标签：import cto max nta ima als 宽度 linear set
本文在调参记录21的基础上，增加卷积核的个数，也就是增加深度神经网络的宽度，继续尝试深度残差网络+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激活函数的原理如下：
Keras程序：
#!/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
@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()
# Noised 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 150 epoches
def 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 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 = 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 model
inputs = Input(shape=(32, 32, 3))
net = Conv2D(32, 3, padding=‘same‘, kernel_initializer=‘he_normal‘, kernel_regularizer=l2(1e-4))(inputs)
net = residual_block(net, 20, 32, downsample=False)
net = residual_block(net, 1, 64, downsample=True)
net = residual_block(net, 19, 64, downsample=False)
net = residual_block(net, 1, 128, downsample=True)
net = residual_block(net, 19,128, 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=100),
validation_data=(x_test, y_test), epochs=500,
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：
Using TensorFlow backend. x_train shape: (50000, 32, 32, 3) 50000 train samples 10000 test samples Epoch 1/500 177s 354ms/step - loss: 5.4649 - acc: 0.3787 - val_loss: 4.7056 - val_acc: 0.5364 Epoch 2/500 134s 268ms/step - loss: 4.3931 - acc: 0.5448 - val_loss: 3.8649 - val_acc: 0.6462 Epoch 3/500 134s 267ms/step - loss: 3.6521 - acc: 0.6280 - val_loss: 3.2183 - val_acc: 0.7019 Epoch 4/500 134s 267ms/step - loss: 3.0934 - acc: 0.6764 - val_loss: 2.7007 - val_acc: 0.7536 Epoch 5/500 134s 267ms/step - loss: 2.6582 - acc: 0.7114 - val_loss: 2.3136 - val_acc: 0.7880 Epoch 6/500 134s 267ms/step - loss: 2.3051 - acc: 0.7361 - val_loss: 2.0292 - val_acc: 0.7951 Epoch 7/500 134s 267ms/step - loss: 2.0207 - acc: 0.7557 - val_loss: 1.7687 - val_acc: 0.8134 Epoch 8/500 134s 267ms/step - loss: 1.7859 - acc: 0.7732 - val_loss: 1.5536 - val_acc: 0.8292 Epoch 9/500 134s 267ms/step - loss: 1.6043 - acc: 0.7845 - val_loss: 1.3916 - val_acc: 0.8385 Epoch 10/500 134s 267ms/step - loss: 1.4540 - acc: 0.7942 - val_loss: 1.2741 - val_acc: 0.8426 Epoch 11/500 134s 267ms/step - loss: 1.3272 - acc: 0.8037 - val_loss: 1.1760 - val_acc: 0.8461 Epoch 12/500 133s 267ms/step - loss: 1.2285 - acc: 0.8110 - val_loss: 1.0927 - val_acc: 0.8514 Epoch 13/500 133s 267ms/step - loss: 1.1442 - acc: 0.8174 - val_loss: 0.9914 - val_acc: 0.8639 Epoch 14/500 133s 267ms/step - loss: 1.0733 - acc: 0.8252 - val_loss: 0.9532 - val_acc: 0.8634 Epoch 15/500 133s 267ms/step - loss: 1.0186 - acc: 0.8297 - val_loss: 0.9055 - val_acc: 0.8638 Epoch 16/500 133s 267ms/step - loss: 0.9759 - acc: 0.8329 - val_loss: 0.8713 - val_acc: 0.8664 Epoch 17/500 133s 267ms/step - loss: 0.9408 - acc: 0.8357 - val_loss: 0.8173 - val_acc: 0.8798 Epoch 18/500 133s 267ms/step - loss: 0.9071 - acc: 0.8412 - val_loss: 0.8102 - val_acc: 0.8730 Epoch 19/500 133s 267ms/step - loss: 0.8804 - acc: 0.8437 - val_loss: 0.7703 - val_acc: 0.8819 Epoch 20/500 133s 267ms/step - loss: 0.8613 - acc: 0.8478 - val_loss: 0.7809 - val_acc: 0.8739 Epoch 21/500 133s 267ms/step - loss: 0.8408 - acc: 0.8498 - val_loss: 0.7378 - val_acc: 0.8870 Epoch 22/500 133s 267ms/step - loss: 0.8260 - acc: 0.8515 - val_loss: 0.7501 - val_acc: 0.8812 Epoch 23/500 133s 267ms/step - loss: 0.8093 - acc: 0.8561 - val_loss: 0.7612 - val_acc: 0.8719 Epoch 24/500 134s 267ms/step - loss: 0.8009 - acc: 0.8579 - val_loss: 0.7348 - val_acc: 0.8814 Epoch 25/500 133s 267ms/step - loss: 0.7908 - acc: 0.8585 - val_loss: 0.7542 - val_acc: 0.8741 Epoch 26/500 133s 266ms/step - loss: 0.7787 - acc: 0.8636 - val_loss: 0.7407 - val_acc: 0.8771 Epoch 27/500 133s 266ms/step - loss: 0.7765 - acc: 0.8622 - val_loss: 0.6996 - val_acc: 0.8934 Epoch 28/500 133s 266ms/step - loss: 0.7662 - acc: 0.8658 - val_loss: 0.7118 - val_acc: 0.8855 Epoch 29/500 133s 266ms/step - loss: 0.7623 - acc: 0.8661 - val_loss: 0.7267 - val_acc: 0.8808 Epoch 30/500 133s 266ms/step - loss: 0.7654 - acc: 0.8652 - val_loss: 0.7112 - val_acc: 0.8846 Epoch 31/500 133s 266ms/step - loss: 0.7575 - acc: 0.8675 - val_loss: 0.6885 - val_acc: 0.8944 Epoch 32/500 133s 267ms/step - loss: 0.7513 - acc: 0.8691 - val_loss: 0.6925 - val_acc: 0.8930 Epoch 33/500 133s 267ms/step - loss: 0.7455 - acc: 0.8724 - val_loss: 0.6935 - val_acc: 0.8910 Epoch 34/500 133s 267ms/step - loss: 0.7411 - acc: 0.8722 - val_loss: 0.6856 - val_acc: 0.8938 Epoch 35/500 133s 267ms/step - loss: 0.7418 - acc: 0.8729 - val_loss: 0.7001 - val_acc: 0.8881 Epoch 36/500 133s 267ms/step - loss: 0.7354 - acc: 0.8739 - val_loss: 0.6869 - val_acc: 0.8895 Epoch 37/500 133s 266ms/step - loss: 0.7337 - acc: 0.8767 - val_loss: 0.6840 - val_acc: 0.8962 Epoch 38/500 133s 266ms/step - loss: 0.7360 - acc: 0.8765 - val_loss: 0.6967 - val_acc: 0.8914 Epoch 39/500 133s 267ms/step - loss: 0.7316 - acc: 0.8780 - val_loss: 0.6687 - val_acc: 0.8993 Epoch 40/500 133s 267ms/step - loss: 0.7253 - acc: 0.8811 - val_loss: 0.6886 - val_acc: 0.8949 Epoch 41/500 133s 267ms/step - loss: 0.7240 - acc: 0.8809 - val_loss: 0.7086 - val_acc: 0.8887 Epoch 42/500 133s 267ms/step - loss: 0.7247 - acc: 0.8802 - val_loss: 0.6879 - val_acc: 0.8944 Epoch 43/500 133s 267ms/step - loss: 0.7266 - acc: 0.8794 - val_loss: 0.6762 - val_acc: 0.9009 Epoch 44/500 133s 266ms/step - loss: 0.7206 - acc: 0.8820 - val_loss: 0.7067 - val_acc: 0.8874 Epoch 45/500 133s 266ms/step - loss: 0.7233 - acc: 0.8823 - val_loss: 0.6840 - val_acc: 0.8944 Epoch 46/500 133s 266ms/step - loss: 0.7163 - acc: 0.8839 - val_loss: 0.6924 - val_acc: 0.8926 Epoch 47/500 133s 266ms/step - loss: 0.7189 - acc: 0.8842 - val_loss: 0.6761 - val_acc: 0.8982 Epoch 48/500 133s 266ms/step - loss: 0.7137 - acc: 0.8841 - val_loss: 0.7079 - val_acc: 0.8931 Epoch 49/500 133s 266ms/step - loss: 0.7139 - acc: 0.8851 - val_loss: 0.6882 - val_acc: 0.8954 Epoch 50/500 133s 266ms/step - loss: 0.7129 - acc: 0.8859 - val_loss: 0.6681 - val_acc: 0.9011 Epoch 51/500 133s 266ms/step - loss: 0.7157 - acc: 0.8838 - val_loss: 0.6726 - val_acc: 0.9000 Epoch 52/500 133s 266ms/step - loss: 0.7108 - acc: 0.8858 - val_loss: 0.6720 - val_acc: 0.9002 Epoch 53/500 133s 266ms/step - loss: 0.7137 - acc: 0.8866 - val_loss: 0.6790 - val_acc: 0.8982 