本文介绍哈工大团队提出的一种动态ReLU（Dynamic ReLU）激活函数，即自适应参数化ReLU激活函数，原本是应用在基于一维振动信号的故障诊断，能够让每个样本有自己独特的ReLU参数，在2019年5月3日投稿至IEEE Transactions on Industrial Electronics，2020年1月24日录用，2020年2月13日在IEEE官网公布。
在之前的调参记录18中，是将深度残差网络ResNet中的所有ReLU都替换成了自适应参数化ReLU（Adaptively Parametric ReLU，APReLU）。
由于APReLU的输入特征图与输出特征图的尺寸是完全一致的，所以APReLU可以被嵌入到神经网络的任意部分。
本文将APReLU放在每个残差模块的第二个卷积层之后。这种结构与Squeeze-and-Excitation Network是非常相似的，其区别在于APReLU额外地包含了非线性变换。
同时，迭代次数也从5000个epoch减少到了500个epoch。时间耗不起。
APReLU激活函数的原理如下图所示：
自适应参数化ReLU：一种Dynamic ReLU（动态ReLU）激活函数整体Keras代码如下：
代码语言：python代码运行次数：0复制#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 14 04:17:45 2020
Implemented using TensorFlow 1.0.1 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, DOI: 10.1109/TIE.2020.2972458,
Date of Publication: 13 February 2020
@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()
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 150 epoches
def scheduler(epoch):
if epoch % 150 == 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//16, 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 = Activation('relu')(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 = Activation('relu')(residual)
residual = Conv2D(out_channels, 3, padding='same', kernel_initializer='he_normal',
kernel_regularizer=l2(1e-4))(residual)
residual = aprelu(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, 32, 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,
# Range for random zoom
zoom_range = 0.2,
# 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=500,
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])实验结果如下：
代码语言：javascript复制Using TensorFlow backend.
x_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
Epoch 1/500
70s 140ms/step - loss: 2.5742 - acc: 0.4145 - val_loss: 2.1224 - val_acc: 0.5538
Epoch 2/500
52s 104ms/step - loss: 2.0566 - acc: 0.5631 - val_loss: 1.7678 - val_acc: 0.6502
Epoch 3/500
52s 104ms/step - loss: 1.7697 - acc: 0.6317 - val_loss: 1.5134 - val_acc: 0.7114
Epoch 4/500
52s 103ms/step - loss: 1.5790 - acc: 0.6694 - val_loss: 1.3269 - val_acc: 0.7508
Epoch 5/500
52s 104ms/step - loss: 1.4270 - acc: 0.6971 - val_loss: 1.2040 - val_acc: 0.7703
Epoch 6/500
52s 104ms/step - loss: 1.3109 - acc: 0.7165 - val_loss: 1.1187 - val_acc: 0.7809
Epoch 7/500
52s 104ms/step - loss: 1.2249 - acc: 0.7302 - val_loss: 1.0393 - val_acc: 0.7919
Epoch 8/500
52s 103ms/step - loss: 1.1457 - acc: 0.7482 - val_loss: 0.9639 - val_acc: 0.8084
Epoch 9/500
52s 104ms/step - loss: 1.0931 - acc: 0.7555 - val_loss: 0.9324 - val_acc: 0.8130
Epoch 10/500
52s 104ms/step - loss: 1.0418 - acc: 0.7693 - val_loss: 0.9043 - val_acc: 0.8138
Epoch 11/500
52s 104ms/step - loss: 1.0049 - acc: 0.7747 - val_loss: 0.8600 - val_acc: 0.8281
Epoch 12/500
51s 102ms/step - loss: 0.9835 - acc: 0.7796 - val_loss: 0.8364 - val_acc: 0.8288
Epoch 13/500
51s 103ms/step - loss: 0.9480 - acc: 0.7878 - val_loss: 0.7905 - val_acc: 0.8432
Epoch 14/500
52s 104ms/step - loss: 0.9246 - acc: 0.7906 - val_loss: 0.7895 - val_acc: 0.8400
Epoch 15/500
52s 103ms/step - loss: 0.9067 - acc: 0.7967 - val_loss: 0.7709 - val_acc: 0.8451
Epoch 16/500
52s 103ms/step - loss: 0.8928 - acc: 0.8000 - val_loss: 0.7728 - val_acc: 0.8447
Epoch 17/500
52s 104ms/step - loss: 0.8787 - acc: 0.8042 - val_loss: 0.7842 - val_acc: 0.8367
Epoch 18/500
52s 104ms/step - loss: 0.8644 - acc: 0.8095 - val_loss: 0.7588 - val_acc: 0.8489
Epoch 19/500
52s 103ms/step - loss: 0.8531 - acc: 0.8111 - val_loss: 0.7626 - val_acc: 0.8427
Epoch 20/500
52s 103ms/step - loss: 0.8463 - acc: 0.8140 - val_loss: 0.7256 - val_acc: 0.8620
Epoch 21/500
52s 103ms/step - loss: 0.8402 - acc: 0.8158 - val_loss: 0.7499 - val_acc: 0.8502
Epoch 22/500
52s 103ms/step - loss: 0.8347 - acc: 0.8170 - val_loss: 0.7154 - val_acc: 0.8629
Epoch 23/500
52s 103ms/step - loss: 0.8262 - acc: 0.8219 - val_loss: 0.7074 - val_acc: 0.8625
Epoch 24/500
51s 102ms/step - loss: 0.8251 - acc: 0.8211 - val_loss: 0.7165 - val_acc: 0.8601
Epoch 25/500
51s 102ms/step - loss: 0.8166 - acc: 0.8250 - val_loss: 0.7233 - val_acc: 0.8564
Epoch 26/500
51s 102ms/step - loss: 0.8090 - acc: 0.8266 - val_loss: 0.7401 - val_acc: 0.8481
Epoch 27/500
51s 102ms/step - loss: 0.8049 - acc: 0.8290 - val_loss: 0.6991 - val_acc: 0.8658
Epoch 28/500
51s 102ms/step - loss: 0.8037 - acc: 0.8302 - val_loss: 0.7159 - val_acc: 0.8630
Epoch 29/500
51s 102ms/step - loss: 0.7977 - acc: 0.8327 - val_loss: 0.7189 - val_acc: 0.8614
Epoch 30/500
51s 102ms/step - loss: 0.7968 - acc: 0.8334 - val_loss: 0.7030 - val_acc: 0.8706
Epoch 31/500
51s 102ms/step - loss: 0.7956 - acc: 0.8339 - val_loss: 0.6863 - val_acc: 0.8764
Epoch 32/500
51s 102ms/step - loss: 0.7875 - acc: 0.8377 - val_loss: 0.7160 - val_acc: 0.8647
Epoch 33/500
51s 102ms/step - loss: 0.7874 - acc: 0.8368 - val_loss: 0.7002 - val_acc: 0.8704
Epoch 34/500
51s 102ms/step - loss: 0.7917 - acc: 0.8357 - val_loss: 0.6829 - val_acc: 0.8783
Epoch 35/500
51s 102ms/step - loss: 0.7808 - acc: 0.8423 - val_loss: 0.7057 - val_acc: 0.8685
Epoch 36/500
51s 103ms/step - loss: 0.7795 - acc: 0.8413 - val_loss: 0.7044 - val_acc: 0.8710
Epoch 37/500
52s 103ms/step - loss: 0.7768 - acc: 0.8429 - val_loss: 0.6871 - val_acc: 0.8743
Epoch 38/500
