自适应参数化ReLU是一种动态激活函数，对所有输入不是“一视同仁”，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。
本文在调参记录21的基础上，将残差模块的个数，从60个增加到120个，测试深度残差网络+自适应参数化ReLU激活函数在Cifar10数据集上的效果。
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, 2020, 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, Minimumfrom 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)# 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 150 epochesdef 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 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 = 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 modelinputs = 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, 40, 32, downsample=False)net = residual_block(net, 1, 32, downsample=True)net = residual_block(net, 39, 32, downsample=False)net = residual_block(net, 1, 64, downsample=True)net = residual_block(net, 39, 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 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=500, 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 1/500318s 637ms/step - loss: 6.0165 - acc: 0.3791 - val_loss: 5.2157 - val_acc: 0.5307Epoch 2/500221s 443ms/step - loss: 4.8584 - acc: 0.5361 - val_loss: 4.2761 - val_acc: 0.6383Epoch 3/500221s 442ms/step - loss: 4.0487 - acc: 0.6159 - val_loss: 3.5837 - val_acc: 0.6913Epoch 4/500221s 442ms/step - loss: 3.4323 - acc: 0.6610 - val_loss: 3.0189 - val_acc: 0.7396Epoch 5/500221s 442ms/step - loss: 2.9384 - acc: 0.6943 - val_loss: 2.5795 - val_acc: 0.7697Epoch 6/500221s 442ms/step - loss: 2.5470 - acc: 0.7181 - val_loss: 2.2296 - val_acc: 0.7848Epoch 7/500221s 442ms/step - loss: 2.2227 - acc: 0.7400 - val_loss: 1.9631 - val_acc: 0.7931Epoch 8/500222s 444ms/step - loss: 1.9632 - acc: 0.7546 - val_loss: 1.7318 - val_acc: 0.8098Epoch 9/500221s 443ms/step - loss: 1.7535 - acc: 0.7685 - val_loss: 1.5313 - val_acc: 0.8197Epoch 10/500221s 442ms/step - loss: 1.5759 - acc: 0.7798 - val_loss: 1.4001 - val_acc: 0.8214Epoch 11/500221s 442ms/step - loss: 1.4432 - acc: 0.7859 - val_loss: 1.2776 - val_acc: 0.8309Epoch 12/500221s 442ms/step - loss: 1.3201 - acc: 0.7977 - val_loss: 1.1707 - val_acc: 0.8349Epoch 13/500222s 443ms/step - loss: 1.2295 - acc: 0.8028 - val_loss: 1.0760 - val_acc: 0.8454Epoch 14/500222s 443ms/step - loss: 1.1552 - acc: 0.8069 - val_loss: 1.0225 - val_acc: 0.8432Epoch 15/500221s 441ms/step - loss: 1.0964 - acc: 0.8119 - val_loss: 0.9549 - val_acc: 0.8514Epoch 16/500221s 442ms/step - loss: 1.0386 - acc: 0.8174 - val_loss: 0.9072 - val_acc: 0.8614Epoch 17/500221s 442ms/step - loss: 0.9979 - acc: 0.8204 - val_loss: 0.8765 - val_acc: 0.8566Epoch 18/500221s 441ms/step - loss: 0.9611 - acc: 0.8260 - val_loss: 0.8820 - val_acc: 0.8502Epoch 19/500220s 441ms/step - loss: 0.9351 - acc: 0.8290 - val_loss: 0.8319 - val_acc: 0.8601Epoch 20/500221s 441ms/step - loss: 0.9130 - acc: 0.8295 - val_loss: 0.8077 - val_acc: 0.8643Epoch 21/500221s 441ms/step - loss: 0.8837 - acc: 0.8347 - val_loss: 