Epoch 54/500 133s 266ms/step - loss: 0.7151 - acc: 0.8859 - val_loss: 0.6823 - val_acc: 0.8998 Epoch 55/500 133s 266ms/step - loss: 0.7139 - acc: 0.8870 - val_loss: 0.7120 - val_acc: 0.8894 Epoch 56/500 133s 266ms/step - loss: 0.7093 - acc: 0.8884 - val_loss: 0.6790 - val_acc: 0.9013 Epoch 57/500 133s 266ms/step - loss: 0.7113 - acc: 0.8880 - val_loss: 0.6772 - val_acc: 0.9038 Epoch 58/500 133s 266ms/step - loss: 0.7042 - acc: 0.8908 - val_loss: 0.6758 - val_acc: 0.9042 Epoch 59/500 133s 266ms/step - loss: 0.7107 - acc: 0.8881 - val_loss: 0.6771 - val_acc: 0.9001 Epoch 60/500 133s 266ms/step - loss: 0.7082 - acc: 0.8878 - val_loss: 0.6848 - val_acc: 0.8998 Epoch 61/500 133s 266ms/step - loss: 0.7039 - acc: 0.8920 - val_loss: 0.6842 - val_acc: 0.9002 Epoch 62/500 133s 266ms/step - loss: 0.7049 - acc: 0.8908 - val_loss: 0.6577 - val_acc: 0.9076 Epoch 63/500 133s 265ms/step - loss: 0.7005 - acc: 0.8914 - val_loss: 0.6904 - val_acc: 0.8962 Epoch 64/500 133s 266ms/step - loss: 0.7042 - acc: 0.8916 - val_loss: 0.7025 - val_acc: 0.8910 Epoch 65/500 133s 266ms/step - loss: 0.7037 - acc: 0.8904 - val_loss: 0.6811 - val_acc: 0.9038 Epoch 66/500 133s 266ms/step - loss: 0.7085 - acc: 0.8908 - val_loss: 0.7166 - val_acc: 0.8915 Epoch 67/500 133s 265ms/step - loss: 0.6981 - acc: 0.8939 - val_loss: 0.6934 - val_acc: 0.8978 Epoch 68/500 133s 266ms/step - loss: 0.7087 - acc: 0.8917 - val_loss: 0.6868 - val_acc: 0.9026 Epoch 69/500 133s 266ms/step - loss: 0.6994 - acc: 0.8932 - val_loss: 0.6792 - val_acc: 0.9016 Epoch 70/500 133s 266ms/step - loss: 0.7040 - acc: 0.8931 - val_loss: 0.6695 - val_acc: 0.9042 Epoch 71/500 133s 266ms/step - loss: 0.7022 - acc: 0.8933 - val_loss: 0.6771 - val_acc: 0.9039 Epoch 72/500 133s 266ms/step - loss: 0.6975 - acc: 0.8954 - val_loss: 0.6789 - val_acc: 0.9043 Epoch 73/500 133s 266ms/step - loss: 0.6935 - acc: 0.8953 - val_loss: 0.6664 - val_acc: 0.9070 Epoch 74/500 133s 266ms/step - loss: 0.6956 - acc: 0.8943 - val_loss: 0.6633 - val_acc: 0.9124 Epoch 75/500 133s 266ms/step - loss: 0.6966 - acc: 0.8934 - val_loss: 0.6719 - val_acc: 0.9057 Epoch 76/500 133s 266ms/step - loss: 0.7008 - acc: 0.8942 - val_loss: 0.6872 - val_acc: 0.8993 Epoch 77/500 133s 266ms/step - loss: 0.6923 - acc: 0.8950 - val_loss: 0.6961 - val_acc: 0.9007 Epoch 78/500 133s 266ms/step - loss: 0.6966 - acc: 0.8951 - val_loss: 0.6771 - val_acc: 0.9010 Epoch 79/500 133s 266ms/step - loss: 0.6988 - acc: 0.8952 - val_loss: 0.6752 - val_acc: 0.9046 Epoch 80/500 133s 266ms/step - loss: 0.6946 - acc: 0.8970 - val_loss: 0.6716 - val_acc: 0.9073 Epoch 81/500 133s 266ms/step - loss: 0.6979 - acc: 0.8950 - val_loss: 0.6785 - val_acc: 0.9049 Epoch 82/500 133s 266ms/step - loss: 0.6956 - acc: 0.8968 - val_loss: 0.6916 - val_acc: 0.8987 Epoch 83/500 133s 266ms/step - loss: 0.6946 - acc: 0.8964 - val_loss: 0.6816 - val_acc: 0.9054 Epoch 84/500 133s 266ms/step - loss: 0.6921 - acc: 0.8972 - val_loss: 0.6834 - val_acc: 0.9044 Epoch 85/500 133s 265ms/step - loss: 0.6909 - acc: 0.8983 - val_loss: 0.6983 - val_acc: 0.8966 Epoch 86/500 133s 266ms/step - loss: 0.6991 - acc: 0.8959 - val_loss: 0.6677 - val_acc: 0.9096 Epoch 87/500 133s 266ms/step - loss: 0.6932 - acc: 0.8996 - val_loss: 0.6768 - val_acc: 0.9078 Epoch 88/500 133s 266ms/step - loss: 0.6961 - acc: 0.8974 - val_loss: 0.6895 - val_acc: 0.9016 Epoch 89/500 133s 266ms/step - loss: 0.6919 - acc: 0.9001 - val_loss: 0.6846 - val_acc: 0.9060 Epoch 90/500 133s 267ms/step - loss: 0.6937 - acc: 0.8986 - val_loss: 0.6677 - val_acc: 0.9106 Epoch 91/500 134s 268ms/step - loss: 0.6880 - acc: 0.9007 - val_loss: 0.6800 - val_acc: 0.9038 Epoch 92/500 134s 268ms/step - loss: 0.6910 - acc: 0.8982 - val_loss: 0.6843 - val_acc: 0.9035 Epoch 93/500 134s 268ms/step - loss: 0.6888 - acc: 0.8995 - val_loss: 0.7000 - val_acc: 0.8988 Epoch 94/500 134s 268ms/step - loss: 0.6865 - acc: 0.8998 - val_loss: 0.6852 - val_acc: 0.9047 Epoch 95/500 134s 268ms/step - loss: 0.6970 - acc: 0.8963 - val_loss: 0.7136 - val_acc: 0.8964 Epoch 96/500 134s 268ms/step - loss: 0.6883 - acc: 0.9005 - val_loss: 0.6620 - val_acc: 0.9128 Epoch 97/500 134s 268ms/step - loss: 0.6923 - acc: 0.8986 - val_loss: 0.6725 - val_acc: 0.9088 Epoch 98/500 134s 268ms/step - loss: 0.6889 - acc: 0.9005 - val_loss: 0.6813 - val_acc: 0.9058 Epoch 99/500 134s 268ms/step - loss: 0.6915 - acc: 0.8992 - val_loss: 0.6781 - val_acc: 0.9048 Epoch 100/500 134s 268ms/step - loss: 0.6876 - acc: 0.9011 - val_loss: 0.6740 - val_acc: 0.9062 Epoch 101/500 134s 268ms/step - loss: 0.6886 - acc: 0.9015 - val_loss: 0.6744 - val_acc: 0.9074 Epoch 102/500 134s 268ms/step - loss: 0.6904 - acc: 0.8995 - val_loss: 0.6853 - val_acc: 0.9028 Epoch 103/500 134s 268ms/step - loss: 0.6860 - acc: 0.9018 - val_loss: 0.6714 - val_acc: 0.9111 Epoch 104/500 134s 268ms/step - loss: 0.6921 - acc: 0.8997 - val_loss: 0.6827 - val_acc: 0.9026 Epoch 105/500 134s 268ms/step - loss: 0.6849 - acc: 0.9013 - val_loss: 0.7103 - val_acc: 0.8968 Epoch 106/500 134s 268ms/step - loss: 0.6896 - acc: 0.9010 - val_loss: 0.6898 - val_acc: 0.9017 Epoch 107/500 134s 268ms/step - loss: 0.6874 - acc: 0.9018 - val_loss: 0.6835 - val_acc: 0.9019 Epoch 108/500 134s 268ms/step - loss: 0.6879 - acc: 0.9017 - val_loss: 0.6864 - val_acc: 0.9057 Epoch 109/500 134s 268ms/step - loss: 0.6900 - acc: 0.9024 - val_loss: 0.6853 - val_acc: 0.9032 Epoch 110/500 134s 268ms/step - loss: 0.6811 - acc: 0.9038 - val_loss: 0.6793 - val_acc: 0.9062 Epoch 111/500 134s 268ms/step - loss: 0.6848 - acc: 0.9011 - val_loss: 0.6824 - val_acc: 0.9069 Epoch 112/500 134s 268ms/step - loss: 0.6864 - acc: 0.9025 - val_loss: 0.6786 - val_acc: 0.9075 Epoch 113/500 134s 268ms/step - loss: 0.6831 - acc: 0.9023 - val_loss: 0.6813 - val_acc: 0.9018 Epoch 114/500 134s 268ms/step - loss: 0.6832 - acc: 0.9033 - val_loss: 0.6756 - val_acc: 0.9078 Epoch 115/500 134s 268ms/step - loss: 0.6813 - acc: 0.9049 - val_loss: 0.6847 - val_acc: 0.9030 Epoch 116/500 134s 268ms/step - loss: 0.6899 - acc: 0.8999 - val_loss: 0.6872 - val_acc: 0.9067 Epoch 117/500 134s 268ms/step - loss: 0.6816 - acc: 0.9038 - val_loss: 0.6873 - val_acc: 0.9084 Epoch 118/500 134s 268ms/step - loss: 0.6832 - acc: 0.9025 - val_loss: 0.6646 - val_acc: 0.9142 