[Harbin Institute of Technology] Dynamic ReLU: adaptive parameterized ReLU activation function (parameter record 13)

Posted May 25, 202033 min read

Adaptive parameterized ReLU is a dynamic ReLU(Dynamic ReLU), submitted to IEEE Transactions on Industrial Electronics on May 3, 2019, accepted on January 24, 2020, on 2020 2 Published on the IEEE's official website on the 13th.

Judging from the results of past parameter adjustments, overfitting is the most important issue. Based on Adjustment Record 12 , this paper reduces the number of layers to 9 residual modules and try again.

The principle of adaptive parameterized ReLU 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 Feb. 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, 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 = 625),
                    validation_data =(x_test, y_test), epochs = 5000,
                    verbose = 1, callbacks = [reduce_lr], workers = 10)

# get results
K.set_learning_phase(0)
DRSN_train_score = model.evaluate(x_train, y_train, batch_size = 625, 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 = 625, verbose = 0)
print('Test loss:', DRSN_test_score [0])
print('Test accuracy:', DRSN_test_score [1])

The experimental results are as follows:

Epoch 2500/5000
12s 151ms/step-loss:0.1258-acc:0.9867-val_loss:0.4697-val_acc:0.9024
Epoch 2501/5000
12s 151ms/step-loss:0.1274-acc:0.9852-val_loss:0.4688-val_acc:0.9026
Epoch 2502/5000
12s 151ms/step-loss:0.1260-acc:0.9861-val_loss:0.4585-val_acc:0.9040
Epoch 2503/5000
12s 152ms/step-loss:0.1241-acc:0.9869-val_loss:0.4489-val_acc:0.9066
Epoch 2504/5000
12s 152ms/step-loss:0.1236-acc:0.9869-val_loss:0.4469-val_acc:0.9106
Epoch 2505/5000
12s 151ms/step-loss:0.1276-acc:0.9850-val_loss:0.4515-val_acc:0.9034
Epoch 2506/5000
12s 151ms/step-loss:0.1252-acc:0.9864-val_loss:0.4586-val_acc:0.9074
Epoch 2507/5000
12s 151ms/step-loss:0.1289-acc:0.9852-val_loss:0.4585-val_acc:0.9057
Epoch 2508/5000
12s 151ms/step-loss:0.1285-acc:0.9853-val_loss:0.4485-val_acc:0.9077
Epoch 2509/5000
12s 151ms/step-loss:0.1284-acc:0.9851-val_loss:0.4529-val_acc:0.9032
Epoch 2510/5000
12s 151ms/step-loss:0.1287-acc:0.9855-val_loss:0.4567-val_acc:0.9040
Epoch 2511/5000
12s 151ms/step-loss:0.1253-acc:0.9862-val_loss:0.4554-val_acc:0.9080
Epoch 2512/5000
12s 152ms/step-loss:0.1262-acc:0.9859-val_loss:0.4477-val_acc:0.9086
Epoch 2513/5000
12s 151ms/step-loss:0.1241-acc:0.9864-val_loss:0.4531-val_acc:0.9063
Epoch 2514/5000
12s 150ms/step-loss:0.1247-acc:0.9866-val_loss:0.4484-val_acc:0.9073
Epoch 2515/5000
12s 151ms/step-loss:0.1239-acc:0.9869-val_loss:0.4502-val_acc:0.9078
Epoch 2516/5000
12s 151ms/step-loss:0.1275-acc:0.9857-val_loss:0.4790-val_acc:0.8981
