瞎采新闻 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。
本文在调参记录10的基础上，将残差模块的数量从27个增加到60个，测试采用自适应参数化ReLU（APReLU）激活函数的深度残差网络，在Cifar10图像集上的效果。
自适应参数化ReLU：一种动态ReLU激活函数Keras程序如下：
#!/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 300 epoches def scheduler(epoch): if epoch % 300 == 0 and epoch != 0: lr = K.get_value(model.optimizer.lr) K.set_value(model.optimizer.lr, lr * 0.1) print("lr changed to {}".format(lr * 0.1)) return K.get_value(model.optimizer.lr) # An adaptively parametric rectifier linear unit (APReLU) def aprelu(inputs): # get the number of channels channels = inputs.get_shape().as_list()[-1] # get a zero feature map zeros_input = keras.layers.subtract([inputs, inputs]) # get a feature map with only positive features pos_input = Activation('relu')(inputs) # get a feature map with only negative features neg_input = Minimum()([inputs,zeros_input]) # define a network to obtain the scaling coefficients scales_p = GlobalAveragePooling2D()(pos_input) scales_n = GlobalAveragePooling2D()(neg_input) scales = Concatenate()([scales_n, scales_p]) scales = Dense(channels, activation='linear', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(scales) scales = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(scales) scales = Activation('relu')(scales) scales = Dense(channels, activation='linear', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(scales) scales = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(scales) scales = Activation('sigmoid')(scales) scales = Reshape((1,1,channels))(scales) # apply a paramtetric relu neg_part = keras.layers.multiply([scales, neg_input]) return keras.layers.add([pos_input, neg_part]) # Residual Block def residual_block(incoming, nb_blocks, out_channels, downsample=False, downsample_strides=2): residual = incoming in_channels = incoming.get_shape().as_list()[-1] for i in range(nb_blocks): identity = residual if not downsample: downsample_strides = 1 residual = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(residual) residual = aprelu(residual) residual = Conv2D(out_channels, 3, strides=(downsample_strides, downsample_strides), padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(residual) residual = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(residual) residual = aprelu(residual) residual = Conv2D(out_channels, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(residual) # Downsampling if downsample_strides > 1: identity = AveragePooling2D(pool_size=(1,1), strides=(2,2))(identity) # Zero_padding to match channels if in_channels != out_channels: zeros_identity = keras.layers.subtract([identity, identity]) identity = keras.layers.concatenate([identity, zeros_identity]) in_channels = out_channels residual = keras.layers.add([residual, identity]) return residual # define and train a model inputs = Input(shape=(32, 32, 3)) net = Conv2D(16, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(inputs) net = residual_block(net, 20, 16, downsample=False) net = residual_block(net, 1, 32, downsample=True) net = residual_block(net, 19, 32, downsample=False) net = residual_block(net, 1, 64, downsample=True) net = residual_block(net, 19, 64, downsample=False) net = BatchNormalization(momentum=0.9, gamma_regularizer=l2(1e-4))(net) net = Activation('relu')(net) net = GlobalAveragePooling2D()(net) outputs = Dense(10, activation='softmax', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(net) model = Model(inputs=inputs, outputs=outputs) sgd = optimizers.SGD(lr=0.1, decay=0., momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # data augmentation datagen = ImageDataGenerator( # randomly rotate images in the range (deg 0 to 180) rotation_range=30, # shear angle in counter-clockwise direction in degrees shear_range = 30, # randomly flip images horizontal_flip=True, # randomly shift images horizontally width_shift_range=0.125, # randomly shift images vertically height_shift_range=0.125) reduce_lr = LearningRateScheduler(scheduler) # fit the model on the batches generated by datagen.flow(). model.fit_generator(datagen.flow(x_train, y_train, batch_size=100), validation_data=(x_test, y_test), epochs=1000, verbose=1, callbacks=[reduce_lr], workers=4) # get results K.set_learning_phase(0) DRSN_train_score = model.evaluate(x_train, y_train, batch_size=100, verbose=0) print('Train loss:', DRSN_train_score[0]) print('Train accuracy:', DRSN_train_score[1]) DRSN_test_score = model.evaluate(x_test, y_test, batch_size=100, verbose=0) print('Test loss:', DRSN_test_score[0]) print('Test accuracy:', DRSN_test_score[1])实验结果如下（跑得好慢，不知道能不能跑完）：
Using TensorFlow backend. x_train shape: (50000, 32, 32, 3) 50000 train samples 10000 test samples Epoch 1/1000 216s 433ms/step - loss: 5.3303 - acc: 0.3881 - val_loss: 4.6744 - val_acc: 0.5067 Epoch 2/1000 142s 284ms/step - loss: 4.3438 - acc: 0.5292 - val_loss: 3.8578 - val_acc: 0.6084 Epoch 3/1000 142s 284ms/step - loss: 3.6504 - acc: 0.5949 - val_loss: 3.2425 - val_acc: 0.6673 Epoch 4/1000 142s 284ms/step - loss: 3.1230 - acc: 0.6384 - val_loss: 2.8284 - val_acc: 0.6826 Epoch 5/1000 142s 284ms/step - loss: 2.7009 - acc: 0.6656 - val_loss: 2.4285 - val_acc: 0.7164 Epoch 6/1000 142s 284ms/step - loss: 2.3806 - acc: 0.6838 - val_loss: 2.1267 - val_acc: 0.7293 Epoch 7/1000 142s 284ms/step - loss: 2.1009 - acc: 0.7026 - val_loss: 1.9077 - val_acc: 0.7389 Epoch 8/1000 142s 284ms/step - loss: 1.8769 - acc: 0.7181 - val_loss: 1.7067 - val_acc: 0.7544 