Epoch 119/500 134s 268ms/step - loss: 0.6754 - acc: 0.9053 - val_loss: 0.6790 - val_acc: 0.9053 Epoch 120/500 134s 268ms/step - loss: 0.6800 - acc: 0.9050 - val_loss: 0.6888 - val_acc: 0.9062 Epoch 121/500 134s 268ms/step - loss: 0.6821 - acc: 0.9043 - val_loss: 0.6804 - val_acc: 0.9076 Epoch 122/500 134s 268ms/step - loss: 0.6821 - acc: 0.9047 - val_loss: 0.6873 - val_acc: 0.9074 Epoch 123/500 134s 268ms/step - loss: 0.6862 - acc: 0.9017 - val_loss: 0.6817 - val_acc: 0.9061 Epoch 124/500 134s 267ms/step - loss: 0.6827 - acc: 0.9034 - val_loss: 0.6852 - val_acc: 0.9070 Epoch 125/500 133s 266ms/step - loss: 0.6801 - acc: 0.9050 - val_loss: 0.6793 - val_acc: 0.9080 Epoch 126/500 133s 266ms/step - loss: 0.6857 - acc: 0.9038 - val_loss: 0.6788 - val_acc: 0.9059 Epoch 127/500 133s 266ms/step - loss: 0.6817 - acc: 0.9042 - val_loss: 0.6804 - val_acc: 0.9065 Epoch 128/500 133s 266ms/step - loss: 0.6851 - acc: 0.9036 - val_loss: 0.7013 - val_acc: 0.9027 Epoch 129/500 133s 266ms/step - loss: 0.6850 - acc: 0.9024 - val_loss: 0.6965 - val_acc: 0.9042 Epoch 130/500 133s 266ms/step - loss: 0.6846 - acc: 0.9050 - val_loss: 0.6797 - val_acc: 0.9104 Epoch 131/500 133s 266ms/step - loss: 0.6814 - acc: 0.9058 - val_loss: 0.6740 - val_acc: 0.9107 Epoch 132/500 133s 266ms/step - loss: 0.6835 - acc: 0.9044 - val_loss: 0.7089 - val_acc: 0.8962 Epoch 133/500 133s 266ms/step - loss: 0.6808 - acc: 0.9066 - val_loss: 0.6767 - val_acc: 0.9105 Epoch 134/500 133s 265ms/step - loss: 0.6847 - acc: 0.9035 - val_loss: 0.6932 - val_acc: 0.9055 Epoch 135/500 133s 266ms/step - loss: 0.6832 - acc: 0.9058 - val_loss: 0.6916 - val_acc: 0.9058 Epoch 136/500 133s 265ms/step - loss: 0.6801 - acc: 0.9041 - val_loss: 0.6851 - val_acc: 0.9073 Epoch 137/500 133s 266ms/step - loss: 0.6809 - acc: 0.9056 - val_loss: 0.6726 - val_acc: 0.9108 Epoch 138/500 133s 266ms/step - loss: 0.6813 - acc: 0.9053 - val_loss: 0.6590 - val_acc: 0.9143 Epoch 139/500 133s 266ms/step - loss: 0.6814 - acc: 0.9057 - val_loss: 0.6746 - val_acc: 0.9085 Epoch 140/500 133s 266ms/step - loss: 0.6804 - acc: 0.9060 - val_loss: 0.6839 - val_acc: 0.9068 Epoch 141/500 133s 266ms/step - loss: 0.6810 - acc: 0.9065 - val_loss: 0.7121 - val_acc: 0.8978 Epoch 142/500 133s 266ms/step - loss: 0.6831 - acc: 0.9054 - val_loss: 0.6893 - val_acc: 0.9067 Epoch 143/500 133s 266ms/step - loss: 0.6785 - acc: 0.9069 - val_loss: 0.6754 - val_acc: 0.9105 Epoch 144/500 133s 266ms/step - loss: 0.6810 - acc: 0.9049 - val_loss: 0.6889 - val_acc: 0.9064 Epoch 145/500 133s 266ms/step - loss: 0.6807 - acc: 0.9074 - val_loss: 0.7067 - val_acc: 0.9023 Epoch 146/500 133s 266ms/step - loss: 0.6845 - acc: 0.9057 - val_loss: 0.6855 - val_acc: 0.9055 Epoch 147/500 133s 267ms/step - loss: 0.6779 - acc: 0.9055 - val_loss: 0.6928 - val_acc: 0.9040 Epoch 148/500 134s 269ms/step - loss: 0.6781 - acc: 0.9069 - val_loss: 0.6760 - val_acc: 0.9086 Epoch 149/500 133s 267ms/step - loss: 0.6834 - acc: 0.9064 - val_loss: 0.6991 - val_acc: 0.9012 Epoch 150/500 135s 270ms/step - loss: 0.6809 - acc: 0.9071 - val_loss: 0.6887 - val_acc: 0.9069 Epoch 151/500 lr changed to 0.010000000149011612 133s 267ms/step - loss: 0.5790 - acc: 0.9415 - val_loss: 0.5901 - val_acc: 0.9381 Epoch 152/500 134s 267ms/step - loss: 0.5211 - acc: 0.9595 - val_loss: 0.5735 - val_acc: 0.9413 Epoch 153/500 133s 267ms/step - loss: 0.4983 - acc: 0.9645 - val_loss: 0.5626 - val_acc: 0.9440 Epoch 154/500 134s 267ms/step - loss: 0.4793 - acc: 0.9686 - val_loss: 0.5532 - val_acc: 0.9434 Epoch 155/500 135s 269ms/step - loss: 0.4689 - acc: 0.9709 - val_loss: 0.5510 - val_acc: 0.9434 Epoch 156/500 133s 267ms/step - loss: 0.4579 - acc: 0.9716 - val_loss: 0.5398 - val_acc: 0.9444 Epoch 157/500 133s 266ms/step - loss: 0.4461 - acc: 0.9739 - val_loss: 0.5347 - val_acc: 0.9459 Epoch 158/500 133s 266ms/step - loss: 0.4325 - acc: 0.9769 - val_loss: 0.5237 - val_acc: 0.9461 Epoch 159/500 133s 266ms/step - loss: 0.4263 - acc: 0.9767 - val_loss: 0.5284 - val_acc: 0.9435 Epoch 160/500 133s 266ms/step - loss: 0.4159 - acc: 0.9773 - val_loss: 0.5137 - val_acc: 0.9458 Epoch 161/500 133s 266ms/step - loss: 0.4084 - acc: 0.9782 - val_loss: 0.5121 - val_acc: 0.9457 Epoch 162/500 133s 266ms/step - loss: 0.4002 - acc: 0.9792 - val_loss: 0.5061 - val_acc: 0.9463 Epoch 163/500 133s 266ms/step - loss: 0.3892 - acc: 0.9812 - val_loss: 0.5056 - val_acc: 0.9454 Epoch 164/500 133s 266ms/step - loss: 0.3828 - acc: 0.9816 - val_loss: 0.5098 - val_acc: 0.9438 Epoch 165/500 133s 266ms/step - loss: 0.3795 - acc: 0.9811 - val_loss: 0.4993 - val_acc: 0.9436 Epoch 166/500 133s 266ms/step - loss: 0.3708 - acc: 0.9829 - val_loss: 0.4963 - val_acc: 0.9439 Epoch 167/500 133s 266ms/step - loss: 0.3640 - acc: 0.9835 - val_loss: 0.4935 - val_acc: 0.9428 Epoch 168/500 133s 266ms/step - loss: 0.3581 - acc: 0.9835 - val_loss: 0.4856 - val_acc: 0.9440 Epoch 169/500 133s 266ms/step - loss: 0.3534 - acc: 0.9836 - val_loss: 0.4830 - val_acc: 0.9441 Epoch 170/500 133s 266ms/step - loss: 0.3478 - acc: 0.9841 - val_loss: 0.4819 - val_acc: 0.9452 Epoch 171/500 133s 266ms/step - loss: 0.3438 - acc: 0.9836 - val_loss: 0.4810 - val_acc: 0.9432 Epoch 172/500 133s 266ms/step - loss: 0.3365 - acc: 0.9847 - val_loss: 0.4694 - val_acc: 0.9430 Epoch 173/500 133s 266ms/step - loss: 0.3307 - acc: 0.9859 - val_loss: 0.4657 - val_acc: 0.9454 Epoch 174/500 133s 266ms/step - loss: 0.3266 - acc: 0.9849 - val_loss: 0.4566 - val_acc: 0.9474 Epoch 175/500 133s 265ms/step - loss: 0.3199 - acc: 0.9860 - val_loss: 0.4570 - val_acc: 0.9442 Epoch 176/500 133s 266ms/step - loss: 0.3156 - acc: 0.9863 - val_loss: 0.4640 - val_acc: 0.9426 Epoch 177/500 133s 266ms/step - loss: 0.3134 - acc: 0.9863 - val_loss: 0.4648 - val_acc: 0.9405 Epoch 178/500 133s 266ms/step - loss: 0.3089 - acc: 0.9856 - val_loss: 0.4527 - val_acc: 0.9450 Epoch 179/500 134s 267ms/step - loss: 0.3021 - acc: 0.9871 - val_loss: 0.4506 - val_acc: 0.9429 Epoch 180/500 133s 266ms/step - loss: 0.2990 - acc: 0.9868 - val_loss: 0.4441 - val_acc: 0.9460 Epoch 181/500 133s 267ms/step - loss: 0.2960 - acc: 0.9870 - val_loss: 0.4532 - val_acc: 0.9413 Epoch 182/500 133s 266ms/step - loss: 0.2916 - acc: 0.9874 - val_loss: 0.4430 - val_acc: 0.9435 Epoch 183/500 