[Harbin Institute of Technology] Dynamic ReLU: Adaptively Parametric ReLU (reference record 14)

Posted May 25, 202026 min read

Adaptively Parametric ReLU is a dynamic ReLU(Dynamic ReLU), submitted to IEEE Transactions on Industrial Electronics on May 3, 2019, accepted on January 24, 2020, February 13, 2020 IEEE official website announced.

This time, try to solve the overfitting again and reduce the number of residual modules to 2. The weight of the first fully connected layer in Adaptively Parametric ReLU is reduced to 1/8, and the batch size is set to 1000(mainly To save time).

The basic principle of Adaptively Parametric ReLU activation function is as follows:
aprelu.png

The Keras program is as follows:

#!/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, 2020, DOI:10.1109/TIE.2020.2972458,
Date of Publication:13 February 2020

@author:Minghang Zhao
"" "

from __future__ import print_function
import keras
import numpy as np
from keras.datasets import cifar10
from keras.layers import Dense, Conv2D, BatchNormalization, Activation, Minimum
from keras.layers import AveragePooling2D, Input, GlobalAveragePooling2D, Concatenate, Reshape
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import LearningRateScheduler
K.set_learning_phase(1)

# The data, split between train and test sets

(x_train, y_train),(x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32')/255.
x_test = x_test.astype('float32')/255.
x_test = x_test-np.mean(x_train)
x_train = x_train-np.mean(x_train)
print('x_train shape:', x_train.shape)
print(x_train.shape [0], 'train samples')
print(x_test.shape [0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

# Schedule the learning rate, multiply 0.1 every 1500 epoches
def scheduler(epoch):
    if epoch%1500 == 0 and epoch! = 0:
        lr = K.get_value(model.optimizer.lr)
        K.set_value(model.optimizer.lr, lr * 0.1)
        print("lr changed to {}". format(lr * 0.1))
    return K.get_value(model.optimizer.lr)

# An adaptively parametric rectifier linear unit(APReLU)
def aprelu(inputs):
    # get the number of channels
    channels = inputs.get_shape(). as_list() [-1]
    # get a zero feature map
    zeros_input = keras.layers.subtract([inputs, inputs])
    # get a feature map with only positive features
    pos_input = Activation('relu')(inputs)
    # get a feature map with only negative features
    neg_input = Minimum()([inputs, zeros_input])
    # define a network to obtain the scaling coefficients
    scales_p = GlobalAveragePooling2D()(pos_input)
    scales_n = GlobalAveragePooling2D()(neg_input)
    scales = Concatenate()([scales_n, scales_p])
    scales = Dense(channels //8, activation = 'linear', kernel_initializer = 'he_normal', kernel_regularizer = l2(1e-4))(scales)
    scales = BatchNormalization(momentum = 0.9, gamma_regularizer = l2(1e-4))(scales)
    scales = Activation('relu')(scales)
    scales = Dense(channels, activation = 'linear', kernel_initializer = 'he_normal', kernel_regularizer = l2(1e-4))(scales)
    scales = BatchNormalization(momentum = 0.9, gamma_regularizer = l2(1e-4))(scales)
    scales = Activation('sigmoid')(scales)
    scales = Reshape((1,1, channels))(scales)
    # apply a paramtetric relu
    neg_part = keras.layers.multiply([scales, neg_input])
    return keras.layers.add([pos_input, neg_part])

# Residual Block
def residual_block(incoming, nb_blocks, out_channels, downsample = False,
                   downsample_strides = 2):

    residual = incoming
    in_channels = incoming.get_shape(). as_list() [-1]

    for i in range(nb_blocks):

        identity = residual

        if not downsample:
            downsample_strides = 1

        residual = BatchNormalization(momentum = 0.9, gamma_regularizer = l2(1e-4))(residual)
        residual = aprelu(residual)
        residual = Conv2D(out_channels, 3, strides =(downsample_strides, downsample_strides),
                          padding = 'same', kernel_initializer = 'he_normal',
                          kernel_regularizer = l2(1e-4))(residual)

        residual = BatchNormalization(momentum = 0.9, gamma_regularizer = l2(1e-4))(residual)
        residual = aprelu(residual)
        residual = Conv2D(out_channels, 3, padding = 'same', kernel_initializer = 'he_normal',
                          kernel_regularizer = l2(1e-4))(residual)

        # Downsampling
        if downsample_strides> 1:
            identity = AveragePooling2D(pool_size =(1,1), strides =(2,2))(identity)

        # Zero_padding to match channels
        if in_channels! = out_channels:
            zeros_identity = keras.layers.subtract([identity, identity])
            identity = keras.layers.concatenate([identity, zeros_identity])
            in_channels = out_channels

        residual = keras.layers.add([residual, identity])

    return residual


# define and train a model
inputs = Input(shape =(32, 32, 3))
net = Conv2D(16, 3, padding = 'same', kernel_initializer = 'he_normal', kernel_regularizer = l2(1e-4))(inputs)
# net = residual_block(net, 3, 16, downsample = False)
net = residual_block(net, 1, 32, downsample = True)
# net = residual_block(net, 2, 32, downsample = False)
net = residual_block(net, 1, 64, downsample = True)
# net = residual_block(net, 2, 64, downsample = False)
net = BatchNormalization(momentum = 0.9, gamma_regularizer = l2(1e-4))(net)
net = Activation('relu')(net)
net = GlobalAveragePooling2D()(net)
outputs = Dense(10, activation = 'softmax', kernel_initializer = 'he_normal', kernel_regularizer = l2(1e-4))(net)
model = Model(inputs = inputs, outputs = outputs)
sgd = optimizers.SGD(lr = 0.1, decay = 0., momentum = 0.9, nesterov = True)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])

# data augmentation
datagen = ImageDataGenerator(
    # randomly rotate images in the range(deg 0 to 180)
    rotation_range = 30,
    # Range for random zoom
    zoom_range = 0.2,
    # shear angle in counter-clockwise direction in degrees
    shear_range = 30,
    # randomly flip images
    horizontal_flip = True,
    # randomly shift images horizontally
    width_shift_range = 0.125,
    # randomly shift images vertically
    height_shift_range = 0.125)

reduce_lr = LearningRateScheduler(scheduler)
# fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size = 1000),
                    validation_data =(x_test, y_test), epochs = 5000,
                    verbose = 1, callbacks = [reduce_lr], workers = 4)

# get results
K.set_learning_phase(0)
DRSN_train_score = model.evaluate(x_train, y_train, batch_size = 1000, verbose = 0)
print('Train loss:', DRSN_train_score [0])
print('Train accuracy:', DRSN_train_score [1])
DRSN_test_score = model.evaluate(x_test, y_test, batch_size = 1000, verbose = 0)
print('Test loss:', DRSN_test_score [0])
print('Test accuracy:', DRSN_test_score [1])

The experimental results are as follows(for ease of viewing, some equal signs have been deleted):