0.7924 - val_acc: 0.8683Epoch 22/500221s 441ms/step - loss: 0.8741 - acc: 0.8349 - val_loss: 0.7675 - val_acc: 0.8747Epoch 23/500221s 442ms/step - loss: 0.8536 - acc: 0.8403 - val_loss: 0.7988 - val_acc: 0.8599Epoch 24/500221s 441ms/step - loss: 0.8457 - acc: 0.8395 - val_loss: 0.7619 - val_acc: 0.8698Epoch 25/500221s 441ms/step - loss: 0.8354 - acc: 0.8422 - val_loss: 0.7466 - val_acc: 0.8708Epoch 26/500221s 441ms/step - loss: 0.8210 - acc: 0.8449 - val_loss: 0.7481 - val_acc: 0.8714Epoch 27/500220s 441ms/step - loss: 0.8155 - acc: 0.8473 - val_loss: 0.7636 - val_acc: 0.8669Epoch 28/500220s 441ms/step - loss: 0.8154 - acc: 0.8470 - val_loss: 0.7301 - val_acc: 0.8785Epoch 29/500220s 441ms/step - loss: 0.7967 - acc: 0.8537 - val_loss: 0.7206 - val_acc: 0.8811Epoch 30/500220s 441ms/step - loss: 0.7961 - acc: 0.8510 - val_loss: 0.7203 - val_acc: 0.8814Epoch 31/500221s 441ms/step - loss: 0.7932 - acc: 0.8534 - val_loss: 0.7010 - val_acc: 0.8835Epoch 32/500220s 441ms/step - loss: 0.7783 - acc: 0.8585 - val_loss: 0.7239 - val_acc: 0.8797Epoch 33/500221s 441ms/step - loss: 0.7744 - acc: 0.8577 - val_loss: 0.7140 - val_acc: 0.8795Epoch 34/500221s 442ms/step - loss: 0.7753 - acc: 0.8591 - val_loss: 0.7185 - val_acc: 0.8811Epoch 35/500221s 441ms/step - loss: 0.7737 - acc: 0.8575 - val_loss: 0.7251 - val_acc: 0.8752Epoch 36/500220s 441ms/step - loss: 0.7666 - acc: 0.8632 - val_loss: 0.7151 - val_acc: 0.8814Epoch 37/500221s 441ms/step - loss: 0.7746 - acc: 0.8593 - val_loss: 0.7119 - val_acc: 0.8792Epoch 38/500220s 441ms/step - loss: 0.7644 - acc: 0.8631 - val_loss: 0.7091 - val_acc: 0.8819Epoch 39/500221s 442ms/step - loss: 0.7620 - acc: 0.8639 - val_loss: 0.7190 - val_acc: 0.8809Epoch 40/500221s 441ms/step - loss: 0.7507 - acc: 0.8660 - val_loss: 0.7065 - val_acc: 0.8840Epoch 41/500221s 441ms/step - loss: 0.7550 - acc: 0.8658 - val_loss: 0.6998 - val_acc: 0.8858Epoch 42/500220s 441ms/step - loss: 0.7546 - acc: 0.8666 - val_loss: 0.7195 - val_acc: 0.8803Epoch 43/500221s 441ms/step - loss: 0.7514 - acc: 0.8680 - val_loss: 0.6949 - val_acc: 0.8895Epoch 44/500220s 441ms/step - loss: 0.7511 - acc: 0.8661 - val_loss: 0.7011 - val_acc: 0.8872Epoch 45/500221s 441ms/step - loss: 0.7431 - acc: 0.8688 - val_loss: 0.7057 - val_acc: 0.8848Epoch 46/500221s 441ms/step - loss: 0.7464 - acc: 0.8683 - val_loss: 0.7014 - val_acc: 0.8827Epoch 47/500220s 441ms/step - loss: 0.7487 - acc: 0.8678 - val_loss: 0.7002 - val_acc: 0.8859Epoch 48/500220s 441ms/step - loss: 0.7453 - acc: 0.8701 - val_loss: 0.6912 - val_acc: 0.8891Epoch 49/500220s 441ms/step - loss: 0.7431 - acc: 0.8694 - val_loss: 0.6798 - val_acc: 0.8932Epoch 50/500221s 441ms/step - loss: 0.7409 - acc: 0.8726 - val_loss: 0.6813 - val_acc: 0.8949Epoch 51/500220s 440ms/step - loss: 0.7370 - acc: 0.8732 - val_loss: 0.7049 - val_acc: 0.8886Epoch 52/500220s 441ms/step - loss: 0.7315 - acc: 0.8748 - val_loss: 0.6921 - val_acc: 0.8881Epoch 53/500220s 441ms/step - loss: 0.7374 - acc: 0.8728 - val_loss: 0.6728 - val_acc: 0.8990Epoch 54/500220s 441ms/step - loss: 0.7326 - acc: 0.8749 - val_loss: 0.6982 - val_acc: 0.8861Epoch 55/500221s 441ms/step - loss: 0.7353 - acc: 0.8723 - val_loss: 0.6776 - val_acc: 0.8918Epoch 56/500220s 441ms/step - loss: 0.7300 - acc: 0.8752 - val_loss: 0.6822 - val_acc: 0.8923Epoch 57/500221s 441ms/step - loss: 0.7321 - acc: 0.8756 - val_loss: 0.6854 - val_acc: 0.8963Epoch 58/500221s 441ms/step - loss: 0.7341 - acc: 0.8742 - val_loss: 0.7065 - val_acc: 0.8861Epoch 59/500220s 441ms/step - loss: 0.7334 - acc: 0.8749 - val_loss: 0.6815 - val_acc: 0.8960Epoch 60/500221s 443ms/step - loss: 0.7250 - acc: 0.8774 - val_loss: 0.6798 - val_acc: 0.8977Epoch 61/500222s 444ms/step - loss: 0.7309 - acc: 0.8759 - val_loss: 0.6892 - val_acc: 0.8964Epoch 62/500225s 451ms/step - loss: 0.7249 - acc: 0.8784 - val_loss: 0.6967 - val_acc: 0.8923Epoch 63/500226s 451ms/step - loss: 0.7291 - acc: 0.8770 - val_loss: 0.7028 - val_acc: 0.8907Epoch 64/500225s 451ms/step - loss: 0.7234 - acc: 0.8817 - val_loss: 0.6920 - val_acc: 0.8903Epoch 65/500225s 451ms/step - loss: 0.7279 - acc: 0.8787 - val_loss: 0.6723 - val_acc: 0.9003Epoch 66/500225s 451ms/step - loss: 0.7229 - acc: 0.8801 - val_loss: 0.6937 - val_acc: 0.8939Epoch 67/500225s 450ms/step - loss: 0.7207 - acc: 0.8795 - val_loss: 0.7028 - val_acc: 0.8928Epoch 68/500226s 451ms/step - loss: 0.7226 - acc: 0.8804 - val_loss: 0.6830 - val_acc: 0.8942Epoch 69/500225s 451ms/step - loss: 0.7210 - acc: 0.8801 - val_loss: 0.6928 - val_acc: 0.8941Epoch 70/500225s 451ms/step - loss: 0.7197 - acc: 0.8817 - val_loss: 0.6946 - val_acc: 0.8912Epoch 71/500225s 450ms/step - loss: 0.7228 - acc: 0.8799 - val_loss: 0.6721 - val_acc: 0.9010Epoch 72/500226s 451ms/step - loss: 0.7195 - acc: 0.8836 - val_loss: 0.6764 - val_acc: 0.9032Epoch 73/500225s 450ms/step - loss: 0.7210 - acc: 0.8810 - val_loss: 0.6776 - val_acc: 0.8979Epoch 74/500225s 451ms/step - loss: 0.7174 - acc: 0.8819 - val_loss: 0.6784 - val_acc: 0.8965Epoch 75/500225s 451ms/step - loss: 0.7144 - acc: 0.8838 - val_loss: 0.6799 - val_acc: 0.8988Epoch 76/500225s 451ms/step - loss: 0.7188 - acc: 0.8814 - val_loss: 0.6884 - val_acc: 0.8945Epoch 77/500225s 451ms/step - loss: 0.7188 - acc: 0.8833 - val_loss: 0.7054 - val_acc: 0.8915Epoch 78/500225s 451ms/step - loss: 0.7147 - acc: 0.8835 - val_loss: 0.6905 - val_acc: 0.8957Epoch 79/500225s 451ms/step - loss: 0.7168 - acc: 0.8830 - val_loss: 0.6794 - val_acc: 0.9000Epoch 80/500225s 451ms/step - loss: 0.7150 - acc: 0.8829 - val_loss: 0.6843 - val_acc: 0.8957Epoch 81/500225s 451ms/step - loss: 0.7102 - acc: 0.8846 - val_loss: 0.6813 - val_acc: 0.8954Epoch 82/500226s 451ms/step - loss: 0.7093 - acc: 0.8844 - val_loss: 0.6944 - val_acc: 0.8913Epoch 83/500225s 451ms/step - loss: 0.7105 - acc: 0.8840 - val_loss: 0.6791 - val_acc: 0.8964Epoch 84/500225s 451ms/step - loss: 0.7068 - acc: 0.8872 - val_loss: 0.6921 - val_acc: 0.8905Epoch 85/500225s 451ms/step - loss: 0.7118 - acc: 0.8866 - val_loss: 0.6970 - val_acc: 0.8921Epoch 86/500225s 451ms/step - loss: 0.7108 - acc: 0.8842 - val_loss: 0.6891 - val_acc: 0.8955Epoch 87/500225s 451ms/step - loss: 0.7105 - acc: 0.8832 - val_loss: 0.6872 - val_acc: 0.8949Epoch 88/500225s 451ms/step - loss: 0.7133 - acc: 0.8846 - val_loss: 0.6777 - val_acc: 0.8978Epoch 89/500225s 451ms/step - loss: 0.7105 - acc: 0.8853 - val_loss: 0.6784 - val_acc: 0.8953Epoch 90/500225s 451ms/step - loss: 0.7031 - acc: 0.8884 - val_loss: 0.6937 - val_acc: 0.8952Epoch 91/500225s 451ms/step - loss: 0.7002 - acc: 0.8892 - val_loss: 0.6709 - val_acc: 0.9001Epoch 92/500225s 451ms/step - loss: 0.7098 - acc: 0.8863 - val_loss: 0.6674 - val_acc: 0.9002Epoch 93/500225s 451ms/step - loss: 0.7034 - acc: 0.8882 - val_loss: 0.7211 - val_acc: 0.8831Epoch 94/500225s 450ms/step - loss: 0.7056 - acc: 0.8870 - val_loss: 0.6597 - val_acc: 0.9043Epoch 95/500225s 450ms/step - loss: 0.7070 - acc: 0.8861 - val_loss: 0.6682 - val_acc: 0.9026Epoch 96/500221s 442ms/step - loss: 0.7015 - acc: 0.8893 - val_loss: 0.6766 - val_acc: 0.9009Epoch 97/500224s 448ms/step - loss: 0.7089 - acc: 0.8855 - val_loss: 0.6844 - val_acc: 0.8970Epoch 98/500225s 450ms/step - loss: 0.7052 - acc: 0.8885 - val_loss: 0.6668 - val_acc: 0.9040Epoch 99/500225s 451ms/step - loss: 0.7072 - acc: 0.8879 - val_loss: 0.6808 - val_acc: 0.8978Epoch 100/500225s 451ms/step - loss: 0.7016 - acc: 0.8891 - val_loss: 0.6898 - val_acc: 0.8935Epoch 101/500225s 451ms/step - loss: 0.7018 - acc: 0.8888 - val_loss: 0.6803 - val_acc: 0.8980Epoch 102/500225s 451ms/step - loss: 0.7099 - acc: 0.8865 - val_loss: 0.6773 - val_acc: 0.8986Epoch 103/500225s 451ms/step - loss: 0.7075 - acc: 0.8875 - val_loss: 0.6743 - val_acc: 0.9014Epoch 104/500225s 451ms/step - loss: 0.7048 - acc: 0.8881 - val_loss: 0.6627 - val_acc: 0.9064Epoch 105/500225s 451ms/step - loss: 0.7041 - acc: 0.8890 - val_loss: 0.6741 - val_acc: 0.9032Epoch 106/500226s 451ms/step - loss: 0.7036 - acc: 0.8884 - val_loss: 0.6736 - val_acc: 0.9037Epoch 107/500225s 451ms/step - loss: 0.7043 - acc: 0.8882 - val_loss: 0.6758 - val_acc: 0.9005Epoch 108/500225s 451ms/step - loss: 0.7024 - acc: 0.8891 - val_loss: 0.6812 - val_acc: 0.8990Epoch 109/500225s 450ms/step - loss: 0.7044 - acc: 0.8872 - val_loss: 0.6736 - val_acc: 0.9016Epoch 110/500225s 451ms/step - loss: 0.6999 - acc: 0.8913 - val_loss: 0.6756 - val_acc: 0.9007Epoch 111/500226s 451ms/step - loss: 0.6951 - acc: 0.8930 - val_loss: 0.6871 - val_acc: 0.8945Epoch 112/500226s 451ms/step - loss: 0.6970 - acc: 0.8898 - val_loss: 0.6875 - val_acc: 0.8950Epoch 113/500225s 450ms/step - loss: 0.7006 - acc: 0.8902 - val_loss: 0.6711 - val_acc: 0.9032Epoch 114/500225s 451ms/step - loss: 0.7000 - acc: 0.8896 - val_loss: 0.6824 - val_acc: 0.8962Epoch 115/500225s 450ms/step - loss: 0.6969 - acc: 0.8904 - val_loss: 0.6761 - val_acc: 0.8975Epoch 116/500225s 