Epoch 9/1000 142s 284ms/step - loss: 1.6922 - acc: 0.7336 - val_loss: 1.5801 - val_acc: 0.7518 Epoch 10/1000 142s 284ms/step - loss: 1.5452 - acc: 0.7440 - val_loss: 1.4281 - val_acc: 0.7685 Epoch 11/1000 142s 284ms/step - loss: 1.4296 - acc: 0.7495 - val_loss: 1.3131 - val_acc: 0.7802 Epoch 12/1000 142s 284ms/step - loss: 1.3341 - acc: 0.7572 - val_loss: 1.2388 - val_acc: 0.7803 Epoch 13/1000 142s 284ms/step - loss: 1.2588 - acc: 0.7623 - val_loss: 1.1707 - val_acc: 0.7887 Epoch 14/1000 142s 284ms/step - loss: 1.1930 - acc: 0.7688 - val_loss: 1.0920 - val_acc: 0.8042 Epoch 15/1000 142s 284ms/step - loss: 1.1506 - acc: 0.7699 - val_loss: 1.0500 - val_acc: 0.8034 Epoch 16/1000 142s 284ms/step - loss: 1.1056 - acc: 0.7766 - val_loss: 1.0199 - val_acc: 0.8052 Epoch 17/1000 142s 284ms/step - loss: 1.0735 - acc: 0.7772 - val_loss: 0.9737 - val_acc: 0.8178 Epoch 18/1000 142s 284ms/step - loss: 1.0420 - acc: 0.7833 - val_loss: 0.9912 - val_acc: 0.8025 Epoch 19/1000 142s 284ms/step - loss: 1.0156 - acc: 0.7860 - val_loss: 0.9525 - val_acc: 0.8041 Epoch 20/1000 142s 284ms/step - loss: 0.9980 - acc: 0.7892 - val_loss: 0.9304 - val_acc: 0.8140 Epoch 21/1000 142s 284ms/step - loss: 0.9773 - acc: 0.7910 - val_loss: 0.9240 - val_acc: 0.8116 Epoch 22/1000 142s 284ms/step - loss: 0.9600 - acc: 0.7931 - val_loss: 0.8714 - val_acc: 0.8248 Epoch 23/1000 142s 284ms/step - loss: 0.9449 - acc: 0.7969 - val_loss: 0.8751 - val_acc: 0.8234 Epoch 24/1000 142s 284ms/step - loss: 0.9424 - acc: 0.7958 - val_loss: 0.8551 - val_acc: 0.8261 Epoch 25/1000 142s 284ms/step - loss: 0.9224 - acc: 0.8039 - val_loss: 0.8438 - val_acc: 0.8336 Epoch 26/1000 142s 284ms/step - loss: 0.9131 - acc: 0.8023 - val_loss: 0.8542 - val_acc: 0.8272 Epoch 27/1000 142s 284ms/step - loss: 0.8975 - acc: 0.8069 - val_loss: 0.8719 - val_acc: 0.8196 Epoch 28/1000 142s 284ms/step - loss: 0.8987 - acc: 0.8085 - val_loss: 0.8269 - val_acc: 0.8355 Epoch 29/1000 142s 284ms/step - loss: 0.8824 - acc: 0.8122 - val_loss: 0.8305 - val_acc: 0.8324 Epoch 30/1000 142s 284ms/step - loss: 0.8837 - acc: 0.8102 - val_loss: 0.8332 - val_acc: 0.8247 Epoch 31/1000 142s 284ms/step - loss: 0.8727 - acc: 0.8130 - val_loss: 0.8075 - val_acc: 0.8386 Epoch 32/1000 142s 284ms/step - loss: 0.8686 - acc: 0.8154 - val_loss: 0.8198 - val_acc: 0.8350 Epoch 33/1000 142s 284ms/step - loss: 0.8608 - acc: 0.8150 - val_loss: 0.8006 - val_acc: 0.8396 Epoch 34/1000 142s 284ms/step - loss: 0.8553 - acc: 0.8188 - val_loss: 0.8249 - val_acc: 0.8324 Epoch 35/1000 142s 284ms/step - loss: 0.8474 - acc: 0.8197 - val_loss: 0.7876 - val_acc: 0.8437 Epoch 36/1000 142s 284ms/step - loss: 0.8473 - acc: 0.8218 - val_loss: 0.7648 - val_acc: 0.8555 Epoch 37/1000 142s 284ms/step - loss: 0.8410 - acc: 0.8235 - val_loss: 0.7866 - val_acc: 0.8432 Epoch 38/1000 142s 285ms/step - loss: 0.8334 - acc: 0.8245 - val_loss: 0.7785 - val_acc: 0.8473 Epoch 39/1000 142s 284ms/step - loss: 0.8336 - acc: 0.8263 - val_loss: 0.7783 - val_acc: 0.8486 Epoch 40/1000 142s 284ms/step - loss: 0.8337 - acc: 0.8245 - val_loss: 0.7782 - val_acc: 0.8461 Epoch 41/1000 142s 284ms/step - loss: 0.8292 - acc: 0.8257 - val_loss: 0.7696 - val_acc: 0.8498 Epoch 42/1000 142s 284ms/step - loss: 0.8203 - acc: 0.8298 - val_loss: 0.7618 - val_acc: 0.8511 Epoch 43/1000 142s 284ms/step - loss: 0.8209 - acc: 0.8303 - val_loss: 0.7634 - val_acc: 0.8551 Epoch 44/1000 142s 284ms/step - loss: 0.8163 - acc: 0.8327 - val_loss: 0.7719 - val_acc: 0.8449 Epoch 45/1000 142s 285ms/step - loss: 0.8072 - acc: 0.8328 - val_loss: 0.7635 - val_acc: 0.8493 Epoch 46/1000 142s 284ms/step - loss: 0.8127 - acc: 0.8324 - val_loss: 0.7725 - val_acc: 0.8495 Epoch 47/1000 142s 285ms/step - loss: 0.8081 - acc: 0.8343 - val_loss: 0.7576 - val_acc: 0.8537 Epoch 48/1000 142s 284ms/step - loss: 0.8090 - acc: 0.8322 - val_loss: 0.7421 - val_acc: 0.8603 Epoch 49/1000 142s 285ms/step - loss: 0.8041 - acc: 0.8344 - val_loss: 0.7422 - val_acc: 0.8576 Epoch 50/1000 142s 284ms/step - loss: 0.8008 - acc: 0.8361 - val_loss: 0.7472 - val_acc: 0.8566 Epoch 51/1000 142s 284ms/step - loss: 0.8013 - acc: 0.8379 - val_loss: 0.7385 - val_acc: 0.8585 Epoch 52/1000 142s 285ms/step - loss: 0.7964 - acc: 0.8381 - val_loss: 0.7805 - val_acc: 0.8453 Epoch 53/1000 142s 285ms/step - loss: 0.7929 - acc: 0.8387 - val_loss: 0.7597 - val_acc: 0.8516 Epoch 54/1000 142s 284ms/step - loss: 0.7945 - acc: 0.8388 - val_loss: 0.7596 - val_acc: 0.8529 Epoch 55/1000 142s 285ms/step - loss: 0.7904 - acc: 0.8407 - val_loss: 0.7376 - val_acc: 0.8594 Epoch 56/1000 142s 284ms/step - loss: 0.7806 - acc: 0.8443 - val_loss: 0.7478 - val_acc: 0.8551 Epoch 57/1000 142s 284ms/step - loss: 0.7807 - acc: 0.8444 - val_loss: 0.7536 - val_acc: 0.8547 Epoch 58/1000 142s 284ms/step - loss: 0.7838 - acc: 0.8440 - val_loss: 0.7164 - val_acc: 0.8686 Epoch 59/1000 142s 284ms/step - loss: 0.7777 - acc: 0.8444 - val_loss: 0.7441 - val_acc: 0.8601 Epoch 60/1000 142s 284ms/step - loss: 0.7786 - acc: 0.8461 - val_loss: 0.7339 - val_acc: 0.8603 Epoch 61/1000 142s 284ms/step - loss: 0.7765 - acc: 0.8438 - val_loss: 0.7224 - val_acc: 0.8649 Epoch 62/1000 142s 284ms/step - loss: 0.7733 - acc: 0.8462 - val_loss: 0.7340 - val_acc: 0.8584 Epoch 63/1000 142s 284ms/step - loss: 0.7694 - acc: 0.8475 - val_loss: 0.7215 - val_acc: 0.8658 Epoch 64/1000 142s 284ms/step - loss: 0.7734 - acc: 0.8451 - val_loss: 0.7256 - val_acc: 0.8662 Epoch 65/1000 142s 284ms/step - loss: 0.7726 - acc: 0.8461 - val_loss: 0.7094 - val_acc: 0.8699 Epoch 66/1000 142s 284ms/step - loss: 0.7731 - acc: 0.8464 - val_loss: 0.7434 - val_acc: 0.8636 Epoch 67/1000 142s 284ms/step - loss: 0.7707 - acc: 0.8470 - val_loss: 0.7170 - val_acc: 0.8668 Epoch 68/1000 142s 284ms/step - loss: 0.7649 - acc: 0.8481 - val_loss: 0.7423 - val_acc: 0.8611 Epoch 69/1000 142s 284ms/step - loss: 0.7691 - acc: 0.8477 - val_loss: 0.7237 - val_acc: 0.8621 Epoch 70/1000 142s 284ms/step - loss: 0.7679 - acc: 0.8482 - val_loss: 0.7110 - val_acc: 0.8717 Epoch 71/1000 142s 284ms/step - loss: 0.7633 - acc: 0.8492 - val_loss: 0.7444 - val_acc: 0.8622 Epoch 72/1000 142s 284ms/step - loss: 0.7622 - acc: 0.8502 - val_loss: 0.7188 - val_acc: 0.8630 Epoch 73/1000 142s 284ms/step - loss: 0.7578 - acc: 0.8515 - val_loss: 0.7131 - val_acc: 0.8706 Epoch 74/1000 142s 284ms/step - loss: 0.7600 - acc: 0.8499 - val_loss: 0.7096 - val_acc: 0.8716 Epoch 75/1000 142s 284ms/step - loss: 0.7576 - acc: 0.8506 - val_loss: 0.7224 - val_acc: 0.8640 Epoch 76/1000 142s 284ms/step - loss: 0.7571 - acc: 0.8519 - val_loss: 0.7212 - val_acc: 0.8660 Epoch 77/1000 142s 284ms/step - loss: 0.7566 - acc: 0.8537 - val_loss: 0.7008 - val_acc: 0.8733 Epoch 78/1000 142s 284ms/step - loss: 0.7559 - acc: 0.8516 - val_loss: 0.7283 - val_acc: 0.8635 Epoch 79/1000 142s 284ms/step - loss: 0.7524 - acc: 0.8541 - val_loss: 0.7403 - val_acc: 0.8573 Epoch 80/1000 142s 284ms/step - loss: 0.7504 - acc: 0.8536 - val_loss: 0.7243 - val_acc: 0.8656 Epoch 81/1000 142s 284ms/step - loss: 0.7499 - acc: 0.8536 - val_loss: 0.7063 - val_acc: 0.8732 Epoch 82/1000 142s 284ms/step - loss: 0.7473 - acc: 0.8565 - val_loss: 0.6971 - val_acc: 0.8747 Epoch 83/1000 142s 284ms/step - loss: 0.7473 - acc: 0.8551 - val_loss: 0.7468 - val_acc: 0.8552 Epoch 84/1000 142s 284ms/step - loss: 0.7482 - acc: 0.8553 - val_loss: 0.7314 - val_acc: 0.8598 Epoch 85/1000 142s 285ms/step - loss: 0.7482 - acc: 0.8535 - val_loss: 0.6948 - val_acc: 0.8744 Epoch 86/1000 142s 284ms/step - loss: 0.7483 - acc: 0.8534 - val_loss: 0.7078 - val_acc: 0.8709 Epoch 87/1000 143s 285ms/step - loss: 0.7423 - acc: 0.8562 - val_loss: 0.7032 - val_acc: 0.8722 Epoch 88/1000 142s 284ms/step - loss: 0.7454 - acc: 0.8552 - val_loss: 0.7115 - val_acc: 0.8688 Epoch 89/1000 142s 284ms/step - loss: 0.7392 - acc: 0.8578 - val_loss: 0.7133 - val_acc: 0.8657 Epoch 90/1000 142s 284ms/step - loss: 0.7432 - acc: 0.8582 - val_loss: 0.6976 - val_acc: 0.8736 Epoch 91/1000 142s 284ms/step - loss: 0.7391 - acc: 0.8568 - val_loss: 0.6976 - val_acc: 0.8726 Epoch 92/1000 142s 284ms/step - loss: 0.7423 - acc: 0.8551 - val_loss: 0.7116 - val_acc: 0.8693 Epoch 93/1000 142s 284ms/step - loss: 0.7412 - acc: 0.8559 - val_loss: 0.7249 - val_acc: 0.8657 Epoch 94/1000 142s 284ms/step - loss: 0.7374 - acc: 0.8579 - val_loss: 0.6937 - val_acc: 0.8782 Epoch 95/1000 142s 284ms/step - loss: 0.7339 - acc: 0.8578 - val_loss: 0.6872 - val_acc: 0.8770 Epoch 96/1000 142s 284ms/step - loss: 0.7422 - acc: 0.8561 - val_loss: 0.7079 - val_acc: 0.8712 Epoch 97/1000 142s 284ms/step - loss: 0.7376 - acc: 0.8598 - val_loss: 0.7335 - val_acc: 0.8619 Epoch 98/1000 142s 284ms/step - loss: 0.7357 - acc: 0.8585 - val_loss: 0.6998 - val_acc: 0.8762 Epoch 99/1000 142s 284ms/step - loss: 0.7355 - acc: 0.8589 - val_loss: 0.6954 - val_acc: 0.8751 Epoch 100/1000 142s 284ms/step - loss: 0.7331 - acc: 0.8608 - val_loss: 0.7237 - val_acc: 0.8646 Epoch 101/1000 142s 284ms/step - loss: 0.7293 - acc: 0.8610 - val_loss: 0.7088 - val_acc: 0.8710 Epoch 102/1000 142s 284ms/step - loss: 0.7336 - acc: 0.8597 - val_loss: 0.7064 - val_acc: 0.8712 Epoch 103/1000 142s 284ms/step - loss: 0.7329 - acc: 0.8599 - val_loss: 0.6799 - val_acc: 0.8843 Epoch 104/1000 142s 284ms/step - loss: 0.7279 - acc: 0.8624 - val_loss: 0.6911 - val_acc: 0.8754 Epoch 105/1000 142s 284ms/step - loss: 0.7301 - acc: 0.8616 - val_loss: 0.7133 - val_acc: 0.8665 Epoch 106/1000 142s 284ms/step - loss: 0.7348 - acc: 0.8580 - val_loss: 0.7112 - val_acc: 0.8689 Epoch 107/1000 142s 283ms/step - loss: 0.7331 - acc: 0.8608 - val_loss: 0.7015 - val_acc: 0.8733 Epoch 108/1000 141s 283ms/step - loss: 0.7302 - acc: 0.8614 - val_loss: 0.7154 - val_acc: 0.8663 Epoch 109/1000 142s 283ms/step - loss: 0.7274 - acc: 0.8618 - val_loss: 0.7076 - val_acc: 0.8682 Epoch 110/1000 142s 283ms/step - loss: 0.7303 - acc: 0.8604 - val_loss: 0.7166 - val_acc: 0.8689 Epoch 111/1000 142s 284ms/step - loss: 0.7253 - acc: 0.8616 - val_loss: 0.6957 - val_acc: 0.8788 Epoch 112/1000 142s 284ms/step - loss: 0.7317 - acc: 0.8603 - val_loss: 0.6839 - val_acc: 0.8784 Epoch 113/1000 142s 284ms/step - loss: 0.7245 - acc: 0.8631 - val_loss: 0.7076 - val_acc: 0.8711 Epoch 114/1000 142s 284ms/step - loss: 0.7302 - acc: 0.8622 - val_loss: 0.7022 - val_acc: 0.8759 Epoch 115/1000 142s 284ms/step - loss: 0.7247 - acc: 0.8630 - val_loss: 0.6978 - val_acc: 0.8745 Epoch 116/1000 142s 284ms/step - loss: 0.7179 - acc: 0.8648 - val_loss: 0.6849 - val_acc: 0.8812 Epoch 117/1000 142s 284ms/step - loss: 0.7267 - acc: 0.8636 - val_loss: 0.6885 - val_acc: 0.8771 Epoch 118/1000 142s 284ms/step - loss: 0.7215 - acc: 0.8616 - val_loss: 0.6948 - val_acc: 0.8755 Epoch 119/1000 142s 284ms/step - loss: 0.7246 - acc: 0.8634 - val_loss: 0.7062 - val_acc: 0.8697 Epoch 120/1000 142s 284ms/step - loss: 0.7213 - acc: 0.8641 - val_loss: 0.6994 - val_acc: 0.8754 Epoch 121/1000 142s 284ms/step - loss: 0.7216 - acc: 0.8649 - val_loss: 0.6949 - val_acc: 