52s 103ms/step - loss: 0.7763 - acc: 0.8418 - val_loss: 0.6995 - val_acc: 0.8687
Epoch 39/500
52s 103ms/step - loss: 0.7665 - acc: 0.8459 - val_loss: 0.6907 - val_acc: 0.8736
Epoch 40/500
52s 103ms/step - loss: 0.7751 - acc: 0.8428 - val_loss: 0.6947 - val_acc: 0.8717
Epoch 41/500
52s 103ms/step - loss: 0.7691 - acc: 0.8456 - val_loss: 0.6960 - val_acc: 0.8709
Epoch 42/500
52s 103ms/step - loss: 0.7704 - acc: 0.8460 - val_loss: 0.6690 - val_acc: 0.8851
Epoch 43/500
52s 103ms/step - loss: 0.7641 - acc: 0.8484 - val_loss: 0.6825 - val_acc: 0.8792
Epoch 44/500
52s 103ms/step - loss: 0.7630 - acc: 0.8499 - val_loss: 0.6765 - val_acc: 0.8781
Epoch 45/500
52s 103ms/step - loss: 0.7583 - acc: 0.8502 - val_loss: 0.6955 - val_acc: 0.8724
Epoch 46/500
52s 103ms/step - loss: 0.7599 - acc: 0.8493 - val_loss: 0.6750 - val_acc: 0.8773
Epoch 47/500
51s 102ms/step - loss: 0.7617 - acc: 0.8497 - val_loss: 0.6925 - val_acc: 0.8757
Epoch 48/500
51s 102ms/step - loss: 0.7589 - acc: 0.8501 - val_loss: 0.6848 - val_acc: 0.8775
Epoch 49/500
51s 102ms/step - loss: 0.7572 - acc: 0.8506 - val_loss: 0.6834 - val_acc: 0.8767
Epoch 50/500
51s 103ms/step - loss: 0.7512 - acc: 0.8523 - val_loss: 0.6908 - val_acc: 0.8742
Epoch 51/500
51s 102ms/step - loss: 0.7553 - acc: 0.8523 - val_loss: 0.6987 - val_acc: 0.8705
Epoch 52/500
51s 103ms/step - loss: 0.7501 - acc: 0.8523 - val_loss: 0.7237 - val_acc: 0.8665
Epoch 53/500
52s 103ms/step - loss: 0.7544 - acc: 0.8535 - val_loss: 0.6973 - val_acc: 0.8747
Epoch 54/500
52s 103ms/step - loss: 0.7467 - acc: 0.8561 - val_loss: 0.6836 - val_acc: 0.8814
Epoch 55/500
52s 103ms/step - loss: 0.7490 - acc: 0.8544 - val_loss: 0.6914 - val_acc: 0.8767
Epoch 56/500
52s 103ms/step - loss: 0.7451 - acc: 0.8568 - val_loss: 0.6881 - val_acc: 0.8803
Epoch 57/500
52s 103ms/step - loss: 0.7488 - acc: 0.8554 - val_loss: 0.6867 - val_acc: 0.8786
Epoch 58/500
52s 103ms/step - loss: 0.7465 - acc: 0.8576 - val_loss: 0.6845 - val_acc: 0.8759
Epoch 59/500
52s 103ms/step - loss: 0.7443 - acc: 0.8570 - val_loss: 0.6715 - val_acc: 0.8851
Epoch 60/500
52s 103ms/step - loss: 0.7472 - acc: 0.8562 - val_loss: 0.7045 - val_acc: 0.8751
Epoch 61/500
52s 103ms/step - loss: 0.7425 - acc: 0.8596 - val_loss: 0.6793 - val_acc: 0.8836
Epoch 62/500
52s 103ms/step - loss: 0.7433 - acc: 0.8587 - val_loss: 0.6963 - val_acc: 0.8752
Epoch 63/500
52s 103ms/step - loss: 0.7472 - acc: 0.8560 - val_loss: 0.6911 - val_acc: 0.8773
Epoch 64/500
52s 103ms/step - loss: 0.7423 - acc: 0.8577 - val_loss: 0.6808 - val_acc: 0.8809
Epoch 65/500
52s 103ms/step - loss: 0.7404 - acc: 0.8594 - val_loss: 0.7071 - val_acc: 0.8703
Epoch 66/500
52s 103ms/step - loss: 0.7418 - acc: 0.8580 - val_loss: 0.6881 - val_acc: 0.8776
Epoch 67/500
52s 103ms/step - loss: 0.7404 - acc: 0.8584 - val_loss: 0.6973 - val_acc: 0.8770
Epoch 68/500
52s 103ms/step - loss: 0.7458 - acc: 0.8569 - val_loss: 0.6871 - val_acc: 0.8783
Epoch 69/500
52s 103ms/step - loss: 0.7342 - acc: 0.8625 - val_loss: 0.6856 - val_acc: 0.8778
Epoch 70/500
52s 103ms/step - loss: 0.7358 - acc: 0.8603 - val_loss: 0.6999 - val_acc: 0.8742
Epoch 71/500
52s 103ms/step - loss: 0.7419 - acc: 0.8600 - val_loss: 0.6921 - val_acc: 0.8816
Epoch 72/500
52s 103ms/step - loss: 0.7368 - acc: 0.8607 - val_loss: 0.6825 - val_acc: 0.8813
Epoch 73/500
51s 103ms/step - loss: 0.7376 - acc: 0.8581 - val_loss: 0.6771 - val_acc: 0.8846
Epoch 74/500
51s 102ms/step - loss: 0.7371 - acc: 0.8598 - val_loss: 0.6963 - val_acc: 0.8787
Epoch 75/500
51s 102ms/step - loss: 0.7409 - acc: 0.8593 - val_loss: 0.6884 - val_acc: 0.8794
Epoch 76/500
51s 102ms/step - loss: 0.7348 - acc: 0.8633 - val_loss: 0.6655 - val_acc: 0.8904
Epoch 77/500
51s 102ms/step - loss: 0.7358 - acc: 0.8624 - val_loss: 0.6860 - val_acc: 0.8812
Epoch 78/500
51s 102ms/step - loss: 0.7392 - acc: 0.8616 - val_loss: 0.7251 - val_acc: 0.8686
Epoch 79/500
51s 102ms/step - loss: 0.7342 - acc: 0.8645 - val_loss: 0.6891 - val_acc: 0.8804
Epoch 80/500
51s 102ms/step - loss: 0.7341 - acc: 0.8635 - val_loss: 0.6761 - val_acc: 0.8847
Epoch 81/500
51s 102ms/step - loss: 0.7382 - acc: 0.8605 - val_loss: 0.7034 - val_acc: 0.8752ETA: 43s - loss: 0.6977 - acc: 0.8793
Epoch 82/500
51s 102ms/step - loss: 0.7377 - acc: 0.8617 - val_loss: 0.6670 - val_acc: 0.8867
Epoch 83/500
51s 102ms/step - loss: 0.7347 - acc: 0.8643 - val_loss: 0.6862 - val_acc: 0.8815
Epoch 84/500
51s 102ms/step - loss: 0.7301 - acc: 0.8660 - val_loss: 0.6837 - val_acc: 0.8818
Epoch 85/500
51s 102ms/step - loss: 0.7322 - acc: 0.8648 - val_loss: 0.6842 - val_acc: 0.8809
Epoch 86/500
51s 102ms/step - loss: 0.7303 - acc: 0.8640 - val_loss: 0.6906 - val_acc: 0.8823
Epoch 87/500
51s 102ms/step - loss: 0.7285 - acc: 0.8651 - val_loss: 0.6701 - val_acc: 0.8847
Epoch 88/500
51s 102ms/step - loss: 0.7313 - acc: 0.8645 - val_loss: 0.6774 - val_acc: 0.8832
Epoch 89/500
51s 102ms/step - loss: 0.7303 - acc: 0.8625 - val_loss: 0.6827 - val_acc: 0.8851
Epoch 90/500
51s 102ms/step - loss: 0.7283 - acc: 0.8647 - val_loss: 0.6886 - val_acc: 0.8821
Epoch 91/500
51s 103ms/step - loss: 0.7256 - acc: 0.8662 - val_loss: 0.6889 - val_acc: 0.8803
Epoch 92/500
52s 103ms/step - loss: 0.7313 - acc: 0.8634 - val_loss: 0.6747 - val_acc: 0.8865
Epoch 93/500
