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。
续上一篇：
【哈工大团队】动态ReLU：自适应参数化ReLU及Keras代码（调参记录13）
这次再尝试解决过拟合，把残差模块的数量减少到2个，自适应参数化ReLU激活函数里面第一个全连接层的权重数量，减少为之前的1/8，批量大小设置为1000（主要是为了省时间）。
自适应参数化ReLU激活函数的基本原理如下：
自适应参数化ReLU：一种Dynamic ReLU（动态ReLU）激活函数Keras程序如下：
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 1500 epoches
def scheduler(epoch):
if epoch % 1500 == 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//8, 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, 3, 16, downsample=False)
net = residual_block(net, 1, 32, downsample=True)
# net = residual_block(net, 2, 32, downsample=False)
net = residual_block(net, 1, 64, downsample=True)
# net = residual_block(net, 2, 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=1000),
validation_data=(x_test, y_test), epochs=5000,
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=1000, 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=1000, 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Epoch 2000/5000
10s 191ms/step - loss: 0.5083 - acc: 0.8658 - val_loss: 0.5190 - val_acc: 0.8644
Epoch 2001/5000
10s 194ms/step - loss: 0.5102 - acc: 0.8651 - val_loss: 0.5203 - val_acc: 0.8656
Epoch 2002/5000
10s 192ms/step - loss: 0.5073 - acc: 0.8659 - val_loss: 0.5245 - val_acc: 0.8612
Epoch 2003/5000
10s 190ms/step - loss: 0.5105 - acc: 0.8646 - val_loss: 0.5181 - val_acc: 0.8636
Epoch 2004/5000
9s 186ms/step - loss: 0.5080 - acc: 0.8661 - val_loss: 0.5217 - val_acc: 0.8631
Epoch 2005/5000
9s 186ms/step - loss: 0.5074 - acc: 0.8641 - val_loss: 0.5237 - val_acc: 0.8614
Epoch 2006/5000
9s 186ms/step - loss: 0.5060 - acc: 0.8651 - val_loss: 0.5241 - val_acc: 0.8641
Epoch 2007/5000
10s 190ms/step - loss: 0.5096 - acc: 0.8651 - val_loss: 0.5185 - val_acc: 0.8660
Epoch 2008/5000
9s 190ms/step - loss: 0.5053 - acc: 0.8686 - val_loss: 0.5186 - val_acc: 0.8624
Epoch 2009/5000
10s 191ms/step - loss: 0.5057 - acc: 0.8670 - val_loss: 0.5208 - val_acc: 0.8636
Epoch 2010/5000
10s 190ms/step - loss: 0.5102 - acc: 0.8653 - val_loss: 0.5214 - val_acc: 0.8614
Epoch 2011/5000
9s 188ms/step - loss: 0.5091 - acc: 0.8651 - val_loss: 0.5211 - val_acc: 0.8629
Epoch 2012/5000
9s 187ms/step - loss: 0.5069 - acc: 0.8672 - val_loss: 0.5221 - val_acc: 0.8616
Epoch 2013/5000
9s 184ms/step - loss: 0.5098 - acc: 0.8652 - val_loss: 0.5241 - val_acc: 0.8625
Epoch 2014/5000
9s 187ms/step - loss: 0.5056 - acc: 0.8666 - val_loss: 0.5188 - val_acc: 0.8623
Epoch 2015/5000
9s 189ms/step - loss: 0.5050 - acc: 0.8672 - val_loss: 0.5237 - val_acc: 0.8621
Epoch 2016/5000
10s 193ms/step - loss: 0.5057 - acc: 0.8667 - val_loss: 0.5207 - val_acc: 0.8607
Epoch 2017/5000
10s 192ms/step - loss: 0.5092 - acc: 0.8642 - val_loss: 0.5172 - val_acc: 0.8637
Epoch 2018/5000