0.8742 Epoch 122/1000 142s 284ms/step - loss: 0.7252 - acc: 0.8634 - val_loss: 0.6923 - val_acc: 0.8772 Epoch 123/1000 142s 284ms/step - loss: 0.7219 - acc: 0.8639 - val_loss: 0.6769 - val_acc: 0.8797 Epoch 124/1000 142s 284ms/step - loss: 0.7191 - acc: 0.8650 - val_loss: 0.7037 - val_acc: 0.8727 Epoch 125/1000 142s 284ms/step - loss: 0.7196 - acc: 0.8652 - val_loss: 0.6791 - val_acc: 0.8809 Epoch 126/1000 142s 284ms/step - loss: 0.7211 - acc: 0.8651 - val_loss: 0.6945 - val_acc: 0.8768 Epoch 127/1000 142s 284ms/step - loss: 0.7178 - acc: 0.8650 - val_loss: 0.7042 - val_acc: 0.8745 Epoch 128/1000 142s 284ms/step - loss: 0.7214 - acc: 0.8654 - val_loss: 0.6981 - val_acc: 0.8744 Epoch 129/1000 142s 284ms/step - loss: 0.7195 - acc: 0.8652 - val_loss: 0.6753 - val_acc: 0.8834 Epoch 130/1000 142s 284ms/step - loss: 0.7148 - acc: 0.8675 - val_loss: 0.6814 - val_acc: 0.8768 Epoch 131/1000 142s 284ms/step - loss: 0.7188 - acc: 0.8648 - val_loss: 0.6965 - val_acc: 0.8718 Epoch 132/1000 142s 284ms/step - loss: 0.7161 - acc: 0.8661 - val_loss: 0.6995 - val_acc: 0.8713 Epoch 133/1000 142s 284ms/step - loss: 0.7176 - acc: 0.8645 - val_loss: 0.6922 - val_acc: 0.8764 Epoch 134/1000 142s 284ms/step - loss: 0.7151 - acc: 0.8646 - val_loss: 0.6790 - val_acc: 0.8806 Epoch 135/1000 142s 284ms/step - loss: 0.7167 - acc: 0.8644 - val_loss: 0.6733 - val_acc: 0.8828 Epoch 136/1000 142s 284ms/step - loss: 0.7163 - acc: 0.8657 - val_loss: 0.6853 - val_acc: 0.8809 Epoch 137/1000 142s 284ms/step - loss: 0.7088 - acc: 0.8698 - val_loss: 0.6670 - val_acc: 0.8843 Epoch 138/1000 142s 284ms/step - loss: 0.7098 - acc: 0.8662 - val_loss: 0.6837 - val_acc: 0.8793 Epoch 139/1000 142s 284ms/step - loss: 0.7109 - acc: 0.8671 - val_loss: 0.6929 - val_acc: 0.8767 Epoch 140/1000 142s 284ms/step - loss: 0.7109 - acc: 0.8682 - val_loss: 0.6977 - val_acc: 0.8751 Epoch 141/1000 142s 284ms/step - loss: 0.7152 - acc: 0.8666 - val_loss: 0.6836 - val_acc: 0.8769 Epoch 142/1000 142s 284ms/step - loss: 0.7100 - acc: 0.8669 - val_loss: 0.6742 - val_acc: 0.8822 Epoch 143/1000 143s 286ms/step - loss: 0.7144 - acc: 0.8661 - val_loss: 0.6953 - val_acc: 0.8777 Epoch 144/1000 142s 284ms/step - loss: 0.7067 - acc: 0.8692 - val_loss: 0.6899 - val_acc: 0.8761 Epoch 145/1000 142s 284ms/step - loss: 0.7109 - acc: 0.8655 - val_loss: 0.6713 - val_acc: 0.8829 Epoch 146/1000 142s 284ms/step - loss: 0.7063 - acc: 0.8675 - val_loss: 0.7086 - val_acc: 0.8714 Epoch 147/1000 142s 284ms/step - loss: 0.7129 - acc: 0.8666 - val_loss: 0.6727 - val_acc: 0.8836 Epoch 148/1000 142s 284ms/step - loss: 0.7027 - acc: 0.8698 - val_loss: 0.6494 - val_acc: 0.8887 Epoch 149/1000 142s 284ms/step - loss: 0.7073 - acc: 0.8666 - val_loss: 0.6780 - val_acc: 0.8815 Epoch 150/1000 142s 284ms/step - loss: 0.7070 - acc: 0.8700 - val_loss: 0.6805 - val_acc: 0.8806 Epoch 151/1000 142s 284ms/step - loss: 0.7108 - acc: 0.8678 - val_loss: 0.6577 - val_acc: 0.8856 Epoch 152/1000 142s 284ms/step - loss: 0.7040 - acc: 0.8711 - val_loss: 0.6734 - val_acc: 0.8844 Epoch 153/1000 142s 283ms/step - loss: 0.7087 - acc: 0.8688 - val_loss: 0.6897 - val_acc: 0.8765 Epoch 154/1000 142s 284ms/step - loss: 0.7074 - acc: 0.8694 - val_loss: 0.6765 - val_acc: 0.8838 Epoch 155/1000 142s 284ms/step - loss: 0.7035 - acc: 0.8697 - val_loss: 0.6951 - val_acc: 0.8793 Epoch 156/1000 142s 284ms/step - loss: 0.7086 - acc: 0.8694 - val_loss: 0.6608 - val_acc: 0.8847 Epoch 157/1000 142s 284ms/step - loss: 0.7095 - acc: 0.8678 - val_loss: 0.6774 - val_acc: 0.8786 Epoch 158/1000 142s 284ms/step - loss: 0.7077 - acc: 0.8696 - val_loss: 0.6807 - val_acc: 0.8792 Epoch 159/1000 142s 284ms/step - loss: 0.7113 - acc: 0.8687 - val_loss: 0.6760 - val_acc: 0.8847 Epoch 160/1000 142s 284ms/step - loss: 0.7078 - acc: 0.8688 - val_loss: 0.6829 - val_acc: 0.8789 Epoch 161/1000 142s 284ms/step - loss: 0.7034 - acc: 0.8707 - val_loss: 0.6821 - val_acc: 0.8816 Epoch 162/1000 142s 284ms/step - loss: 0.7044 - acc: 0.8685 - val_loss: 0.6610 - val_acc: 0.8828 Epoch 163/1000 142s 284ms/step - loss: 0.6975 - acc: 0.8738 - val_loss: 0.6520 - val_acc: 0.8910 Epoch 164/1000 142s 284ms/step - loss: 0.7046 - acc: 0.8709 - val_loss: 0.6711 - val_acc: 0.8845 Epoch 165/1000 142s 284ms/step - loss: 0.7067 - acc: 0.8699 - val_loss: 0.6878 - val_acc: 0.8732 Epoch 166/1000 142s 284ms/step - loss: 0.7055 - acc: 0.8692 - val_loss: 0.6733 - val_acc: 0.8795 Epoch 167/1000 142s 284ms/step - loss: 0.7055 - acc: 0.8703 - val_loss: 0.6827 - val_acc: 0.8806 Epoch 168/1000 142s 284ms/step - loss: 0.6999 - acc: 0.8719 - val_loss: 0.6782 - val_acc: 0.8779 Epoch 169/1000 142s 284ms/step - loss: 0.7011 - acc: 0.8713 - val_loss: 0.6690 - val_acc: 0.8869 Epoch 170/1000 142s 284ms/step - loss: 0.7037 - acc: 0.8697 - val_loss: 0.6687 - val_acc: 0.8835 Epoch 171/1000 142s 284ms/step - loss: 0.7050 - acc: 0.8687 - val_loss: 0.6669 - val_acc: 0.8845 Epoch 172/1000 142s 284ms/step - loss: 0.6990 - acc: 0.8723 - val_loss: 0.6920 - val_acc: 0.8777 Epoch 173/1000 142s 284ms/step - loss: 0.7064 - acc: 0.8682 - val_loss: 0.6815 - val_acc: 0.8770 Epoch 174/1000 142s 284ms/step - loss: 0.7060 - acc: 0.8685 - val_loss: 0.6752 - val_acc: 0.8814 Epoch 175/1000 142s 284ms/step - loss: 0.7041 - acc: 0.8684 - val_loss: 0.6824 - val_acc: 0.8807 Epoch 176/1000 142s 284ms/step - loss: 0.6979 - acc: 0.8711 - val_loss: 0.6680 - val_acc: 0.8861 Epoch 177/1000 142s 284ms/step - loss: 0.7055 - acc: 0.8709 - val_loss: 0.6766 - val_acc: 