Epoch 2000/5000
10s 191ms/step-loss:0.5083-acc:0.8658-val_loss:0.5190-val_acc:0.8644
Epoch 2001/5000
10s 194ms/step-loss:0.5102-acc:0.8651-val_loss:0.5203-val_acc:0.8656
Epoch 2002/5000
10s 192ms/step-loss:0.5073-acc:0.8659-val_loss:0.5245-val_acc:0.8612
Epoch 2003/5000
10s 190ms/step-loss:0.5105-acc:0.8646-val_loss:0.5181-val_acc:0.8636
Epoch 2004/5000
9s 186ms/step-loss:0.5080-acc:0.8661-val_loss:0.5217-val_acc:0.8631
Epoch 2005/5000
9s 186ms/step-loss:0.5074-acc:0.8641-val_loss:0.5237-val_acc:0.8614
Epoch 2006/5000
9s 186ms/step-loss:0.5060-acc:0.8651-val_loss:0.5241-val_acc:0.8641
Epoch 2007/5000
10s 190ms/step-loss:0.5096-acc:0.8651-val_loss:0.5185-val_acc:0.8660
Epoch 2008/5000
9s 190ms/step-loss:0.5053-acc:0.8686-val_loss:0.5186-val_acc:0.8624
Epoch 2009/5000
10s 191ms/step-loss:0.5057-acc:0.8670-val_loss:0.5208-val_acc:0.8636
Epoch 2010/5000
10s 190ms/step-loss:0.5102-acc:0.8653-val_loss:0.5214-val_acc:0.8614
Epoch 2011/5000
9s 188ms/step-loss:0.5091-acc:0.8651-val_loss:0.5211-val_acc:0.8629
Epoch 2012/5000
9s 187ms/step-loss:0.5069-acc:0.8672-val_loss:0.5221-val_acc:0.8616
Epoch 2013/5000
9s 184ms/step-loss:0.5098-acc:0.8652-val_loss:0.5241-val_acc:0.8625
Epoch 2014/5000
9s 187ms/step-loss:0.5056-acc:0.8666-val_loss:0.5188-val_acc:0.8623
Epoch 2015/5000
9s 189ms/step-loss:0.5050-acc:0.8672-val_loss:0.5237-val_acc:0.8621
Epoch 2016/5000
10s 193ms/step-loss:0.5057-acc:0.8667-val_loss:0.5207-val_acc:0.8607
Epoch 2017/5000
10s 192ms/step-loss:0.5092-acc:0.8642-val_loss:0.5172-val_acc:0.8637
Epoch 2018/5000
9s 190ms/step-loss:0.5062-acc:0.8671-val_loss:0.5265-val_acc:0.8612
Epoch 2019/5000
9s 188ms/step-loss:0.5112-acc:0.8648-val_loss:0.5256-val_acc:0.8617
Epoch 2020/5000
9s 185ms/step-loss:0.5071-acc:0.8663-val_loss:0.5241-val_acc:0.8622
Epoch 2021/5000
9s 186ms/step-loss:0.5079-acc:0.8660-val_loss:0.5212-val_acc:0.8627
Epoch 2022/5000
9s 185ms/step-loss:0.5058-acc:0.8667-val_loss:0.5227-val_acc:0.8600
Epoch 2023/5000
9s 189ms/step-loss:0.5070-acc:0.8661-val_loss:0.5259-val_acc:0.8608
Epoch 2024/5000
10s 193ms/step-loss:0.5053-acc:0.8663-val_loss:0.5219-val_acc:0.8606
Epoch 2025/5000
10s 191ms/step-loss:0.5106-acc:0.8655-val_loss:0.5205-val_acc:0.8625
Epoch 2026/5000
10s 191ms/step-loss:0.5090-acc:0.8649-val_loss:0.5221-val_acc:0.8610
Epoch 2027/5000
9s 189ms/step-loss:0.5103-acc:0.8648-val_loss:0.5242-val_acc:0.8631
Epoch 2028/5000
9s 189ms/step-loss:0.5051-acc:0.8663-val_loss:0.5253-val_acc:0.8596
Epoch 2029/5000
9s 185ms/step-loss:0.5091-acc:0.8654-val_loss:0.5237-val_acc:0.8612
Epoch 2030/5000
9s 189ms/step-loss:0.5076-acc:0.8654-val_loss:0.5197-val_acc:0.8627
Epoch 2031/5000
9s 189ms/step-loss:0.5058-acc:0.8657-val_loss:0.5226-val_acc:0.8625
Epoch 2032/5000
10s 194ms/step-loss:0.5078-acc:0.8669-val_loss:0.5225-val_acc:0.8639
Epoch 2033/5000
10s 193ms/step-loss:0.5101-acc:0.8643-val_loss:0.5195-val_acc:0.8632
Epoch 2034/5000
10s 191ms/step-loss:0.5088-acc:0.8665-val_loss:0.5237-val_acc:0.8634
Epoch 2035/5000
10s 191ms/step-loss:0.5091-acc:0.8644-val_loss:0.5182-val_acc:0.8652
Epoch 2036/5000
9s 186ms/step-loss:0.5090-acc:0.8658-val_loss:0.5199-val_acc:0.8615
Epoch 2037/5000
9s 187ms/step-loss:0.5056-acc:0.8670-val_loss:0.5256-val_acc:0.8600
Epoch 2038/5000
9s 187ms/step-loss:0.5057-acc:0.8665-val_loss:0.5261-val_acc:0.8600
Epoch 2039/5000
9s 190ms/step-loss:0.5088-acc:0.8647-val_loss:0.5242-val_acc:0.8625
Epoch 2040/5000
10s 192ms/step-loss:0.5068-acc:0.8662-val_loss:0.5223-val_acc:0.8620
Epoch 2041/5000
10s 191ms/step-loss:0.5068-acc:0.8655-val_loss:0.5189-val_acc:0.8594
Epoch 2042/5000
10s 191ms/step-loss:0.5070-acc:0.8661-val_loss:0.5244-val_acc:0.8613
Epoch 2043/5000
9s 187ms/step-loss:0.5008-acc:0.8683-val_loss:0.5238-val_acc:0.8603
Epoch 2044/5000
9s 189ms/step-loss:0.5100-acc:0.8652-val_loss:0.5233-val_acc:0.8609
Epoch 2045/5000
9s 186ms/step-loss:0.5007-acc:0.8692-val_loss:0.5206-val_acc:0.8651
Epoch 2046/5000
9s 188ms/step-loss:0.5043-acc:0.8662-val_loss:0.5246-val_acc:0.8623
Epoch 2047/5000
10s 190ms/step-loss:0.5084-acc:0.8644-val_loss:0.5211-val_acc:0.8620
Epoch 2048/5000
10s 192ms/step-loss:0.5068-acc:0.8658-val_loss:0.5206-val_acc:0.8635
Epoch 2049/5000
10s 194ms/step-loss:0.5033-acc:0.8669-val_loss:0.5230-val_acc:0.8589
Epoch 2050/5000
10s 192ms/step-loss:0.5096-acc:0.8653-val_loss:0.5217-val_acc:0.8610
Epoch 2051/5000
10s 191ms/step-loss:0.5081-acc:0.8651-val_loss:0.5238-val_acc:0.8600
Epoch 2052/5000
9s 186ms/step-loss:0.5061-acc:0.8665-val_loss:0.5258-val_acc:0.8617
Epoch 2053/5000
9s 187ms/step-loss:0.5036-acc:0.8683-val_loss:0.5233-val_acc:0.8606
Epoch 2054/5000
9s 187ms/step-loss:0.5075-acc:0.8652-val_loss:0.5242-val_acc:0.8599
Epoch 2055/5000
9s 189ms/step-loss:0.5105-acc:0.8656-val_loss:0.5201-val_acc:0.8621
Epoch 2056/5000
10s 191ms/step-loss:0.5064-acc:0.8664-val_loss:0.5264-val_acc:0.8586
Epoch 2057/5000
10s 192ms/step-loss:0.5049-acc:0.8655-val_loss:0.5225-val_acc:0.8621
Epoch 2058/5000
10s 190ms/step-loss:0.5083-acc:0.8657-val_loss:0.5218-val_acc:0.8648