Epoch 2517/5000
12s 152ms/step-loss:0.1259-acc:0.9862-val_loss:0.4625-val_acc:0.9063
Epoch 2518/5000
12s 151ms/step-loss:0.1278-acc:0.9853-val_loss:0.4751-val_acc:0.9009
Epoch 2519/5000
12s 151ms/step-loss:0.1283-acc:0.9857-val_loss:0.4655-val_acc:0.9056
Epoch 2520/5000
12s 151ms/step-loss:0.1275-acc:0.9859-val_loss:0.4386-val_acc:0.9085
Epoch 2521/5000
12s 151ms/step-loss:0.1245-acc:0.9871-val_loss:0.4699-val_acc:0.9006
Epoch 2522/5000
12s 151ms/step-loss:0.1278-acc:0.9860-val_loss:0.4520-val_acc:0.9050
Epoch 2523/5000
12s 151ms/step-loss:0.1249-acc:0.9864-val_loss:0.4566-val_acc:0.9056
Epoch 2524/5000
12s 152ms/step-loss:0.1278-acc:0.9855-val_loss:0.4650-val_acc:0.9018
Epoch 2525/5000
12s 151ms/step-loss:0.1235-acc:0.9873-val_loss:0.4555-val_acc:0.9061
Epoch 2526/5000
12s 151ms/step-loss:0.1260-acc:0.9862-val_loss:0.4556-val_acc:0.9061
Epoch 2527/5000
12s 152ms/step-loss:0.1261-acc:0.9866-val_loss:0.4667-val_acc:0.9040
Epoch 2528/5000
12s 152ms/step-loss:0.1240-acc:0.9874-val_loss:0.4539-val_acc:0.9083
Epoch 2529/5000
12s 152ms/step-loss:0.1281-acc:0.9856-val_loss:0.4584-val_acc:0.9048
Epoch 2530/5000
12s 151ms/step-loss:0.1234-acc:0.9871-val_loss:0.4538-val_acc:0.9048
Epoch 2531/5000
12s 151ms/step-loss:0.1235-acc:0.9868-val_loss:0.4504-val_acc:0.9056
Epoch 2532/5000
12s 151ms/step-loss:0.1247-acc:0.9871-val_loss:0.4529-val_acc:0.9053
Epoch 2533/5000
12s 150ms/step-loss:0.1241-acc:0.9872-val_loss:0.4591-val_acc:0.9034
Epoch 2534/5000
12s 152ms/step-loss:0.1255-acc:0.9865-val_loss:0.4502-val_acc:0.9058
Epoch 2535/5000
12s 151ms/step-loss:0.1254-acc:0.9865-val_loss:0.4596-val_acc:0.9039
Epoch 2536/5000
12s 152ms/step-loss:0.1239-acc:0.9872-val_loss:0.4488-val_acc:0.9040
Epoch 2537/5000
12s 151ms/step-loss:0.1260-acc:0.9865-val_loss:0.4494-val_acc:0.9042
Epoch 2538/5000
12s 150ms/step-loss:0.1288-acc:0.9851-val_loss:0.4621-val_acc:0.9039
Epoch 2539/5000
12s 152ms/step-loss:0.1267-acc:0.9855-val_loss:0.4497-val_acc:0.9068
Epoch 2540/5000
12s 151ms/step-loss:0.1250-acc:0.9869-val_loss:0.4626-val_acc:0.9024
Epoch 2541/5000
12s 152ms/step-loss:0.1272-acc:0.9856-val_loss:0.4621-val_acc:0.9038
Epoch 2542/5000
12s 151ms/step-loss:0.1258-acc:0.9862-val_loss:0.4738-val_acc:0.9044
Epoch 2543/5000
12s 152ms/step-loss:0.1257-acc:0.9862-val_loss:0.4597-val_acc:0.9061
Epoch 2544/5000
12s 151ms/step-loss:0.1271-acc:0.9854-val_loss:0.4571-val_acc:0.9008
Epoch 2545/5000
12s 151ms/step-loss:0.1247-acc:0.9861-val_loss:0.4450-val_acc:0.9065
Epoch 2546/5000
12s 152ms/step-loss:0.1273-acc:0.9860-val_loss:0.4568-val_acc:0.9031
Epoch 2547/5000
12s 151ms/step-loss:0.1291-acc:0.9855-val_loss:0.4558-val_acc:0.9034
Epoch 2548/5000
12s 152ms/step-loss:0.1280-acc:0.9849-val_loss:0.4463-val_acc:0.9077
