ResNet+自适应参数化ReLU（调参记录25）Cifar10~95.77%

在之前调参记录的基础上，首先，大幅度削减了自适应参数化ReLU中全连接神经元的个数，想着可以减轻训练的难度，也可以减少过拟合；然后，将Epoch增加到1000个，继续测试ResNet+自适应参数化ReLU激活函数在Cifar10上的效果。

自适应参数化ReLU激活函数的基本原理如下：

Keras程序：

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Tue Apr 14 04:17:45 2020Implemented using TensorFlow 1.0.1 and Keras 2.2.1Minghang 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_functionimport kerasimport numpy as npfrom keras.datasets import cifar10from keras.layers import Dense, Conv2D, BatchNormalization, Activation, Minimum, Lambdafrom keras.layers import AveragePooling2D, Input, GlobalAveragePooling2D, Concatenate, Reshapefrom keras.regularizers import l2from keras import backend as Kfrom keras.models import Modelfrom keras import optimizersfrom keras.preprocessing.image import ImageDataGeneratorfrom keras.callbacks import LearningRateSchedulerK.set_learning_phase(1)def cal_mean(inputs):    outputs = K.mean(inputs, axis=1, keepdims=True)    return outputs# 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 matricesy_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 epochesdef 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 = Lambda(cal_mean)(GlobalAveragePooling2D()(pos_input))    scales_n = Lambda(cal_mean)(GlobalAveragePooling2D()(neg_input))    scales = Concatenate()([scales_n, scales_p])    scales = Dense(2, 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(1, 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((1,1,1))(scales)    # apply a paramtetric relu    neg_part = keras.layers.multiply([scales, neg_input])    return keras.layers.add([pos_input, neg_part])# Residual Blockdef 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 modelinputs = Input(shape=(32, 32, 3))net = Conv2D(64, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(inputs)net = residual_block(net, 20,  64, downsample=False)net = residual_block(net, 1,  128, downsample=True)net = residual_block(net, 19, 128, downsample=False)net = residual_block(net, 1,  256, downsample=True)net = residual_block(net, 19, 256, downsample=False)net = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(net)net = aprelu(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 augmentationdatagen = 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=1000,                     verbose=1, callbacks=[reduce_lr], workers=4)# get resultsK.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：

Using TensorFlow backend.x_train shape: (50000, 32, 32, 3)50000 train samples10000 test samplesEpoch 1/1000413s 826ms/step - loss: 6.7483 - acc: 0.3694 - val_loss: 5.9301 - val_acc: 0.4997Epoch 2/1000330s 659ms/step - loss: 5.5312 - acc: 0.4941 - val_loss: 4.9301 - val_acc: 0.5792Epoch 3/1000330s 659ms/step - loss: 4.6439 - acc: 0.5593 - val_loss: 4.0912 - val_acc: 0.6487Epoch 4/1000329s 659ms/step - loss: 3.9263 - acc: 0.6101 - val_loss: 3.4891 - val_acc: 0.6778Epoch 5/1000329s 659ms/step - loss: 3.3595 - acc: 0.6415 - val_loss: 2.9574 - val_acc: 0.7220Epoch 6/1000334s 668ms/step - loss: 2.8985 - acc: 0.6704 - val_loss: 2.5622 - val_acc: 0.7322Epoch 7/1000330s 660ms/step - loss: 2.5229 - acc: 