Epoch 2059/5000
9s 188ms/step-loss:0.5118-acc:0.8657-val_loss:0.5233-val_acc:0.8627
Epoch 2060/5000
9s 187ms/step-loss:0.5037-acc:0.8668-val_loss:0.5183-val_acc:0.8618
Epoch 2061/5000
9s 185ms/step-loss:0.5092-acc:0.8661-val_loss:0.5207-val_acc:0.8642
Epoch 2062/5000
9s 188ms/step-loss:0.5089-acc:0.8649-val_loss:0.5175-val_acc:0.8625
Epoch 2063/5000
10s 190ms/step-loss:0.5056-acc:0.8667-val_loss:0.5175-val_acc:0.8635
Epoch 2064/5000
10s 192ms/step-loss:0.5078-acc:0.8659-val_loss:0.5217-val_acc:0.8602
Epoch 2065/5000
10s 192ms/step-loss:0.5065-acc:0.8685-val_loss:0.5195-val_acc:0.8627
Epoch 2066/5000
10s 190ms/step-loss:0.5099-acc:0.8660-val_loss:0.5223-val_acc:0.8608
Epoch 2067/5000
10s 190ms/step-loss:0.5048-acc:0.8663-val_loss:0.5188-val_acc:0.8629
Epoch 2068/5000
9s 187ms/step-loss:0.5047-acc:0.8659-val_loss:0.5176-val_acc:0.8611
Epoch 2069/5000
9s 185ms/step-loss:0.5055-acc:0.8680-val_loss:0.5182-val_acc:0.8636
Epoch 2070/5000
9s 186ms/step-loss:0.5076-acc:0.8659-val_loss:0.5235-val_acc:0.8619
Epoch 2071/5000
9s 189ms/step-loss:0.5100-acc:0.8664-val_loss:0.5185-val_acc:0.8630
Epoch 2072/5000
10s 192ms/step-loss:0.5052-acc:0.8663-val_loss:0.5214-val_acc:0.8611
Epoch 2073/5000
10s 192ms/step-loss:0.5052-acc:0.8662-val_loss:0.5250-val_acc:0.8630
Epoch 2074/5000
10s 193ms/step-loss:0.5054-acc:0.8668-val_loss:0.5162-val_acc:0.8650
Epoch 2075/5000
9s 189ms/step-loss:0.5081-acc:0.8647-val_loss:0.5238-val_acc:0.8598
Epoch 2076/5000
9s 189ms/step-loss:0.5075-acc:0.8647-val_loss:0.5237-val_acc:0.8609
Epoch 2077/5000
9s 185ms/step-loss:0.5127-acc:0.8642-val_loss:0.5209-val_acc:0.8662
Epoch 2078/5000
9s 186ms/step-loss:0.5063-acc:0.8655-val_loss:0.5192-val_acc:0.8639
Epoch 2079/5000
9s 189ms/step-loss:0.5113-acc:0.8650-val_loss:0.5193-val_acc:0.8613
Epoch 2080/5000
10s 191ms/step-loss:0.5087-acc:0.8660-val_loss:0.5199-val_acc:0.8623
Epoch 2081/5000
10s 191ms/step-loss:0.5097-acc:0.8648-val_loss:0.5187-val_acc:0.8611
Epoch 2082/5000
10s 191ms/step-loss:0.5059-acc:0.8674-val_loss:0.5229-val_acc:0.8608
Epoch 2083/5000
10s 191ms/step-loss:0.5100-acc:0.8641-val_loss:0.5251-val_acc:0.8599
Epoch 2084/5000
9s 187ms/step-loss:0.5098-acc:0.8645-val_loss:0.5195-val_acc:0.8631
Epoch 2085/5000
9s 187ms/step-loss:0.5023-acc:0.8680-val_loss:0.5185-val_acc:0.8638
Epoch 2086/5000
9s 187ms/step-loss:0.5077-acc:0.8660-val_loss:0.5249-val_acc:0.8628
Epoch 2087/5000
9s 189ms/step-loss:0.5076-acc:0.8645-val_loss:0.5219-val_acc:0.8599
Epoch 2088/5000
9s 189ms/step-loss:0.5074-acc:0.8665-val_loss:0.5228-val_acc:0.8633
Epoch 2089/5000
10s 191ms/step-loss:0.5064-acc:0.8658-val_loss:0.5219-val_acc:0.8626
Epoch 2090/5000
10s 191ms/step-loss:0.5064-acc:0.8673-val_loss:0.5207-val_acc:0.8626
Epoch 2091/5000
9s 188ms/step-loss:0.5064-acc:0.8673-val_loss:0.5229-val_acc:0.8616
Epoch 2092/5000
9s 190ms/step-loss:0.5055-acc:0.8670-val_loss:0.5236-val_acc:0.8613
Epoch 2093/5000
9s 187ms/step-loss:0.5075-acc:0.8657-val_loss:0.5197-val_acc:0.8627
Epoch 2094/5000
9s 186ms/step-loss:0.5082-acc:0.8664-val_loss:0.5228-val_acc:0.8602
Epoch 2095/5000
9s 189ms/step-loss:0.5072-acc:0.8672-val_loss:0.5258-val_acc:0.8618
Epoch 2096/5000
10s 192ms/step-loss:0.5099-acc:0.8641-val_loss:0.5190-val_acc:0.8631
Epoch 2097/5000
10s 192ms/step-loss:0.5069-acc:0.8662-val_loss:0.5212-val_acc:0.8603
Epoch 2098/5000
10s 191ms/step-loss:0.5080-acc:0.8656-val_loss:0.5203-val_acc:0.8611
Epoch 2099/5000
10s 192ms/step-loss:0.5051-acc:0.8663-val_loss:0.5177-val_acc:0.8604
Epoch 2100/5000
9s 186ms/step-loss:0.5059-acc:0.8643-val_loss:0.5206-val_acc:0.8633
Epoch 2101/5000
9s 186ms/step-loss:0.5081-acc:0.8651-val_loss:0.5215-val_acc:0.8622
Epoch 2102/5000
9s 188ms/step-loss:0.5060-acc:0.8655-val_loss:0.5183-val_acc:0.8645
Epoch 2103/5000
10s 190ms/step-loss:0.5062-acc:0.8650-val_loss:0.5229-val_acc:0.8610
Epoch 2104/5000
10s 192ms/step-loss:0.5060-acc:0.8664-val_loss:0.5250-val_acc:0.8595
Epoch 2105/5000
10s 194ms/step-loss:0.5072-acc:0.8653-val_loss:0.5233-val_acc:0.8634
Epoch 2106/5000
10s 193ms/step-loss:0.5073-acc:0.8645-val_loss:0.5187-val_acc:0.8626
Epoch 2107/5000
9s 187ms/step-loss:0.5070-acc:0.8644-val_loss:0.5202-val_acc:0.8636
Epoch 2108/5000
9s 186ms/step-loss:0.5075-acc:0.8648-val_loss:0.5222-val_acc:0.8609
Epoch 2109/5000
9s 186ms/step-loss:0.5049-acc:0.8649-val_loss:0.5200-val_acc:0.8623
Epoch 2110/5000
9s 188ms/step-loss:0.5025-acc:0.8691-val_loss:0.5246-val_acc:0.8613
Epoch 2111/5000
10s 190ms/step-loss:0.5086-acc:0.8646-val_loss:0.5212-val_acc:0.8598
Epoch 2112/5000
10s 191ms/step-loss:0.5071-acc:0.8658-val_loss:0.5213-val_acc:0.8636
Epoch 2113/5000
10s 193ms/step-loss:0.5063-acc:0.8657-val_loss:0.5229-val_acc:0.8599
Epoch 2114/5000
9s 190ms/step-loss:0.5070-acc:0.8652-val_loss:0.5194-val_acc:0.8629
Epoch 2115/5000
10s 191ms/step-loss:0.5089-acc:0.8648-val_loss:0.5208-val_acc:0.8608
Epoch 2116/5000
9s 188ms/step-loss:0.5021-acc:0.8666-val_loss:0.5199-val_acc:0.8595
Epoch 2117/5000