0.8774 Epoch 178/1000 142s 284ms/step - loss: 0.7005 - acc: 0.8715 - val_loss: 0.6983 - val_acc: 0.8748 Epoch 179/1000 142s 284ms/step - loss: 0.6979 - acc: 0.8722 - val_loss: 0.6873 - val_acc: 0.8777 Epoch 180/1000 142s 284ms/step - loss: 0.7041 - acc: 0.8692 - val_loss: 0.6644 - val_acc: 0.8874 Epoch 181/1000 142s 284ms/step - loss: 0.6983 - acc: 0.8711 - val_loss: 0.6860 - val_acc: 0.8800 Epoch 182/1000 142s 284ms/step - loss: 0.6964 - acc: 0.8730 - val_loss: 0.6701 - val_acc: 0.8851 Epoch 183/1000 142s 284ms/step - loss: 0.6949 - acc: 0.8740 - val_loss: 0.6826 - val_acc: 0.8826 Epoch 184/1000 142s 284ms/step - loss: 0.6990 - acc: 0.8720 - val_loss: 0.6650 - val_acc: 0.8883 Epoch 185/1000 142s 284ms/step - loss: 0.6946 - acc: 0.8735 - val_loss: 0.6783 - val_acc: 0.8813 Epoch 186/1000 142s 283ms/step - loss: 0.6986 - acc: 0.8737 - val_loss: 0.6683 - val_acc: 0.8848 Epoch 187/1000 142s 284ms/step - loss: 0.6934 - acc: 0.8729 - val_loss: 0.6800 - val_acc: 0.8801 Epoch 188/1000 142s 284ms/step - loss: 0.7006 - acc: 0.8711 - val_loss: 0.6956 - val_acc: 0.8757 Epoch 189/1000 142s 284ms/step - loss: 0.6959 - acc: 0.8712 - val_loss: 0.6650 - val_acc: 0.8876 Epoch 190/1000 142s 284ms/step - loss: 0.6991 - acc: 0.8718 - val_loss: 0.6821 - val_acc: 0.8785 Epoch 191/1000 142s 284ms/step - loss: 0.7015 - acc: 0.8704 - val_loss: 0.6750 - val_acc: 0.8830 Epoch 192/1000 142s 284ms/step - loss: 0.7000 - acc: 0.8715 - val_loss: 0.6775 - val_acc: 0.8804 Epoch 193/1000 142s 284ms/step - loss: 0.6978 - acc: 0.8719 - val_loss: 0.6919 - val_acc: 0.8782 Epoch 194/1000 142s 283ms/step - loss: 0.6958 - acc: 0.8732 - val_loss: 0.6706 - val_acc: 0.8852 Epoch 195/1000 142s 284ms/step - loss: 0.6995 - acc: 0.8717 - val_loss: 0.6769 - val_acc: 0.8802 Epoch 196/1000 142s 284ms/step - loss: 0.6975 - acc: 0.8712 - val_loss: 0.6609 - val_acc: 0.8888 Epoch 197/1000 142s 284ms/step - loss: 0.6955 - acc: 0.8725 - val_loss: 0.6624 - val_acc: 0.8870 Epoch 198/1000 142s 284ms/step - loss: 0.6981 - acc: 0.8726 - val_loss: 0.6550 - val_acc: 0.8912 Epoch 199/1000 142s 284ms/step - loss: 0.6961 - acc: 0.8730 - val_loss: 0.6892 - val_acc: 0.8796 Epoch 200/1000 142s 284ms/step - loss: 0.6936 - acc: 0.8744 - val_loss: 0.6906 - val_acc: 0.8792 Epoch 201/1000 142s 284ms/step - loss: 0.6940 - acc: 0.8746 - val_loss: 0.6571 - val_acc: 0.8881 Epoch 202/1000 142s 284ms/step - loss: 0.6899 - acc: 0.8751 - val_loss: 0.6537 - val_acc: 0.8904 Epoch 203/1000 142s 284ms/step - loss: 0.6970 - acc: 0.8720 - val_loss: 0.6717 - val_acc: 0.8848 Epoch 204/1000 142s 284ms/step - loss: 0.6917 - acc: 0.8743 - val_loss: 0.6643 - val_acc: 0.8850 Epoch 205/1000 142s 284ms/step - loss: 0.6927 - acc: 0.8745 - val_loss: 0.6841 - val_acc: 0.8804 Epoch 206/1000 142s 284ms/step - loss: 0.6957 - acc: 0.8723 - val_loss: 0.6947 - val_acc: 0.8750 Epoch 207/1000 142s 284ms/step - loss: 0.6913 - acc: 0.8760 - val_loss: 0.6755 - val_acc: 0.8827 Epoch 208/1000 142s 284ms/step - loss: 0.6975 - acc: 0.8723 - val_loss: 0.6626 - val_acc: 0.8837 Epoch 209/1000 142s 284ms/step - loss: 0.6920 - acc: 0.8748 - val_loss: 0.6797 - val_acc: 0.8803 Epoch 210/1000 142s 284ms/step - loss: 0.6958 - acc: 0.8737 - val_loss: 0.6869 - val_acc: 0.8791 Epoch 211/1000 142s 284ms/step - loss: 0.6906 - acc: 0.8731 - val_loss: 0.6656 - val_acc: 0.8865 Epoch 212/1000 142s 284ms/step - loss: 0.6946 - acc: 0.8726 - val_loss: 0.6841 - val_acc: 0.8813 Epoch 213/1000 142s 284ms/step - loss: 0.6930 - acc: 0.8738 - val_loss: 0.6858 - val_acc: 0.8770 Epoch 214/1000 142s 284ms/step - loss: 0.6955 - acc: 0.8717 - val_loss: 0.6848 - val_acc: 0.8851 Epoch 215/1000 142s 284ms/step - loss: 0.6964 - acc: 0.8728 - val_loss: 0.6671 - val_acc: 0.8836 Epoch 216/1000 142s 284ms/step - loss: 0.6889 - acc: 0.8743 - val_loss: 0.6633 - val_acc: 0.8885 Epoch 217/1000 142s 284ms/step - loss: 0.6965 - acc: 0.8724 - val_loss: 0.6691 - val_acc: 0.8833 Epoch 218/1000 142s 284ms/step - loss: 0.6906 - acc: 0.8749 - val_loss: 0.6752 - val_acc: 0.8843 Epoch 219/1000 142s 284ms/step - loss: 0.6926 - acc: 0.8733 - val_loss: 0.6759 - val_acc: 0.8821 Epoch 220/1000 142s 284ms/step - loss: 0.6953 - acc: 0.8736 - val_loss: 0.6813 - val_acc: 0.8796 Epoch 221/1000 142s 284ms/step - loss: 0.6904 - acc: 0.8745 - val_loss: 0.6864 - val_acc: 0.8803 Epoch 222/1000 142s 284ms/step - loss: 0.6912 - acc: 0.8754 - val_loss: 0.6892 - val_acc: 0.8775 Epoch 223/1000 142s 284ms/step - loss: 0.6887 - acc: 0.8757 - val_loss: 0.6630 - val_acc: 0.8857 Epoch 224/1000 142s 284ms/step - loss: 0.6940 - acc: 0.8746 - val_loss: 0.6808 - val_acc: 0.8789 Epoch 225/1000 142s 284ms/step - loss: 0.6901 - acc: 0.8739 - val_loss: 0.6795 - val_acc: 0.8786 Epoch 226/1000 142s 284ms/step - loss: 0.6932 - acc: 0.8741 - val_loss: 0.6934 - val_acc: 0.8785 Epoch 227/1000 142s 284ms/step - loss: 0.6949 - acc: 0.8734 - val_loss: 0.6660 - val_acc: 0.8854 Epoch 228/1000 142s 284ms/step - loss: 0.6909 - acc: 0.8758 - val_loss: 0.6684 - val_acc: 0.8836 Epoch 229/1000 142s 284ms/step - loss: 0.6910 - acc: 0.8759 - val_loss: 0.6811 - val_acc: 0.8853 Epoch 230/1000 142s 284ms/step - loss: 0.6958 - acc: 0.8736 - val_loss: 0.6751 - val_acc: 0.8847 Epoch 231/1000 142s 284ms/step - loss: 0.6937 - acc: 0.8742 - val_loss: 0.6626 - val_acc: 0.8904 Epoch 232/1000 142s 284ms/step - loss: 0.6904 - acc: 0.8763 - val_loss: 0.6724 - val_acc: 0.8850 Epoch 233/1000 142s 284ms/step - loss: 0.6860 - acc: 0.8769 - val_loss: 0.6722 - val_acc: 