9s 190ms/step - loss: 0.5062 - acc: 0.8671 - val_loss: 0.5265 - val_acc: 0.8612
Epoch 2019/5000
9s 188ms/step - loss: 0.5112 - acc: 0.8648 - val_loss: 0.5256 - val_acc: 0.8617
Epoch 2020/5000
9s 185ms/step - loss: 0.5071 - acc: 0.8663 - val_loss: 0.5241 - val_acc: 0.8622
Epoch 2021/5000
9s 186ms/step - loss: 0.5079 - acc: 0.8660 - val_loss: 0.5212 - val_acc: 0.8627
Epoch 2022/5000
9s 185ms/step - loss: 0.5058 - acc: 0.8667 - val_loss: 0.5227 - val_acc: 0.8600
Epoch 2023/5000
9s 189ms/step - loss: 0.5070 - acc: 0.8661 - val_loss: 0.5259 - val_acc: 0.8608
Epoch 2024/5000
10s 193ms/step - loss: 0.5053 - acc: 0.8663 - val_loss: 0.5219 - val_acc: 0.8606
Epoch 2025/5000
10s 191ms/step - loss: 0.5106 - acc: 0.8655 - val_loss: 0.5205 - val_acc: 0.8625
Epoch 2026/5000
10s 191ms/step - loss: 0.5090 - acc: 0.8649 - val_loss: 0.5221 - val_acc: 0.8610
Epoch 2027/5000
9s 189ms/step - loss: 0.5103 - acc: 0.8648 - val_loss: 0.5242 - val_acc: 0.8631
Epoch 2028/5000
9s 189ms/step - loss: 0.5051 - acc: 0.8663 - val_loss: 0.5253 - val_acc: 0.8596
Epoch 2029/5000
9s 185ms/step - loss: 0.5091 - acc: 0.8654 - val_loss: 0.5237 - val_acc: 0.8612
Epoch 2030/5000
9s 189ms/step - loss: 0.5076 - acc: 0.8654 - val_loss: 0.5197 - val_acc: 0.8627
Epoch 2031/5000
9s 189ms/step - loss: 0.5058 - acc: 0.8657 - val_loss: 0.5226 - val_acc: 0.8625
Epoch 2032/5000
10s 194ms/step - loss: 0.5078 - acc: 0.8669 - val_loss: 0.5225 - val_acc: 0.8639
Epoch 2033/5000
10s 193ms/step - loss: 0.5101 - acc: 0.8643 - val_loss: 0.5195 - val_acc: 0.8632
Epoch 2034/5000
10s 191ms/step - loss: 0.5088 - acc: 0.8665 - val_loss: 0.5237 - val_acc: 0.8634
Epoch 2035/5000
10s 191ms/step - loss: 0.5091 - acc: 0.8644 - val_loss: 0.5182 - val_acc: 0.8652
Epoch 2036/5000
9s 186ms/step - loss: 0.5090 - acc: 0.8658 - val_loss: 0.5199 - val_acc: 0.8615
Epoch 2037/5000
9s 187ms/step - loss: 0.5056 - acc: 0.8670 - val_loss: 0.5256 - val_acc: 0.8600
Epoch 2038/5000
9s 187ms/step - loss: 0.5057 - acc: 0.8665 - val_loss: 0.5261 - val_acc: 0.8600
Epoch 2039/5000
9s 190ms/step - loss: 0.5088 - acc: 0.8647 - val_loss: 0.5242 - val_acc: 0.8625
Epoch 2040/5000
10s 192ms/step - loss: 0.5068 - acc: 0.8662 - val_loss: 0.5223 - val_acc: 0.8620
Epoch 2041/5000
10s 191ms/step - loss: 0.5068 - acc: 0.8655 - val_loss: 0.5189 - val_acc: 0.8594
Epoch 2042/5000
10s 191ms/step - loss: 0.5070 - acc: 0.8661 - val_loss: 0.5244 - val_acc: 0.8613
Epoch 2043/5000
9s 187ms/step - loss: 0.5008 - acc: 0.8683 - val_loss: 0.5238 - val_acc: 0.8603
Epoch 2044/5000
9s 189ms/step - loss: 0.5100 - acc: 0.8652 - val_loss: 0.5233 - val_acc: 0.8609
Epoch 2045/5000
9s 186ms/step - loss: 0.5007 - acc: 0.8692 - val_loss: 0.5206 - val_acc: 0.8651
Epoch 2046/5000