0.6917 - val_loss: 2.2063 - val_acc: 0.7612Epoch 8/1000329s 659ms/step - loss: 2.2063 - acc: 0.7167 - val_loss: 1.9074 - val_acc: 0.7861Epoch 9/1000329s 659ms/step - loss: 1.9480 - acc: 0.7339 - val_loss: 1.6790 - val_acc: 0.8023Epoch 10/1000330s 659ms/step - loss: 1.7331 - acc: 0.7527 - val_loss: 1.5017 - val_acc: 0.8113Epoch 11/1000330s 659ms/step - loss: 1.5656 - acc: 0.7652 - val_loss: 1.3648 - val_acc: 0.8174Epoch 12/1000330s 659ms/step - loss: 1.4253 - acc: 0.7756 - val_loss: 1.2259 - val_acc: 0.8327Epoch 13/1000329s 659ms/step - loss: 1.3032 - acc: 0.7877 - val_loss: 1.1070 - val_acc: 0.8442Epoch 14/1000330s 659ms/step - loss: 1.2136 - acc: 0.7941 - val_loss: 1.0533 - val_acc: 0.8408Epoch 15/1000329s 659ms/step - loss: 1.1363 - acc: 0.8009 - val_loss: 0.9825 - val_acc: 0.8517Epoch 16/1000331s 661ms/step - loss: 1.0679 - acc: 0.8111 - val_loss: 0.9233 - val_acc: 0.8563Epoch 17/1000330s 660ms/step - loss: 1.0143 - acc: 0.8168 - val_loss: 0.8913 - val_acc: 0.8569Epoch 18/1000330s 660ms/step - loss: 0.9731 - acc: 0.8215 - val_loss: 0.8578 - val_acc: 0.8561Epoch 19/1000330s 660ms/step - loss: 0.9397 - acc: 0.8261 - val_loss: 0.8226 - val_acc: 0.8675Epoch 20/1000330s 659ms/step - loss: 0.9073 - acc: 0.8301 - val_loss: 0.7973 - val_acc: 0.8697Epoch 21/1000329s 659ms/step - loss: 0.8823 - acc: 0.8342 - val_loss: 0.7837 - val_acc: 0.8693Epoch 22/1000331s 662ms/step - loss: 0.8688 - acc: 0.8369 - val_loss: 0.7693 - val_acc: 0.8736Epoch 23/1000332s 665ms/step - loss: 0.8479 - acc: 0.8425 - val_loss: 0.7626 - val_acc: 0.8730Epoch 24/1000329s 659ms/step - loss: 0.8329 - acc: 0.8453 - val_loss: 0.7745 - val_acc: 0.8671Epoch 25/1000330s 660ms/step - loss: 0.8271 - acc: 0.8462 - val_loss: 0.7271 - val_acc: 0.8833Epoch 26/1000331s 661ms/step - loss: 0.8121 - acc: 0.8516 - val_loss: 0.7155 - val_acc: 0.8883Epoch 27/1000330s 659ms/step - loss: 0.8008 - acc: 0.8543 - val_loss: 0.7522 - val_acc: 0.8707Epoch 28/1000330s 659ms/step - loss: 0.7959 - acc: 0.8558 - val_loss: 0.7200 - val_acc: 0.8799Epoch 29/1000329s 659ms/step - loss: 0.7937 - acc: 0.8563 - val_loss: 0.7196 - val_acc: 0.8855Epoch 30/1000329s 659ms/step - loss: 0.7843 - acc: 0.8602 - val_loss: 0.7285 - val_acc: 0.8808Epoch 31/1000330s 659ms/step - loss: 0.7791 - acc: 0.8606 - val_loss: 0.7120 - val_acc: 0.8879Epoch 32/1000329s 658ms/step - loss: 0.7781 - acc: 0.8612 - val_loss: 0.7139 - val_acc: 0.8840Epoch 33/1000329s 659ms/step - loss: 0.7650 - acc: 0.8660 - val_loss: 0.7243 - val_acc: 0.8814Epoch 34/1000329s 658ms/step - loss: 0.7686 - acc: 0.8669 - val_loss: 0.7223 - val_acc: 0.8811Epoch 35/1000329s 659ms/step - loss: 0.7694 - acc: 0.8662 - val_loss: 0.6995 - val_acc: 0.8938Epoch 36/1000329s 659ms/step - loss: 0.7618 - acc: 0.8679 - val_loss: 0.7032 - val_acc: 0.8919Epoch 37/1000329s 658ms/step - loss: 0.7542 - acc: 0.8709 - val_loss: 0.7115 - val_acc: 0.8868Epoch 38/1000329s 659ms/step - loss: 0.7525 - acc: 0.8719 - val_loss: 0.7282 - val_acc: 0.8791Epoch 39/1000329s 658ms/step - loss: 0.7528 - acc: 0.8727 - val_loss: 0.7046 - val_acc: 0.8898Epoch 40/1000329s 658ms/step - loss: 0.7476 - acc: 0.8748 - val_loss: 