Epoch 2549/5000
12s 151ms/step-loss:0.1237-acc:0.9868-val_loss:0.4427-val_acc:0.9083
Epoch 2550/5000
12s 151ms/step-loss:0.1247-acc:0.9865-val_loss:0.4486-val_acc:0.9060
Epoch 2551/5000
12s 152ms/step-loss:0.1265-acc:0.9864-val_loss:0.4414-val_acc:0.9047
Epoch 2552/5000
12s 151ms/step-loss:0.1275-acc:0.9859-val_loss:0.4652-val_acc:0.9003
Epoch 2553/5000
12s 151ms/step-loss:0.1241-acc:0.9864-val_loss:0.4713-val_acc:0.8976
Epoch 2554/5000
12s 152ms/step-loss:0.1258-acc:0.9862-val_loss:0.4549-val_acc:0.9048
Epoch 2555/5000
12s 151ms/step-loss:0.1249-acc:0.9866-val_loss:0.4376-val_acc:0.9069
Epoch 2556/5000
12s 152ms/step-loss:0.1251-acc:0.9866-val_loss:0.4519-val_acc:0.9062
Epoch 2557/5000
12s 151ms/step-loss:0.1269-acc:0.9857-val_loss:0.4479-val_acc:0.9069
Epoch 2558/5000
12s 151ms/step-loss:0.1240-acc:0.9870-val_loss:0.4629-val_acc:0.9023
Epoch 2559/5000
12s 151ms/step-loss:0.1257-acc:0.9866-val_loss:0.4487-val_acc:0.9039
Epoch 2560/5000
12s 151ms/step-loss:0.1272-acc:0.9859-val_loss:0.4574-val_acc:0.9029
Epoch 2561/5000
12s 152ms/step-loss:0.1238-acc:0.9872-val_loss:0.4530-val_acc:0.9073
Epoch 2562/5000
12s 152ms/step-loss:0.1226-acc:0.9872-val_loss:0.4589-val_acc:0.9048
Epoch 2563/5000
12s 151ms/step-loss:0.1283-acc:0.9854-val_loss:0.4525-val_acc:0.9032
Epoch 2564/5000
12s 151ms/step-loss:0.1286-acc:0.9851-val_loss:0.4488-val_acc:0.9063
Epoch 2565/5000
12s 150ms/step-loss:0.1263-acc:0.9862-val_loss:0.4520-val_acc:0.9044
Epoch 2566/5000
12s 152ms/step-loss:0.1280-acc:0.9854-val_loss:0.4561-val_acc:0.9025
Epoch 2567/5000
12s 151ms/step-loss:0.1259-acc:0.9860-val_loss:0.4532-val_acc:0.9034
Epoch 2568/5000
12s 156ms/step-loss:0.1249-acc:0.9864-val_loss:0.4449-val_acc:0.9072
Epoch 2569/5000
12s 152ms/step-loss:0.1269-acc:0.9857-val_loss:0.4465-val_acc:0.9056
Epoch 2570/5000
12s 153ms/step-loss:0.1282-acc:0.9853-val_loss:0.4445-val_acc:0.9074
Epoch 2571/5000
12s 153ms/step-loss:0.1268-acc:0.9857-val_loss:0.4496-val_acc:0.9028
Epoch 2572/5000
12s 152ms/step-loss:0.1255-acc:0.9860-val_loss:0.4600-val_acc:0.9038
Epoch 2573/5000
12s 153ms/step-loss:0.1206-acc:0.9884-val_loss:0.4555-val_acc:0.9057
Epoch 2574/5000
12s 152ms/step-loss:0.1242-acc:0.9867-val_loss:0.4483-val_acc:0.9071
Epoch 2575/5000
12s 153ms/step-loss:0.1225-acc:0.9871-val_loss:0.4497-val_acc:0.9054
Epoch 2576/5000
12s 152ms/step-loss:0.1233-acc:0.9876-val_loss:0.4645-val_acc:0.9039
Epoch 2577/5000
12s 153ms/step-loss:0.1247-acc:0.9865-val_loss:0.4584-val_acc:0.9036
Epoch 2578/5000
12s 153ms/step-loss:0.1248-acc:0.9864-val_loss:0.4666-val_acc:0.9045
Epoch 2579/5000
12s 153ms/step-loss:0.1245-acc:0.9868-val_loss:0.4668-val_acc:0.9063
Epoch 2580/5000