0.8854 Epoch 234/1000 142s 284ms/step - loss: 0.6957 - acc: 0.8731 - val_loss: 0.6722 - val_acc: 0.8829 Epoch 235/1000 142s 284ms/step - loss: 0.6909 - acc: 0.8755 - val_loss: 0.6749 - val_acc: 0.8835 Epoch 236/1000 142s 284ms/step - loss: 0.6891 - acc: 0.8758 - val_loss: 0.6551 - val_acc: 0.8885 Epoch 237/1000 142s 284ms/step - loss: 0.6888 - acc: 0.8742 - val_loss: 0.6953 - val_acc: 0.8778 Epoch 238/1000 142s 284ms/step - loss: 0.6907 - acc: 0.8760 - val_loss: 0.6752 - val_acc: 0.8844 Epoch 239/1000 142s 284ms/step - loss: 0.6894 - acc: 0.8764 - val_loss: 0.6801 - val_acc: 0.8820 Epoch 240/1000 142s 284ms/step - loss: 0.6893 - acc: 0.8761 - val_loss: 0.6842 - val_acc: 0.8816 Epoch 241/1000 142s 284ms/step - loss: 0.6895 - acc: 0.8754 - val_loss: 0.6722 - val_acc: 0.8817 Epoch 242/1000 142s 284ms/step - loss: 0.6895 - acc: 0.8767 - val_loss: 0.6942 - val_acc: 0.8757 Epoch 243/1000 142s 284ms/step - loss: 0.6934 - acc: 0.8734 - val_loss: 0.6603 - val_acc: 0.8851 Epoch 244/1000 141s 283ms/step - loss: 0.6851 - acc: 0.8772 - val_loss: 0.6947 - val_acc: 0.8764 Epoch 245/1000 142s 283ms/step - loss: 0.6875 - acc: 0.8759 - val_loss: 0.6707 - val_acc: 0.8863 Epoch 246/1000 142s 284ms/step - loss: 0.6858 - acc: 0.8747 - val_loss: 0.6729 - val_acc: 0.8814 Epoch 247/1000 142s 284ms/step - loss: 0.6881 - acc: 0.8778 - val_loss: 0.6919 - val_acc: 0.8765 Epoch 248/1000 142s 284ms/step - loss: 0.6844 - acc: 0.8776 - val_loss: 0.6899 - val_acc: 0.8821 Epoch 249/1000 142s 284ms/step - loss: 0.6890 - acc: 0.8763 - val_loss: 0.6534 - val_acc: 0.8901 Epoch 250/1000 142s 284ms/step - loss: 0.6825 - acc: 0.8784 - val_loss: 0.6682 - val_acc: 0.8849 Epoch 251/1000 142s 284ms/step - loss: 0.6847 - acc: 0.8777 - val_loss: 0.6655 - val_acc: 0.8860 Epoch 252/1000 142s 284ms/step - loss: 0.6814 - acc: 0.8791 - val_loss: 0.6657 - val_acc: 0.8860 Epoch 253/1000 142s 284ms/step - loss: 0.6873 - acc: 0.8742 - val_loss: 0.6804 - val_acc: 0.8793 Epoch 254/1000 142s 284ms/step - loss: 0.6887 - acc: 0.8754 - val_loss: 0.6719 - val_acc: 0.8835 Epoch 255/1000 142s 284ms/step - loss: 0.6847 - acc: 0.8764 - val_loss: 0.6631 - val_acc: 0.8857 Epoch 256/1000 142s 284ms/step - loss: 0.6896 - acc: 0.8743 - val_loss: 0.6694 - val_acc: 0.8846 Epoch 257/1000 142s 284ms/step - loss: 0.6900 - acc: 0.8756 - val_loss: 0.6771 - val_acc: 0.8810 Epoch 258/1000 142s 284ms/step - loss: 0.6860 - acc: 0.8764 - val_loss: 0.6694 - val_acc: 0.8843 Epoch 259/1000 142s 284ms/step - loss: 0.6875 - acc: 0.8786 - val_loss: 0.6747 - val_acc: 0.8807 Epoch 260/1000 142s 284ms/step - loss: 0.6857 - acc: 0.8768 - val_loss: 0.6458 - val_acc: 0.8938 Epoch 261/1000 142s 284ms/step - loss: 0.6880 - acc: 0.8771 - val_loss: 0.6855 - val_acc: 0.8788 Epoch 262/1000 142s 284ms/step - loss: 0.6839 - acc: 0.8777 - val_loss: 0.6723 - val_acc: 0.8851 Epoch 263/1000 142s 284ms/step - loss: 0.6819 - acc: 0.8783 - val_loss: 0.6738 - val_acc: 0.8845 Epoch 264/1000 142s 284ms/step - loss: 0.6867 - acc: 0.8784 - val_loss: 0.6809 - val_acc: 0.8790 Epoch 265/1000 142s 284ms/step - loss: 0.6805 - acc: 0.8810 - val_loss: 0.6750 - val_acc: 0.8846 Epoch 266/1000 141s 283ms/step - loss: 0.6809 - acc: 0.8781 - val_loss: 0.6584 - val_acc: 0.8878 Epoch 267/1000 142s 283ms/step - loss: 0.6944 - acc: 0.8722 - val_loss: 0.6598 - val_acc: 0.8875 Epoch 268/1000 141s 283ms/step - loss: 0.6847 - acc: 0.8779 - val_loss: 0.6825 - val_acc: 0.8817 Epoch 269/1000 141s 283ms/step - loss: 0.6824 - acc: 0.8786 - val_loss: 0.6552 - val_acc: 0.8908 Epoch 270/1000 141s 283ms/step - loss: 0.6830 - acc: 0.8783 - val_loss: 0.6820 - val_acc: 0.8767 Epoch 271/1000 141s 283ms/step - loss: 0.6903 - acc: 0.8752 - val_loss: 0.6685 - val_acc: 0.8855 Epoch 272/1000 141s 283ms/step - loss: 0.6861 - acc: 0.8760 - val_loss: 0.6707 - val_acc: 0.8873 Epoch 273/1000 142s 283ms/step - loss: 0.6823 - acc: 0.8782 - val_loss: 0.6721 - val_acc: 0.8864 Epoch 274/1000 141s 283ms/step - loss: 0.6862 - acc: 0.8769 - val_loss: 0.6764 - val_acc: 0.8866 Epoch 275/1000 141s 283ms/step - loss: 0.6825 - acc: 0.8785 - val_loss: 0.6673 - val_acc: 0.8861 Epoch 276/1000 142s 283ms/step - loss: 0.6842 - acc: 0.8771 - val_loss: 0.6757 - val_acc: 0.8835 Epoch 277/1000 142s 283ms/step - loss: 0.6855 - acc: 0.8777 - val_loss: 0.6769 - val_acc: 0.8814 Epoch 278/1000 142s 284ms/step - loss: 0.6793 - acc: 0.8802 - val_loss: 0.6618 - val_acc: 0.8883 Epoch 279/1000 142s 284ms/step - loss: 0.6854 - acc: 0.8766 - val_loss: 0.6965 - val_acc: 0.8743 Epoch 280/1000 142s 284ms/step - loss: 0.6824 - acc: 0.8792 - val_loss: 0.6720 - val_acc: 0.8842 Epoch 281/1000 142s 284ms/step - loss: 0.6786 - acc: 0.8790 - val_loss: 0.6589 - val_acc: 0.8883 Epoch 282/1000 142s 284ms/step - loss: 0.6781 - acc: 0.8797 - val_loss: 0.6620 - val_acc: 0.8862 Epoch 283/1000 142s 284ms/step - loss: 0.6845 - acc: 0.8786 - val_loss: 0.6936 - val_acc: 0.8802 Epoch 284/1000 142s 284ms/step - loss: 0.6866 - acc: 0.8772 - val_loss: 0.6678 - val_acc: 0.8890 Epoch 285/1000 142s 284ms/step - loss: 0.6829 - acc: 0.8787 - val_loss: 0.6630 - val_acc: 0.8866 Epoch 286/1000 142s 284ms/step - loss: 0.6763 - acc: 0.8796 - val_loss: 0.6597 - val_acc: 0.8893 Epoch 287/1000 142s 284ms/step - loss: 0.6833 - acc: 0.8774 - val_loss: 0.6752 - val_acc: 0.8866 Epoch 288/1000 142s 284ms/step - loss: 0.6858 - acc: 0.8768 - val_loss: 0.6617 - val_acc: 0.8902 Epoch 289/1000 142s 284ms/step - loss: 0.6784 - acc: 0.8799 - val_loss: 0.6634 - val_acc: 