0.7071 - val_acc: 0.8927Epoch 41/1000329s 659ms/step - loss: 0.7429 - acc: 0.8762 - val_loss: 0.7195 - val_acc: 0.8852Epoch 42/1000329s 658ms/step - loss: 0.7487 - acc: 0.8758 - val_loss: 0.7064 - val_acc: 0.8936Epoch 43/1000329s 658ms/step - loss: 0.7423 - acc: 0.8769 - val_loss: 0.6941 - val_acc: 0.8959Epoch 44/1000330s 660ms/step - loss: 0.7357 - acc: 0.8802 - val_loss: 0.7097 - val_acc: 0.8921Epoch 45/1000329s 659ms/step - loss: 0.7385 - acc: 0.8796 - val_loss: 0.6845 - val_acc: 0.8997Epoch 46/1000332s 664ms/step - loss: 0.7331 - acc: 0.8821 - val_loss: 0.7265 - val_acc: 0.8870Epoch 47/1000329s 658ms/step - loss: 0.7358 - acc: 0.8804 - val_loss: 0.7062 - val_acc: 0.8947Epoch 48/1000329s 658ms/step - loss: 0.7357 - acc: 0.8812 - val_loss: 0.7007 - val_acc: 0.8932Epoch 49/1000329s 658ms/step - loss: 0.7338 - acc: 0.8832 - val_loss: 0.6983 - val_acc: 0.8989Epoch 50/1000330s 659ms/step - loss: 0.7268 - acc: 0.8852 - val_loss: 0.7136 - val_acc: 0.8940Epoch 51/1000329s 659ms/step - loss: 0.7327 - acc: 0.8845 - val_loss: 0.7076 - val_acc: 0.8957Epoch 52/1000329s 658ms/step - loss: 0.7255 - acc: 0.8858 - val_loss: 0.6909 - val_acc: 0.8995Epoch 53/1000329s 658ms/step - loss: 0.7298 - acc: 0.8860 - val_loss: 0.7088 - val_acc: 0.8956Epoch 54/1000331s 661ms/step - loss: 0.7237 - acc: 0.8884 - val_loss: 0.7069 - val_acc: 0.8960Epoch 55/1000329s 659ms/step - loss: 0.7288 - acc: 0.8863 - val_loss: 0.6929 - val_acc: 0.8984Epoch 56/1000329s 658ms/step - loss: 0.7208 - acc: 0.8890 - val_loss: 0.6925 - val_acc: 0.9017Epoch 57/1000330s 659ms/step - loss: 0.7187 - acc: 0.8898 - val_loss: 0.7150 - val_acc: 0.8944Epoch 58/1000330s 659ms/step - loss: 0.7148 - acc: 0.8920 - val_loss: 0.6766 - val_acc: 0.9060Epoch 59/1000329s 659ms/step - loss: 0.7185 - acc: 0.8905 - val_loss: 0.7031 - val_acc: 0.8951Epoch 60/1000329s 658ms/step - loss: 0.7192 - acc: 0.8904 - val_loss: 0.6956 - val_acc: 0.9020Epoch 61/1000329s 659ms/step - loss: 0.7209 - acc: 0.8895 - val_loss: 0.6967 - val_acc: 0.8997Epoch 62/1000329s 658ms/step - loss: 0.7083 - acc: 0.8936 - val_loss: 0.6806 - val_acc: 0.9048Epoch 63/1000329s 658ms/step - loss: 0.7123 - acc: 0.8926 - val_loss: 0.6832 - val_acc: 0.9042Epoch 64/1000329s 658ms/step - loss: 0.7085 - acc: 0.8939 - val_loss: 0.7000 - val_acc: 0.8973Epoch 65/1000330s 660ms/step - loss: 0.7049 - acc: 0.8963 - val_loss: 0.6993 - val_acc: 0.8969Epoch 66/1000330s 660ms/step - loss: 0.7062 - acc: 0.8950 - val_loss: 0.6977 - val_acc: 0.9044Epoch 67/1000329s 659ms/step - loss: 0.7127 - acc: 0.8938 - val_loss: 0.7069 - val_acc: 0.8977Epoch 68/1000329s 657ms/step - loss: 0.7010 - acc: 0.8960 - val_loss: 0.7052 - val_acc: 0.8994Epoch 69/1000329s 658ms/step - loss: 0.7096 - acc: 0.8934 - val_loss: 0.6982 - val_acc: 0.9027Epoch 70/1000329s 658ms/step - loss: 0.7118 - acc: 0.8947 - val_loss: 0.6850 - val_acc: 0.9053Epoch 71/1000329s 658ms/step - loss: 0.7076 - acc: 0.8953 - val_loss: 0.6798 - val_acc: 0.9053Epoch 72/1000329s 658ms/step - loss: 0.6998 - acc: 0.8981 - val_loss: 0.7312 - val_acc: 0.8905Epoch 73/1000329s 658ms/step - loss: 0.7049 - acc: 0.8969 - val_loss: 0.7042 - val_acc: 