9s 188ms/step-loss:0.5027-acc:0.8682-val_loss:0.5228-val_acc:0.8613
Epoch 2118/5000
9s 186ms/step-loss:0.5099-acc:0.8648-val_loss:0.5247-val_acc:0.8596
Epoch 2119/5000
10s 190ms/step-loss:0.5082-acc:0.8667-val_loss:0.5223-val_acc:0.8598
Epoch 2120/5000
10s 191ms/step-loss:0.5107-acc:0.8639-val_loss:0.5200-val_acc:0.8608
Epoch 2121/5000
10s 191ms/step-loss:0.5075-acc:0.8651-val_loss:0.5216-val_acc:0.8601
Epoch 2122/5000
10s 191ms/step-loss:0.5030-acc:0.8646-val_loss:0.5156-val_acc:0.8647
Epoch 2123/5000
9s 187ms/step-loss:0.5031-acc:0.8668-val_loss:0.5280-val_acc:0.8588
Epoch 2124/5000
9s 187ms/step-loss:0.5066-acc:0.8663-val_loss:0.5184-val_acc:0.8622
Epoch 2125/5000
9s 187ms/step-loss:0.5025-acc:0.8660-val_loss:0.5189-val_acc:0.8624
Epoch 2126/5000
9s 189ms/step-loss:0.5038-acc:0.8676-val_loss:0.5244-val_acc:0.8605
Epoch 2127/5000
10s 191ms/step-loss:0.5106-acc:0.8634-val_loss:0.5201-val_acc:0.8634
Epoch 2128/5000
10s 193ms/step-loss:0.5068-acc:0.8653-val_loss:0.5264-val_acc:0.8617
Epoch 2129/5000
10s 193ms/step-loss:0.5054-acc:0.8635-val_loss:0.5238-val_acc:0.8604
Epoch 2130/5000
9s 189ms/step-loss:0.5082-acc:0.8652-val_loss:0.5238-val_acc:0.8599
Epoch 2131/5000
10s 191ms/step-loss:0.5055-acc:0.8659-val_loss:0.5204-val_acc:0.8627
Epoch 2132/5000
9s 186ms/step-loss:0.5059-acc:0.8656-val_loss:0.5225-val_acc:0.8619
Epoch 2133/5000
9s 189ms/step-loss:0.5044-acc:0.8671-val_loss:0.5210-val_acc:0.8617
Epoch 2134/5000
9s 187ms/step-loss:0.5066-acc:0.8676-val_loss:0.5216-val_acc:0.8609
Epoch 2135/5000
10s 191ms/step-loss:0.5031-acc:0.8667-val_loss:0.5206-val_acc:0.8612
Epoch 2136/5000
10s 190ms/step-loss:0.5058-acc:0.8651-val_loss:0.5200-val_acc:0.8621
Epoch 2137/5000
10s 192ms/step-loss:0.5065-acc:0.8653-val_loss:0.5189-val_acc:0.8634
Epoch 2138/5000
10s 191ms/step-loss:0.5080-acc:0.8654-val_loss:0.5173-val_acc:0.8638
Epoch 2139/5000
9s 188ms/step-loss:0.5008-acc:0.8676-val_loss:0.5229-val_acc:0.8609
Epoch 2140/5000
9s 188ms/step-loss:0.5061-acc:0.8642-val_loss:0.5203-val_acc:0.8622
Epoch 2141/5000
9s 186ms/step-loss:0.5077-acc:0.8655-val_loss:0.5212-val_acc:0.8614
Epoch 2142/5000
9s 187ms/step-loss:0.5080-acc:0.8662-val_loss:0.5197-val_acc:0.8609
Epoch 2143/5000
9s 189ms/step-loss:0.5042-acc:0.8669-val_loss:0.5261-val_acc:0.8592
Epoch 2144/5000
10s 192ms/step-loss:0.5115-acc:0.8645-val_loss:0.5163-val_acc:0.8638
Epoch 2145/5000
10s 193ms/step-loss:0.5032-acc:0.8663-val_loss:0.5202-val_acc:0.8608
Epoch 2146/5000
10s 192ms/step-loss:0.5061-acc:0.8647-val_loss:0.5189-val_acc:0.8614
Epoch 2147/5000
10s 191ms/step-loss:0.5073-acc:0.8665-val_loss:0.5224-val_acc:0.8612
Epoch 2148/5000
9s 186ms/step-loss:0.5074-acc:0.8647-val_loss:0.5217-val_acc:0.8605
Epoch 2149/5000
9s 186ms/step-loss:0.5077-acc:0.8646-val_loss:0.5221-val_acc:0.8611
Epoch 2150/5000
9s 186ms/step-loss:0.5079-acc:0.8641-val_loss:0.5205-val_acc:0.8609
Epoch 2151/5000
10s 191ms/step-loss:0.5036-acc:0.8665-val_loss:0.5173-val_acc:0.8648
Epoch 2152/5000
10s 193ms/step-loss:0.5104-acc:0.8642-val_loss:0.5295-val_acc:0.8571
Epoch 2153/5000
10s 192ms/step-loss:0.5030-acc:0.8656-val_loss:0.5240-val_acc:0.8589
Epoch 2154/5000
10s 192ms/step-loss:0.5046-acc:0.8664-val_loss:0.5217-val_acc:0.8620
Epoch 2155/5000
9s 188ms/step-loss:0.5104-acc:0.8644-val_loss:0.5199-val_acc:0.8598
Epoch 2156/5000
9s 187ms/step-loss:0.5042-acc:0.8657-val_loss:0.5159-val_acc:0.8643
Epoch 2157/5000
9s 185ms/step-loss:0.5019-acc:0.8681-val_loss:0.5242-val_acc:0.8617
Epoch 2158/5000
9s 188ms/step-loss:0.5101-acc:0.8635-val_loss:0.5187-val_acc:0.8628
Epoch 2159/5000
9s 190ms/step-loss:0.5064-acc:0.8645-val_loss:0.5147-val_acc:0.8623
Epoch 2160/5000
10s 191ms/step-loss:0.5034-acc:0.8663-val_loss:0.5175-val_acc:0.8623
Epoch 2161/5000
10s 191ms/step-loss:0.5051-acc:0.8670-val_loss:0.5236-val_acc:0.8593
Epoch 2162/5000
9s 189ms/step-loss:0.5020-acc:0.8666-val_loss:0.5165-val_acc:0.8641
Epoch 2163/5000
9s 189ms/step-loss:0.5059-acc:0.8657-val_loss:0.5250-val_acc:0.8592
Epoch 2164/5000
9s 186ms/step-loss:0.5047-acc:0.8649-val_loss:0.5237-val_acc:0.8613
Epoch 2165/5000
9s 186ms/step-loss:0.5071-acc:0.8654-val_loss:0.5203-val_acc:0.8624
Epoch 2166/5000
9s 185ms/step-loss:0.5001-acc:0.8673-val_loss:0.5168-val_acc:0.8634
Epoch 2167/5000
9s 188ms/step-loss:0.5043-acc:0.8654-val_loss:0.5185-val_acc:0.8608
Epoch 2168/5000
10s 193ms/step-loss:0.5071-acc:0.8649-val_loss:0.5225-val_acc:0.8629
Epoch 2169/5000
10s 192ms/step-loss:0.5060-acc:0.8655-val_loss:0.5231-val_acc:0.8597
Epoch 2170/5000
10s 191ms/step-loss:0.5072-acc:0.8665-val_loss:0.5236-val_acc:0.8621
Epoch 2171/5000
9s 187ms/step-loss:0.5050-acc:0.8676-val_loss:0.5193-val_acc:0.8618
Epoch 2172/5000
9s 187ms/step-loss:0.5016-acc:0.8674-val_loss:0.5173-val_acc:0.8625
Epoch 2173/5000
9s 184ms/step-loss:0.5027-acc:0.8657-val_loss:0.5197-val_acc:0.8621
Epoch 2174/5000
9s 185ms/step-loss:0.5039-acc:0.8668-val_loss:0.5183-val_acc:0.8637
Epoch 2175/5000
9s 186ms/step-loss:0.5056-acc:0.8670-val_loss:0.5257-val_acc:0.8614
Epoch 2176/5000