451ms/step - loss: 0.6939 - acc: 0.8913 - val_loss: 0.6924 - val_acc: 0.8974Epoch 117/500225s 451ms/step - loss: 0.7028 - acc: 0.8895 - val_loss: 0.6773 - val_acc: 0.9014Epoch 118/500225s 450ms/step - loss: 0.6994 - acc: 0.8906 - val_loss: 0.7111 - val_acc: 0.8884Epoch 119/500225s 451ms/step - loss: 0.7059 - acc: 0.8889 - val_loss: 0.6947 - val_acc: 0.8955Epoch 120/500226s 451ms/step - loss: 0.7000 - acc: 0.8902 - val_loss: 0.6832 - val_acc: 0.8976Epoch 121/500225s 451ms/step - loss: 0.6976 - acc: 0.8911 - val_loss: 0.6770 - val_acc: 0.9027Epoch 122/500226s 451ms/step - loss: 0.6962 - acc: 0.8918 - val_loss: 0.7034 - val_acc: 0.8925Epoch 123/500225s 451ms/step - loss: 0.6908 - acc: 0.8940 - val_loss: 0.6872 - val_acc: 0.8974Epoch 124/500225s 450ms/step - loss: 0.7004 - acc: 0.8896 - val_loss: 0.6788 - val_acc: 0.8979Epoch 125/500225s 451ms/step - loss: 0.6953 - acc: 0.8924 - val_loss: 0.6973 - val_acc: 0.8933Epoch 126/500225s 451ms/step - loss: 0.6998 - acc: 0.8913 - val_loss: 0.6845 - val_acc: 0.8960Epoch 127/500225s 451ms/step - loss: 0.7004 - acc: 0.8908 - val_loss: 0.6787 - val_acc: 0.9009Epoch 128/500225s 451ms/step - loss: 0.7020 - acc: 0.8898 - val_loss: 0.6899 - val_acc: 0.8970Epoch 129/500225s 451ms/step - loss: 0.6948 - acc: 0.8928 - val_loss: 0.6748 - val_acc: 0.9026Epoch 130/500221s 443ms/step - loss: 0.6958 - acc: 0.8922 - val_loss: 0.6656 - val_acc: 0.9032Epoch 131/500220s 440ms/step - 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val_loss: 0.3124 - val_acc: 0.9525Epoch 459/500221s 441ms/step - loss: 0.1294 - acc: 0.9988 - val_loss: 0.3126 - val_acc: 0.9526Epoch 460/500221s 441ms/step - loss: 0.1296 - acc: 0.9987 - val_loss: 0.3129 - val_acc: 0.9524Epoch 461/500221s 442ms/step - loss: 0.1301 - acc: 0.9985 - val_loss: 0.3133 - val_acc: 0.9526Epoch 462/500221s 441ms/step - loss: 0.1293 - acc: 0.9987 - val_loss: 0.3133 - val_acc: 0.9526Epoch 463/500221s 442ms/step - loss: 0.1290 - acc: 0.9988 - val_loss: 0.3130 - val_acc: 0.9527Epoch 464/500221s 441ms/step - loss: 0.1298 - acc: 0.9984 - val_loss: 0.3126 - val_acc: 0.9530Epoch 465/500221s 441ms/step - loss: 0.1290 - acc: 0.9986 - val_loss: 0.3122 - val_acc: 0.9525Epoch 466/500221s 442ms/step - loss: 0.1292 - acc: 0.9986 - val_loss: 0.3121 - val_acc: 0.9526Epoch 467/500221s 441ms/step - loss: 0.1286 - acc: 0.9989 - val_loss: 0.3122 - val_acc: 0.9524Epoch 468/500221s 442ms/step - loss: 0.1288 - acc: 0.9989 - val_loss: 0.3123 - val_acc: 0.9526Epoch 469/500221s 441ms/step - loss: 0.1284 - acc: 0.9989 - val_loss: 0.3130 - val_acc: 0.9522Epoch 470/500223s 445ms/step - loss: 0.1284 - acc: 0.9989 - val_loss: 0.3136 - val_acc: 0.9522Epoch 471/500221s 442ms/step - loss: 0.1282 - acc: 0.9990 - val_loss: 0.3138 - val_acc: 0.9517Epoch 472/500221s 442ms/step - loss: 0.1291 - acc: 0.9988 - val_loss: 0.3133 - val_acc: 0.9523Epoch 473/500221s 441ms/step - loss: 0.1296 - acc: 0.9984 - val_loss: 0.3130 - val_acc: 0.9524Epoch 474/500221s 