0.8996Epoch 74/1000329s 658ms/step - loss: 0.7094 - acc: 0.8959 - val_loss: 0.6987 - val_acc: 0.9020Epoch 75/1000329s 658ms/step - loss: 0.7045 - acc: 0.8963 - val_loss: 0.6909 - val_acc: 0.9056Epoch 76/1000329s 658ms/step - loss: 0.7028 - acc: 0.8980 - val_loss: 0.7257 - val_acc: 0.8918Epoch 77/1000329s 657ms/step - loss: 0.7059 - acc: 0.8956 - val_loss: 0.6813 - val_acc: 0.9097Epoch 78/1000329s 658ms/step - loss: 0.6996 - acc: 0.9006 - val_loss: 0.6942 - val_acc: 0.9081Epoch 79/1000329s 658ms/step - loss: 0.6988 - acc: 0.8996 - val_loss: 0.6801 - val_acc: 0.9059Epoch 80/1000329s 657ms/step - loss: 0.6983 - acc: 0.8990 - val_loss: 0.6759 - val_acc: 0.9081Epoch 81/1000329s 658ms/step - loss: 0.6968 - acc: 0.9012 - val_loss: 0.6868 - val_acc: 0.9052Epoch 82/1000329s 658ms/step - loss: 0.6976 - acc: 0.9000 - val_loss: 0.6945 - val_acc: 0.9028Epoch 83/1000329s 658ms/step - loss: 0.6974 - acc: 0.8999 - val_loss: 0.7020 - val_acc: 0.9010Epoch 84/1000329s 658ms/step - loss: 0.6984 - acc: 0.9003 - val_loss: 0.7220 - val_acc: 0.8956Epoch 85/1000329s 658ms/step - loss: 0.7054 - acc: 0.8974 - val_loss: 0.6846 - val_acc: 0.9082Epoch 86/1000329s 657ms/step - loss: 0.6974 - acc: 0.9005 - val_loss: 0.6880 - val_acc: 0.9085Epoch 87/1000329s 658ms/step - loss: 0.6989 - acc: 0.8998 - val_loss: 0.6956 - val_acc: 0.9035Epoch 88/1000329s 658ms/step - loss: 0.7002 - acc: 0.8998 - val_loss: 0.6852 - val_acc: 0.9056Epoch 89/1000329s 657ms/step - loss: 0.7005 - acc: 0.9004 - val_loss: 0.6891 - val_acc: 0.9077Epoch 90/1000329s 658ms/step - loss: 0.6945 - acc: 0.9030 - val_loss: 0.6880 - val_acc: 0.9071Epoch 91/1000329s 658ms/step - loss: 0.6999 - acc: 0.9011 - val_loss: 0.6868 - val_acc: 0.9064Epoch 92/1000329s 658ms/step - loss: 0.6976 - acc: 0.9028 - val_loss: 0.6964 - val_acc: 0.9022Epoch 93/1000329s 658ms/step - loss: 0.6923 - acc: 0.9045 - val_loss: 0.6712 - val_acc: 0.9126Epoch 94/1000329s 657ms/step - loss: 0.6981 - acc: 0.9020 - val_loss: 0.6813 - val_acc: 0.9084Epoch 95/1000329s 657ms/step - loss: 0.6952 - acc: 0.9029 - val_loss: 0.6713 - val_acc: 0.9135Epoch 96/1000329s 657ms/step - loss: 0.6958 - acc: 0.9032 - val_loss: 0.6835 - val_acc: 0.9084Epoch 97/1000329s 657ms/step - loss: 0.6889 - acc: 0.9045 - val_loss: 0.6818 - val_acc: 0.9053Epoch 98/1000329s 657ms/step - loss: 0.6997 - acc: 0.9009 - val_loss: 0.6886 - val_acc: 0.9075Epoch 99/1000329s 657ms/step - loss: 0.6948 - acc: 0.9029 - val_loss: 0.6872 - val_acc: 0.9057Epoch 100/1000329s 657ms/step - loss: 0.6925 - acc: 0.9044 - val_loss: 0.6992 - val_acc: 0.9051Epoch 101/1000329s 657ms/step - loss: 0.6948 - acc: 0.9033 - val_loss: 0.7153 - val_acc: 0.8990Epoch 102/1000328s 657ms/step - loss: 0.6998 - acc: 0.9025 - val_loss: 0.6735 - val_acc: 0.9155Epoch 103/1000329s 657ms/step - loss: 0.6931 - acc: 0.9037 - val_loss: 0.6907 - val_acc: 0.9052Epoch 104/1000329s 657ms/step - loss: 0.6941 - acc: 0.9048 - val_loss: 0.7109 - val_acc: 0.9046Epoch 105/1000329s 658ms/step - loss: 0.6902 - acc: 0.9066 - val_loss: 0.6744 - val_acc: 0.9114Epoch 106/1000329s 657ms/step - loss: 0.6944 - acc: 0.9038 - val_loss: 0.6758 - val_acc: 