9s 189ms/step-loss:0.5045-acc:0.8651-val_loss:0.5171-val_acc:0.8641
Epoch 2177/5000
9s 190ms/step-loss:0.5045-acc:0.8661-val_loss:0.5215-val_acc:0.8637
Epoch 2178/5000
9s 190ms/step-loss:0.5073-acc:0.8629-val_loss:0.5189-val_acc:0.8600
Epoch 2179/5000
9s 190ms/step-loss:0.5054-acc:0.8658-val_loss:0.5240-val_acc:0.8620
Epoch 2180/5000
9s 185ms/step-loss:0.5089-acc:0.8648-val_loss:0.5281-val_acc:0.8587
Epoch 2181/5000
9s 185ms/step-loss:0.5047-acc:0.8660-val_loss:0.5253-val_acc:0.8614
Epoch 2182/5000
9s 185ms/step-loss:0.5057-acc:0.8654-val_loss:0.5213-val_acc:0.8594
Epoch 2183/5000
9s 188ms/step-loss:0.5088-acc:0.8657-val_loss:0.5214-val_acc:0.8608
Epoch 2184/5000
9s 189ms/step-loss:0.5038-acc:0.8663-val_loss:0.5253-val_acc:0.8584
Epoch 2185/5000
9s 190ms/step-loss:0.5070-acc:0.8658-val_loss:0.5223-val_acc:0.8584
Epoch 2186/5000
10s 190ms/step-loss:0.5065-acc:0.8650-val_loss:0.5250-val_acc:0.8602
Epoch 2187/5000
9s 185ms/step-loss:0.5032-acc:0.8669-val_loss:0.5148-val_acc:0.8636
Epoch 2188/5000
9s 184ms/step-loss:0.5025-acc:0.8668-val_loss:0.5234-val_acc:0.8595
Epoch 2189/5000
9s 184ms/step-loss:0.5077-acc:0.8638-val_loss:0.5249-val_acc:0.8600
Epoch 2190/5000
9s 185ms/step-loss:0.5090-acc:0.8635-val_loss:0.5256-val_acc:0.8606
Epoch 2191/5000
9s 188ms/step-loss:0.5072-acc:0.8648-val_loss:0.5211-val_acc:0.8605
Epoch 2192/5000
9s 187ms/step-loss:0.5054-acc:0.8649-val_loss:0.5203-val_acc:0.8608
Epoch 2193/5000
9s 189ms/step-loss:0.5024-acc:0.8666-val_loss:0.5207-val_acc:0.8618
Epoch 2194/5000
9s 190ms/step-loss:0.5049-acc:0.8652-val_loss:0.5231-val_acc:0.8608
Epoch 2195/5000
9s 188ms/step-loss:0.5061-acc:0.8658-val_loss:0.5225-val_acc:0.8606
Epoch 2196/5000
9s 185ms/step-loss:0.5104-acc:0.8637-val_loss:0.5259-val_acc:0.8589
Epoch 2197/5000
9s 184ms/step-loss:0.5042-acc:0.8657-val_loss:0.5193-val_acc:0.8633
Epoch 2198/5000
9s 185ms/step-loss:0.5036-acc:0.8664-val_loss:0.5178-val_acc:0.8663
Epoch 2199/5000
9s 187ms/step-loss:0.5066-acc:0.8675-val_loss:0.5172-val_acc:0.8633
Epoch 2200/5000
9s 188ms/step-loss:0.5095-acc:0.8641-val_loss:0.5165-val_acc:0.8624
Epoch 2201/5000
10s 190ms/step-loss:0.5089-acc:0.8634-val_loss:0.5183-val_acc:0.8635
Epoch 2202/5000
9s 189ms/step-loss:0.5069-acc:0.8649-val_loss:0.5179-val_acc:0.8624
Epoch 2203/5000
9s 184ms/step-loss:0.5116-acc:0.8632-val_loss:0.5259-val_acc:0.8591
Epoch 2204/5000
9s 187ms/step-loss:0.5066-acc:0.8659-val_loss:0.5187-val_acc:0.8611
Epoch 2205/5000
9s 181ms/step-loss:0.5052-acc:0.8655-val_loss:0.5187-val_acc:0.8618
Epoch 2206/5000
9s 186ms/step-loss:0.5067-acc:0.8650-val_loss:0.5182-val_acc:0.8619
Epoch 2207/5000
9s 185ms/step-loss:0.5091-acc:0.8644-val_loss:0.5177-val_acc:0.8624
Epoch 2208/5000
10s 192ms/step-loss:0.5038-acc:0.8661-val_loss:0.5194-val_acc:0.8607
Epoch 2209/5000
10s 190ms/step-loss:0.5070-acc:0.8640-val_loss:0.5176-val_acc:0.8623
Epoch 2210/5000
10s 190ms/step-loss:0.5076-acc:0.8637-val_loss:0.5210-val_acc:0.8609
Epoch 2211/5000
9s 189ms/step-loss:0.5018-acc:0.8666-val_loss:0.5191-val_acc:0.8606
Epoch 2212/5000
9s 186ms/step-loss:0.5055-acc:0.8661-val_loss:0.5209-val_acc:0.8609
Epoch 2213/5000
9s 185ms/step-loss:0.5056-acc:0.8640-val_loss:0.5210-val_acc:0.8596
Epoch 2214/5000
9s 184ms/step-loss:0.5034-acc:0.8672-val_loss:0.5182-val_acc:0.8618
Epoch 2215/5000
9s 187ms/step-loss:0.5066-acc:0.8649-val_loss:0.5201-val_acc:0.8603
Epoch 2216/5000
9s 189ms/step-loss:0.5060-acc:0.8652-val_loss:0.5190-val_acc:0.8597
Epoch 2217/5000
10s 190ms/step-loss:0.5062-acc:0.8649-val_loss:0.5234-val_acc:0.8583
Epoch 2218/5000
10s 192ms/step-loss:0.5044-acc:0.8663-val_loss:0.5167-val_acc:0.8638
Epoch 2219/5000
9s 185ms/step-loss:0.5063-acc:0.8647-val_loss:0.5253-val_acc:0.8575
Epoch 2220/5000
9s 186ms/step-loss:0.5096-acc:0.8649-val_loss:0.5245-val_acc:0.8585
Epoch 2221/5000
9s 184ms/step-loss:0.5083-acc:0.8634-val_loss:0.5212-val_acc:0.8588
Epoch 2222/5000
9s 187ms/step-loss:0.5060-acc:0.8656-val_loss:0.5202-val_acc:0.8603
Epoch 2223/5000
9s 188ms/step-loss:0.5032-acc:0.8669-val_loss:0.5179-val_acc:0.8603
Epoch 2224/5000
9s 190ms/step-loss:0.5051-acc:0.8658-val_loss:0.5224-val_acc:0.8608
Epoch 2225/5000
10s 191ms/step-loss:0.5022-acc:0.8663-val_loss:0.5204-val_acc:0.8615
Epoch 2226/5000
9s 189ms/step-loss:0.5044-acc:0.8668-val_loss:0.5194-val_acc:0.8603
Epoch 2227/5000
9s 188ms/step-loss:0.5066-acc:0.8658-val_loss:0.5194-val_acc:0.8612
Epoch 2228/5000
9s 183ms/step-loss:0.5091-acc:0.8642-val_loss:0.5199-val_acc:0.8590
Epoch 2229/5000
9s 185ms/step-loss:0.5023-acc:0.8648-val_loss:0.5202-val_acc:0.8637
Epoch 2230/5000
9s 185ms/step-loss:0.5041-acc:0.8646-val_loss:0.5196-val_acc:0.8622
Epoch 2231/5000
9s 189ms/step-loss:0.5014-acc:0.8668-val_loss:0.5193-val_acc:0.8612
Epoch 2232/5000
9s 188ms/step-loss:0.5031-acc:0.8663-val_loss:0.5227-val_acc:0.8614
Epoch 2233/5000
10s 192ms/step-loss:0.5037-acc:0.8661-val_loss:0.5181-val_acc:0.8610
Epoch 2234/5000
10s 192ms/step-loss:0.5067-acc:0.8666-val_loss:0.5257-val_acc:0.8587