52s 103ms/step - loss: 0.7257 - acc: 0.8656 - val_loss: 0.6831 - val_acc: 0.8836
Epoch 94/500
52s 103ms/step - loss: 0.7303 - acc: 0.8645 - val_loss: 0.7008 - val_acc: 0.8772
Epoch 95/500
52s 103ms/step - loss: 0.7268 - acc: 0.8657 - val_loss: 0.6998 - val_acc: 0.8783
Epoch 96/500
51s 102ms/step - loss: 0.7245 - acc: 0.8659 - val_loss: 0.6927 - val_acc: 0.8789
Epoch 97/500
51s 102ms/step - loss: 0.7236 - acc: 0.8685 - val_loss: 0.6620 - val_acc: 0.8919
Epoch 98/500
51s 102ms/step - loss: 0.7249 - acc: 0.8669 - val_loss: 0.6741 - val_acc: 0.8857
Epoch 99/500
51s 102ms/step - loss: 0.7252 - acc: 0.8659 - val_loss: 0.6770 - val_acc: 0.8844
Epoch 100/500
51s 102ms/step - loss: 0.7239 - acc: 0.8672 - val_loss: 0.6815 - val_acc: 0.8851
Epoch 101/500
51s 102ms/step - loss: 0.7272 - acc: 0.8667 - val_loss: 0.6752 - val_acc: 0.8867
Epoch 102/500
51s 102ms/step - loss: 0.7268 - acc: 0.8651 - val_loss: 0.6985 - val_acc: 0.8766
Epoch 103/500
51s 102ms/step - loss: 0.7243 - acc: 0.8685 - val_loss: 0.7136 - val_acc: 0.8714
Epoch 104/500
51s 102ms/step - loss: 0.7293 - acc: 0.8646 - val_loss: 0.6930 - val_acc: 0.8813
Epoch 105/500
51s 102ms/step - loss: 0.7237 - acc: 0.8665 - val_loss: 0.6919 - val_acc: 0.8812
Epoch 106/500
51s 102ms/step - loss: 0.7275 - acc: 0.8669 - val_loss: 0.6722 - val_acc: 0.8857
Epoch 107/500
51s 102ms/step - loss: 0.7149 - acc: 0.8703 - val_loss: 0.6730 - val_acc: 0.8827
Epoch 108/500
51s 102ms/step - loss: 0.7214 - acc: 0.8689 - val_loss: 0.6530 - val_acc: 0.8930
Epoch 109/500
51s 102ms/step - loss: 0.7212 - acc: 0.8678 - val_loss: 0.7015 - val_acc: 0.8784
Epoch 110/500
51s 102ms/step - loss: 0.7238 - acc: 0.8676 - val_loss: 0.6730 - val_acc: 0.8833
Epoch 111/500
51s 102ms/step - loss: 0.7260 - acc: 0.8670 - val_loss: 0.6541 - val_acc: 0.8919
Epoch 112/500
51s 102ms/step - loss: 0.7209 - acc: 0.8688 - val_loss: 0.6577 - val_acc: 0.8926
Epoch 113/500
51s 102ms/step - loss: 0.7258 - acc: 0.8654 - val_loss: 0.6864 - val_acc: 0.8832
Epoch 114/500
51s 102ms/step - loss: 0.7211 - acc: 0.8695 - val_loss: 0.6749 - val_acc: 0.8873
Epoch 115/500
51s 102ms/step - loss: 0.7196 - acc: 0.8707 - val_loss: 0.6660 - val_acc: 0.8873
Epoch 116/500
51s 102ms/step - loss: 0.7189 - acc: 0.8684 - val_loss: 0.6945 - val_acc: 0.8809
Epoch 117/500
51s 102ms/step - loss: 0.7246 - acc: 0.8672 - val_loss: 0.7153 - val_acc: 0.8706
Epoch 118/500
51s 102ms/step - loss: 0.7189 - acc: 0.8719 - val_loss: 0.6718 - val_acc: 0.8849
Epoch 119/500
51s 102ms/step - loss: 0.7214 - acc: 0.8695 - val_loss: 0.6883 - val_acc: 0.8802
Epoch 120/500
51s 102ms/step - loss: 0.7154 - acc: 0.8706 - val_loss: 0.6846 - val_acc: 0.8844
Epoch 121/500
51s 102ms/step - loss: 0.7150 - acc: 0.8691 - val_loss: 0.6820 - val_acc: 0.8836
Epoch 122/500
51s 102ms/step - loss: 0.7190 - acc: 0.8696 - val_loss: 0.6737 - val_acc: 0.8857
Epoch 123/500
51s 102ms/step - loss: 0.7191 - acc: 0.8686 - val_loss: 0.6752 - val_acc: 0.8848
Epoch 124/500
51s 102ms/step - loss: 0.7185 - acc: 0.8701 - val_loss: 0.6841 - val_acc: 0.8828
Epoch 125/500
51s 103ms/step - loss: 0.7221 - acc: 0.8689 - val_loss: 0.6739 - val_acc: 0.8837
Epoch 126/500
51s 102ms/step - loss: 0.7202 - acc: 0.8699 - val_loss: 0.6787 - val_acc: 0.8888
Epoch 127/500
51s 102ms/step - loss: 0.7172 - acc: 0.8703 - val_loss: 0.6889 - val_acc: 0.8815
Epoch 128/500
51s 102ms/step - loss: 0.7157 - acc: 0.8719 - val_loss: 0.6832 - val_acc: 0.8852
Epoch 129/500
51s 102ms/step - loss: 0.7149 - acc: 0.8704 - val_loss: 0.6777 - val_acc: 0.8859
Epoch 130/500
51s 102ms/step - loss: 0.7205 - acc: 0.8698 - val_loss: 0.6675 - val_acc: 0.8908
Epoch 131/500
51s 102ms/step - loss: 0.7146 - acc: 0.8721 - val_loss: 0.6741 - val_acc: 0.8916
Epoch 132/500
51s 102ms/step - loss: 0.7140 - acc: 0.8720 - val_loss: 0.6649 - val_acc: 0.8891
Epoch 133/500
52s 103ms/step - loss: 0.7128 - acc: 0.8714 - val_loss: 0.6883 - val_acc: 0.8834
Epoch 134/500
52s 103ms/step - loss: 0.7203 - acc: 0.8699 - val_loss: 0.6899 - val_acc: 0.8854
Epoch 135/500
52s 103ms/step - loss: 0.7131 - acc: 0.8716 - val_loss: 0.6907 - val_acc: 0.8823
Epoch 136/500
52s 103ms/step - loss: 0.7127 - acc: 0.8706 - val_loss: 0.6957 - val_acc: 0.8786
Epoch 137/500
52s 103ms/step - loss: 0.7147 - acc: 0.8726 - val_loss: 0.7036 - val_acc: 0.8764
Epoch 138/500
52s 103ms/step - loss: 0.7125 - acc: 0.8735 - val_loss: 0.6704 - val_acc: 0.8896
Epoch 139/500
52s 103ms/step - loss: 0.7153 - acc: 0.8692 - val_loss: 0.6620 - val_acc: 0.8923
Epoch 140/500
52s 103ms/step - loss: 0.7125 - acc: 0.8707 - val_loss: 0.6862 - val_acc: 0.8839
Epoch 141/500
52s 103ms/step - loss: 0.7149 - acc: 0.8722 - val_loss: 0.6573 - val_acc: 0.8951
Epoch 142/500
51s 103ms/step - loss: 0.7154 - acc: 0.8726 - val_loss: 0.6658 - val_acc: 0.8898
Epoch 143/500
52s 103ms/step - loss: 0.7115 - acc: 0.8728 - val_loss: 0.6868 - val_acc: 0.8848
Epoch 144/500
52s 103ms/step - loss: 0.7116 - acc: 0.8733 - val_loss: 0.6679 - val_acc: 0.8894
Epoch 145/500
52s 103ms/step - loss: 0.7198 - acc: 0.8696 - val_loss: 0.6865 - val_acc: 0.8824
Epoch 146/500
52s 103ms/step - loss: 0.7146 - acc: 0.8726 - val_loss: 0.6906 - val_acc: 0.8826
Epoch 147/500
52s 103ms/step - loss: 0.7165 - acc: 0.8718 - val_loss: 0.6597 - val_acc: 0.8898