9s 188ms/step - loss: 0.5043 - acc: 0.8662 - val_loss: 0.5246 - val_acc: 0.8623
Epoch 2047/5000
10s 190ms/step - loss: 0.5084 - acc: 0.8644 - val_loss: 0.5211 - val_acc: 0.8620
Epoch 2048/5000
10s 192ms/step - loss: 0.5068 - acc: 0.8658 - val_loss: 0.5206 - val_acc: 0.8635
Epoch 2049/5000
10s 194ms/step - loss: 0.5033 - acc: 0.8669 - val_loss: 0.5230 - val_acc: 0.8589
Epoch 2050/5000
10s 192ms/step - loss: 0.5096 - acc: 0.8653 - val_loss: 0.5217 - val_acc: 0.8610
Epoch 2051/5000
10s 191ms/step - loss: 0.5081 - acc: 0.8651 - val_loss: 0.5238 - val_acc: 0.8600
Epoch 2052/5000
9s 186ms/step - loss: 0.5061 - acc: 0.8665 - val_loss: 0.5258 - val_acc: 0.8617
Epoch 2053/5000
9s 187ms/step - loss: 0.5036 - acc: 0.8683 - val_loss: 0.5233 - val_acc: 0.8606
Epoch 2054/5000
9s 187ms/step - loss: 0.5075 - acc: 0.8652 - val_loss: 0.5242 - val_acc: 0.8599
Epoch 2055/5000
9s 189ms/step - loss: 0.5105 - acc: 0.8656 - val_loss: 0.5201 - val_acc: 0.8621
Epoch 2056/5000
10s 191ms/step - loss: 0.5064 - acc: 0.8664 - val_loss: 0.5264 - val_acc: 0.8586
Epoch 2057/5000
10s 192ms/step - loss: 0.5049 - acc: 0.8655 - val_loss: 0.5225 - val_acc: 0.8621
Epoch 2058/5000
10s 190ms/step - loss: 0.5083 - acc: 0.8657 - val_loss: 0.5218 - val_acc: 0.8648
Epoch 2059/5000
9s 188ms/step - loss: 0.5118 - acc: 0.8657 - val_loss: 0.5233 - val_acc: 0.8627
Epoch 2060/5000
9s 187ms/step - loss: 0.5037 - acc: 0.8668 - val_loss: 0.5183 - val_acc: 0.8618
Epoch 2061/5000
9s 185ms/step - loss: 0.5092 - acc: 0.8661 - val_loss: 0.5207 - val_acc: 0.8642
Epoch 2062/5000
9s 188ms/step - loss: 0.5089 - acc: 0.8649 - val_loss: 0.5175 - val_acc: 0.8625
Epoch 2063/5000
10s 190ms/step - loss: 0.5056 - acc: 0.8667 - val_loss: 0.5175 - val_acc: 0.8635
Epoch 2064/5000
10s 192ms/step - loss: 0.5078 - acc: 0.8659 - val_loss: 0.5217 - val_acc: 0.8602
Epoch 2065/5000
10s 192ms/step - loss: 0.5065 - acc: 0.8685 - val_loss: 0.5195 - val_acc: 0.8627
Epoch 2066/5000
10s 190ms/step - loss: 0.5099 - acc: 0.8660 - val_loss: 0.5223 - val_acc: 0.8608
Epoch 2067/5000
10s 190ms/step - loss: 0.5048 - acc: 0.8663 - val_loss: 0.5188 - val_acc: 0.8629
Epoch 2068/5000
9s 187ms/step - loss: 0.5047 - acc: 0.8659 - val_loss: 0.5176 - val_acc: 0.8611
Epoch 2069/5000
9s 185ms/step - loss: 0.5055 - acc: 0.8680 - val_loss: 0.5182 - val_acc: 0.8636
Epoch 2070/5000
9s 186ms/step - loss: 0.5076 - acc: 0.8659 - val_loss: 0.5235 - val_acc: 0.8619
Epoch 2071/5000
9s 189ms/step - loss: 0.5100 - acc: 0.8664 - val_loss: 0.5185 - val_acc: 0.8630
Epoch 2072/5000
10s 192ms/step - loss: 0.5052 - acc: 0.8663 - val_loss: 0.5214 - val_acc: 0.8611
Epoch 2073/5000
10s 192ms/step - loss: 0.5052 - acc: 0.8662 - val_loss: 0.5250 - val_acc: 0.8630
Epoch 2074/5000
10s 193ms/step - loss: 0.5054 - acc: 0.8668 - val_loss: 0.5162 - val_acc: 0.8650
