc: 0.9324 Epoch 974/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.1000 - acc: 0.9985 - val_loss: 0.3623 - val_acc: 0.9321 Epoch 975/1000 500/500 [==============================] - 68s 135ms/step - loss: 0.0989 - acc: 0.9990 - val_loss: 0.3619 - val_acc: 0.9321 Epoch 976/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0992 - acc: 0.9987 - val_loss: 0.3612 - val_acc: 0.9323 Epoch 977/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0996 - acc: 0.9986 - val_loss: 0.3612 - val_acc: 0.9317 Epoch 978/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0997 - acc: 0.9986 - val_loss: 0.3610 - val_acc: 0.9326 Epoch 979/1000 500/500 [==============================] - 68s 136ms/step - loss: 0.0991 - acc: 0.9987 - val_loss: 0.3611 - val_acc: 0.9327 Epoch 980/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0988 - acc: 0.9989 - val_loss: 0.3615 - val_acc: 0.9326 Epoch 981/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0992 - acc: 0.9987 - val_loss: 0.3619 - val_acc: 0.9324 Epoch 982/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0994 - acc: 0.9986 - val_loss: 0.3619 - val_acc: 0.9332 Epoch 983/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0995 - acc: 0.9986 - val_loss: 0.3617 - val_acc: 0.9329 Epoch 984/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0991 - acc: 0.9987 - val_loss: 0.3622 - val_acc: 0.9328 Epoch 985/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0991 - acc: 0.9987 - val_loss: 0.3628 - val_acc: 0.9322 Epoch 986/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0993 - acc: 0.9987 - val_loss: 0.3625 - val_acc: 0.9319 Epoch 987/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0995 - acc: 0.9986 - val_loss: 0.3629 - val_acc: 0.9317 Epoch 988/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0993 - acc: 0.9985 - val_loss: 0.3628 - val_acc: 0.9319 Epoch 989/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0997 - acc: 0.9984 - val_loss: 0.3624 - val_acc: 0.9322 Epoch 990/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0993 - acc: 0.9986 - val_loss: 0.3622 - val_acc: 0.9323 Epoch 991/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0993 - acc: 0.9986 - val_loss: 0.3625 - val_acc: 0.9327 Epoch 992/1000 500/500 [==============================] - 69s 137ms/step - loss: 0.0993 - acc: 0.9988 - val_loss: 0.3630 - val_acc: 0.9325 Epoch 993/1000 500/500 [==============================] - 68s 137ms/step - loss: 0.0992 - acc: 0.9984 - val_loss: 0.3634 - val_acc: 0.9320 Epoch 994/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0991 - acc: 0.9988 - val_loss: 0.3627 - val_acc: 0.9328 Epoch 995/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0989 - acc: 0.9989 - val_loss: 0.3637 - val_acc: 0.9321 Epoch 996/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0994 - acc: 0.9986 - val_loss: 0.3623 - val_acc: 0.9319 Epoch 997/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0987 - acc: 0.9987 - val_loss: 0.3622 - val_acc: 0.9322 Epoch 998/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0989 - acc: 0.9988 - val_loss: 0.3621 - val_acc: 0.9325 Epoch 999/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0993 - acc: 0.9984 - val_loss: 0.3615 - val_acc: 0.9326 Epoch 1000/1000 500/500 [==============================] - 69s 138ms/step - loss: 0.0986 - acc: 0.9988 - val_loss: 0.3614 - val_acc: 0.9323 Train loss: 0.09943642792105675 Train accuracy: 0.9982600016593933 Test loss: 0.3614072059094906 Test accuracy: 0.9322999995946885
在使用了shear_range = 30的資料增強以後，準確率降了呢。。
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
————————————————
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/105712292
來自 “ ITPUB部落格 ” ，連結：http://blog.itpub.net/69972329/viewspace-2689901/，如需轉載，請註明出處，否則將追究法律責任。
相關文章
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄11）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄12）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄13）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄14）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄15）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄16）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄17）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄1）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄2）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄3）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄4）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄5）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄6）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄7）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄8）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄9）函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄18）Cifar10~94.28%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄19）Cifar10~93.96%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄26）Cifar10~95.92%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄20）Cifar10~94.17%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄21）Cifar10~95.12%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄22）Cifar10~95.25%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄23）Cifar10~95.47%函式
- 深度殘差網路+自適應引數化ReLU啟用函式（調參記錄24）Cifar10~95.80%函式
- 注意力機制下的啟用函式：自適應引數化ReLU函式
- 深度殘差網路(ResNet)
- 深度學習之殘差網路深度學習
- 殘差網路再升級之深度殘差收縮網路（附Keras程式碼）Keras
- 深度殘差收縮網路：（三）網路結構
- 深度學習故障診斷——深度殘差收縮網路深度學習
- 深度殘差收縮網路：（一）背景知識
- 深度殘差收縮網路：（二）整體思路
- 學習筆記16：殘差網路筆記
- 從ReLU到GELU，一文概覽神經網路的啟用函式神經網路函式
- 十分鐘弄懂深度殘差收縮網路
- 深度殘差收縮網路：（五）實驗驗證
- 深度殘差收縮網路：（六）程式碼實現
- 殘差網路（Residual Networks, ResNets）