Epoch 148/500
52s 103ms/step - loss: 0.7130 - acc: 0.8725 - val_loss: 0.6596 - val_acc: 0.8926
Epoch 149/500
52s 103ms/step - loss: 0.7096 - acc: 0.8728 - val_loss: 0.6855 - val_acc: 0.8816
Epoch 150/500
52s 103ms/step - loss: 0.7131 - acc: 0.8730 - val_loss: 0.6872 - val_acc: 0.8838
Epoch 151/500
lr changed to 0.010000000149011612
52s 103ms/step - loss: 0.5994 - acc: 0.9120 - val_loss: 0.5846 - val_acc: 0.9190
Epoch 152/500
51s 103ms/step - loss: 0.5434 - acc: 0.9286 - val_loss: 0.5670 - val_acc: 0.9214
Epoch 153/500
52s 103ms/step - loss: 0.5249 - acc: 0.9328 - val_loss: 0.5552 - val_acc: 0.9216
Epoch 154/500
51s 103ms/step - loss: 0.5089 - acc: 0.9363 - val_loss: 0.5436 - val_acc: 0.9275
Epoch 155/500
51s 102ms/step - loss: 0.4992 - acc: 0.9393 - val_loss: 0.5400 - val_acc: 0.9263
Epoch 156/500
51s 102ms/step - loss: 0.4838 - acc: 0.9424 - val_loss: 0.5373 - val_acc: 0.9269
Epoch 157/500
51s 102ms/step - loss: 0.4733 - acc: 0.9442 - val_loss: 0.5283 - val_acc: 0.9264ETA: 24s - loss: 0.4741 - acc: 0.9435
Epoch 158/500
51s 103ms/step - loss: 0.4641 - acc: 0.9458 - val_loss: 0.5195 - val_acc: 0.9296
Epoch 159/500
51s 102ms/step - loss: 0.4579 - acc: 0.9462 - val_loss: 0.5159 - val_acc: 0.9316
Epoch 160/500
51s 102ms/step - loss: 0.4515 - acc: 0.9467 - val_loss: 0.5058 - val_acc: 0.9299
Epoch 161/500
51s 102ms/step - loss: 0.4420 - acc: 0.9485 - val_loss: 0.5039 - val_acc: 0.9296
Epoch 162/500
51s 102ms/step - loss: 0.4357 - acc: 0.9485 - val_loss: 0.4972 - val_acc: 0.9325
Epoch 163/500
51s 102ms/step - loss: 0.4238 - acc: 0.9514 - val_loss: 0.4934 - val_acc: 0.9320
Epoch 164/500
51s 102ms/step - loss: 0.4187 - acc: 0.9523 - val_loss: 0.4884 - val_acc: 0.9314
Epoch 165/500
51s 102ms/step - loss: 0.4139 - acc: 0.9523 - val_loss: 0.4825 - val_acc: 0.9311
Epoch 166/500
51s 102ms/step - loss: 0.4071 - acc: 0.9538 - val_loss: 0.4861 - val_acc: 0.9308
Epoch 167/500
51s 102ms/step - loss: 0.4004 - acc: 0.9539 - val_loss: 0.4780 - val_acc: 0.9292
Epoch 168/500
51s 102ms/step - loss: 0.3950 - acc: 0.9550 - val_loss: 0.4754 - val_acc: 0.9311
Epoch 169/500
51s 102ms/step - loss: 0.3948 - acc: 0.9541 - val_loss: 0.4717 - val_acc: 0.9328
Epoch 170/500
51s 102ms/step - loss: 0.3828 - acc: 0.9570 - val_loss: 0.4741 - val_acc: 0.9313
Epoch 171/500
51s 102ms/step - loss: 0.3831 - acc: 0.9554 - val_loss: 0.4666 - val_acc: 0.9302
Epoch 172/500
51s 102ms/step - loss: 0.3781 - acc: 0.9561 - val_loss: 0.4653 - val_acc: 0.9324
Epoch 173/500
51s 102ms/step - loss: 0.3707 - acc: 0.9575 - val_loss: 0.4633 - val_acc: 0.9300
Epoch 174/500
51s 102ms/step - loss: 0.3679 - acc: 0.9573 - val_loss: 0.4688 - val_acc: 0.9262
Epoch 175/500
51s 102ms/step - loss: 0.3621 - acc: 0.9585 - val_loss: 0.4521 - val_acc: 0.9307
Epoch 176/500
51s 102ms/step - loss: 0.3619 - acc: 0.9571 - val_loss: 0.4465 - val_acc: 0.9329ETA: 42s - loss: 0.3637 - acc: 0.9586
Epoch 177/500
51s 102ms/step - loss: 0.3586 - acc: 0.9569 - val_loss: 0.4481 - val_acc: 0.9315
Epoch 178/500
51s 102ms/step - loss: 0.3485 - acc: 0.9598 - val_loss: 0.4531 - val_acc: 0.9305
Epoch 179/500
51s 102ms/step - loss: 0.3469 - acc: 0.9590 - val_loss: 0.4452 - val_acc: 0.9324
Epoch 180/500
51s 102ms/step - loss: 0.3456 - acc: 0.9590 - val_loss: 0.4466 - val_acc: 0.9312
Epoch 181/500
51s 102ms/step - loss: 0.3440 - acc: 0.9584 - val_loss: 0.4395 - val_acc: 0.9324
Epoch 182/500
51s 102ms/step - loss: 0.3393 - acc: 0.9595 - val_loss: 0.4456 - val_acc: 0.9295
Epoch 183/500
51s 102ms/step - loss: 0.3372 - acc: 0.9591 - val_loss: 0.4441 - val_acc: 0.9268
Epoch 184/500
51s 102ms/step - loss: 0.3342 - acc: 0.9592 - val_loss: 0.4347 - val_acc: 0.9283
Epoch 185/500
51s 102ms/step - loss: 0.3313 - acc: 0.9604 - val_loss: 0.4392 - val_acc: 0.9277
Epoch 186/500
51s 102ms/step - loss: 0.3245 - acc: 0.9618 - val_loss: 0.4281 - val_acc: 0.9318
Epoch 187/500
51s 102ms/step - loss: 0.3279 - acc: 0.9596 - val_loss: 0.4449 - val_acc: 0.9266
Epoch 188/500
51s 102ms/step - loss: 0.3244 - acc: 0.9593 - val_loss: 0.4367 - val_acc: 0.9262
Epoch 189/500
51s 102ms/step - loss: 0.3223 - acc: 0.9601 - val_loss: 0.4250 - val_acc: 0.9316
Epoch 190/500
51s 102ms/step - loss: 0.3197 - acc: 0.9597 - val_loss: 0.4245 - val_acc: 0.9273
Epoch 191/500
51s 102ms/step - loss: 0.3179 - acc: 0.9591 - val_loss: 0.4279 - val_acc: 0.9299
Epoch 192/500
51s 102ms/step - loss: 0.3166 - acc: 0.9603 - val_loss: 0.4243 - val_acc: 0.9285
Epoch 193/500
51s 102ms/step - loss: 0.3156 - acc: 0.9600 - val_loss: 0.4209 - val_acc: 0.9299
Epoch 194/500
51s 103ms/step - loss: 0.3102 - acc: 0.9622 - val_loss: 0.4253 - val_acc: 0.9267
Epoch 195/500
51s 102ms/step - loss: 0.3044 - acc: 0.9627 - val_loss: 0.4237 - val_acc: 0.9283
Epoch 196/500
51s 102ms/step - loss: 0.3085 - acc: 0.9609 - val_loss: 0.4174 - val_acc: 0.9297
Epoch 197/500
51s 102ms/step - loss: 0.3044 - acc: 0.9617 - val_loss: 0.4135 - val_acc: 0.9283
Epoch 198/500
51s 102ms/step - loss: 0.2983 - acc: 0.9631 - val_loss: 0.4152 - val_acc: 0.9312
Epoch 199/500
51s 102ms/step - loss: 0.3054 - acc: 0.9601 - val_loss: 0.4156 - val_acc: 0.9296
Epoch 200/500
51s 102ms/step - loss: 0.3007 - acc: 0.9619 - val_loss: 0.4084 - val_acc: 0.9305
Epoch 201/500