Epoch 2075/5000
9s 189ms/step - loss: 0.5081 - acc: 0.8647 - val_loss: 0.5238 - val_acc: 0.8598
Epoch 2076/5000
9s 189ms/step - loss: 0.5075 - acc: 0.8647 - val_loss: 0.5237 - val_acc: 0.8609
Epoch 2077/5000
9s 185ms/step - loss: 0.5127 - acc: 0.8642 - val_loss: 0.5209 - val_acc: 0.8662
Epoch 2078/5000
9s 186ms/step - loss: 0.5063 - acc: 0.8655 - val_loss: 0.5192 - val_acc: 0.8639
Epoch 2079/5000
9s 189ms/step - loss: 0.5113 - acc: 0.8650 - val_loss: 0.5193 - val_acc: 0.8613
Epoch 2080/5000
10s 191ms/step - loss: 0.5087 - acc: 0.8660 - val_loss: 0.5199 - val_acc: 0.8623
Epoch 2081/5000
10s 191ms/step - loss: 0.5097 - acc: 0.8648 - val_loss: 0.5187 - val_acc: 0.8611
Epoch 2082/5000
10s 191ms/step - loss: 0.5059 - acc: 0.8674 - val_loss: 0.5229 - val_acc: 0.8608
Epoch 2083/5000
10s 191ms/step - loss: 0.5100 - acc: 0.8641 - val_loss: 0.5251 - val_acc: 0.8599
Epoch 2084/5000
9s 187ms/step - loss: 0.5098 - acc: 0.8645 - val_loss: 0.5195 - val_acc: 0.8631
Epoch 2085/5000
9s 187ms/step - loss: 0.5023 - acc: 0.8680 - val_loss: 0.5185 - val_acc: 0.8638
Epoch 2086/5000
9s 187ms/step - loss: 0.5077 - acc: 0.8660 - val_loss: 0.5249 - val_acc: 0.8628
Epoch 2087/5000
9s 189ms/step - loss: 0.5076 - acc: 0.8645 - val_loss: 0.5219 - val_acc: 0.8599
Epoch 2088/5000
9s 189ms/step - loss: 0.5074 - acc: 0.8665 - val_loss: 0.5228 - val_acc: 0.8633
Epoch 2089/5000
10s 191ms/step - loss: 0.5064 - acc: 0.8658 - val_loss: 0.5219 - val_acc: 0.8626
Epoch 2090/5000
10s 191ms/step - loss: 0.5064 - acc: 0.8673 - val_loss: 0.5207 - val_acc: 0.8626
Epoch 2091/5000
9s 188ms/step - loss: 0.5064 - acc: 0.8673 - val_loss: 0.5229 - val_acc: 0.8616
Epoch 2092/5000
9s 190ms/step - loss: 0.5055 - acc: 0.8670 - val_loss: 0.5236 - val_acc: 0.8613
Epoch 2093/5000
9s 187ms/step - loss: 0.5075 - acc: 0.8657 - val_loss: 0.5197 - val_acc: 0.8627
Epoch 2094/5000
9s 186ms/step - loss: 0.5082 - acc: 0.8664 - val_loss: 0.5228 - val_acc: 0.8602
Epoch 2095/5000
9s 189ms/step - loss: 0.5072 - acc: 0.8672 - val_loss: 0.5258 - val_acc: 0.8618
Epoch 2096/5000
10s 192ms/step - loss: 0.5099 - acc: 0.8641 - val_loss: 0.5190 - val_acc: 0.8631
Epoch 2097/5000
10s 192ms/step - loss: 0.5069 - acc: 0.8662 - val_loss: 0.5212 - val_acc: 0.8603
Epoch 2098/5000
10s 191ms/step - loss: 0.5080 - acc: 0.8656 - val_loss: 0.5203 - val_acc: 0.8611
Epoch 2099/5000
10s 192ms/step - loss: 0.5051 - acc: 0.8663 - val_loss: 0.5177 - val_acc: 0.8604
Epoch 2100/5000
9s 186ms/step - loss: 0.5059 - acc: 0.8643 - val_loss: 0.5206 - val_acc: 0.8633
Epoch 2101/5000
9s 186ms/step - loss: 0.5081 - acc: 0.8651 - val_loss: 0.5215 - val_acc: 0.8622
Epoch 2102/5000
9s 188ms/step - loss: 0.5060 - acc: 0.8655 - val_loss: 0.5183 - val_acc: 0.8645