0.8872 Epoch 290/1000 142s 284ms/step - loss: 0.6807 - acc: 0.8778 - val_loss: 0.6564 - val_acc: 0.8896 Epoch 291/1000 142s 284ms/step - loss: 0.6835 - acc: 0.8769 - val_loss: 0.6628 - val_acc: 0.8877 Epoch 292/1000 142s 284ms/step - loss: 0.6783 - acc: 0.8798 - val_loss: 0.6887 - val_acc: 0.8813 Epoch 293/1000 142s 284ms/step - loss: 0.6795 - acc: 0.8810 - val_loss: 0.6590 - val_acc: 0.8899 Epoch 294/1000 142s 284ms/step - loss: 0.6799 - acc: 0.8798 - val_loss: 0.6599 - val_acc: 0.8873 Epoch 295/1000 142s 284ms/step - loss: 0.6856 - acc: 0.8792 - val_loss: 0.6636 - val_acc: 0.8880 Epoch 296/1000 142s 284ms/step - loss: 0.6832 - acc: 0.8802 - val_loss: 0.6513 - val_acc: 0.8926 Epoch 297/1000 142s 284ms/step - loss: 0.6785 - acc: 0.8794 - val_loss: 0.6568 - val_acc: 0.8886 Epoch 298/1000 142s 284ms/step - loss: 0.6832 - acc: 0.8782 - val_loss: 0.6697 - val_acc: 0.8872 Epoch 299/1000 142s 284ms/step - loss: 0.6771 - acc: 0.8813 - val_loss: 0.6714 - val_acc: 0.8825 Epoch 300/1000 142s 285ms/step - loss: 0.6814 - acc: 0.8784 - val_loss: 0.6857 - val_acc: 0.8821 Epoch 301/1000 lr changed to 0.010000000149011612 142s 284ms/step - loss: 0.5714 - acc: 0.9156 - val_loss: 0.5648 - val_acc: 0.9171 Epoch 302/1000 142s 284ms/step - loss: 0.5073 - acc: 0.9362 - val_loss: 0.5481 - val_acc: 0.9236 Epoch 303/1000 142s 284ms/step - loss: 0.4913 - acc: 0.9412 - val_loss: 0.5391 - val_acc: 0.9228 Epoch 304/1000 142s 284ms/step - loss: 0.4714 - acc: 0.9455 - val_loss: 0.5304 - val_acc: 0.9255 Epoch 305/1000 142s 285ms/step - loss: 0.4592 - acc: 0.9481 - val_loss: 0.5223 - val_acc: 0.9253 Epoch 306/1000 142s 284ms/step - loss: 0.4452 - acc: 0.9512 - val_loss: 0.5173 - val_acc: 0.9271 Epoch 307/1000 142s 284ms/step - loss: 0.4350 - acc: 0.9520 - val_loss: 0.5130 - val_acc: 0.9272 Epoch 308/1000 142s 285ms/step - loss: 0.4268 - acc: 0.9528 - val_loss: 0.5095 - val_acc: 0.9247 Epoch 309/1000 142s 284ms/step - loss: 0.4178 - acc: 0.9562 - val_loss: 0.5078 - val_acc: 0.9272 Epoch 310/1000 142s 284ms/step - loss: 0.4143 - acc: 0.9540 - val_loss: 0.5075 - val_acc: 0.9279 Epoch 311/1000 142s 284ms/step - loss: 0.4027 - acc: 0.9576 - val_loss: 0.4964 - val_acc: 0.9266 Epoch 312/1000 142s 284ms/step - loss: 0.3964 - acc: 0.9572 - val_loss: 0.4957 - val_acc: 0.9264 Epoch 313/1000 142s 285ms/step - loss: 0.3920 - acc: 0.9581 - val_loss: 0.4919 - val_acc: 0.9276 Epoch 314/1000 142s 284ms/step - loss: 0.3829 - acc: 0.9602 - val_loss: 0.4879 - val_acc: 0.9271 Epoch 315/1000 142s 284ms/step - loss: 0.3751 - acc: 0.9609 - val_loss: 0.4864 - val_acc: 0.9285 Epoch 316/1000 142s 284ms/step - loss: 0.3736 - acc: 0.9605 - val_loss: 0.4832 - val_acc: 0.9264 Epoch 317/1000 142s 284ms/step - loss: 0.3669 - acc: 0.9609 - val_loss: 0.4763 - val_acc: 0.9280 Epoch 318/1000 142s 284ms/step - loss: 0.3610 - acc: 0.9625 - val_loss: 0.4739 - val_acc: 0.9295 ... Epoch 861/1000 142s 284ms/step - loss: 0.1070 - acc: 0.9982 - val_loss: 0.3575 - val_acc: 0.9367 Epoch 862/1000 142s 284ms/step - loss: 0.1074 - acc: 0.9980 - val_loss: 0.3581 - val_acc: 0.9357 Epoch 863/1000 142s 284ms/step - loss: 0.1070 - acc: 0.9982 - val_loss: 0.3527 - val_acc: 0.9374 Epoch 864/1000 142s 284ms/step - loss: 0.1063 - acc: 0.9984 - val_loss: 0.3543 - val_acc: 0.9374 Epoch 865/1000 142s 284ms/step - loss: 0.1057 - acc: 0.9986 - val_loss: 0.3533 - val_acc: 0.9377 Epoch 866/1000 142s 285ms/step - loss: 0.1062 - acc: 0.9978 - val_loss: 0.3545 - val_acc: 0.9369 Epoch 867/1000 142s 284ms/step - loss: 0.1054 - acc: 0.9984 - val_loss: 0.3542 - val_acc: 0.9355 Epoch 868/1000 142s 284ms/step - loss: 0.1060 - acc: 0.9983 - val_loss: 0.3482 - val_acc: 0.9394 Epoch 869/1000 142s 285ms/step - loss: 0.1054 - acc: 0.9984 - val_loss: 0.3560 - val_acc: 0.9375 Epoch 870/1000 142s 284ms/step - loss: 0.1064 - acc: 0.9978 - val_loss: 0.3537 - val_acc: 0.9370 Epoch 871/1000 142s 284ms/step - loss: 0.1050 - acc: 0.9984 - val_loss: 0.3555 - val_acc: 0.9374 Epoch 872/1000 142s 284ms/step - loss: 0.1049 - acc: 0.9985 - val_loss: 0.3539 - val_acc: 0.9367 Epoch 873/1000 142s 284ms/step - loss: 0.1050 - acc: 0.9984 - val_loss: 0.3574 - val_acc: 0.9373 Epoch 874/1000 143s 285ms/step - loss: 0.1044 - acc: 0.9987 - val_loss: 0.3623 - val_acc: 0.9359 Epoch 875/1000 142s 283ms/step - loss: 0.1048 - acc: 0.9982 - val_loss: 0.3600 - val_acc: 0.9370 Epoch 876/1000 142s 284ms/step - loss: 0.1051 - acc: 0.9982 - val_loss: 0.3594 - val_acc: 0.9366 Epoch 877/1000 142s 284ms/step - loss: 0.1042 - acc: 0.9985 - val_loss: 0.3558 - val_acc: 0.9357 Epoch 878/1000 142s 284ms/step - loss: 0.1046 - acc: 0.9982 - val_loss: 0.3549 - val_acc: 0.9360 Epoch 879/1000 142s 284ms/step - loss: 0.1042 - acc: 0.9984 - val_loss: 0.3520 - val_acc: 0.9385 Epoch 880/1000 142s 285ms/step - loss: 0.1040 - acc: 0.9984 - val_loss: 0.3598 - val_acc: 0.9367 Epoch 881/1000 142s 285ms/step - loss: 0.1036 - acc: 0.9984 - val_loss: 0.3550 - val_acc: 0.9364 Epoch 882/1000 142s 284ms/step - loss: 0.1031 - acc: 0.9985 - val_loss: 0.3544 - val_acc: 0.9381 Epoch 883/1000 142s 285ms/step - loss: 0.1042 - acc: 0.9981 - val_loss: 0.3513 - val_acc: 0.9380 Epoch 884/1000 142s 284ms/step - loss: 0.1036 - acc: 0.9982 - val_loss: 0.3541 - val_acc: 0.9364 Epoch 885/1000 142s 284ms/step - loss: 0.1033 - acc: 0.9985 - val_loss: 0.3532 - val_acc: 0.9376 Epoch 886/1000 142s 284ms/step - loss: 0.1032 - acc: 0.9981 - val_loss: 0.3566 - val_acc: 0.9376 Epoch 887/1000 142s 284ms/step - loss: 0.1033 - acc: 