51s 102ms/step - loss: 0.3018 - acc: 0.9608 - val_loss: 0.4119 - val_acc: 0.9285
Epoch 202/500
51s 102ms/step - loss: 0.3023 - acc: 0.9596 - val_loss: 0.4075 - val_acc: 0.9312
Epoch 203/500
51s 102ms/step - loss: 0.2984 - acc: 0.9608 - val_loss: 0.4125 - val_acc: 0.9315
Epoch 204/500
51s 102ms/step - loss: 0.2937 - acc: 0.9621 - val_loss: 0.4088 - val_acc: 0.9267
Epoch 205/500
51s 102ms/step - loss: 0.2918 - acc: 0.9620 - val_loss: 0.4138 - val_acc: 0.9258
Epoch 206/500
51s 102ms/step - loss: 0.2922 - acc: 0.9617 - val_loss: 0.4048 - val_acc: 0.9310
Epoch 207/500
51s 102ms/step - loss: 0.2914 - acc: 0.9615 - val_loss: 0.3929 - val_acc: 0.9343
Epoch 208/500
51s 102ms/step - loss: 0.2915 - acc: 0.9615 - val_loss: 0.4042 - val_acc: 0.9291
Epoch 209/500
51s 102ms/step - loss: 0.2880 - acc: 0.9619 - val_loss: 0.4065 - val_acc: 0.9257
Epoch 210/500
51s 102ms/step - loss: 0.2912 - acc: 0.9601 - val_loss: 0.4155 - val_acc: 0.9238
Epoch 211/500
51s 102ms/step - loss: 0.2896 - acc: 0.9616 - val_loss: 0.3948 - val_acc: 0.9304
Epoch 212/500
51s 102ms/step - loss: 0.2890 - acc: 0.9605 - val_loss: 0.4077 - val_acc: 0.9288
Epoch 213/500
51s 102ms/step - loss: 0.2913 - acc: 0.9596 - val_loss: 0.3965 - val_acc: 0.9282
Epoch 214/500
51s 102ms/step - loss: 0.2865 - acc: 0.9599 - val_loss: 0.4121 - val_acc: 0.9240
Epoch 215/500
51s 102ms/step - loss: 0.2841 - acc: 0.9610 - val_loss: 0.4025 - val_acc: 0.9302
Epoch 216/500
51s 102ms/step - loss: 0.2870 - acc: 0.9603 - val_loss: 0.4064 - val_acc: 0.9244
Epoch 217/500
51s 102ms/step - loss: 0.2811 - acc: 0.9610 - val_loss: 0.4086 - val_acc: 0.9276
Epoch 218/500
51s 102ms/step - loss: 0.2837 - acc: 0.9606 - val_loss: 0.3963 - val_acc: 0.9269
Epoch 219/500
51s 102ms/step - loss: 0.2801 - acc: 0.9623 - val_loss: 0.3981 - val_acc: 0.9290
Epoch 220/500
51s 102ms/step - loss: 0.2786 - acc: 0.9631 - val_loss: 0.4038 - val_acc: 0.9261
Epoch 221/500
51s 103ms/step - loss: 0.2792 - acc: 0.9617 - val_loss: 0.3992 - val_acc: 0.9275
Epoch 222/500
52s 104ms/step - loss: 0.2780 - acc: 0.9619 - val_loss: 0.3951 - val_acc: 0.9290
Epoch 223/500
51s 103ms/step - loss: 0.2816 - acc: 0.9602 - val_loss: 0.3909 - val_acc: 0.9290
Epoch 224/500
51s 102ms/step - loss: 0.2766 - acc: 0.9622 - val_loss: 0.4014 - val_acc: 0.9276
Epoch 225/500
51s 102ms/step - loss: 0.2801 - acc: 0.9612 - val_loss: 0.3954 - val_acc: 0.9264
Epoch 226/500
51s 102ms/step - loss: 0.2803 - acc: 0.9595 - val_loss: 0.3920 - val_acc: 0.9309
Epoch 227/500
51s 102ms/step - loss: 0.2759 - acc: 0.9608 - val_loss: 0.3968 - val_acc: 0.9281
Epoch 228/500
51s 102ms/step - loss: 0.2814 - acc: 0.9586 - val_loss: 0.3927 - val_acc: 0.9288
Epoch 229/500
51s 102ms/step - loss: 0.2732 - acc: 0.9636 - val_loss: 0.4051 - val_acc: 0.9249
Epoch 230/500
51s 102ms/step - loss: 0.2770 - acc: 0.9606 - val_loss: 0.3944 - val_acc: 0.9276
Epoch 231/500
51s 102ms/step - loss: 0.2765 - acc: 0.9616 - val_loss: 0.3943 - val_acc: 0.9279
Epoch 232/500
51s 102ms/step - loss: 0.2762 - acc: 0.9603 - val_loss: 0.3855 - val_acc: 0.9271
Epoch 233/500
51s 102ms/step - loss: 0.2747 - acc: 0.9609 - val_loss: 0.3948 - val_acc: 0.9272
Epoch 234/500
51s 102ms/step - loss: 0.2776 - acc: 0.9595 - val_loss: 0.3861 - val_acc: 0.9285
Epoch 235/500
51s 102ms/step - loss: 0.2763 - acc: 0.9606 - val_loss: 0.3802 - val_acc: 0.9301
Epoch 236/500
51s 102ms/step - loss: 0.2747 - acc: 0.9616 - val_loss: 0.3883 - val_acc: 0.9295
Epoch 237/500
51s 102ms/step - loss: 0.2763 - acc: 0.9604 - val_loss: 0.3882 - val_acc: 0.9288
Epoch 238/500
51s 103ms/step - loss: 0.2699 - acc: 0.9616 - val_loss: 0.3973 - val_acc: 0.9260
Epoch 239/500
52s 103ms/step - loss: 0.2720 - acc: 0.9612 - val_loss: 0.3893 - val_acc: 0.9255
Epoch 240/500
51s 102ms/step - loss: 0.2758 - acc: 0.9595 - val_loss: 0.3975 - val_acc: 0.9267
Epoch 241/500
51s 102ms/step - loss: 0.2724 - acc: 0.9619 - val_loss: 0.3955 - val_acc: 0.9242
Epoch 242/500
51s 102ms/step - loss: 0.2704 - acc: 0.9622 - val_loss: 0.3889 - val_acc: 0.9263
Epoch 243/500
51s 102ms/step - loss: 0.2699 - acc: 0.9612 - val_loss: 0.4002 - val_acc: 0.9267
Epoch 244/500
51s 102ms/step - loss: 0.2733 - acc: 0.9605 - val_loss: 0.3858 - val_acc: 0.9290
Epoch 245/500
51s 102ms/step - loss: 0.2733 - acc: 0.9603 - val_loss: 0.3944 - val_acc: 0.9272
Epoch 246/500
51s 102ms/step - loss: 0.2682 - acc: 0.9616 - val_loss: 0.3787 - val_acc: 0.9306
Epoch 247/500
51s 103ms/step - loss: 0.2679 - acc: 0.9623 - val_loss: 0.3862 - val_acc: 0.9281
Epoch 248/500
52s 103ms/step - loss: 0.2688 - acc: 0.9619 - val_loss: 0.3904 - val_acc: 0.9266
Epoch 249/500
52s 103ms/step - loss: 0.2683 - acc: 0.9626 - val_loss: 0.3898 - val_acc: 0.9269
Epoch 250/500
52s 103ms/step - loss: 0.2706 - acc: 0.9600 - val_loss: 0.3884 - val_acc: 0.9294
Epoch 251/500
52s 103ms/step - loss: 0.2681 - acc: 0.9612 - val_loss: 0.3848 - val_acc: 0.9289
Epoch 252/500
52s 103ms/step - loss: 0.2696 - acc: 0.9618 - val_loss: 0.3852 - val_acc: 0.9268
Epoch 253/500
52s 103ms/step - loss: 0.2717 - acc: 0.9603 - val_loss: 0.3837 - val_acc: 0.9270
Epoch 254/500
51s 102ms/step - loss: 0.2718 - acc: 0.9596 - val_loss: 0.3855 - val_acc: 0.9264
Epoch 255/500
51s 103ms/step - loss: 0.2658 - acc: 0.9626 - val_loss: 0.3890 - val_acc: 0.9286