441ms/step - loss: 0.1284 - acc: 0.9988 - val_loss: 0.3128 - val_acc: 0.9527Epoch 475/500221s 441ms/step - loss: 0.1283 - acc: 0.9989 - val_loss: 0.3126 - val_acc: 0.9523Epoch 476/500221s 442ms/step - loss: 0.1290 - acc: 0.9987 - val_loss: 0.3125 - val_acc: 0.9524Epoch 477/500221s 442ms/step - loss: 0.1287 - acc: 0.9988 - val_loss: 0.3121 - val_acc: 0.9521Epoch 478/500221s 442ms/step - loss: 0.1291 - acc: 0.9986 - val_loss: 0.3123 - val_acc: 0.9521Epoch 479/500221s 442ms/step - loss: 0.1292 - acc: 0.9986 - val_loss: 0.3124 - val_acc: 0.9522Epoch 480/500221s 442ms/step - loss: 0.1291 - acc: 0.9986 - val_loss: 0.3123 - val_acc: 0.9519Epoch 481/500221s 442ms/step - loss: 0.1282 - acc: 0.9989 - val_loss: 0.3125 - val_acc: 0.9521Epoch 482/500221s 442ms/step - loss: 0.1291 - acc: 0.9988 - val_loss: 0.3125 - val_acc: 0.9522Epoch 483/500221s 442ms/step - loss: 0.1286 - acc: 0.9988 - val_loss: 0.3125 - val_acc: 0.9516Epoch 484/500220s 441ms/step - loss: 0.1280 - acc: 0.9991 - val_loss: 0.3123 - val_acc: 0.9518Epoch 485/500220s 441ms/step - loss: 0.1281 - acc: 0.9989 - val_loss: 0.3128 - val_acc: 0.9519Epoch 486/500221s 441ms/step - loss: 0.1281 - acc: 0.9990 - val_loss: 0.3127 - val_acc: 0.9520Epoch 487/500221s 441ms/step - loss: 0.1282 - acc: 0.9990 - val_loss: 0.3127 - val_acc: 0.9520Epoch 488/500221s 441ms/step - loss: 0.1283 - acc: 0.9988 - val_loss: 0.3129 - val_acc: 0.9520Epoch 489/500221s 442ms/step - loss: 0.1282 - acc: 0.9988 - val_loss: 0.3131 - val_acc: 0.9521Epoch 490/500221s 441ms/step - loss: 0.1283 - acc: 0.9987 - val_loss: 0.3131 - val_acc: 0.9522Epoch 491/500221s 441ms/step - loss: 0.1280 - acc: 0.9990 - val_loss: 0.3133 - val_acc: 0.9526Epoch 492/500221s 442ms/step - loss: 0.1281 - acc: 0.9989 - val_loss: 0.3132 - val_acc: 0.9524Epoch 493/500221s 441ms/step - loss: 0.1282 - acc: 0.9988 - val_loss: 0.3126 - val_acc: 0.9527Epoch 494/500221s 441ms/step - loss: 0.1281 - acc: 0.9989 - val_loss: 0.3125 - val_acc: 0.9522Epoch 495/500221s 441ms/step - loss: 0.1278 - acc: 0.9991 - val_loss: 0.3118 - val_acc: 0.9524Epoch 496/500221s 441ms/step - loss: 0.1280 - acc: 0.9990 - val_loss: 0.3118 - val_acc: 0.9522Epoch 497/500221s 442ms/step - loss: 0.1279 - acc: 0.9988 - val_loss: 0.3118 - val_acc: 0.9527Epoch 498/500222s 443ms/step - loss: 0.1275 - acc: 0.9991 - val_loss: 0.3113 - val_acc: 0.9523Epoch 499/500221s 443ms/step - loss: 0.1275 - acc: 0.9990 - val_loss: 0.3115 - val_acc: 0.9524Epoch 500/500221s 443ms/step - loss: 0.1278 - acc: 0.9989 - val_loss: 0.3119 - val_acc: 0.9525Train loss: 0.12599779877066614Train accuracy: 0.9992800006866455Test loss: 0.3119203564524651Test accuracy: 0.9525000017881393相较于调参记录21的95.12%，只提高了0.13%。
Minghang Zhao, Shisheng Zhong, Xuyun Fu, Baoping Tang, Shaojiang Dong, Michael Pecht, Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis, IEEE Transactions on Industrial Electronics, 2020, DOI: 10.1109/TIE.2020.2972458
https://ieeexplore.ieee.org/d...