Epoch 2103/5000
10s 190ms/step - loss: 0.5062 - acc: 0.8650 - val_loss: 0.5229 - val_acc: 0.8610
Epoch 2104/5000
10s 192ms/step - loss: 0.5060 - acc: 0.8664 - val_loss: 0.5250 - val_acc: 0.8595
Epoch 2105/5000
10s 194ms/step - loss: 0.5072 - acc: 0.8653 - val_loss: 0.5233 - val_acc: 0.8634
Epoch 2106/5000
10s 193ms/step - loss: 0.5073 - acc: 0.8645 - val_loss: 0.5187 - val_acc: 0.8626
Epoch 2107/5000
9s 187ms/step - loss: 0.5070 - acc: 0.8644 - val_loss: 0.5202 - val_acc: 0.8636
Epoch 2108/5000
9s 186ms/step - loss: 0.5075 - acc: 0.8648 - val_loss: 0.5222 - val_acc: 0.8609
Epoch 2109/5000
9s 186ms/step - loss: 0.5049 - acc: 0.8649 - val_loss: 0.5200 - val_acc: 0.8623
Epoch 2110/5000
9s 188ms/step - loss: 0.5025 - acc: 0.8691 - val_loss: 0.5246 - val_acc: 0.8613
Epoch 2111/5000
10s 190ms/step - loss: 0.5086 - acc: 0.8646 - val_loss: 0.5212 - val_acc: 0.8598
Epoch 2112/5000
10s 191ms/step - loss: 0.5071 - acc: 0.8658 - val_loss: 0.5213 - val_acc: 0.8636
Epoch 2113/5000
10s 193ms/step - loss: 0.5063 - acc: 0.8657 - val_loss: 0.5229 - val_acc: 0.8599
Epoch 2114/5000
9s 190ms/step - loss: 0.5070 - acc: 0.8652 - val_loss: 0.5194 - val_acc: 0.8629
Epoch 2115/5000
10s 191ms/step - loss: 0.5089 - acc: 0.8648 - val_loss: 0.5208 - val_acc: 0.8608
Epoch 2116/5000
9s 188ms/step - loss: 0.5021 - acc: 0.8666 - val_loss: 0.5199 - val_acc: 0.8595
Epoch 2117/5000
9s 188ms/step - loss: 0.5027 - acc: 0.8682 - val_loss: 0.5228 - val_acc: 0.8613
Epoch 2118/5000
9s 186ms/step - loss: 0.5099 - acc: 0.8648 - val_loss: 0.5247 - val_acc: 0.8596
Epoch 2119/5000
10s 190ms/step - loss: 0.5082 - acc: 0.8667 - val_loss: 0.5223 - val_acc: 0.8598
Epoch 2120/5000
10s 191ms/step - loss: 0.5107 - acc: 0.8639 - val_loss: 0.5200 - val_acc: 0.8608
Epoch 2121/5000
10s 191ms/step - loss: 0.5075 - acc: 0.8651 - val_loss: 0.5216 - val_acc: 0.8601
Epoch 2122/5000
10s 191ms/step - loss: 0.5030 - acc: 0.8646 - val_loss: 0.5156 - val_acc: 0.8647
Epoch 2123/5000
9s 187ms/step - loss: 0.5031 - acc: 0.8668 - val_loss: 0.5280 - val_acc: 0.8588
Epoch 2124/5000
9s 187ms/step - loss: 0.5066 - acc: 0.8663 - val_loss: 0.5184 - val_acc: 0.8622
Epoch 2125/5000
9s 187ms/step - loss: 0.5025 - acc: 0.8660 - val_loss: 0.5189 - val_acc: 0.8624
Epoch 2126/5000
9s 189ms/step - loss: 0.5038 - acc: 0.8676 - val_loss: 0.5244 - val_acc: 0.8605
Epoch 2127/5000
10s 191ms/step - loss: 0.5106 - acc: 0.8634 - val_loss: 0.5201 - val_acc: 0.8634
Epoch 2128/5000
10s 193ms/step - loss: 0.5068 - acc: 0.8653 - val_loss: 0.5264 - val_acc: 0.8617
Epoch 2129/5000
10s 193ms/step - loss: 0.5054 - acc: 0.8635 - val_loss: 0.5238 - val_acc: 0.8604
Epoch 2130/5000
9s 189ms/step - loss: 0.5082 - acc: 0.8652 - val_loss: 0.5238 - val_acc: 0.8599
...