0.9119Epoch 107/1000329s 658ms/step - loss: 0.6895 - acc: 0.9036 - val_loss: 0.7041 - val_acc: 0.9024Epoch 108/1000329s 657ms/step - loss: 0.6976 - acc: 0.9026 - val_loss: 0.6959 - val_acc: 0.9065Epoch 109/1000329s 657ms/step - loss: 0.6890 - acc: 0.9063 - val_loss: 0.6866 - val_acc: 0.9125Epoch 110/1000329s 657ms/step - loss: 0.6897 - acc: 0.9062 - val_loss: 0.6786 - val_acc: 0.9150Epoch 111/1000329s 657ms/step - loss: 0.6941 - acc: 0.9061 - val_loss: 0.6870 - val_acc: 0.9100Epoch 112/1000329s 657ms/step - loss: 0.6916 - acc: 0.9052 - val_loss: 0.6836 - val_acc: 0.9069Epoch 113/1000329s 657ms/step - loss: 0.6908 - acc: 0.9040 - val_loss: 0.6841 - val_acc: 0.9108Epoch 114/1000329s 657ms/step - loss: 0.6907 - acc: 0.9059 - val_loss: 0.6831 - val_acc: 0.9102Epoch 115/1000328s 657ms/step - loss: 0.6904 - acc: 0.9063 - val_loss: 0.6852 - val_acc: 0.9101Epoch 116/1000329s 657ms/step - loss: 0.6940 - acc: 0.9051 - val_loss: 0.6977 - val_acc: 0.9069Epoch 117/1000329s 657ms/step - loss: 0.6909 - acc: 0.9075 - val_loss: 0.6904 - val_acc: 0.9110Epoch 118/1000329s 658ms/step - loss: 0.6888 - acc: 0.9066 - val_loss: 0.6845 - val_acc: 0.9144Epoch 119/1000328s 657ms/step - loss: 0.6890 - acc: 0.9058 - val_loss: 0.6954 - val_acc: 0.9076Epoch 120/1000329s 657ms/step - loss: 0.6893 - acc: 0.9059 - val_loss: 0.6790 - val_acc: 0.9125Epoch 121/1000329s 657ms/step - loss: 0.6840 - acc: 0.9082 - val_loss: 0.6813 - val_acc: 0.9088Epoch 122/1000329s 657ms/step - loss: 0.6966 - acc: 0.9032 - val_loss: 0.6816 - val_acc: 0.9079Epoch 123/1000329s 657ms/step - loss: 0.6869 - acc: 0.9093 - val_loss: 0.6970 - val_acc: 0.9057Epoch 124/1000329s 657ms/step - loss: 0.6907 - acc: 0.9052 - val_loss: 0.6734 - val_acc: 0.9143Epoch 125/1000328s 657ms/step - loss: 0.6886 - acc: 0.9074 - val_loss: 0.6811 - val_acc: 0.9102Epoch 126/1000329s 657ms/step - loss: 0.6862 - acc: 0.9082 - val_loss: 0.7166 - val_acc: 0.8981Epoch 127/1000329s 657ms/step - loss: 0.6882 - acc: 0.9078 - val_loss: 0.6780 - val_acc: 0.9125Epoch 128/1000329s 657ms/step - loss: 0.6972 - acc: 0.9036 - val_loss: 0.6861 - val_acc: 0.9110Epoch 129/1000329s 657ms/step - loss: 0.6848 - acc: 0.9099 - val_loss: 0.6905 - val_acc: 0.9069Epoch 130/1000329s 657ms/step - loss: 0.6868 - acc: 0.9072 - val_loss: 0.6773 - val_acc: 0.9150Epoch 131/1000329s 657ms/step - 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loss: 0.0914 - acc: 0.9996 - val_loss: 0.2554 - val_acc: 0.9575Epoch 900/1000334s 667ms/step - loss: 0.0909 - acc: 0.9995 - val_loss: 0.2557 - val_acc: 0.9576Epoch 901/1000lr changed to 9.999999310821295e-05333s 667ms/step - loss: 0.0910 - acc: 0.9995 - val_loss: 0.2555 - val_acc: 0.9575Epoch 902/1000330s 660ms/step - loss: 0.0911 - acc: 0.9995 - val_loss: 0.2548 - val_acc: 0.9576Epoch 903/1000327s 654ms/step - loss: 0.0912 - acc: 0.9995 - val_loss: 0.2552 - val_acc: 0.9576Epoch 904/1000327s 653ms/step - loss: 0.0910 - acc: 0.9995 - val_loss: 0.2545 - val_acc: 0.9572Epoch 905/1000327s 654ms/step - loss: 0.0904 - acc: 0.9998 - val_loss: 0.2548 - val_acc: 0.9575Epoch 906/1000327s 653ms/step - loss: 0.0905 - acc: 0.9998 - val_loss: 0.2549 - val_acc: 0.9572Epoch 907/1000327s 654ms/step - loss: 0.0908 - acc: 0.9995 - val_loss: 0.2546 - val_acc: 0.9573Epoch 908/1000327s 654ms/step - loss: 0.0908 - acc: 0.9995 - val_loss: 0.2542 - val_acc: 0.9573Epoch 909/1000327s 654ms/step - loss: 0.0910 - acc: 0.9994 - val_loss: 0.2539 - val_acc: 0.9578Epoch 910/1000327s 654ms/step - loss: 0.0905 - acc: 0.9996 - val_loss: 0.2538 - val_acc: 0.9575Epoch 911/1000327s 653ms/step - loss: 0.0908 - acc: 0.9996 - val_loss: 0.2537 - val_acc: 0.9574Epoch 912/1000327s 654ms/step - loss: 0.0906 - acc: 0.9996 - val_loss: 0.2534 - val_acc: 0.9573Epoch 913/1000327s 653ms/step - loss: 0.0903 - acc: 0.9998 - val_loss: 0.2538 - val_acc: 0.9575Epoch 914/1000331s 662ms/step - loss: 0.0905 - acc: 0.9996 - val_loss: 0.2542 - val_acc: 0.9577Epoch 915/1000333s 667ms/step - loss: 0.0904 - acc: 0.9997 - val_loss: 0.2544 - val_acc: 0.9572Epoch 916/1000331s 662ms/step - loss: 0.0905 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9574Epoch 917/1000327s 654ms/step - loss: 0.0905 - acc: 0.9995 - val_loss: 0.2543 - val_acc: 0.9576Epoch 918/1000327s 653ms/step - loss: 0.0901 - acc: 0.9998 - val_loss: 0.2541 - val_acc: 0.9578Epoch 919/1000327s 654ms/step - loss: 0.0904 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9578Epoch 920/1000327s 653ms/step - loss: 0.0902 - acc: 0.9998 - val_loss: 0.2545 - val_acc: 0.9575Epoch 921/1000327s 654ms/step - loss: 0.0903 - acc: 0.9997 - val_loss: 0.2544 - val_acc: 0.9578Epoch 922/1000327s 653ms/step - loss: 0.0903 - acc: 0.9996 - val_loss: 0.2542 - val_acc: 0.9575Epoch 923/1000327s 654ms/step - loss: 0.0903 - acc: 0.9997 - val_loss: 0.2544 - val_acc: 0.9577Epoch 924/1000327s 654ms/step - loss: 0.0905 - acc: 0.9996 - val_loss: 0.2548 - val_acc: 0.9573Epoch 925/1000327s 654ms/step - loss: 0.0904 - acc: 0.9997 - val_loss: 0.2547 - val_acc: 0.9574Epoch 926/1000327s 654ms/step - loss: 0.0903 - acc: 0.9997 - val_loss: 0.2546 - val_acc: 0.9575Epoch 927/1000327s 653ms/step - loss: 0.0905 - acc: 0.9996 - val_loss: 0.2545 - val_acc: 0.9576Epoch 928/1000327s 654ms/step - loss: 0.0901 - acc: 0.9998 - val_loss: 0.2543 - val_acc: 0.9576Epoch 929/1000327s 654ms/step - loss: 0.0901 - acc: 0.9998 - val_loss: 0.2544 - val_acc: 0.9579Epoch 930/1000327s 654ms/step - loss: 0.0902 - acc: 0.9997 - val_loss: 0.2538 - val_acc: 0.9577Epoch 931/1000327s 653ms/step - loss: 0.0904 - acc: 0.9995 - val_loss: 0.2539 - val_acc: 0.9576Epoch 932/1000327s 655ms/step - loss: 0.0901 - acc: 0.9997 - val_loss: 0.2535 - val_acc: 0.9577Epoch 933/1000327s 654ms/step - loss: 0.0902 - acc: 0.9997 - val_loss: 0.2541 - val_acc: 0.9578Epoch 934/1000327s 654ms/step - loss: 0.0899 - acc: 0.9998 - val_loss: 0.2541 - val_acc: 0.9579Epoch 935/1000327s 654ms/step - loss: 0.0907 - acc: 0.9995 - val_loss: 0.2542 - val_acc: 0.9579Epoch 936/1000327s 654ms/step - loss: 0.0900 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9576Epoch 937/1000327s 655ms/step - loss: 0.0900 - acc: 0.9998 - val_loss: 0.2544 - val_acc: 0.9575Epoch 938/1000328s 655ms/step - loss: 0.0901 - acc: 0.9997 - val_loss: 0.2544 - val_acc: 0.9577Epoch 939/1000329s 659ms/step - loss: 0.0900 - acc: 0.9997 - val_loss: 0.2544 - val_acc: 0.9580Epoch 940/1000328s 657ms/step - loss: 0.0901 - acc: 0.9996 - val_loss: 0.2544 - val_acc: 0.9577Epoch 941/1000328s 657ms/step - loss: 0.0900 - acc: 0.9997 - val_loss: 0.2541 - val_acc: 0.9575Epoch 942/1000333s 666ms/step - loss: 0.0900 - acc: 0.9996 - val_loss: 0.2536 - val_acc: 0.9577Epoch 943/1000333s 667ms/step - loss: 0.0901 - acc: 0.9996 - val_loss: 0.2536 - val_acc: 0.9575Epoch 944/1000329s 657ms/step - loss: 0.0896 - acc: 0.9998 - val_loss: 0.2536 - val_acc: 0.9575Epoch 945/1000326s 653ms/step - loss: 0.0899 - acc: 0.9998 - val_loss: 0.2537 - val_acc: 0.9577Epoch 946/1000327s 654ms/step - loss: 0.0899 - acc: 0.9997 - val_loss: 0.2535 - val_acc: 0.9577Epoch 947/1000326s 652ms/step - loss: 0.0901 - acc: 0.9996 - val_loss: 0.2533 - val_acc: 0.9580Epoch 948/1000326s 652ms/step - loss: 0.0899 - acc: 0.9996 - val_loss: 0.2529 - val_acc: 0.9576Epoch 949/1000326s 652ms/step - loss: 0.0901 - acc: 0.9997 - val_loss: 0.2530 - val_acc: 0.9582Epoch 950/1000326s 653ms/step - loss: 0.0898 - acc: 0.9998 - val_loss: 0.2528 - val_acc: 0.9578Epoch 951/1000326s 652ms/step - loss: 0.0897 - acc: 0.9998 - val_loss: 0.2531 - val_acc: 0.9581Epoch 952/1000326s 653ms/step - loss: 0.0894 - acc: 0.9999 - val_loss: 0.2533 - val_acc: 0.9584Epoch 953/1000327s 654ms/step - loss: 0.0900 - acc: 0.9997 - val_loss: 0.2534 - val_acc: 0.9580Epoch 954/1000327s 654ms/step - loss: 0.0896 - acc: 0.9998 - val_loss: 0.2535 - val_acc: 0.9583Epoch 955/1000327s 654ms/step - loss: 0.0900 - acc: 0.9996 - val_loss: 0.2534 - val_acc: 0.9579Epoch 956/1000327s 653ms/step - loss: 0.0896 - acc: 0.9998 - val_loss: 0.2533 - val_acc: 0.9581Epoch 957/1000327s 654ms/step - loss: 0.0898 - acc: 0.9997 - val_loss: 0.2528 - val_acc: 0.9581Epoch 958/1000327s 654ms/step - loss: 0.0895 - acc: 0.9998 - val_loss: 0.2530 - val_acc: 0.9580Epoch 959/1000327s 654ms/step - loss: 0.0897 - acc: 0.9997 - val_loss: 0.2537 - val_acc: 0.9582Epoch 960/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2535 - val_acc: 0.9578Epoch 961/1000327s 654ms/step - loss: 0.0895 - acc: 0.9998 - val_loss: 0.2533 - val_acc: 0.9580Epoch 962/1000327s 654ms/step - loss: 0.0897 - acc: 0.9997 - val_loss: 0.2529 - val_acc: 0.9579Epoch 963/1000327s 653ms/step - loss: 0.0899 - acc: 0.9996 - val_loss: 0.2534 - val_acc: 0.9576Epoch 964/1000327s 653ms/step - loss: 0.0896 - acc: 0.9997 - val_loss: 0.2536 - val_acc: 0.9580Epoch 965/1000327s 653ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2535 - val_acc: 0.9583Epoch 966/1000327s 654ms/step - loss: 0.0898 - acc: 0.9997 - val_loss: 0.2538 - val_acc: 0.9581Epoch 967/1000327s 654ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2536 - val_acc: 0.9582Epoch 968/1000327s 654ms/step - loss: 0.0895 - acc: 0.9998 - val_loss: 0.2538 - val_acc: 0.9583Epoch 969/1000327s 654ms/step - loss: 0.0896 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9580Epoch 970/1000327s 654ms/step - loss: 0.0896 - acc: 0.9996 - val_loss: 0.2545 - val_acc: 0.9584Epoch 971/1000327s 655ms/step - loss: 0.0898 - acc: 0.9997 - val_loss: 0.2545 - val_acc: 0.9582Epoch 972/1000327s 