Epoch 256/500
51s 102ms/step - loss: 0.2660 - acc: 0.9623 - val_loss: 0.3866 - val_acc: 0.9314
Epoch 257/500
51s 103ms/step - loss: 0.2713 - acc: 0.9605 - val_loss: 0.3863 - val_acc: 0.9271
Epoch 258/500
52s 103ms/step - loss: 0.2715 - acc: 0.9599 - val_loss: 0.3833 - val_acc: 0.9301
Epoch 259/500
52s 103ms/step - loss: 0.2705 - acc: 0.9596 - val_loss: 0.3897 - val_acc: 0.9257
Epoch 260/500
52s 103ms/step - loss: 0.2682 - acc: 0.9613 - val_loss: 0.3911 - val_acc: 0.9274
Epoch 261/500
52s 103ms/step - loss: 0.2692 - acc: 0.9601 - val_loss: 0.3798 - val_acc: 0.9292
Epoch 262/500
52s 103ms/step - loss: 0.2650 - acc: 0.9622 - val_loss: 0.3910 - val_acc: 0.9270
Epoch 263/500
52s 103ms/step - loss: 0.2639 - acc: 0.9620 - val_loss: 0.3870 - val_acc: 0.9285
Epoch 264/500
52s 103ms/step - loss: 0.2651 - acc: 0.9620 - val_loss: 0.3994 - val_acc: 0.9263
Epoch 265/500
52s 103ms/step - loss: 0.2694 - acc: 0.9601 - val_loss: 0.3807 - val_acc: 0.9314
Epoch 266/500
52s 103ms/step - loss: 0.2691 - acc: 0.9613 - val_loss: 0.3811 - val_acc: 0.9282
Epoch 267/500
52s 103ms/step - loss: 0.2618 - acc: 0.9631 - val_loss: 0.3789 - val_acc: 0.9334
Epoch 268/500
51s 102ms/step - loss: 0.2636 - acc: 0.9631 - val_loss: 0.3905 - val_acc: 0.9254
Epoch 269/500
51s 102ms/step - loss: 0.2695 - acc: 0.9609 - val_loss: 0.4035 - val_acc: 0.9235
Epoch 270/500
51s 102ms/step - loss: 0.2661 - acc: 0.9615 - val_loss: 0.3942 - val_acc: 0.9264
Epoch 271/500
51s 102ms/step - loss: 0.2640 - acc: 0.9623 - val_loss: 0.3878 - val_acc: 0.9294
Epoch 272/500
52s 103ms/step - loss: 0.2658 - acc: 0.9620 - val_loss: 0.3952 - val_acc: 0.9260
Epoch 273/500
52s 103ms/step - loss: 0.2654 - acc: 0.9623 - val_loss: 0.4001 - val_acc: 0.9225
Epoch 274/500
51s 102ms/step - loss: 0.2648 - acc: 0.9621 - val_loss: 0.3885 - val_acc: 0.9285
Epoch 275/500
51s 102ms/step - loss: 0.2618 - acc: 0.9625 - val_loss: 0.3925 - val_acc: 0.9260
Epoch 276/500
51s 103ms/step - loss: 0.2615 - acc: 0.9628 - val_loss: 0.3860 - val_acc: 0.9279
Epoch 277/500
51s 103ms/step - loss: 0.2740 - acc: 0.9584 - val_loss: 0.3831 - val_acc: 0.9279
Epoch 278/500
51s 102ms/step - loss: 0.2653 - acc: 0.9616 - val_loss: 0.3898 - val_acc: 0.9257
Epoch 279/500
51s 102ms/step - loss: 0.2623 - acc: 0.9630 - val_loss: 0.3902 - val_acc: 0.9271
Epoch 280/500
52s 103ms/step - loss: 0.2652 - acc: 0.9624 - val_loss: 0.3899 - val_acc: 0.9274
Epoch 281/500
51s 103ms/step - loss: 0.2674 - acc: 0.9614 - val_loss: 0.3948 - val_acc: 0.9232
Epoch 282/500
51s 102ms/step - loss: 0.2641 - acc: 0.9622 - val_loss: 0.3880 - val_acc: 0.9284
Epoch 283/500
51s 103ms/step - loss: 0.2689 - acc: 0.9600 - val_loss: 0.3856 - val_acc: 0.9281
Epoch 284/500
51s 102ms/step - loss: 0.2616 - acc: 0.9618 - val_loss: 0.3795 - val_acc: 0.9294
Epoch 285/500
51s 102ms/step - loss: 0.2645 - acc: 0.9618 - val_loss: 0.3904 - val_acc: 0.9270
Epoch 286/500
51s 102ms/step - loss: 0.2656 - acc: 0.9612 - val_loss: 0.3848 - val_acc: 0.9264
Epoch 287/500
51s 102ms/step - loss: 0.2645 - acc: 0.9621 - val_loss: 0.3850 - val_acc: 0.9273
Epoch 288/500
51s 103ms/step - loss: 0.2607 - acc: 0.9628 - val_loss: 0.3806 - val_acc: 0.9295
Epoch 289/500
51s 102ms/step - loss: 0.2627 - acc: 0.9619 - val_loss: 0.3875 - val_acc: 0.9279
Epoch 290/500
51s 103ms/step - loss: 0.2641 - acc: 0.9618 - val_loss: 0.3967 - val_acc: 0.9284
Epoch 291/500
52s 103ms/step - loss: 0.2624 - acc: 0.9633 - val_loss: 0.3906 - val_acc: 0.9253
Epoch 292/500
51s 103ms/step - loss: 0.2651 - acc: 0.9623 - val_loss: 0.3843 - val_acc: 0.9273
Epoch 293/500
52s 103ms/step - loss: 0.2604 - acc: 0.9632 - val_loss: 0.3887 - val_acc: 0.9264
Epoch 294/500
51s 103ms/step - loss: 0.2605 - acc: 0.9633 - val_loss: 0.3969 - val_acc: 0.9244
Epoch 295/500
51s 102ms/step - loss: 0.2636 - acc: 0.9615 - val_loss: 0.3853 - val_acc: 0.9295
Epoch 296/500
51s 103ms/step - loss: 0.2620 - acc: 0.9625 - val_loss: 0.3993 - val_acc: 0.9248
Epoch 297/500
52s 103ms/step - loss: 0.2657 - acc: 0.9616 - val_loss: 0.3840 - val_acc: 0.9283
Epoch 298/500
52s 103ms/step - loss: 0.2630 - acc: 0.9627 - val_loss: 0.3899 - val_acc: 0.9257
Epoch 299/500
52s 103ms/step - loss: 0.2623 - acc: 0.9619 - val_loss: 0.3894 - val_acc: 0.9249
Epoch 300/500
52s 103ms/step - loss: 0.2709 - acc: 0.9589 - val_loss: 0.3775 - val_acc: 0.9304
Epoch 301/500
lr changed to 0.0009999999776482583
52s 103ms/step - loss: 0.2329 - acc: 0.9743 - val_loss: 0.3555 - val_acc: 0.9362
Epoch 302/500
52s 103ms/step - loss: 0.2176 - acc: 0.9795 - val_loss: 0.3499 - val_acc: 0.9380
Epoch 303/500
52s 103ms/step - loss: 0.2121 - acc: 0.9810 - val_loss: 0.3486 - val_acc: 0.9394
Epoch 304/500
52s 103ms/step - loss: 0.2088 - acc: 0.9825 - val_loss: 0.3498 - val_acc: 0.9401
Epoch 305/500
52s 103ms/step - loss: 0.2045 - acc: 0.9837 - val_loss: 0.3475 - val_acc: 0.9406
Epoch 306/500
52s 103ms/step - loss: 0.2032 - acc: 0.9846 - val_loss: 0.3479 - val_acc: 0.9404
Epoch 307/500
52s 103ms/step - loss: 0.2013 - acc: 0.9845 - val_loss: 0.3481 - val_acc: 0.9407
Epoch 308/500
51s 103ms/step - loss: 0.2013 - acc: 0.9846 - val_loss: 0.3519 - val_acc: 0.9397
Epoch 309/500
51s 103ms/step - loss: 0.1980 - acc: 0.9865 - val_loss: 0.3485 - val_acc: 0.9411
...