Epoch 2235/5000
9s 186ms/step-loss:0.5068-acc:0.8649-val_loss:0.5163-val_acc:0.8618
Epoch 2236/5000
9s 186ms/step-loss:0.5071-acc:0.8639-val_loss:0.5234-val_acc:0.8599
Epoch 2237/5000
9s 185ms/step-loss:0.5042-acc:0.8648-val_loss:0.5147-val_acc:0.8623
Epoch 2238/5000
9s 185ms/step-loss:0.5053-acc:0.8652-val_loss:0.5217-val_acc:0.8588
Epoch 2239/5000
9s 186ms/step-loss:0.5070-acc:0.8636-val_loss:0.5205-val_acc:0.8624
Epoch 2240/5000
10s 192ms/step-loss:0.5024-acc:0.8658-val_loss:0.5219-val_acc:0.8639
Epoch 2241/5000
10s 191ms/step-loss:0.5069-acc:0.8651-val_loss:0.5211-val_acc:0.8619
Epoch 2242/5000
9s 188ms/step-loss:0.5040-acc:0.8658-val_loss:0.5152-val_acc:0.8622
Epoch 2243/5000
9s 188ms/step-loss:0.5036-acc:0.8651-val_loss:0.5222-val_acc:0.8599
Epoch 2244/5000
9s 185ms/step-loss:0.5068-acc:0.8635-val_loss:0.5177-val_acc:0.8611
Epoch 2245/5000
9s 186ms/step-loss:0.5059-acc:0.8650-val_loss:0.5247-val_acc:0.8600
Epoch 2246/5000
9s 184ms/step-loss:0.5041-acc:0.8661-val_loss:0.5194-val_acc:0.8609
Epoch 2247/5000
9s 188ms/step-loss:0.5058-acc:0.8652-val_loss:0.5250-val_acc:0.8607
Epoch 2248/5000
10s 191ms/step-loss:0.5072-acc:0.8649-val_loss:0.5225-val_acc:0.8623
Epoch 2249/5000
10s 191ms/step-loss:0.5071-acc:0.8650-val_loss:0.5214-val_acc:0.8609
Epoch 2250/5000
10s 192ms/step-loss:0.5040-acc:0.8660-val_loss:0.5235-val_acc:0.8594
Epoch 2251/5000
9s 188ms/step-loss:0.5052-acc:0.8667-val_loss:0.5213-val_acc:0.8615
Epoch 2252/5000
9s 187ms/step-loss:0.5055-acc:0.8664-val_loss:0.5178-val_acc:0.8604
Epoch 2253/5000
9s 184ms/step-loss:0.5074-acc:0.8666-val_loss:0.5228-val_acc:0.8605
Epoch 2254/5000
9s 187ms/step-loss:0.5065-acc:0.8657-val_loss:0.5181-val_acc:0.8640
Epoch 2255/5000
9s 187ms/step-loss:0.5033-acc:0.8668-val_loss:0.5194-val_acc:0.8625
Epoch 2256/5000
9s 190ms/step-loss:0.5065-acc:0.8644-val_loss:0.5209-val_acc:0.8599
Epoch 2257/5000
9s 190ms/step-loss:0.5064-acc:0.8645-val_loss:0.5248-val_acc:0.8588
Epoch 2258/5000
9s 188ms/step-loss:0.5070-acc:0.8645-val_loss:0.5210-val_acc:0.8633
Epoch 2259/5000
9s 189ms/step-loss:0.5039-acc:0.8642-val_loss:0.5231-val_acc:0.8633
Epoch 2260/5000
9s 185ms/step-loss:0.5078-acc:0.8641-val_loss:0.5208-val_acc:0.8609
Epoch 2261/5000
9s 185ms/step-loss:0.5001-acc:0.8675-val_loss:0.5218-val_acc:0.8633
Epoch 2262/5000
9s 186ms/step-loss:0.5063-acc:0.8653-val_loss:0.5183-val_acc:0.8602
Epoch 2263/5000
9s 186ms/step-loss:0.5039-acc:0.8665-val_loss:0.5264-val_acc:0.8582
Epoch 2264/5000
10s 190ms/step-loss:0.5058-acc:0.8660-val_loss:0.5223-val_acc:0.8605
Epoch 2265/5000
9s 190ms/step-loss:0.5027-acc:0.8659-val_loss:0.5194-val_acc:0.8629
Epoch 2266/5000
9s 189ms/step-loss:0.5042-acc:0.8663-val_loss:0.5171-val_acc:0.8610
Epoch 2267/5000
9s 185ms/step-loss:0.5032-acc:0.8662-val_loss:0.5224-val_acc:0.8594
Epoch 2268/5000
9s 186ms/step-loss:0.5089-acc:0.8636-val_loss:0.5156-val_acc:0.8617
Epoch 2269/5000
9s 183ms/step-loss:0.5065-acc:0.8629-val_loss:0.5170-val_acc:0.8615
Epoch 2270/5000
9s 185ms/step-loss:0.5052-acc:0.8653-val_loss:0.5179-val_acc:0.8612
Epoch 2271/5000
9s 187ms/step-loss:0.5058-acc:0.8646-val_loss:0.5214-val_acc:0.8610
Epoch 2272/5000
9s 188ms/step-loss:0.5054-acc:0.8655-val_loss:0.5210-val_acc:0.8616
Epoch 2273/5000
10s 196ms/step-loss:0.5026-acc:0.8650-val_loss:0.5182-val_acc:0.8612
Epoch 2274/5000
10s 194ms/step-loss:0.5045-acc:0.8653-val_loss:0.5181-val_acc:0.8621
Epoch 2275/5000
9s 187ms/step-loss:0.5019-acc:0.8674-val_loss:0.5220-val_acc:0.8617
Epoch 2276/5000
9s 185ms/step-loss:0.5060-acc:0.8633-val_loss:0.5245-val_acc:0.8592
Epoch 2277/5000
9s 182ms/step-loss:0.5051-acc:0.8664-val_loss:0.5211-val_acc:0.8593
Epoch 2278/5000
9s 186ms/step-loss:0.5048-acc:0.8640-val_loss:0.5253-val_acc:0.8605
Epoch 2279/5000
9s 189ms/step-loss:0.5082-acc:0.8631-val_loss:0.5198-val_acc:0.8642
Epoch 2280/5000
10s 192ms/step-loss:0.5044-acc:0.8660-val_loss:0.5190-val_acc:0.8618
Epoch 2281/5000
10s 192ms/step-loss:0.5053-acc:0.8660-val_loss:0.5215-val_acc:0.8586
Epoch 2282/5000
10s 191ms/step-loss:0.5032-acc:0.8647-val_loss:0.5280-val_acc:0.8604
Epoch 2283/5000
9s 186ms/step-loss:0.5086-acc:0.8635-val_loss:0.5234-val_acc:0.8564
Epoch 2284/5000
9s 187ms/step-loss:0.5015-acc:0.8669-val_loss:0.5199-val_acc:0.8601
Epoch 2285/5000
9s 183ms/step-loss:0.5049-acc:0.8642-val_loss:0.5206-val_acc:0.8607
Epoch 2286/5000
9s 187ms/step-loss:0.5046-acc:0.8644-val_loss:0.5229-val_acc:0.8590
Epoch 2287/5000
9s 186ms/step-loss:0.5042-acc:0.8663-val_loss:0.5194-val_acc:0.8618
Epoch 2288/5000
9s 188ms/step-loss:0.5001-acc:0.8680-val_loss:0.5266-val_acc:0.8598
Epoch 2289/5000
10s 191ms/step-loss:0.5044-acc:0.8660-val_loss:0.5180-val_acc:0.8626
Epoch 2290/5000
9s 189ms/step-loss:0.5053-acc:0.8638-val_loss:0.5227-val_acc:0.8600
Epoch 2291/5000
9s 189ms/step-loss:0.5054-acc:0.8645-val_loss:0.5171-val_acc:0.8617
Epoch 2292/5000
9s 182ms/step-loss:0.5035-acc:0.8667-val_loss:0.5225-val_acc:0.8587
Epoch 2293/5000
9s 186ms/step-loss:0.5067-acc:0.8657-val_loss:0.5227-val_acc:0.8621