12s 153ms/step-loss:0.1253-acc:0.9862-val_loss:0.4609-val_acc:0.9023
Epoch 2581/5000
12s 153ms/step-loss:0.1242-acc:0.9869-val_loss:0.4450-val_acc:0.9058
Epoch 2582/5000
12s 152ms/step-loss:0.1253-acc:0.9863-val_loss:0.4391-val_acc:0.9068
Epoch 2583/5000
12s 153ms/step-loss:0.1266-acc:0.9861-val_loss:0.4420-val_acc:0.9066
Epoch 2584/5000
12s 153ms/step-loss:0.1255-acc:0.9865-val_loss:0.4480-val_acc:0.9056
Epoch 2585/5000
12s 152ms/step-loss:0.1281-acc:0.9851-val_loss:0.4449-val_acc:0.9052
Epoch 2586/5000
12s 152ms/step-loss:0.1247-acc:0.9868-val_loss:0.4536-val_acc:0.9050
Epoch 2587/5000
12s 152ms/step-loss:0.1273-acc:0.9857-val_loss:0.4712-val_acc:0.9007
Epoch 2588/5000
12s 153ms/step-loss:0.1292-acc:0.9852-val_loss:0.4495-val_acc:0.9059
Epoch 2589/5000
12s 153ms/step-loss:0.1253-acc:0.9866-val_loss:0.4626-val_acc:0.9051
Epoch 2590/5000
12s 153ms/step-loss:0.1248-acc:0.9867-val_loss:0.4609-val_acc:0.9021
Epoch 2591/5000
12s 152ms/step-loss:0.1273-acc:0.9855-val_loss:0.4594-val_acc:0.9039
Epoch 2592/5000
12s 152ms/step-loss:0.1257-acc:0.9857-val_loss:0.4519-val_acc:0.9023
Epoch 2593/5000
12s 152ms/step-loss:0.1317-acc:0.9845-val_loss:0.4526-val_acc:0.9063
Epoch 2594/5000
12s 153ms/step-loss:0.1255-acc:0.9864-val_loss:0.4529-val_acc:0.9066
Epoch 2595/5000
12s 153ms/step-loss:0.1244-acc:0.9863-val_loss:0.4540-val_acc:0.9076
Epoch 2596/5000
12s 153ms/step-loss:0.1268-acc:0.9859-val_loss:0.4632-val_acc:0.9022
Epoch 2597/5000
12s 153ms/step-loss:0.1250-acc:0.9864-val_loss:0.4440-val_acc:0.9057
Epoch 2598/5000
12s 153ms/step-loss:0.1246-acc:0.9870-val_loss:0.4489-val_acc:0.9035
Epoch 2599/5000
12s 153ms/step-loss:0.1252-acc:0.9857-val_loss:0.4671-val_acc:0.9035
Epoch 2600/5000
12s 153ms/step-loss:0.1253-acc:0.9866-val_loss:0.4532-val_acc:0.9077
Epoch 2601/5000
12s 153ms/step-loss:0.1228-acc:0.9870-val_loss:0.4503-val_acc:0.9026
Epoch 2602/5000
12s 153ms/step-loss:0.1225-acc:0.9873-val_loss:0.4490-val_acc:0.9027
Epoch 2603/5000
12s 152ms/step-loss:0.1238-acc:0.9871-val_loss:0.4430-val_acc:0.9066
Epoch 2604/5000
12s 152ms/step-loss:0.1279-acc:0.9856-val_loss:0.4576-val_acc:0.9054
Epoch 2605/5000
12s 152ms/step-loss:0.1253-acc:0.9864-val_loss:0.4425-val_acc:0.9069
Epoch 2606/5000
12s 152ms/step-loss:0.1269-acc:0.9859-val_loss:0.4542-val_acc:0.9024
Epoch 2607/5000
12s 152ms/step-loss:0.1281-acc:0.9852-val_loss:0.4673-val_acc:0.9023
Epoch 2608/5000
12s 152ms/step-loss:0.1269-acc:0.9864-val_loss:0.4638-val_acc:0.9025
Epoch 2609/5000
12s 152ms/step-loss:0.1261-acc:0.9861-val_loss:0.4499-val_acc:0.9059
Epoch 2610/5000
12s 152ms/step-loss:0.1240-acc:0.9871-val_loss:0.4502-val_acc:0.9070
Epoch 2611/5000
12s 151ms/step-loss:0.1236-acc:0.9874-val_loss:0.4592-val_acc:0.9018
Epoch 2612/5000