0.9981 - val_loss: 0.3518 - val_acc: 0.9368 Epoch 888/1000 142s 285ms/step - loss: 0.1020 - acc: 0.9987 - val_loss: 0.3521 - val_acc: 0.9378 Epoch 889/1000 142s 284ms/step - loss: 0.1020 - acc: 0.9984 - val_loss: 0.3524 - val_acc: 0.9368 Epoch 890/1000 142s 284ms/step - loss: 0.1024 - acc: 0.9983 - val_loss: 0.3523 - val_acc: 0.9364 Epoch 891/1000 142s 284ms/step - loss: 0.1029 - acc: 0.9983 - val_loss: 0.3582 - val_acc: 0.9355 Epoch 892/1000 142s 284ms/step - loss: 0.1018 - acc: 0.9984 - val_loss: 0.3555 - val_acc: 0.9365 Epoch 893/1000 142s 284ms/step - loss: 0.1021 - acc: 0.9985 - val_loss: 0.3559 - val_acc: 0.9367 Epoch 894/1000 142s 284ms/step - loss: 0.1026 - acc: 0.9977 - val_loss: 0.3563 - val_acc: 0.9360 Epoch 895/1000 142s 284ms/step - loss: 0.1027 - acc: 0.9980 - val_loss: 0.3575 - val_acc: 0.9365 Epoch 896/1000 142s 284ms/step - loss: 0.1023 - acc: 0.9980 - val_loss: 0.3541 - val_acc: 0.9375 Epoch 897/1000 142s 284ms/step - loss: 0.1016 - acc: 0.9982 - val_loss: 0.3518 - val_acc: 0.9372 Epoch 898/1000 142s 285ms/step - loss: 0.1018 - acc: 0.9979 - val_loss: 0.3473 - val_acc: 0.9372 Epoch 899/1000 142s 284ms/step - loss: 0.1014 - acc: 0.9986 - val_loss: 0.3507 - val_acc: 0.9376 Epoch 900/1000 142s 284ms/step - loss: 0.1010 - acc: 0.9985 - val_loss: 0.3568 - val_acc: 0.9366 Epoch 901/1000 lr changed to 9.999999310821295e-05 142s 284ms/step - loss: 0.1014 - acc: 0.9982 - val_loss: 0.3548 - val_acc: 0.9366 Epoch 902/1000 142s 284ms/step - loss: 0.1009 - acc: 0.9983 - val_loss: 0.3535 - val_acc: 0.9372 Epoch 903/1000 142s 284ms/step - loss: 0.1008 - acc: 0.9981 - val_loss: 0.3523 - val_acc: 0.9370 Epoch 904/1000 142s 284ms/step - loss: 0.1002 - acc: 0.9986 - val_loss: 0.3526 - val_acc: 0.9375 Epoch 905/1000 142s 285ms/step - loss: 0.1000 - acc: 0.9987 - val_loss: 0.3519 - val_acc: 0.9372 Epoch 906/1000 142s 284ms/step - loss: 0.0997 - acc: 0.9989 - val_loss: 0.3520 - val_acc: 0.9374 Epoch 907/1000 142s 284ms/step - loss: 0.0999 - acc: 0.9989 - val_loss: 0.3520 - val_acc: 0.9377 Epoch 908/1000 142s 284ms/step - loss: 0.0994 - acc: 0.9989 - val_loss: 0.3518 - val_acc: 0.9376 Epoch 909/1000 142s 284ms/step - loss: 0.0991 - acc: 0.9990 - val_loss: 0.3520 - val_acc: 0.9378 Epoch 910/1000 142s 284ms/step - loss: 0.0996 - acc: 0.9988 - val_loss: 0.3515 - val_acc: 0.9375 Epoch 911/1000 142s 284ms/step - loss: 0.0990 - acc: 0.9990 - val_loss: 0.3513 - val_acc: 0.9372 Epoch 912/1000 142s 284ms/step - loss: 0.0994 - acc: 0.9987 - val_loss: 0.3508 - val_acc: 0.9371 Epoch 913/1000 142s 284ms/step - loss: 0.0997 - acc: 0.9988 - val_loss: 0.3510 - val_acc: 0.9373 Epoch 914/1000 142s 284ms/step - loss: 0.0996 - acc: 0.9989 - val_loss: 0.3509 - val_acc: 0.9374 Epoch 915/1000 142s 284ms/step - loss: 0.1001 - acc: 0.9986 - val_loss: 0.3513 - val_acc: 0.9375 Epoch 916/1000 142s 284ms/step - loss: 0.0991 - acc: 0.9990 - val_loss: 0.3508 - val_acc: 0.9388 Epoch 917/1000 142s 284ms/step - loss: 0.0987 - acc: 0.9989 - val_loss: 0.3512 - val_acc: 0.9377 Epoch 918/1000 142s 284ms/step - loss: 0.0990 - acc: 0.9988 - val_loss: 0.3510 - val_acc: 0.9381 Epoch 919/1000 142s 284ms/step - loss: 0.0997 - acc: 0.9986 - val_loss: 0.3515 - val_acc: 0.9380 Epoch 920/1000 142s 284ms/step - loss: 0.0993 - acc: 0.9987 - val_loss: 0.3519 - val_acc: 0.9379 ... Epoch 982/1000 142s 284ms/step - loss: 0.0977 - acc: 0.9990 - val_loss: 0.3512 - val_acc: 0.9391 Epoch 983/1000 142s 284ms/step - loss: 0.0978 - acc: 0.9988 - val_loss: 0.3503 - val_acc: 0.9389 Epoch 984/1000 142s 285ms/step - loss: 0.0975 - acc: 0.9991 - val_loss: 0.3503 - val_acc: 0.9383 Epoch 985/1000 142s 284ms/step - loss: 0.0977 - acc: 0.9989 - val_loss: 0.3497 - val_acc: 0.9388 Epoch 986/1000 142s 284ms/step - loss: 0.0977 - acc: 0.9990 - val_loss: 0.3498 - val_acc: 0.9390 Epoch 987/1000 142s 284ms/step - loss: 0.0972 - acc: 0.9992 - val_loss: 0.3502 - val_acc: 0.9382 Epoch 988/1000 142s 284ms/step - loss: 0.0973 - acc: 0.9991 - val_loss: 0.3506 - val_acc: 0.9391 Epoch 989/1000 142s 284ms/step - loss: 0.0977 - acc: 0.9989 - val_loss: 0.3504 - val_acc: 0.9396 Epoch 990/1000 142s 284ms/step - loss: 0.0978 - acc: 0.9990 - val_loss: 0.3502 - val_acc: 0.9393 Epoch 991/1000 142s 284ms/step - loss: 0.0976 - acc: 0.9988 - val_loss: 0.3501 - val_acc: 0.9391 Epoch 992/1000 142s 284ms/step - loss: 0.0973 - acc: 0.9992 - val_loss: 0.3500 - val_acc: 0.9386 Epoch 993/1000 142s 284ms/step - loss: 0.0970 - acc: 0.9992 - val_loss: 0.3497 - val_acc: 0.9387 Epoch 994/1000 142s 284ms/step - loss: 0.0973 - acc: 0.9990 - val_loss: 0.3499 - val_acc: 0.9391 Epoch 995/1000 142s 284ms/step - loss: 0.0978 - acc: 0.9988 - val_loss: 0.3500 - val_acc: 0.9397 Epoch 996/1000 142s 284ms/step - loss: 0.0975 - acc: 0.9991 - val_loss: 0.3500 - val_acc: 0.9392 Epoch 997/1000 142s 284ms/step - loss: 0.0975 - acc: 0.9990 - val_loss: 0.3495 - val_acc: 0.9393 Epoch 998/1000 142s 285ms/step - loss: 0.0980 - acc: 0.9988 - val_loss: 0.3498 - val_acc: 0.9386 Epoch 999/1000 142s 284ms/step - loss: 0.0976 - acc: 0.9991 - val_loss: 0.3490 - val_acc: 0.9382 Epoch 1000/1000 142s 285ms/step - loss: 0.0974 - acc: 0.9990 - val_loss: 0.3497 - val_acc: 0.9385 Train loss: 0.09523774388432503 Train accuracy: 0.9995200004577637 Test loss: 0.34968149244785307 Test accuracy: 0.938500000834465准确率略有提升，但是这是以残差模块的数量翻了一倍为代价的，运算时间长了很多，似乎没有必要这么多层。
本身网络就比较复杂了，还有那么多层，也加大了训练难度。
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
---来自腾讯云社区的---用户7368967
微信扫一扫打赏 
支付宝扫一扫打赏