Epoch 2294/5000
9s 185ms/step-loss:0.4980-acc:0.8681-val_loss:0.5199-val_acc:0.8593
Epoch 2295/5000
9s 188ms/step-loss:0.5036-acc:0.8653-val_loss:0.5232-val_acc:0.8627
Epoch 2296/5000
10s 190ms/step-loss:0.5046-acc:0.8648-val_loss:0.5222-val_acc:0.8598
Epoch 2297/5000
10s 191ms/step-loss:0.5052-acc:0.8657-val_loss:0.5163-val_acc:0.8621
Epoch 2298/5000
9s 189ms/step-loss:0.5079-acc:0.8636-val_loss:0.5138-val_acc:0.8651
Epoch 2299/5000
9s 189ms/step-loss:0.5021-acc:0.8646-val_loss:0.5184-val_acc:0.8637
Epoch 2300/5000
9s 186ms/step-loss:0.5071-acc:0.8634-val_loss:0.5206-val_acc:0.8629
Epoch 2301/5000
9s 182ms/step-loss:0.5054-acc:0.8657-val_loss:0.5240-val_acc:0.8620
Epoch 2302/5000
9s 185ms/step-loss:0.5034-acc:0.8658-val_loss:0.5235-val_acc:0.8608
Epoch 2303/5000
9s 187ms/step-loss:0.5092-acc:0.8627-val_loss:0.5228-val_acc:0.8604
Epoch 2304/5000
10s 191ms/step-loss:0.5026-acc:0.8664-val_loss:0.5142-val_acc:0.8619
Epoch 2305/5000
9s 190ms/step-loss:0.5031-acc:0.8655-val_loss:0.5167-val_acc:0.8627
Epoch 2306/5000
9s 188ms/step-loss:0.5065-acc:0.8655-val_loss:0.5212-val_acc:0.8631
Epoch 2307/5000
9s 190ms/step-loss:0.5036-acc:0.8675-val_loss:0.5190-val_acc:0.8609
Epoch 2308/5000
9s 184ms/step-loss:0.5017-acc:0.8668-val_loss:0.5247-val_acc:0.8598
Epoch 2309/5000
9s 186ms/step-loss:0.5043-acc:0.8639-val_loss:0.5142-val_acc:0.8641
Epoch 2310/5000
9s 185ms/step-loss:0.5070-acc:0.8637-val_loss:0.5193-val_acc:0.8622
Epoch 2311/5000
9s 188ms/step-loss:0.5043-acc:0.8650-val_loss:0.5229-val_acc:0.8637
Epoch 2312/5000
9s 189ms/step-loss:0.5068-acc:0.8645-val_loss:0.5190-val_acc:0.8607
Epoch 2313/5000
10s 190ms/step-loss:0.5041-acc:0.8653-val_loss:0.5195-val_acc:0.8620
Epoch 2314/5000
10s 191ms/step-loss:0.5037-acc:0.8640-val_loss:0.5208-val_acc:0.8613
Epoch 2315/5000
9s 186ms/step-loss:0.5041-acc:0.8652-val_loss:0.5194-val_acc:0.8625
Epoch 2316/5000
9s 186ms/step-loss:0.5075-acc:0.8632-val_loss:0.5149-val_acc:0.8620
Epoch 2317/5000
9s 185ms/step - loss:0.5038 - acc:0.8657 - val_loss:0.5223 - val_acc:0.8617
Epoch 2318/5000
9s 186ms/step - loss:0.5053 - acc:0.8643 - val_loss:0.5190 - val_acc:0.8634
Epoch 2319/5000
9s 187ms/step - loss:0.5028 - acc:0.8662 - val_loss:0.5242 - val_acc:0.8596
Epoch 2320/5000
9s 189ms/step - loss:0.5017 - acc:0.8669 - val_loss:0.5195 - val_acc:0.8606
Epoch 2321/5000
10s 191ms/step - loss:0.5055 - acc:0.8646 - val_loss:0.5220 - val_acc:0.8609
Epoch 2322/5000
10s 190ms/step - loss:0.5034 - acc:0.8655 - val_loss:0.5179 - val_acc:0.8606
Epoch 2323/5000
9s 189ms/step - loss:0.5033 - acc:0.8668 - val_loss:0.5193 - val_acc:0.8605
Epoch 2324/5000
9s 183ms/step - loss:0.5032 - acc:0.8663 - val_loss:0.5221 - val_acc:0.8610
Epoch 2325/5000
9s 183ms/step - loss:0.5066 - acc:0.8649 - val_loss:0.5202 - val_acc:0.8610
Epoch 2326/5000
9s 184ms/step - loss:0.5042 - acc:0.8660 - val_loss:0.5222 - val_acc:0.8597
Epoch 2327/5000
9s 187ms/step - loss:0.5091 - acc:0.8655 - val_loss:0.5203 - val_acc:0.8598
Epoch 2328/5000
9s 188ms/step - loss:0.5090 - acc:0.8636 - val_loss:0.5196 - val_acc:0.8603
Epoch 2329/5000
10s 192ms/step - loss:0.5030 - acc:0.8658 - val_loss:0.5255 - val_acc:0.8602
Epoch 2330/5000
9s 188ms/step - loss:0.5063 - acc:0.8650 - val_loss:0.5205 - val_acc:0.8606
Epoch 2331/5000
9s 188ms/step - loss:0.5038 - acc:0.8659 - val_loss:0.5256 - val_acc:0.8584
Epoch 2332/5000
9s 186ms/step - loss:0.5077 - acc:0.8642 - val_loss:0.5186 - val_acc:0.8618
Epoch 2333/5000
9s 183ms/step - loss:0.5051 - acc:0.8643 - val_loss:0.5204 - val_acc:0.8586
Epoch 2334/5000
9s 187ms/step - loss:0.5008 - acc:0.8670 - val_loss:0.5186 - val_acc:0.8607
Epoch 2335/5000
9s 185ms/step - loss:0.5044 - acc:0.8664 - val_loss:0.5204 - val_acc:0.8623
Epoch 2336/5000
10s 191ms/step - loss:0.5059 - acc:0.8646 - val_loss:0.5191 - val_acc:0.8616
Epoch 2337/5000
10s 191ms/step - loss:0.5013 - acc:0.8675 - val_loss:0.5189 - val_acc:0.8629
Epoch 2338/5000
9s 189ms/step - loss:0.5069 - acc:0.8645 - val_loss:0.5244 - val_acc:0.8630
Epoch 2339/5000
10s 190ms/step - loss:0.5085 - acc:0.8638 - val_loss:0.5189 - val_acc:0.8609
Epoch 2340/5000
9s 184ms/step - loss:0.5009 - acc:0.8653 - val_loss:0.5210 - val_acc:0.8609
Epoch 2341/5000
9s 187ms/step - loss:0.5008 - acc:0.8657 - val_loss:0.5144 - val_acc:0.8628
Epoch 2342/5000
9s 184ms/step - loss:0.5017 - acc:0.8661 - val_loss:0.5251 - val_acc:0.8606
Epoch 2343/5000
9s 186ms/step - loss:0.5069 - acc:0.8646 - val_loss:0.5204 - val_acc:0.8608
Epoch 2344/5000
9s 189ms/step - loss:0.4997 - acc:0.8675 - val_loss:0.5203 - val_acc:0.8651
Epoch 2345/5000
9s 189ms/step - loss:0.5015 - acc:0.8672 - val_loss:0.5220 - val_acc:0.8610
Epoch 2346/5000
10s 192ms/step - loss:0.5005 - acc:0.8656 - val_loss:0.5200 - val_acc:0.8620
Epoch 2347/5000
9s 186ms/step - loss:0.5070 - acc:0.8636 - val_loss:0.5230 - val_acc:0.8607
Epoch 2348/5000
9s 189ms/step - loss:0.5018 - acc:0.8657 - val_loss:0.5175 - val_acc:0.8611
Epoch 2349/5000
9s 182ms/step - loss:0.5042 - acc:0.8656 - val_loss:0.5244 - val_acc:0.8603