12s 151ms/step-loss:0.1233-acc:0.9874-val_loss:0.4603-val_acc:0.9032
Epoch 2613/5000
12s 151ms/step-loss:0.1265-acc:0.9853-val_loss:0.4574-val_acc:0.9056
Epoch 2614/5000
12s 152ms/step-loss:0.1229-acc:0.9871-val_loss:0.4514-val_acc:0.9052
Epoch 2615/5000
12s 152ms/step-loss:0.1233-acc:0.9869-val_loss:0.4699-val_acc:0.9013
Epoch 2616/5000
12s 151ms/step-loss:0.1248-acc:0.9863-val_loss:0.4715-val_acc:0.8995
Epoch 2617/5000
12s 151ms/step-loss:0.1284-acc:0.9853-val_loss:0.4647-val_acc:0.9043
Epoch 2618/5000
12s 151ms/step-loss:0.1267-acc:0.9857-val_loss:0.4656-val_acc:0.9005
Epoch 2619/5000
12s 152ms/step-loss:0.1232-acc:0.9874-val_loss:0.4657-val_acc:0.9035
Epoch 2620/5000
12s 152ms/step-loss:0.1274-acc:0.9859-val_loss:0.4522-val_acc:0.9051
Epoch 2621/5000
12s 151ms/step-loss:0.1275-acc:0.9859-val_loss:0.4528-val_acc:0.9034
Epoch 2622/5000
12s 152ms/step-loss:0.1258-acc:0.9863-val_loss:0.4600-val_acc:0.9036
Epoch 2623/5000
12s 152ms/step-loss:0.1245-acc:0.9865-val_loss:0.4626-val_acc:0.9047
Epoch 2624/5000
12s 152ms/step-loss:0.1241-acc:0.9866-val_loss:0.4644-val_acc:0.9043
Epoch 2625/5000
12s 152ms/step-loss:0.1245-acc:0.9871-val_loss:0.4762-val_acc:0.9035
Epoch 2626/5000
12s 152ms/step-loss:0.1263-acc:0.9859-val_loss:0.4579-val_acc:0.9033
Epoch 2627/5000
12s 151ms/step-loss:0.1253-acc:0.9867-val_loss:0.4616-val_acc:0.9022
Epoch 2628/5000
12s 151ms/step-loss:0.1268-acc:0.9858-val_loss:0.4721-val_acc:0.9026
Epoch 2629/5000
12s 151ms/step-loss:0.1270-acc:0.9854-val_loss:0.4528-val_acc:0.9048
Epoch 2630/5000
12s 151ms/step-loss:0.1258-acc:0.9863-val_loss:0.4496-val_acc:0.9056
Epoch 2631/5000
12s 152ms/step-loss:0.1241-acc:0.9868-val_loss:0.4469-val_acc:0.9058
Epoch 2632/5000
12s 151ms/step-loss:0.1261-acc:0.9865-val_loss:0.4923-val_acc:0.8972
Epoch 2633/5000
12s 152ms/step-loss:0.1255-acc:0.9860-val_loss:0.4662-val_acc:0.9011
Epoch 2634/5000
12s 151ms/step-loss:0.1230-acc:0.9873-val_loss:0.4461-val_acc:0.9055
Epoch 2635/5000
12s 151ms/step-loss:0.1206-acc:0.9877-val_loss:0.4495-val_acc:0.9055
Epoch 2636/5000
12s 152ms/step-loss:0.1234-acc:0.9874-val_loss:0.4671-val_acc:0.9053
Epoch 2637/5000
12s 152ms/step-loss:0.1233-acc:0.9872-val_loss:0.4637-val_acc:0.9032
Epoch 2638/5000
12s 151ms/step-loss:0.1221-acc:0.9874-val_loss:0.4634-val_acc:0.9042
Epoch 2639/5000
12s 151ms/step-loss:0.1209-acc:0.9877-val_loss:0.4655-val_acc:0.9023
Epoch 2640/5000
12s 152ms/step-loss:0.1258-acc:0.9864-val_loss:0.4556-val_acc:0.9065
Epoch 2641/5000
12s 152ms/step-loss:0.1247-acc:0.9867-val_loss:0.4576-val_acc:0.9018
Epoch 2642/5000
12s 152ms/step-loss:0.1274-acc:0.9855-val_loss:0.4584-val_acc:0.9051
Epoch 2643/5000
12s 152ms/step-loss:0.1282-acc:0.9856-val_loss:0.4528-val_acc:0.9066
Epoch 2644/5000