654ms/step - loss: 0.0897 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9577Epoch 973/1000327s 654ms/step - loss: 0.0896 - acc: 0.9996 - val_loss: 0.2541 - val_acc: 0.9581Epoch 974/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9583Epoch 975/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2541 - val_acc: 0.9580Epoch 976/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9580Epoch 977/1000327s 654ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2542 - val_acc: 0.9576Epoch 978/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9576Epoch 979/1000327s 653ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2546 - val_acc: 0.9576Epoch 980/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2549 - val_acc: 0.9579Epoch 981/1000327s 653ms/step - loss: 0.0896 - acc: 0.9996 - val_loss: 0.2551 - val_acc: 0.9579Epoch 982/1000327s 654ms/step - loss: 0.0899 - acc: 0.9996 - val_loss: 0.2546 - val_acc: 0.9584Epoch 983/1000327s 654ms/step - loss: 0.0895 - acc: 0.9996 - val_loss: 0.2548 - val_acc: 0.9584Epoch 984/1000327s 653ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2545 - val_acc: 0.9583Epoch 985/1000327s 654ms/step - loss: 0.0895 - acc: 0.9997 - val_loss: 0.2545 - val_acc: 0.9581Epoch 986/1000327s 654ms/step - loss: 0.0895 - acc: 0.9996 - val_loss: 0.2540 - val_acc: 0.9577Epoch 987/1000328s 656ms/step - loss: 0.0894 - acc: 0.9998 - val_loss: 0.2537 - val_acc: 0.9575Epoch 988/1000327s 653ms/step - loss: 0.0892 - acc: 0.9998 - val_loss: 0.2540 - val_acc: 0.9579Epoch 989/1000327s 653ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2541 - val_acc: 0.9578Epoch 990/1000327s 654ms/step - loss: 0.0893 - acc: 0.9997 - val_loss: 0.2539 - val_acc: 0.9581Epoch 991/1000327s 654ms/step - loss: 0.0892 - acc: 0.9997 - val_loss: 0.2543 - val_acc: 0.9578Epoch 992/1000327s 654ms/step - loss: 0.0890 - acc: 0.9998 - val_loss: 0.2541 - val_acc: 0.9579Epoch 993/1000327s 654ms/step - loss: 0.0892 - acc: 0.9998 - val_loss: 0.2541 - val_acc: 0.9583Epoch 994/1000327s 653ms/step - loss: 0.0892 - acc: 0.9997 - val_loss: 0.2542 - val_acc: 0.9580Epoch 995/1000326s 653ms/step - loss: 0.0893 - acc: 0.9998 - val_loss: 0.2548 - val_acc: 0.9577Epoch 996/1000327s 654ms/step - loss: 0.0892 - acc: 0.9997 - val_loss: 0.2547 - val_acc: 0.9580Epoch 997/1000327s 653ms/step - loss: 0.0895 - acc: 0.9996 - val_loss: 0.2540 - val_acc: 0.9582Epoch 998/1000326s 652ms/step - loss: 0.0891 - acc: 0.9998 - val_loss: 0.2544 - val_acc: 0.9580Epoch 999/1000326s 652ms/step - loss: 0.0890 - acc: 0.9998 - val_loss: 0.2538 - val_acc: 0.9582Epoch 1000/1000326s 653ms/step - loss: 0.0894 - acc: 0.9997 - val_loss: 0.2538 - val_acc: 0.9577Train loss: 0.08789637273550034Train accuracy: 0.999960000038147Test loss: 0.25383884519338606Test accuracy: 0.9577000015974044

相较于调参记录24，本次的测试准确率还降低了0.03%。

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

更新：

期间遇到一个问题，将一模一样的程序，拿到另一台硬件和工具包完全相同的电脑上，结果loss在第一个epoch就急剧飙升，然后成为nan。反复尝试了很多次，每次都这样。折腾了两天，更换了很多显卡驱动、Cuda、TensorFlow和Keras的版本，最后也没有解决，放弃了。

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原文链接：https://blog.csdn.net/dangqing1988/article/details/106498485