133s 266ms/step - loss: 0.2905 - acc: 0.9860 - val_loss: 0.4414 - val_acc: 0.9445 Epoch 184/500 133s 266ms/step - loss: 0.2851 - acc: 0.9875 - val_loss: 0.4303 - val_acc: 0.9460 Epoch 185/500 133s 266ms/step - loss: 0.2804 - acc: 0.9873 - val_loss: 0.4317 - val_acc: 0.9441 Epoch 186/500 133s 266ms/step - loss: 0.2789 - acc: 0.9872 - val_loss: 0.4294 - val_acc: 0.9449 Epoch 187/500 133s 266ms/step - loss: 0.2748 - acc: 0.9874 - val_loss: 0.4248 - val_acc: 0.9428 Epoch 188/500 133s 266ms/step - loss: 0.2700 - acc: 0.9882 - val_loss: 0.4242 - val_acc: 0.9461 Epoch 189/500 133s 266ms/step - loss: 0.2690 - acc: 0.9875 - val_loss: 0.4235 - val_acc: 0.9402 Epoch 190/500 133s 266ms/step - loss: 0.2669 - acc: 0.9868 - val_loss: 0.4353 - val_acc: 0.9404 Epoch 191/500 133s 266ms/step - loss: 0.2636 - acc: 0.9874 - val_loss: 0.4182 - val_acc: 0.9432 Epoch 192/500 133s 266ms/step - loss: 0.2595 - acc: 0.9882 - val_loss: 0.4160 - val_acc: 0.9439 Epoch 193/500 133s 266ms/step - loss: 0.2579 - acc: 0.9879 - val_loss: 0.4147 - val_acc: 0.9445 Epoch 194/500 133s 266ms/step - loss: 0.2556 - acc: 0.9876 - val_loss: 0.4161 - val_acc: 0.9428 Epoch 195/500 133s 266ms/step - loss: 0.2530 - acc: 0.9875 - val_loss: 0.4120 - val_acc: 0.9420 Epoch 196/500 133s 266ms/step - loss: 0.2525 - acc: 0.9873 - val_loss: 0.4121 - val_acc: 0.9420 Epoch 197/500 133s 266ms/step - loss: 0.2492 - acc: 0.9877 - val_loss: 0.4260 - val_acc: 0.9390 Epoch 198/500 133s 266ms/step - loss: 0.2488 - acc: 0.9867 - val_loss: 0.4126 - val_acc: 0.9424 Epoch 199/500 133s 266ms/step - loss: 0.2458 - acc: 0.9869 - val_loss: 0.3974 - val_acc: 0.9442 Epoch 200/500 133s 266ms/step - loss: 0.2418 - acc: 0.9881 - val_loss: 0.3993 - val_acc: 0.9431 Epoch 201/500 133s 266ms/step - loss: 0.2390 - acc: 0.9878 - val_loss: 0.3998 - val_acc: 0.9448 Epoch 202/500 133s 266ms/step - loss: 0.2387 - acc: 0.9871 - val_loss: 0.3926 - val_acc: 0.9451 Epoch 203/500 133s 266ms/step - loss: 0.2354 - acc: 0.9879 - val_loss: 0.4007 - val_acc: 0.9415 Epoch 204/500 133s 266ms/step - loss: 0.2342 - acc: 0.9878 - val_loss: 0.3996 - val_acc: 0.9410 Epoch 205/500 133s 266ms/step - loss: 0.2376 - acc: 0.9860 - val_loss: 0.3943 - val_acc: 0.9423 Epoch 206/500 133s 266ms/step - loss: 0.2315 - acc: 0.9869 - val_loss: 0.3860 - val_acc: 0.9441 Epoch 207/500 133s 266ms/step - loss: 0.2326 - acc: 0.9865 - val_loss: 0.3913 - val_acc: 0.9417 Epoch 208/500 133s 266ms/step - loss: 0.2282 - acc: 0.9875 - val_loss: 0.3957 - val_acc: 0.9422 Epoch 209/500 133s 266ms/step - loss: 0.2272 - acc: 0.9870 - val_loss: 0.4006 - val_acc: 0.9397 Epoch 210/500 133s 266ms/step - loss: 0.2279 - acc: 0.9867 - val_loss: 0.3860 - val_acc: 0.9437 Epoch 211/500 133s 266ms/step - loss: 0.2283 - acc: 0.9856 - val_loss: 0.3855 - val_acc: 0.9427 Epoch 212/500 133s 266ms/step - loss: 0.2238 - acc: 0.9870 - val_loss: 0.3897 - val_acc: 0.9409 Epoch 213/500 133s 266ms/step - loss: 0.2246 - acc: 0.9864 - val_loss: 0.3808 - val_acc: 0.9425 Epoch 214/500 133s 266ms/step - loss: 0.2247 - acc: 0.9861 - val_loss: 0.4011 - val_acc: 0.9375 Epoch 215/500 133s 266ms/step - loss: 0.2213 - acc: 0.9865 - val_loss: 0.3887 - val_acc: 0.9399 Epoch 216/500 133s 266ms/step - loss: 0.2193 - acc: 0.9867 - val_loss: 0.3850 - val_acc: 0.9420 Epoch 217/500 133s 266ms/step - loss: 0.2193 - acc: 0.9862 - val_loss: 0.3781 - val_acc: 0.9425 Epoch 218/500 133s 266ms/step - loss: 0.2194 - acc: 0.9861 - val_loss: 0.3863 - val_acc: 0.9399 Epoch 219/500 133s 266ms/step - loss: 0.2165 - acc: 0.9869 - val_loss: 0.3795 - val_acc: 0.9417 Epoch 220/500 133s 266ms/step - loss: 0.2149 - acc: 0.9874 - val_loss: 0.3749 - val_acc: 0.9443 Epoch 221/500 133s 266ms/step - loss: 0.2171 - acc: 0.9854 - val_loss: 0.3776 - val_acc: 0.9424 Epoch 222/500 133s 266ms/step - loss: 0.2183 - acc: 0.9845 - val_loss: 0.3854 - val_acc: 0.9397 Epoch 223/500 133s 266ms/step - loss: 0.2163 - acc: 0.9854 - val_loss: 0.3745 - val_acc: 0.9424 Epoch 224/500 133s 266ms/step - loss: 0.2138 - acc: 0.9861 - val_loss: 0.3695 - val_acc: 0.9425 Epoch 225/500 133s 266ms/step - loss: 0.2098 - acc: 0.9868 - val_loss: 0.3634 - val_acc: 0.9459 Epoch 226/500 133s 266ms/step - loss: 0.2120 - acc: 0.9863 - val_loss: 0.3709 - val_acc: 0.9431 Epoch 227/500 133s 266ms/step - loss: 0.2122 - acc: 0.9858 - val_loss: 0.3758 - val_acc: 0.9395 Epoch 228/500 133s 266ms/step - loss: 0.2103 - acc: 0.9861 - val_loss: 0.3628 - val_acc: 0.9423 Epoch 229/500 133s 266ms/step - loss: 0.2105 - acc: 0.9856 - val_loss: 0.3739 - val_acc: 0.9400 Epoch 230/500 133s 266ms/step - loss: 0.2109 - acc: 0.9854 - val_loss: 0.3757 - val_acc: 0.9399 Epoch 231/500 133s 266ms/step - loss: 0.2089 - acc: 0.9860 - val_loss: 0.3677 - val_acc: 0.9412 Epoch 232/500 133s 266ms/step - loss: 0.2062 - acc: 0.9875 - val_loss: 0.3596 - val_acc: 0.9430 Epoch 233/500 133s 266ms/step - loss: 0.2068 - acc: 0.9859 - val_loss: 0.3635 - val_acc: 0.9409 Epoch 234/500 133s 266ms/step - loss: 0.2060 - acc: 0.9863 - val_loss: 0.3792 - val_acc: 0.9381 Epoch 235/500 133s 266ms/step - loss: 0.2061 - acc: 0.9865 - val_loss: 0.3720 - val_acc: 0.9416 Epoch 236/500 133s 266ms/step - loss: 0.2066 - acc: 0.9853 - val_loss: 0.3862 - val_acc: 0.9353 Epoch 237/500 133s 266ms/step - loss: 0.2089 - acc: 0.9846 - val_loss: 0.3698 - val_acc: 0.9387 Epoch 238/500 133s 266ms/step - loss: 0.2065 - acc: 0.9853 - val_loss: 0.3611 - val_acc: 0.9405 Epoch 239/500 133s 266ms/step - loss: 0.2070 - acc: 0.9853 - val_loss: 0.3688 - val_acc: 0.9386 Epoch 240/500 133s 266ms/step - loss: 0.2044 - acc: 0.9858 - val_loss: 0.3689 - val_acc: 0.9398 Epoch 241/500 133s 266ms/step - loss: 0.2055 - acc: 0.9849 - val_loss: 0.3766 - val_acc: 0.9390 Epoch 242/500 133s 266ms/step - loss: 0.2028 - acc: 0.9861 - val_loss: 0.3592 - val_acc: 0.9414 Epoch 243/500 133s 266ms/step - loss: 0.2030 - acc: 0.9863 - val_loss: 0.3616 - val_acc: 0.9431 Epoch 244/500 133s 266ms/step - loss: 0.2024 - acc: 0.9861 - val_loss: 0.3722 - val_acc: 0.9379 Epoch 245/500 133s 266ms/step - loss: 0.2038 - acc: 0.9855 - val_loss: 0.3620 - val_acc: 0.9407 Epoch 246/500 133s 266ms/step - loss: 0.2014 - acc: 0.9865 - val_loss: 0.3740 - val_acc: 0.9376 Epoch 247/500 133s 266ms/step - loss: 0.2012 - acc: 0.9856 - val_loss: 0.3630 - val_acc: 0.9418 Epoch 248/500 133s 266ms/step - loss: 0.2045 - acc: 0.9845 - val_loss: 0.3644 - val_acc: 0.9401 Epoch 249/500 133s 266ms/step - loss: 0.2044 - acc: 0.9845 - val_loss: 0.3605 - val_acc: 0.9384 Epoch 250/500 133s 266ms/step - loss: 0.2066 - acc: 0.9842 - val_loss: 0.3684 - val_acc: 0.9383 Epoch 251/500 133s 266ms/step - loss: 0.2005 - acc: 0.9861 - val_loss: 0.3683 - val_acc: 0.9377 Epoch 252/500 133s 266ms/step - loss: 0.2019 - acc: 0.9856 - val_loss: 0.3663 - val_acc: 0.9382 Epoch 253/500 133s 266ms/step - loss: 0.2026 - acc: 0.9852 - val_loss: 0.3666 - val_acc: 0.9393 Epoch 254/500 133s 266ms/step - loss: 0.2034 - acc: 0.9841 - val_loss: 0.3627 - val_acc: 0.9409 Epoch 255/500 133s 266ms/step - loss: 0.1998 - acc: 0.9857 - val_loss: 0.3609 - val_acc: 0.9408 Epoch 256/500 133s 266ms/step - loss: 0.1987 - acc: 0.9859 - val_loss: 0.3629 - val_acc: 0.9414 Epoch 257/500 133s 266ms/step - loss: 0.1971 - acc: 0.9871 - val_loss: 0.3768 - val_acc: 0.9375 Epoch 258/500 133s 266ms/step - loss: 0.1985 - acc: 0.9860 - val_loss: 0.3706 - val_acc: 0.9375 Epoch 259/500 133s 266ms/step - loss: 0.2006 - acc: 0.9845 - val_loss: 0.3638 - val_acc: 0.9401 Epoch 260/500 133s 266ms/step - loss: 0.1991 - acc: 0.9851 - val_loss: 0.3629 - val_acc: 0.9392 Epoch 261/500 133s 265ms/step - loss: 0.1999 - acc: 0.9854 - val_loss: 0.3603 - val_acc: 0.9420 Epoch 262/500 133s 266ms/step - loss: 0.2021 - acc: 0.9841 - val_loss: 0.3610 - val_acc: 0.9391 Epoch 263/500 133s 265ms/step - loss: 0.2009 - acc: 0.9850 - val_loss: 0.3454 - val_acc: 0.9413 Epoch 264/500 133s 266ms/step - loss: 0.1956 - acc: 0.9866 - val_loss: 0.3662 - val_acc: 0.9382 Epoch 265/500 133s 266ms/step - loss: 0.2038 - acc: 0.9844 - val_loss: 0.3595 - val_acc: 0.9417 Epoch 266/500 133s 266ms/step - loss: 0.1982 - acc: 0.9854 - val_loss: 0.3578 - val_acc: 0.9396 Epoch 267/500 133s 266ms/step - loss: 0.1996 - acc: 0.9844 - val_loss: 0.3662 - val_acc: 0.9397 Epoch 268/500 133s 266ms/step - loss: 0.1978 - acc: 0.9854 - val_loss: 0.3551 - val_acc: 0.9437 Epoch 269/500 133s 266ms/step - loss: 0.1986 - acc: 0.9855 - val_loss: 0.3636 - val_acc: 0.9412 Epoch 270/500 133s 266ms/step - loss: 0.1982 - acc: 0.9851 - val_loss: 0.3495 - val_acc: 0.9415 Epoch 271/500 133s 266ms/step - loss: 0.2001 - acc: 0.9845 - val_loss: 0.3504 - val_acc: 0.9407 Epoch 272/500 133s 266ms/step - loss: 0.1982 - acc: 0.9850 - val_loss: 0.3496 - val_acc: 0.9432 Epoch 273/500 133s 266ms/step - loss: 0.1989 - acc: 0.9849 - val_loss: 0.3589 - val_acc: 0.9395 Epoch 274/500 133s 266ms/step - loss: 0.1954 - acc: 0.9862 - val_loss: 0.3517 - val_acc: 0.9440 Epoch 275/500 133s 266ms/step - loss: 0.1964 - acc: 0.9860 - val_loss: 0.3710 - val_acc: 0.9385 Epoch 276/500 133s 266ms/step - loss: 0.1991 - acc: 0.9848 - val_loss: 0.3572 - val_acc: 0.9375 Epoch 277/500 133s 266ms/step - loss: 0.1986 - acc: 0.9841 - val_loss: 0.3733 - val_acc: 0.9394 Epoch 278/500 133s 266ms/step - loss: 0.2004 - acc: 0.9846 - val_loss: 0.3516 - val_acc: 0.9408 Epoch 279/500 133s 266ms/step - loss: 0.1961 - acc: 0.9854 - val_loss: 0.3687 - val_acc: 0.9387 Epoch 280/500 133s 266ms/step - loss: 0.1952 - acc: 0.9855 - val_loss: 0.3724 - val_acc: 0.9397 Epoch 281/500 133s 266ms/step - loss: 0.1960 - acc: 0.9850 - val_loss: 0.3706 - val_acc: 0.9391 Epoch 282/500 133s 266ms/step - loss: 0.1961 - acc: 0.9855 - val_loss: 0.3711 - val_acc: 0.9376 Epoch 283/500 133s 266ms/step - loss: 0.1975 - acc: 0.9846 - val_loss: 0.3728 - val_acc: 0.9352 Epoch 284/500 133s 266ms/step - loss: 0.1980 - acc: 0.9847 - val_loss: 0.3666 - val_acc: 0.9361 Epoch 285/500 133s 266ms/step - loss: 0.1944 - acc: 0.9862 - val_loss: 0.3651 - val_acc: 0.9382 Epoch 286/500 133s 266ms/step - loss: 0.1932 - acc: 0.9862 - val_loss: 0.3679 - val_acc: 0.9366 Epoch 287/500 133s 266ms/step - loss: 0.1988 - acc: 0.9844 - val_loss: 0.3522 - val_acc: 0.9431 Epoch 288/500 133s 266ms/step - loss: 0.1947 - acc: 0.9860 - val_loss: 0.3574 - val_acc: 0.9394 Epoch 289/500 133s 266ms/step - loss: 0.1964 - acc: 0.9854 - val_loss: 0.3608 - val_acc: 0.9391 Epoch 290/500 133s 266ms/step - loss: 0.1955 - acc: 0.9853 - val_loss: 0.3663 - val_acc: 0.9373 Epoch 291/500 133s 266ms/step - loss: 0.1966 - acc: 0.9849 - val_loss: 0.3614 - val_acc: 0.9392 Epoch 292/500 133s 266ms/step - loss: 0.1951 - acc: 0.9856 - val_loss: 0.3698 - val_acc: 0.9350 Epoch 293/500 133s 266ms/step - loss: 0.1969 - acc: 0.9847 - val_loss: 0.3528 - val_acc: 0.9442 Epoch 294/500 133s 266ms/step - loss: 0.1981 - acc: 0.9846 - val_loss: 0.3644 - val_acc: 0.9395 Epoch 295/500 133s 266ms/step - loss: 0.1946 - acc: 0.9854 - val_loss: 0.3649 - val_acc: 0.9390 Epoch 296/500 133s 266ms/step - loss: 0.1973 - acc: 0.9840 - val_loss: 0.3725 - val_acc: 0.9378 Epoch 297/500 133s 266ms/step - loss: 0.1954 - acc: 0.9849 - val_loss: 0.3412 - val_acc: 0.9448 Epoch 298/500 133s 266ms/step - loss: 0.1889 - acc: 0.9877 - val_loss: 0.3610 - val_acc: 0.9377 Epoch 299/500 133s 266ms/step - loss: 0.1894 - acc: 0.9872 - val_loss: 0.3702 - val_acc: 0.9367 Epoch 300/500 133s 266ms/step - loss: 0.1957 - acc: 0.9846 - val_loss: 0.3614 - val_acc: 0.9400 Epoch 301/500 lr changed to 0.0009999999776482583 133s 266ms/step - loss: 0.1796 - acc: 0.9907 - val_loss: 0.3285 - val_acc: 0.9484 Epoch 302/500 133s 266ms/step - loss: 0.1694 - acc: 0.9946 - val_loss: 0.3244 - val_acc: 0.9480 Epoch 303/500 133s 266ms/step - loss: 0.1670 - acc: 0.9951 - val_loss: 0.3218 - val_acc: 0.9495 Epoch 304/500 133s 266ms/step - loss: 0.1638 - acc: 0.9963 - val_loss: 0.3213 - val_acc: 0.9500 Epoch 305/500 133s 266ms/step - loss: 0.1625 - acc: 0.9963 - val_loss: 0.3204 - val_acc: 0.9513 Epoch 306/500 133s 266ms/step - loss: 0.1614 - acc: 0.9967 - val_loss: 0.3201 - val_acc: 0.9507 Epoch 307/500 133s 266ms/step - loss: 0.1616 - acc: 0.9965 - val_loss: 0.3205 - val_acc: 0.9510 Epoch 308/500 133s 266ms/step - loss: 0.1600 - acc: 0.9968 - val_loss: 0.3193 - val_acc: 0.9518 Epoch 309/500 133s 266ms/step - loss: 0.1593 - acc: 0.9970 - val_loss: 0.3213 - val_acc: 0.9520 Epoch 310/500 133s 266ms/step - loss: 0.1574 - acc: 0.9975 - val_loss: 0.3204 - val_acc: 0.9517 Epoch 311/500 133s 266ms/step - loss: 0.1578 - acc: 0.9975 - val_loss: 0.3205 - val_acc: 0.9511 Epoch 312/500 133s 266ms/step - loss: 0.1575 - acc: 0.9972 - val_loss: 0.3199 - val_acc: 0.9518 Epoch 313/500 133s 266ms/step - loss: 0.1565 - acc: 0.9977 - val_loss: 0.3192 - val_acc: 0.9521 Epoch 314/500 133s 266ms/step - loss: 0.1563 - acc: 0.9977 - val_loss: 0.3186 - val_acc: 0.9528 Epoch 315/500 133s 266ms/step - loss: 0.1555 - acc: 0.9980 - val_loss: 0.3194 - val_acc: 0.9523 Epoch 316/500 133s 266ms/step - loss: 0.1551 - acc: 0.9978 - val_loss: 0.3190 - val_acc: 0.9529 Epoch 317/500 133s 266ms/step - loss: 0.1542 - acc: 0.9979 - val_loss: 0.3178 - val_acc: 0.9527 Epoch 318/500 133s 266ms/step - loss: 0.1542 - acc: 0.9981 - val_loss: 0.3160 - val_acc: 0.9535 Epoch 319/500 133s 266ms/step - loss: 0.1540 - acc: 0.9980 - val_loss: 0.3157 - val_acc: 0.9536 Epoch 320/500 133s 266ms/step - loss: 0.1536 - acc: 0.9980 - val_loss: 0.3171 - val_acc: 0.9528 Epoch 321/500 133s 266ms/step - loss: 0.1532 - acc: 0.9979 - val_loss: 0.3203 - val_acc: 0.9519 Epoch 322/500 133s 266ms/step - loss: 0.1522 - acc: 0.9982 - val_loss: 0.3214 - val_acc: 0.9526 Epoch 323/500 133s 266ms/step - loss: 0.1524 - acc: 0.9982 - val_loss: 0.3227 - val_acc: 0.9529 Epoch 324/500 133s 266ms/step - loss: 0.1519 - acc: 0.9983 - val_loss: 0.3229 - val_acc: 0.9527 Epoch 325/500 133s 266ms/step - loss: 0.1519 - acc: 0.9981 - val_loss: 0.3206 - val_acc: 0.9531 Epoch 326/500 133s 265ms/step - loss: 0.1510 - acc: 0.9983 - val_loss: 0.3202 - val_acc: 0.9528 Epoch 327/500 133s 266ms/step - loss: 0.1509 - acc: 0.9984 - val_loss: 0.3216 - val_acc: 0.9530 Epoch 328/500 133s 266ms/step - loss: 0.1513 - acc: 0.9979 - val_loss: 0.3222 - val_acc: 0.9537 Epoch 329/500 133s 266ms/step - loss: 0.1506 - acc: 0.9984 - val_loss: 0.3213 - val_acc: 0.9530 Epoch 330/500 133s 266ms/step - loss: 0.1496 - acc: 0.9985 - val_loss: 0.3221 - val_acc: 0.9527 Epoch 331/500 133s 266ms/step - loss: 0.1498 - acc: 0.9984 - val_loss: 0.3214 - val_acc: 0.9523 Epoch 332/500 133s 266ms/step - loss: 0.1484 - acc: 0.9989 - val_loss: 0.3201 - val_acc: 0.9520 Epoch 333/500 133s 266ms/step - loss: 0.1491 - acc: 0.9983 - val_loss: 0.3206 - val_acc: 0.9520 Epoch 334/500 133s 266ms/step - loss: 0.1491 - acc: 0.9984 - val_loss: 0.3196 - val_acc: 0.9530 Epoch 335/500 133s 266ms/step - loss: 0.1484 - acc: 0.9983 - val_loss: 0.3203 - val_acc: 0.9531 Epoch 336/500 133s 266ms/step - loss: 0.1476 - acc: 0.9986 - val_loss: 0.3195 - val_acc: 0.9527 Epoch 337/500 133s 266ms/step - loss: 0.1473 - acc: 0.9986 - val_loss: 0.3177 - val_acc: 0.9525 Epoch 338/500 133s 266ms/step - loss: 0.1470 - acc: 0.9987 - val_loss: 0.3186 - val_acc: 0.9537 Epoch 339/500 133s 266ms/step - loss: 0.1466 - acc: 0.9986 - val_loss: 0.3175 - val_acc: 0.9546 Epoch 340/500 133s 266ms/step - loss: 0.1465 - acc: 0.9986 - val_loss: 0.3148 - val_acc: 0.9540 Epoch 341/500 133s 265ms/step - loss: 0.1464 - acc: 0.9985 - val_loss: 0.3157 - val_acc: 0.9545 Epoch 342/500 133s 266ms/step - loss: 0.1464 - acc: 0.9983 - val_loss: 0.3149 - val_acc: 0.9540 Epoch 343/500 133s 266ms/step - loss: 0.1454 - acc: 0.9988 - val_loss: 0.3154 - val_acc: 0.9547 Epoch 344/500 133s 266ms/step - loss: 0.1449 - acc: 0.9990 - val_loss: 0.3146 - val_acc: 0.9536 Epoch 345/500 133s 266ms/step - loss: 0.1446 - acc: 0.9988 - val_loss: 0.3149 - val_acc: 0.9537 Epoch 346/500 133s 266ms/step - loss: 0.1439 - acc: 0.9992 - val_loss: 0.3148 - val_acc: 0.9540 Epoch 347/500 133s 266ms/step - loss: 0.1450 - acc: 0.9985 - val_loss: 0.3170 - val_acc: 0.9535 Epoch 348/500 133s 266ms/step - loss: 0.1449 - acc: 0.9985 - val_loss: 0.3167 - val_acc: 0.9539 Epoch 349/500 133s 266ms/step - loss: 0.1441 - acc: 0.9988 - val_loss: 0.3159 - val_acc: 0.9537 Epoch 350/500 133s 266ms/step - loss: 0.1439 - acc: 0.9986 - val_loss: 0.3160 - val_acc: 0.9540 Epoch 351/500 133s 266ms/step - loss: 0.1428 - acc: 0.9988 - val_loss: 0.3141 - val_acc: 0.9537 Epoch 352/500 133s 266ms/step - loss: 0.1431 - acc: 0.9988 - val_loss: 0.3135 - val_acc: 0.9539 Epoch 353/500 133s 266ms/step - loss: 0.1422 - acc: 0.9992 - val_loss: 0.3148 - val_acc: 0.9540 Epoch 354/500 133s 266ms/step - loss: 0.1423 - acc: 0.9988 - val_loss: 0.3155 - val_acc: 0.9540 Epoch 355/500 133s 266ms/step - loss: 0.1417 - acc: 0.9989 - val_loss: 0.3134 - val_acc: 0.9538 Epoch 356/500 133s 266ms/step - loss: 0.1417 - acc: 0.9990 - val_loss: 0.3155 - val_acc: 0.9544 Epoch 357/500 133s 266ms/step - loss: 0.1421 - acc: 0.9985 - val_loss: 0.3138 - val_acc: 0.9546 Epoch 358/500 133s 266ms/step - loss: 0.1411 - acc: 0.9989 - val_loss: 0.3124 - val_acc: 0.9542 Epoch 359/500 133s 266ms/step - loss: 0.1409 - acc: 0.9991 - val_loss: 0.3129 - val_acc: 0.9543 Epoch 360/500 133s 266ms/step - loss: 0.1408 - acc: 0.9989 - val_loss: 0.3110 - val_acc: 0.9556 Epoch 361/500 133s 266ms/step - loss: 0.1411 - acc: 0.9986 - val_loss: 0.3136 - val_acc: 0.9541 Epoch 362/500 133s 266ms/step - loss: 0.1401 - acc: 0.9990 - val_loss: 0.3136 - val_acc: 0.9550 Epoch 363/500 133s 266ms/step - loss: 0.1402 - acc: 0.9986 - val_loss: 0.3134 - val_acc: 0.9553 Epoch 364/500 133s 266ms/step - loss: 0.1401 - acc: 0.9987 - val_loss: 0.3138 - val_acc: 0.9538 Epoch 365/500 133s 266ms/step - loss: 0.1394 - acc: 0.9990 - val_loss: 0.3139 - val_acc: 0.9546 Epoch 366/500 133s 266ms/step - loss: 0.1387 - acc: 0.9992 - val_loss: 0.3130 - val_acc: 0.9545 Epoch 367/500 133s 266ms/step - loss: 0.1390 - acc: 0.9991 - val_loss: 0.3112 - val_acc: 0.9557 Epoch 368/500 133s 266ms/step - loss: 0.1396 - acc: 0.9985 - val_loss: 0.3138 - val_acc: 0.9553 Epoch 369/500 133s 266ms/step - loss: 0.1389 - acc: 0.9989 - val_loss: 0.3153 - val_acc: 0.9545 Epoch 370/500 133s 266ms/step - loss: 0.1384 - acc: 0.9988 - val_loss: 0.3124 - val_acc: 0.9547 Epoch 371/500 133s 266ms/step - loss: 0.1378 - acc: 0.9989 - val_loss: 0.3106 - val_acc: 0.9544 Epoch 372/500 133s 266ms/step - loss: 0.1380 - acc: 0.9990 - val_loss: 0.3092 - val_acc: 0.9556 Epoch 373/500 133s 266ms/step - loss: 0.1377 - acc: 0.9989 - val_loss: 0.3098 - val_acc: 0.9544 Epoch 374/500 133s 266ms/step - loss: 0.1368 - acc: 0.9993 - val_loss: 0.3084 - val_acc: 0.9556 Epoch 375/500 133s 266ms/step - loss: 0.1366 - acc: 0.9991 - val_loss: 0.3074 - val_acc: 0.9558 Epoch 376/500 133s 266ms/step - loss: 0.1370 - acc: 0.9988 - val_loss: 0.3083 - val_acc: 0.9546 Epoch 377/500 133s 266ms/step - loss: 0.1369 - acc: 0.9989 - val_loss: 0.3079 - val_acc: 0.9547 Epoch 378/500 133s 266ms/step - loss: 0.1364 - acc: 0.9989 - val_loss: 0.3075 - val_acc: 0.9551 Epoch 379/500 133s 266ms/step - loss: 0.1360 - acc: 0.9989 - val_loss: 0.3092 - val_acc: 0.9550 Epoch 380/500 133s 266ms/step - loss: 0.1367 - acc: 0.9986 - val_loss: 0.3111 - val_acc: 0.9537 Epoch 381/500 133s 266ms/step - loss: 0.1364 - acc: 0.9986 - val_loss: 0.3126 - val_acc: 0.9548 Epoch 382/500 133s 266ms/step - loss: 0.1352 - acc: 0.9991 - val_loss: 0.3095 - val_acc: 0.9544 Epoch 383/500 133s 266ms/step - loss: 0.1352 - acc: 0.9989 - val_loss: 0.3108 - val_acc: 0.9529 Epoch 384/500 133s 266ms/step - loss: 0.1350 - acc: 0.9989 - val_loss: 0.3122 - val_acc: 0.9534 Epoch 385/500 133s 266ms/step - loss: 0.1347 - acc: 0.9988 - val_loss: 0.3109 - val_acc: 0.9540 Epoch 386/500 133s 266ms/step - loss: 0.1340 - acc: 0.9991 - val_loss: 0.3085 - val_acc: 0.9549 Epoch 387/500 133s 266ms/step - loss: 0.1344 - acc: 0.9988 - val_loss: 0.3081 - val_acc: 0.9554 Epoch 388/500 133s 266ms/step - loss: 0.1341 - acc: 0.9988 - val_loss: 0.3092 - val_acc: 0.9546 Epoch 389/500 133s 266ms/step - loss: 0.1336 - acc: 0.9991 - val_loss: 0.3085 - val_acc: 0.9555 Epoch 390/500 133s 266ms/step - loss: 0.1337 - acc: 0.9989 - val_loss: 0.3086 - val_acc: 0.9557 Epoch 391/500 133s 266ms/step - loss: 0.1337 - acc: 0.9989 - val_loss: 0.3092 - val_acc: 0.9546 Epoch 392/500 133s 266ms/step - loss: 0.1329 - acc: 0.9990 - val_loss: 0.3072 - val_acc: 0.9546 Epoch 393/500 133s 266ms/step - loss: 0.1326 - acc: 0.9991 - val_loss: 0.3077 - val_acc: 0.9547 Epoch 394/500 133s 266ms/step - loss: 0.1330 - acc: 0.9989 - val_loss: 0.3059 - val_acc: 0.9556 Epoch 395/500 133s 266ms/step - loss: 0.1320 - acc: 0.9991 - val_loss: 0.3042 - val_acc: 0.9552 Epoch 396/500 133s 266ms/step - loss: 0.1317 - acc: 0.9993 - val_loss: 0.3054 - val_acc: 0.9549 Epoch 397/500 133s 266ms/step - loss: 0.1313 - acc: 0.9993 - val_loss: 0.3083 - val_acc: 0.9538 Epoch 398/500 133s 266ms/step - loss: 0.1316 - acc: 0.9990 - val_loss: 0.3087 - val_acc: 0.9550 Epoch 399/500 133s 266ms/step - loss: 0.1323 - acc: 0.9988 - val_loss: 0.3056 - val_acc: 0.9546 Epoch 400/500 133s 266ms/step - loss: 0.1315 - acc: 0.9989 - val_loss: 0.3034 - val_acc: 0.9552 Epoch 401/500 133s 266ms/step - loss: 0.1311 - acc: 0.9991 - val_loss: 0.3034 - val_acc: 0.9553 Epoch 402/500 133s 266ms/step - loss: 0.1306 - acc: 0.9991 - val_loss: 0.3006 - val_acc: 0.9556 Epoch 403/500 133s 266ms/step - loss: 0.1302 - acc: 0.9990 - val_loss: 0.3019 - val_acc: 0.9565 Epoch 404/500 133s 266ms/step - loss: 0.1308 - acc: 0.9989 - val_loss: 0.3007 - val_acc: 0.9557 Epoch 405/500 133s 266ms/step - loss: 0.1300 - acc: 0.9992 - val_loss: 0.3011 - val_acc: 0.9561 Epoch 406/500 133s 266ms/step - loss: 0.1294 - acc: 0.9993 - val_loss: 0.3018 - val_acc: 0.9543 Epoch 407/500 133s 266ms/step - loss: 0.1297 - acc: 0.9990 - val_loss: 0.3013 - val_acc: 0.9548 Epoch 408/500 133s 266ms/step - loss: 0.1300 - acc: 0.9989 - val_loss: 0.3017 - val_acc: 0.9554 Epoch 409/500 133s 266ms/step - loss: 0.1292 - acc: 0.9991 - val_loss: 0.2996 - val_acc: 0.9558 Epoch 410/500 133s 266ms/step - loss: 0.1293 - acc: 0.9990 - val_loss: 0.3017 - val_acc: 0.9554 Epoch 411/500 133s 266ms/step - loss: 0.1289 - acc: 0.9990 - val_loss: 0.3029 - val_acc: 0.9545 Epoch 412/500 133s 266ms/step - loss: 0.1290 - acc: 0.9990 - val_loss: 0.2996 - val_acc: 0.9544 Epoch 413/500 133s 266ms/step - loss: 0.1287 - acc: 0.9988 - val_loss: 0.3004 - val_acc: 0.9537 Epoch 414/500 133s 266ms/step - loss: 0.1280 - acc: 0.9992 - val_loss: 0.2980 - val_acc: 0.9540 Epoch 415/500 133s 266ms/step - loss: 0.1281 - acc: 0.9988 - val_loss: 0.3000 - val_acc: 0.9545 Epoch 416/500 133s 266ms/step - loss: 0.1281 - acc: 0.9990 - val_loss: 0.3015 - val_acc: 0.9546 Epoch 417/500 133s 266ms/step - loss: 0.1271 - acc: 0.9993 - val_loss: 0.3014 - val_acc: 0.9552 Epoch 418/500 133s 266ms/step - loss: 0.1273 - acc: 0.9991 - val_loss: 0.2995 - val_acc: 0.9547 Epoch 419/500 133s 266ms/step - loss: 0.1269 - acc: 0.9992 - val_loss: 0.3008 - val_acc: 0.9549 Epoch 420/500 133s 266ms/step - loss: 0.1266 - acc: 0.9992 - val_loss: 0.3001 - val_acc: 0.9533 Epoch 421/500 133s 266ms/step - loss: 0.1269 - acc: 0.9989 - val_loss: 0.3008 - val_acc: 0.9541 Epoch 422/500 133s 266ms/step - loss: 0.1266 - acc: 0.9991 - val_loss: 0.3006 - val_acc: 0.9542 Epoch 423/500 133s 266ms/step - loss: 0.1267 - acc: 0.9991 - val_loss: 0.2985 - val_acc: 0.9546 Epoch 424/500 133s 266ms/step - loss: 0.1258 - acc: 0.9991 - val_loss: 0.2998 - val_acc: 0.9542 Epoch 425/500 133s 266ms/step - loss: 0.1259 - acc: 0.9990 - val_loss: 0.3020 - val_acc: 0.9536 Epoch 426/500 133s 266ms/step - loss: 0.1254 - acc: 0.9990 - val_loss: 0.2985 - val_acc: 0.9547 Epoch 427/500 133s 266ms/step - loss: 0.1260 - acc: 0.9988 - val_loss: 0.3015 - val_acc: 0.9541 Epoch 428/500 133s 266ms/step - loss: 0.1257 - acc: 0.9989 - val_loss: 0.3011 - val_acc: 0.9542 Epoch 429/500 133s 266ms/step - loss: 0.1247 - acc: 0.9992 - val_loss: 0.3011 - val_acc: 0.9541 Epoch 430/500 133s 266ms/step - loss: 0.1249 - acc: 0.9990 - val_loss: 0.3004 - val_acc: 0.9553 Epoch 431/500 133s 266ms/step - loss: 0.1247 - acc: 0.9991 - val_loss: 0.3010 - val_acc: 0.9542 Epoch 432/500 133s 265ms/step - loss: 0.1241 - acc: 0.9992 - val_loss: 0.3029 - val_acc: 0.9541 Epoch 433/500 133s 266ms/step - loss: 0.1244 - acc: 0.9990 - val_loss: 0.3011 - val_acc: 0.9538 Epoch 434/500 133s 265ms/step - loss: 0.1239 - acc: 0.9991 - val_loss: 0.3019 - val_acc: 0.9545 Epoch 435/500 133s 266ms/step - loss: 0.1235 - acc: 0.9992 - val_loss: 0.3008 - val_acc: 0.9548 Epoch 436/500 133s 266ms/step - loss: 0.1235 - acc: 0.9991 - val_loss: 0.2996 - val_acc: 0.9548 Epoch 437/500 133s 266ms/step - loss: 0.1232 - acc: 0.9991 - val_loss: 0.2999 - val_acc: 0.9545 Epoch 438/500 133s 266ms/step - loss: 0.1229 - acc: 0.9991 - val_loss: 0.3015 - val_acc: 0.9542 Epoch 439/500 133s 266ms/step - loss: 0.1225 - acc: 0.9992 - val_loss: 0.3014 - val_acc: 0.9538 Epoch 440/500 133s 266ms/step - loss: 0.1233 - acc: 0.9991 - val_loss: 0.3017 - val_acc: 0.9553 Epoch 441/500 133s 266ms/step - loss: 0.1225 - acc: 0.9992 - val_loss: 0.3018 - val_acc: 0.9546 Epoch 442/500 133s 266ms/step - loss: 0.1223 - acc: 0.9992 - val_loss: 0.3016 - val_acc: 0.9536 Epoch 443/500 133s 267ms/step - loss: 0.1221 - acc: 0.9992 - val_loss: 0.3026 - val_acc: 0.9546 Epoch 444/500 133s 266ms/step - loss: 0.1220 - acc: 0.9990 - val_loss: 0.3024 - val_acc: 0.9553 Epoch 445/500 133s 266ms/step - loss: 0.1220 - acc: 0.9990 - val_loss: 0.3016 - val_acc: 0.9540 Epoch 446/500 133s 266ms/step - loss: 0.1219 - acc: 0.9990 - val_loss: 0.2991 - val_acc: 0.9542 Epoch 447/500 133s 266ms/step - loss: 0.1215 - acc: 0.9990 - val_loss: 0.2986 - val_acc: 0.9546 Epoch 448/500 133s 266ms/step - loss: 0.1214 - acc: 0.9989 - val_loss: 0.3001 - val_acc: 0.9533 Epoch 449/500 133s 266ms/step - loss: 0.1211 - acc: 0.9991 - val_loss: 0.2969 - val_acc: 0.9560 Epoch 450/500 133s 266ms/step - loss: 0.1213 - acc: 0.9991 - val_loss: 0.2956 - val_acc: 0.9547 Epoch 451/500 lr changed to 9.999999310821295e-05 133s 266ms/step - loss: 0.1205 - acc: 0.9993 - val_loss: 0.2953 - val_acc: 0.9552 Epoch 452/500 133s 266ms/step - loss: 0.1205 - acc: 0.9993 - val_loss: 0.2953 - val_acc: 0.9551 Epoch 453/500 133s 265ms/step - loss: 0.1202 - acc: 0.9995 - val_loss: 0.2951 - val_acc: 0.9552 Epoch 454/500 133s 266ms/step - loss: 0.1205 - acc: 0.9991 - val_loss: 0.2949 - val_acc: 0.9550 Epoch 455/500 133s 266ms/step - loss: 0.1204 - acc: 0.9993 - val_loss: 0.2945 - val_acc: 0.9552 Epoch 456/500 133s 266ms/step - loss: 0.1203 - acc: 0.9993 - val_loss: 0.2947 - val_acc: 0.9546 Epoch 457/500 133s 266ms/step - loss: 0.1200 - acc: 0.9995 - val_loss: 0.2948 - val_acc: 0.9545 Epoch 458/500 133s 266ms/step - loss: 0.1204 - acc: 0.9993 - val_loss: 0.2948 - val_acc: 0.9548 Epoch 459/500 133s 266ms/step - loss: 0.1200 - acc: 0.9993 - val_loss: 0.2946 - val_acc: 0.9545 Epoch 460/500 133s 266ms/step - loss: 0.1203 - acc: 0.9992 - val_loss: 0.2942 - val_acc: 0.9551 Epoch 461/500 133s 266ms/step - loss: 0.1199 - acc: 0.9993 - val_loss: 0.2947 - val_acc: 0.9551 Epoch 462/500 133s 266ms/step - loss: 0.1201 - acc: 0.9993 - val_loss: 0.2946 - val_acc: 0.9547 Epoch 463/500 133s 266ms/step - loss: 0.1199 - acc: 0.9994 - val_loss: 0.2945 - val_acc: 0.9548 Epoch 464/500 133s 266ms/step - loss: 0.1200 - acc: 0.9994 - val_loss: 0.2946 - val_acc: 0.9549 Epoch 465/500 133s 266ms/step - loss: 0.1200 - acc: 0.9993 - val_loss: 0.2945 - val_acc: 0.9547 Epoch 466/500 133s 266ms/step - loss: 0.1202 - acc: 0.9990 - val_loss: 0.2941 - val_acc: 0.9552 Epoch 467/500 133s 266ms/step - loss: 0.1198 - acc: 0.9993 - val_loss: 0.2938 - val_acc: 0.9554 Epoch 468/500 133s 266ms/step - loss: 0.1195 - acc: 0.9994 - val_loss: 0.2938 - val_acc: 0.9552 Epoch 469/500 133s 266ms/step - loss: 0.1203 - acc: 0.9993 - val_loss: 0.2938 - val_acc: 0.9553 Epoch 470/500 133s 265ms/step - loss: 0.1199 - acc: 0.9994 - val_loss: 0.2941 - val_acc: 0.9553 Epoch 471/500 133s 266ms/step - loss: 0.1198 - acc: 0.9994 - val_loss: 0.2938 - val_acc: 0.9553 Epoch 472/500 133s 266ms/step - loss: 0.1199 - acc: 0.9993 - val_loss: 0.2938 - val_acc: 0.9551 Epoch 473/500 133s 266ms/step - loss: 0.1201 - acc: 0.9992 - val_loss: 0.2936 - val_acc: 0.9553 Epoch 474/500 133s 266ms/step - loss: 0.1197 - acc: 0.9993 - val_loss: 0.2937 - val_acc: 0.9549 Epoch 475/500 133s 265ms/step - loss: 0.1202 - acc: 0.9991 - val_loss: 0.2936 - val_acc: 0.9553 Epoch 476/500 133s 265ms/step - loss: 0.1201 - acc: 0.9992 - val_loss: 0.2935 - val_acc: 0.9554 Epoch 477/500 133s 265ms/step - loss: 0.1203 - acc: 0.9991 - val_loss: 0.2935 - val_acc: 0.9551 Epoch 478/500 133s 265ms/step - loss: 0.1198 - acc: 0.9992 - val_loss: 0.2938 - val_acc: 0.9553 Epoch 479/500 133s 265ms/step - loss: 0.1199 - acc: 0.9991 - val_loss: 0.2940 - val_acc: 0.9552 Epoch 480/500 133s 265ms/step - loss: 0.1199 - acc: 0.9992 - val_loss: 0.2938 - val_acc: 0.9553 Epoch 481/500 133s 266ms/step - loss: 0.1196 - acc: 0.9994 - val_loss: 0.2936 - val_acc: 0.9553 Epoch 482/500 133s 265ms/step - loss: 0.1196 - acc: 0.9994 - val_loss: 0.2938 - val_acc: 0.9552 Epoch 483/500 133s 265ms/step - loss: 0.1198 - acc: 0.9993 - val_loss: 0.2939 - val_acc: 0.9550 Epoch 484/500 133s 265ms/step - loss: 0.1196 - acc: 0.9994 - val_loss: 0.2942 - val_acc: 0.9549 Epoch 485/500 133s 265ms/step - loss: 0.1200 - acc: 0.9992 - val_loss: 0.2940 - val_acc: 0.9550 Epoch 486/500 133s 265ms/step - loss: 0.1194 - acc: 0.9994 - val_loss: 0.2941 - val_acc: 0.9552 Epoch 487/500 133s 265ms/step - loss: 0.1195 - acc: 0.9993 - val_loss: 0.2936 - val_acc: 0.9549 Epoch 488/500 133s 265ms/step - loss: 0.1196 - acc: 0.9993 - val_loss: 0.2936 - val_acc: 0.9551 Epoch 489/500 133s 265ms/step - loss: 0.1195 - acc: 0.9992 - val_loss: 0.2937 - val_acc: 0.9547 Epoch 490/500 133s 266ms/step - loss: 0.1195 - acc: 0.9992 - val_loss: 0.2936 - val_acc: 0.9550 Epoch 491/500 133s 266ms/step - loss: 0.1192 - acc: 0.9993 - val_loss: 0.2936 - val_acc: 0.9548 Epoch 492/500 133s 266ms/step - loss: 0.1197 - acc: 0.9993 - val_loss: 0.2933 - val_acc: 0.9551 Epoch 493/500 133s 266ms/step - loss: 0.1192 - acc: 0.9994 - val_loss: 0.2930 - val_acc: 0.9550 Epoch 494/500 133s 266ms/step - loss: 0.1195 - acc: 0.9994 - val_loss: 0.2929 - val_acc: 0.9553 Epoch 495/500 133s 266ms/step - loss: 0.1192 - acc: 0.9994 - val_loss: 0.2929 - val_acc: 0.9551 Epoch 496/500 133s 266ms/step - loss: 0.1192 - acc: 0.9993 - val_loss: 0.2930 - val_acc: 0.9553 Epoch 497/500 133s 266ms/step - loss: 0.1191 - acc: 0.9993 - val_loss: 0.2929 - val_acc: 0.9551 Epoch 498/500 133s 266ms/step - loss: 0.1192 - acc: 0.9994 - val_loss: 0.2928 - val_acc: 0.9552 Epoch 499/500 133s 266ms/step - loss: 0.1189 - acc: 0.9993 - val_loss: 0.2925 - val_acc: 0.9548 Epoch 500/500 133s 266ms/step - loss: 0.1197 - acc: 0.9991 - val_loss: 0.2927 - val_acc: 0.9547 Train loss: 0.11755225303769111 Train accuracy: 0.9996800003051758 Test loss: 0.29267876625061034 Test accuracy: 0.9547000050544738
比调参记录21的95.12%高了一点。怎么样能够突破96%呢？
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/document/8998530
深度残差网络+自适应参数化ReLU激活函数（调参记录23）Cifar10~95.47%
标签：import cto max nta ima als 宽度 linear set
原文地址：https://www.cnblogs.com/shisuzanian/p/12915980.html