Epoch 2377/5000
10s 190ms/step - loss: 0.5025 - acc: 0.8661 - val_loss: 0.5171 - val_acc: 0.8600
Epoch 2378/5000
9s 190ms/step - loss: 0.5058 - acc: 0.8643 - val_loss: 0.5229 - val_acc: 0.8596
Epoch 2379/5000
9s 186ms/step - loss: 0.5067 - acc: 0.8649 - val_loss: 0.5204 - val_acc: 0.8585
Epoch 2380/5000
9s 185ms/step - loss: 0.5039 - acc: 0.8648 - val_loss: 0.5211 - val_acc: 0.8616
Epoch 2381/5000
9s 186ms/step - loss: 0.5031 - acc: 0.8650 - val_loss: 0.5184 - val_acc: 0.8609
Epoch 2382/5000
9s 185ms/step - loss: 0.5069 - acc: 0.8644 - val_loss: 0.5213 - val_acc: 0.8594
Epoch 2383/5000
9s 189ms/step - loss: 0.5071 - acc: 0.8639 - val_loss: 0.5227 - val_acc: 0.8589
Epoch 2384/5000
9s 189ms/step - loss: 0.5073 - acc: 0.8663 - val_loss: 0.5190 - val_acc: 0.8623
Epoch 2385/5000
10s 193ms/step - loss: 0.5056 - acc: 0.8662 - val_loss: 0.5171 - val_acc: 0.8633
Epoch 2386/5000
9s 188ms/step - loss: 0.5086 - acc: 0.8638 - val_loss: 0.5144 - val_acc: 0.8637
Epoch 2387/5000
9s 188ms/step - loss: 0.4997 - acc: 0.8667 - val_loss: 0.5166 - val_acc: 0.8624
Epoch 2388/5000
9s 184ms/step - loss: 0.4999 - acc: 0.8663 - val_loss: 0.5198 - val_acc: 0.8641
Epoch 2389/5000
9s 185ms/step - loss: 0.5057 - acc: 0.8638 - val_loss: 0.5206 - val_acc: 0.8614
Epoch 2390/5000
9s 185ms/step - loss: 0.5030 - acc: 0.8666 - val_loss: 0.5173 - val_acc: 0.8608
Epoch 2391/5000
9s 188ms/step - loss: 0.5021 - acc: 0.8650 - val_loss: 0.5237 - val_acc: 0.8592
Epoch 2392/5000
9s 190ms/step - loss: 0.5059 - acc: 0.8660 - val_loss: 0.5201 - val_acc: 0.8632
Epoch 2393/5000
10s 192ms/step - loss: 0.5029 - acc: 0.8650 - val_loss: 0.5208 - val_acc: 0.8596
Epoch 2394/5000
10s 191ms/step - loss: 0.5014 - acc: 0.8664 - val_loss: 0.5260 - val_acc: 0.8575
Epoch 2395/5000
9s 185ms/step - loss: 0.5004 - acc: 0.8677 - val_loss: 0.5158 - val_acc: 0.8635
Epoch 2396/5000
9s 187ms/step - loss: 0.5001 - acc: 0.8665 - val_loss: 0.5234 - val_acc: 0.8596
Epoch 2397/5000
9s 182ms/step - loss: 0.5038 - acc: 0.8644 - val_loss: 0.5157 - val_acc: 0.8631
Epoch 2398/5000
9s 187ms/step - loss: 0.5047 - acc: 0.8655 - val_loss: 0.5172 - val_acc: 0.8619
Epoch 2399/5000
9s 190ms/step - loss: 0.5061 - acc: 0.8647 - val_loss: 0.5225 - val_acc: 0.8602
Epoch 2400/5000
10s 191ms/step - loss: 0.5054 - acc: 0.8632 - val_loss: 0.5205 - val_acc: 0.8616
Epoch 2401/5000
10s 193ms/step - loss: 0.5061 - acc: 0.8649 - val_loss: 0.5271 - val_acc: 0.8580程序还没运行完，看这样子，估计是欠拟合了。
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