Epoch 395/500
51s 103ms/step - loss: 0.1582 - acc: 0.9925 - val_loss: 0.3512 - val_acc: 0.9399
Epoch 396/500
51s 102ms/step - loss: 0.1570 - acc: 0.9931 - val_loss: 0.3508 - val_acc: 0.9406
Epoch 397/500
51s 102ms/step - loss: 0.1573 - acc: 0.9927 - val_loss: 0.3472 - val_acc: 0.9397
Epoch 398/500
51s 102ms/step - loss: 0.1562 - acc: 0.9928 - val_loss: 0.3480 - val_acc: 0.9408ETA: 0s - loss: 0.1562 - acc: 0.9928
Epoch 399/500
51s 102ms/step - loss: 0.1569 - acc: 0.9930 - val_loss: 0.3514 - val_acc: 0.9402
Epoch 400/500
51s 102ms/step - loss: 0.1557 - acc: 0.9932 - val_loss: 0.3516 - val_acc: 0.9390
Epoch 401/500
51s 102ms/step - loss: 0.1567 - acc: 0.9929 - val_loss: 0.3554 - val_acc: 0.9403
Epoch 402/500
51s 102ms/step - loss: 0.1572 - acc: 0.9922 - val_loss: 0.3525 - val_acc: 0.9385
Epoch 403/500
51s 102ms/step - loss: 0.1560 - acc: 0.9929 - val_loss: 0.3521 - val_acc: 0.9395
Epoch 404/500
51s 102ms/step - loss: 0.1564 - acc: 0.9927 - val_loss: 0.3491 - val_acc: 0.9402
Epoch 405/500
51s 103ms/step - loss: 0.1543 - acc: 0.9932 - val_loss: 0.3494 - val_acc: 0.9417
Epoch 406/500
51s 102ms/step - loss: 0.1553 - acc: 0.9926 - val_loss: 0.3483 - val_acc: 0.9408
Epoch 407/500
51s 102ms/step - loss: 0.1548 - acc: 0.9930 - val_loss: 0.3520 - val_acc: 0.9399
Epoch 408/500
51s 102ms/step - loss: 0.1548 - acc: 0.9929 - val_loss: 0.3523 - val_acc: 0.9411
Epoch 409/500
51s 102ms/step - loss: 0.1552 - acc: 0.9926 - val_loss: 0.3506 - val_acc: 0.9399
Epoch 410/500
51s 102ms/step - loss: 0.1530 - acc: 0.9939 - val_loss: 0.3460 - val_acc: 0.9406
Epoch 411/500
51s 102ms/step - loss: 0.1549 - acc: 0.9924 - val_loss: 0.3445 - val_acc: 0.9397
Epoch 412/500
51s 102ms/step - loss: 0.1533 - acc: 0.9931 - val_loss: 0.3445 - val_acc: 0.9410
Epoch 413/500
51s 102ms/step - loss: 0.1534 - acc: 0.9928 - val_loss: 0.3471 - val_acc: 0.9406
Epoch 414/500
51s 102ms/step - loss: 0.1542 - acc: 0.9922 - val_loss: 0.3478 - val_acc: 0.9417
Epoch 415/500
51s 102ms/step - loss: 0.1518 - acc: 0.9934 - val_loss: 0.3527 - val_acc: 0.9414
Epoch 416/500
51s 102ms/step - loss: 0.1529 - acc: 0.9926 - val_loss: 0.3484 - val_acc: 0.9423
Epoch 417/500
51s 102ms/step - loss: 0.1533 - acc: 0.9928 - val_loss: 0.3468 - val_acc: 0.9421
Epoch 418/500
51s 103ms/step - loss: 0.1531 - acc: 0.9927 - val_loss: 0.3497 - val_acc: 0.9418
Epoch 419/500
51s 102ms/step - loss: 0.1519 - acc: 0.9931 - val_loss: 0.3500 - val_acc: 0.9413
Epoch 420/500
51s 102ms/step - loss: 0.1512 - acc: 0.9932 - val_loss: 0.3480 - val_acc: 0.9418
Epoch 421/500
51s 102ms/step - loss: 0.1512 - acc: 0.9931 - val_loss: 0.3491 - val_acc: 0.9414
Epoch 422/500
51s 102ms/step - loss: 0.1518 - acc: 0.9927 - val_loss: 0.3464 - val_acc: 0.9412
Epoch 423/500
51s 102ms/step - loss: 0.1505 - acc: 0.9935 - val_loss: 0.3495 - val_acc: 0.9400
Epoch 424/500
51s 102ms/step - loss: 0.1502 - acc: 0.9930 - val_loss: 0.3467 - val_acc: 0.9418
Epoch 425/500
51s 102ms/step - loss: 0.1513 - acc: 0.9928 - val_loss: 0.3472 - val_acc: 0.9391
Epoch 426/500
51s 102ms/step - loss: 0.1496 - acc: 0.9933 - val_loss: 0.3430 - val_acc: 0.9400
Epoch 427/500
51s 102ms/step - loss: 0.1490 - acc: 0.9935 - val_loss: 0.3452 - val_acc: 0.9398
Epoch 428/500
51s 102ms/step - loss: 0.1499 - acc: 0.9931 - val_loss: 0.3456 - val_acc: 0.9394
Epoch 429/500
51s 102ms/step - loss: 0.1496 - acc: 0.9933 - val_loss: 0.3405 - val_acc: 0.9426
Epoch 430/500
51s 102ms/step - loss: 0.1486 - acc: 0.9933 - val_loss: 0.3467 - val_acc: 0.9404
Epoch 431/500
51s 103ms/step - loss: 0.1481 - acc: 0.9933 - val_loss: 0.3448 - val_acc: 0.9408
Epoch 432/500
51s 102ms/step - loss: 0.1479 - acc: 0.9936 - val_loss: 0.3479 - val_acc: 0.9390
Epoch 433/500
51s 102ms/step - loss: 0.1485 - acc: 0.9934 - val_loss: 0.3484 - val_acc: 0.9380
Epoch 434/500
51s 102ms/step - loss: 0.1479 - acc: 0.9935 - val_loss: 0.3495 - val_acc: 0.9384
Epoch 435/500
51s 102ms/step - loss: 0.1468 - acc: 0.9940 - val_loss: 0.3490 - val_acc: 0.9425
Epoch 436/500
51s 103ms/step - loss: 0.1482 - acc: 0.9929 - val_loss: 0.3538 - val_acc: 0.9386
Epoch 437/500
51s 102ms/step - loss: 0.1478 - acc: 0.9932 - val_loss: 0.3494 - val_acc: 0.9400
Epoch 438/500
51s 102ms/step - loss: 0.1476 - acc: 0.9937 - val_loss: 0.3508 - val_acc: 0.9386
Epoch 439/500
51s 102ms/step - loss: 0.1483 - acc: 0.9932 - val_loss: 0.3483 - val_acc: 0.9386
Epoch 440/500
51s 103ms/step - loss: 0.1475 - acc: 0.9932 - val_loss: 0.3466 - val_acc: 0.9402
Epoch 441/500
51s 102ms/step - loss: 0.1473 - acc: 0.9932 - val_loss: 0.3477 - val_acc: 0.9406
Epoch 442/500
51s 102ms/step - loss: 0.1469 - acc: 0.9931 - val_loss: 0.3461 - val_acc: 0.9402
Epoch 443/500
51s 102ms/step - loss: 0.1460 - acc: 0.9936 - val_loss: 0.3483 - val_acc: 0.9404
Epoch 444/500
51s 102ms/step - loss: 0.1463 - acc: 0.9936 - val_loss: 0.3438 - val_acc: 0.9407
Epoch 445/500
51s 102ms/step - loss: 0.1454 - acc: 0.9936 - val_loss: 0.3488 - val_acc: 0.9401
Epoch 446/500
51s 102ms/step - loss: 0.1452 - acc: 0.9935 - val_loss: 0.3450 - val_acc: 0.9402
Epoch 447/500
51s 102ms/step - loss: 0.1452 - acc: 0.9933 - val_loss: 0.3494 - val_acc: 0.9401
Epoch 448/500
51s 102ms/step - loss: 0.1447 - acc: 0.9936 - val_loss: 0.3448 - val_acc: 0.9418
Epoch 449/500
51s 102ms/step - loss: 0.1446 - acc: 0.9936 - val_loss: 0.3507 - val_acc: 0.9403
Epoch 450/500