12s 151ms/step-loss:0.1270-acc:0.9858-val_loss:0.4617-val_acc:0.9015
Epoch 2645/5000
12s 152ms/step-loss:0.1279-acc:0.9853-val_loss:0.4448-val_acc:0.9063
Epoch 2646/5000
12s 151ms/step-loss:0.1256-acc:0.9865-val_loss:0.4449-val_acc:0.9055
Epoch 2647/5000
12s 152ms/step-loss:0.1259-acc:0.9864-val_loss:0.4429-val_acc:0.9052
Epoch 2648/5000
12s 152ms/step-loss:0.1244-acc:0.9869-val_loss:0.4474-val_acc:0.9038
Epoch 2649/5000
12s 151ms/step-loss:0.1236-acc:0.9874-val_loss:0.4459-val_acc:0.9072
Epoch 2650/5000
12s 151ms/step-loss:0.1246-acc:0.9872-val_loss:0.4469-val_acc:0.9039
Epoch 2651/5000
12s 151ms/step-loss:0.1254-acc:0.9868-val_loss:0.4540-val_acc:0.9056
Epoch 2652/5000
12s 151ms/step-loss:0.1261-acc:0.9866-val_loss:0.4616-val_acc:0.9003
Epoch 2653/5000
12s 151ms/step-loss:0.1254-acc:0.9860-val_loss:0.4525-val_acc:0.9029
Epoch 2654/5000
12s 151ms/step-loss:0.1226-acc:0.9874-val_loss:0.4589-val_acc:0.9032
Epoch 2655/5000
12s 151ms/step-loss:0.1244-acc:0.9868-val_loss:0.4548-val_acc:0.9027
Epoch 2656/5000
12s 151ms/step-loss:0.1252-acc:0.9871-val_loss:0.4438-val_acc:0.9057
Epoch 2657/5000
12s 151ms/step-loss:0.1228-acc:0.9869-val_loss:0.4554-val_acc:0.9045
Epoch 2658/5000
12s 152ms/step-loss:0.1280-acc:0.9857-val_loss:0.4481-val_acc:0.9066
Epoch 2659/5000
12s 152ms/step-loss:0.1251-acc:0.9861-val_loss:0.4492-val_acc:0.9075
Epoch 2660/5000
12s 151ms/step-loss:0.1222-acc:0.9873-val_loss:0.4501-val_acc:0.9045
Epoch 2661/5000
12s 152ms/step-loss:0.1251-acc:0.9864-val_loss:0.4597-val_acc:0.9040
Epoch 2662/5000
12s 151ms/step-loss:0.1258-acc:0.9860-val_loss:0.4588-val_acc:0.9039
Epoch 2663/5000
12s 152ms/step-loss:0.1235-acc:0.9863-val_loss:0.4472-val_acc:0.9056
Epoch 2664/5000
12s 152ms/step-loss:0.1215-acc:0.9874-val_loss:0.4674-val_acc:0.9004
Epoch 2665/5000
12s 151ms/step-loss:0.1239-acc:0.9864-val_loss:0.4674-val_acc:0.9026
Epoch 2666/5000
12s 151ms/step-loss:0.1241-acc:0.9867-val_loss:0.4636-val_acc:0.9023
Epoch 2667/5000
12s 151ms/step-loss:0.1250-acc:0.9866-val_loss:0.4620-val_acc:0.9025
Epoch 2668/5000
12s 151ms/step-loss:0.1252-acc:0.9864-val_loss:0.4758-val_acc:0.8995
Epoch 2669/5000
12s 152ms/step-loss:0.1278-acc:0.9858-val_loss:0.4816-val_acc:0.8986
Epoch 2670/5000
12s 152ms/step-loss:0.1251-acc:0.9864-val_loss:0.4692-val_acc:0.9009
Epoch 2671/5000
12s 151ms/step-loss:0.1281-acc:0.9852-val_loss:0.4615-val_acc:0.9024
Epoch 2672/5000
12s 152ms/step-loss:0.1233-acc:0.9868-val_loss:0.4583-val_acc:0.9055
Epoch 2673/5000
12s 152ms/step-loss:0.1228-acc:0.9871-val_loss:0.4689-val_acc:0.9039
Epoch 2674/5000
12s 152ms/step-loss:0.1267-acc:0.9856-val_loss:0.4596-val_acc:0.9049
Epoch 2675/5000
12s 151ms/step-loss:0.1289-acc:0.9847-val_loss:0.4575-val_acc:0.9020