Epoch 2350/5000
9s 187ms/step - loss:0.5062 - acc:0.8656 - val_loss:0.5182 - val_acc:0.8614
Epoch 2351/5000
9s 184ms/step - loss:0.5053 - acc:0.8642 - val_loss:0.5183 - val_acc:0.8611
Epoch 2352/5000
10s 192ms/step - loss:0.5094 - acc:0.8640 - val_loss:0.5202 - val_acc:0.8589
Epoch 2353/5000
10s 191ms/step - loss:0.5077 - acc:0.8648 - val_loss:0.5176 - val_acc:0.8630
Epoch 2354/5000
9s 189ms/step - loss:0.5082 - acc:0.8635 - val_loss:0.5217 - val_acc:0.8620
Epoch 2355/5000
9s 189ms/step - loss:0.5024 - acc:0.8656 - val_loss:0.5174 - val_acc:0.8627
Epoch 2356/5000
9s 185ms/step - loss:0.5014 - acc:0.8653 - val_loss:0.5185 - val_acc:0.8615
Epoch 2357/5000
9s 183ms/step - loss:0.5070 - acc:0.8650 - val_loss:0.5207 - val_acc:0.8630
Epoch 2358/5000
9s 182ms/step - loss:0.5031 - acc:0.8648 - val_loss:0.5190 - val_acc:0.8649
Epoch 2359/5000
9s 186ms/step - loss:0.5080 - acc:0.8630 - val_loss:0.5172 - val_acc:0.8636
Epoch 2360/5000
9s 189ms/step - loss:0.5041 - acc:0.8661 - val_loss:0.5220 - val_acc:0.8597
Epoch 2361/5000
10s 191ms/step - loss:0.5029 - acc:0.8654 - val_loss:0.5226 - val_acc:0.8602
Epoch 2362/5000
9s 188ms/step - loss:0.4963 - acc:0.8706 - val_loss:0.5257 - val_acc:0.8605
Epoch 2363/5000
9s 189ms/step - loss:0.4999 - acc:0.8673 - val_loss:0.5233 - val_acc:0.8563
Epoch 2364/5000
9s 184ms/step - loss:0.5031 - acc:0.8658 - val_loss:0.5160 - val_acc:0.8612
Epoch 2365/5000
9s 184ms/step - loss:0.5002 - acc:0.8673 - val_loss:0.5206 - val_acc:0.8605
Epoch 2366/5000
9s 185ms/step - loss:0.5039 - acc:0.8649 - val_loss:0.5190 - val_acc:0.8608
Epoch 2367/5000
9s 185ms/step - loss:0.5082 - acc:0.8639 - val_loss:0.5184 - val_acc:0.8623
Epoch 2368/5000
9s 189ms/step - loss:0.5051 - acc:0.8658 - val_loss:0.5175 - val_acc:0.8611
Epoch 2369/5000
10s 191ms/step - loss:0.5079 - acc:0.8635 - val_loss:0.5172 - val_acc:0.8612
Epoch 2370/5000
9s 189ms/step - loss:0.5056 - acc:0.8647 - val_loss:0.5243 - val_acc:0.8609
Epoch 2371/5000
9s 186ms/step - loss:0.5034 - acc:0.8654 - val_loss:0.5149 - val_acc:0.8632
Epoch 2372/5000
9s 185ms/step - loss:0.5055 - acc:0.8625 - val_loss:0.5204 - val_acc:0.8613
Epoch 2373/5000
9s 183ms/step - loss:0.5015 - acc:0.8663 - val_loss:0.5233 - val_acc:0.8620
Epoch 2374/5000
9s 185ms/step - loss:0.5034 - acc:0.8649 - val_loss:0.5199 - val_acc:0.8624
Epoch 2375/5000
9s 186ms/step - loss:0.5043 - acc:0.8667 - val_loss:0.5198 - val_acc:0.8643
Epoch 2376/5000
10s 190ms/step - loss:0.5060 - acc:0.8655 - val_loss:0.5159 - val_acc:0.8641
Epoch 2377/5000
10s 190ms/step - loss:0.5025 - acc:0.8661 - val_loss:0.5171 - val_acc:0.8600
Epoch 2378/5000
9s 190ms/step - loss:0.5058 - acc:0.8643 - val_loss:0.5229 - val_acc:0.8596
Epoch 2379/5000
9s 186ms/step - loss:0.5067 - acc:0.8649 - val_loss:0.5204 - val_acc:0.8585
Epoch 2380/5000
9s 185ms/step - loss:0.5039 - acc:0.8648 - val_loss:0.5211 - val_acc:0.8616
Epoch 2381/5000
9s 186ms/step - loss:0.5031 - acc:0.8650 - val_loss:0.5184 - val_acc:0.8609
Epoch 2382/5000
9s 185ms/step - loss:0.5069 - acc:0.8644 - val_loss:0.5213 - val_acc:0.8594
Epoch 2383/5000
9s 189ms/step - loss:0.5071 - acc:0.8639 - val_loss:0.5227 - val_acc:0.8589
Epoch 2384/5000
9s 189ms/step - loss:0.5073 - acc:0.8663 - val_loss:0.5190 - val_acc:0.8623
Epoch 2385/5000
10s 193ms/step - loss:0.5056 - acc:0.8662 - val_loss:0.5171 - val_acc:0.8633
Epoch 2386/5000
9s 188ms/step - loss:0.5086 - acc:0.8638 - val_loss:0.5144 - val_acc:0.8637
Epoch 2387/5000
9s 188ms/step - loss:0.4997 - acc:0.8667 - val_loss:0.5166 - val_acc:0.8624
Epoch 2388/5000
9s 184ms/step - loss:0.4999 - acc:0.8663 - val_loss:0.5198 - val_acc:0.8641
Epoch 2389/5000
9s 185ms/step - loss:0.5057 - acc:0.8638 - val_loss:0.5206 - val_acc:0.8614
Epoch 2390/5000
9s 185ms/step - loss:0.5030 - acc:0.8666 - val_loss:0.5173 - val_acc:0.8608
Epoch 2391/5000
9s 188ms/step - loss:0.5021 - acc:0.8650 - val_loss:0.5237 - val_acc:0.8592
Epoch 2392/5000
9s 190ms/step - loss:0.5059 - acc:0.8660 - val_loss:0.5201 - val_acc:0.8632
Epoch 2393/5000
10s 192ms/step - loss:0.5029 - acc:0.8650 - val_loss:0.5208 - val_acc:0.8596
Epoch 2394/5000
10s 191ms/step - loss:0.5014 - acc:0.8664 - val_loss:0.5260 - val_acc:0.8575
Epoch 2395/5000
9s 185ms/step - loss:0.5004 - acc:0.8677 - val_loss:0.5158 - val_acc:0.8635
Epoch 2396/5000
9s 187ms/step - loss:0.5001 - acc:0.8665 - val_loss:0.5234 - val_acc:0.8596
Epoch 2397/5000
9s 182ms/step - loss:0.5038 - acc:0.8644 - val_loss:0.5157 - val_acc:0.8631
Epoch 2398/5000
9s 187ms/step - loss:0.5047 - acc:0.8655 - val_loss:0.5172 - val_acc:0.8619
Epoch 2399/5000
9s 190ms/step - loss:0.5061 - acc:0.8647 - val_loss:0.5225 - val_acc:0.8602
Epoch 2400/5000
10s 191ms/step - loss:0.5054 - acc:0.8632 - val_loss:0.5205 - val_acc:0.8616
Epoch 2401/5000
10s 193ms/step - loss:0.5061 - acc:0.8649 - val_loss:0.5271 - val_acc:0.8580

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, Date of Publication :13 February 2020

https://ieeexplore.ieee.org/d...