51s 103ms/step - loss: 0.1442 - acc: 0.9935 - val_loss: 0.3464 - val_acc: 0.9401
Epoch 451/500
lr changed to 9.999999310821295e-05
51s 102ms/step - loss: 0.1437 - acc: 0.9936 - val_loss: 0.3447 - val_acc: 0.9410
Epoch 452/500
51s 102ms/step - loss: 0.1435 - acc: 0.9938 - val_loss: 0.3450 - val_acc: 0.9408ETA: 19s - loss: 0.1439 - acc: 0.9936
Epoch 453/500
51s 102ms/step - loss: 0.1421 - acc: 0.9943 - val_loss: 0.3444 - val_acc: 0.9402
Epoch 454/500
51s 102ms/step - loss: 0.1419 - acc: 0.9945 - val_loss: 0.3443 - val_acc: 0.9407
Epoch 455/500
51s 102ms/step - loss: 0.1424 - acc: 0.9943 - val_loss: 0.3445 - val_acc: 0.9407
Epoch 456/500
51s 102ms/step - loss: 0.1425 - acc: 0.9942 - val_loss: 0.3442 - val_acc: 0.9406
Epoch 457/500
51s 102ms/step - loss: 0.1420 - acc: 0.9946 - val_loss: 0.3440 - val_acc: 0.9408
Epoch 458/500
51s 102ms/step - loss: 0.1422 - acc: 0.9938 - val_loss: 0.3443 - val_acc: 0.9407
Epoch 459/500
51s 102ms/step - loss: 0.1399 - acc: 0.9953 - val_loss: 0.3442 - val_acc: 0.9411
Epoch 460/500
51s 102ms/step - loss: 0.1417 - acc: 0.9944 - val_loss: 0.3446 - val_acc: 0.9415
Epoch 461/500
51s 102ms/step - loss: 0.1420 - acc: 0.9942 - val_loss: 0.3451 - val_acc: 0.9415
Epoch 462/500
51s 102ms/step - loss: 0.1402 - acc: 0.9949 - val_loss: 0.3443 - val_acc: 0.9413
Epoch 463/500
51s 102ms/step - loss: 0.1414 - acc: 0.9944 - val_loss: 0.3437 - val_acc: 0.9407
Epoch 464/500
51s 102ms/step - loss: 0.1411 - acc: 0.9947 - val_loss: 0.3442 - val_acc: 0.9408
Epoch 465/500
51s 102ms/step - loss: 0.1403 - acc: 0.9949 - val_loss: 0.3440 - val_acc: 0.9414
Epoch 466/500
51s 103ms/step - loss: 0.1406 - acc: 0.9948 - val_loss: 0.3439 - val_acc: 0.9408
Epoch 467/500
51s 102ms/step - loss: 0.1407 - acc: 0.9947 - val_loss: 0.3438 - val_acc: 0.9412
Epoch 468/500
51s 102ms/step - loss: 0.1419 - acc: 0.9942 - val_loss: 0.3438 - val_acc: 0.9413
Epoch 469/500
51s 102ms/step - loss: 0.1417 - acc: 0.9945 - val_loss: 0.3443 - val_acc: 0.9406
Epoch 470/500
51s 102ms/step - loss: 0.1409 - acc: 0.9945 - val_loss: 0.3439 - val_acc: 0.9407
Epoch 471/500
51s 102ms/step - loss: 0.1418 - acc: 0.9939 - val_loss: 0.3439 - val_acc: 0.9410
Epoch 472/500
51s 102ms/step - loss: 0.1407 - acc: 0.9948 - val_loss: 0.3434 - val_acc: 0.9408
Epoch 473/500
51s 102ms/step - loss: 0.1413 - acc: 0.9942 - val_loss: 0.3438 - val_acc: 0.9410
Epoch 474/500
51s 102ms/step - loss: 0.1394 - acc: 0.9950 - val_loss: 0.3439 - val_acc: 0.9413
Epoch 475/500
51s 102ms/step - loss: 0.1405 - acc: 0.9946 - val_loss: 0.3438 - val_acc: 0.9414
Epoch 476/500
51s 102ms/step - loss: 0.1396 - acc: 0.9949 - val_loss: 0.3438 - val_acc: 0.9411
Epoch 477/500
51s 102ms/step - loss: 0.1401 - acc: 0.9950 - val_loss: 0.3439 - val_acc: 0.9411
Epoch 478/500
51s 102ms/step - loss: 0.1406 - acc: 0.9947 - val_loss: 0.3442 - val_acc: 0.9408
Epoch 479/500
51s 102ms/step - loss: 0.1394 - acc: 0.9952 - val_loss: 0.3446 - val_acc: 0.9408
Epoch 480/500
51s 102ms/step - loss: 0.1399 - acc: 0.9950 - val_loss: 0.3447 - val_acc: 0.9402
Epoch 481/500
51s 102ms/step - loss: 0.1409 - acc: 0.9945 - val_loss: 0.3451 - val_acc: 0.9405
Epoch 482/500
51s 102ms/step - loss: 0.1392 - acc: 0.9955 - val_loss: 0.3446 - val_acc: 0.9406
Epoch 483/500
51s 102ms/step - loss: 0.1403 - acc: 0.9948 - val_loss: 0.3450 - val_acc: 0.9402
Epoch 484/500
51s 102ms/step - loss: 0.1396 - acc: 0.9950 - val_loss: 0.3450 - val_acc: 0.9403
Epoch 485/500
51s 102ms/step - loss: 0.1395 - acc: 0.9949 - val_loss: 0.3449 - val_acc: 0.9401
Epoch 486/500
51s 102ms/step - loss: 0.1413 - acc: 0.9944 - val_loss: 0.3450 - val_acc: 0.9402
Epoch 487/500
51s 103ms/step - loss: 0.1406 - acc: 0.9945 - val_loss: 0.3447 - val_acc: 0.9403
Epoch 488/500
51s 102ms/step - loss: 0.1403 - acc: 0.9943 - val_loss: 0.3448 - val_acc: 0.9409
Epoch 489/500
51s 102ms/step - loss: 0.1408 - acc: 0.9948 - val_loss: 0.3442 - val_acc: 0.9413
Epoch 490/500
51s 102ms/step - loss: 0.1398 - acc: 0.9951 - val_loss: 0.3438 - val_acc: 0.9416
Epoch 491/500
51s 102ms/step - loss: 0.1398 - acc: 0.9947 - val_loss: 0.3438 - val_acc: 0.9409
Epoch 492/500
51s 102ms/step - loss: 0.1396 - acc: 0.9951 - val_loss: 0.3437 - val_acc: 0.9414
Epoch 493/500
51s 102ms/step - loss: 0.1402 - acc: 0.9947 - val_loss: 0.3434 - val_acc: 0.9416
Epoch 494/500
51s 102ms/step - loss: 0.1400 - acc: 0.9951 - val_loss: 0.3441 - val_acc: 0.9413
Epoch 495/500
51s 102ms/step - loss: 0.1395 - acc: 0.9952 - val_loss: 0.3442 - val_acc: 0.9409
Epoch 496/500
51s 102ms/step - loss: 0.1395 - acc: 0.9948 - val_loss: 0.3446 - val_acc: 0.9413
Epoch 497/500
51s 102ms/step - loss: 0.1386 - acc: 0.9953 - val_loss: 0.3444 - val_acc: 0.9415
Epoch 498/500
51s 102ms/step - loss: 0.1396 - acc: 0.9951 - val_loss: 0.3440 - val_acc: 0.9415
Epoch 499/500
51s 102ms/step - loss: 0.1396 - acc: 0.9950 - val_loss: 0.3444 - val_acc: 0.9414
Epoch 500/500
51s 102ms/step - loss: 0.1386 - acc: 0.9951 - val_loss: 0.3449 - val_acc: 0.9417
Train loss: 0.128365815192461
Train accuracy: 0.9987200012207031
Test loss: 0.3449158281087875
Test accuracy: 0.941700000166893相较于调参记录18的94.28%，这次的测试准确率低了一点。
但是，值得指出的是，这次只训练了500个epoch，而调参记录18训练了5000个epoch。通过观察loss可以发现，其实这次的loss下降得更快。
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, DOI: 10.1109/TIE.2020.2972458, Date of Publication: 13 February 2020
https://ieeexplore.ieee.org/document/8998530