Epoch 2676/5000
12s 152ms/step-loss:0.1234-acc:0.9870-val_loss:0.4527-val_acc:0.9068
Epoch 2677/5000
12s 151ms/step-loss:0.1248-acc:0.9865-val_loss:0.4588-val_acc:0.9035
Epoch 2678/5000
12s 151ms/step-loss:0.1234-acc:0.9866-val_loss:0.4667-val_acc:0.9009
Epoch 2679/5000
12s 152ms/step-loss:0.1234-acc:0.9869-val_loss:0.4613-val_acc:0.9032
Epoch 2680/5000
12s 151ms/step-loss:0.1248-acc:0.9860-val_loss:0.4748-val_acc:0.9014
Epoch 2681/5000
12s 152ms/step-loss:0.1256-acc:0.9856-val_loss:0.4579-val_acc:0.9051
Epoch 2682/5000
12s 151ms/step-loss:0.1276-acc:0.9854-val_loss:0.4688-val_acc:0.9019
Epoch 2683/5000
12s 152ms/step-loss:0.1237-acc:0.9866-val_loss:0.4623-val_acc:0.9023
Epoch 2684/5000
12s 152ms/step-loss:0.1232-acc:0.9872-val_loss:0.4618-val_acc:0.9033
Epoch 2685/5000
12s 151ms/step-loss:0.1253-acc:0.9865-val_loss:0.4712-val_acc:0.9007
Epoch 2686/5000
12s 151ms/step-loss:0.1248-acc:0.9864-val_loss:0.4675-val_acc:0.9035
Epoch 2687/5000
12s 152ms/step-loss:0.1291-acc:0.9851-val_loss:0.4600-val_acc:0.9031
Epoch 2688/5000
12s 151ms/step-loss:0.1255-acc:0.9862-val_loss:0.4623-val_acc:0.9017
Epoch 2689/5000
12s 152ms/step-loss:0.1273-acc:0.9854-val_loss:0.4609-val_acc:0.9021
Epoch 2690/5000
12s 152ms/step-loss:0.1262-acc:0.9862-val_loss:0.4454-val_acc:0.9048
Epoch 2691/5000
12s 151ms/step-loss:0.1231-acc:0.9869-val_loss:0.4612-val_acc:0.9040
Epoch 2692/5000
12s 151ms/step-loss:0.1254-acc:0.9867-val_loss:0.4524-val_acc:0.9045
Epoch 2693/5000
12s 152ms/step-loss:0.1233-acc:0.9874-val_loss:0.4567-val_acc:0.9045
Epoch 2694/5000
12s 151ms/step-loss:0.1243-acc:0.9864-val_loss:0.4603-val_acc:0.9023
Epoch 2695/5000
12s 151ms/step-loss:0.1269-acc:0.9861-val_loss:0.4714-val_acc:0.8998
Epoch 2696/5000
12s 152ms/step-loss:0.1240-acc:0.9866-val_loss:0.4402-val_acc:0.9068
Epoch 2697/5000
12s 156ms/step-loss:0.1245-acc:0.9864-val_loss:0.4597-val_acc:0.9040
Epoch 2698/5000
12s 151ms/step-loss:0.1255-acc:0.9863-val_loss:0.4499-val_acc:0.9045
Epoch 2699/5000
12s 152ms/step-loss:0.1223-acc:0.9876-val_loss:0.4660-val_acc:0.9054
Epoch 2700/5000
12s 152ms/step-loss:0.1248-acc:0.9865-val_loss:0.4537-val_acc:0.9045
Epoch 2701/5000
12s 154ms/step-loss:0.1252-acc:0.9864-val_loss:0.4683-val_acc:0.9019
Epoch 2702/5000
12s 153ms/step-loss:0.1254-acc:0.9859-val_loss:0.4657-val_acc:0.9039
Epoch 2703/5000
12s 153ms/step-loss:0.1234-acc:0.9874-val_loss:0.4679-val_acc:0.9006
Epoch 2704/5000
12s 153ms/step-loss:0.1268-acc:0.9856-val_loss:0.4724-val_acc:0.8994
Epoch 2705/5000
12s 153ms/step-loss:0.1244-acc:0.9869-val_loss:0.4762-val_acc:0.8988
Epoch 2706/5000
12s 153ms/step-loss:0.1245-acc:0.9869-val_loss:0.4669-val_acc:0.9034
Epoch 2707/5000
12s 153ms/step-loss:0.1226-acc:0.9873-val_loss:0.4629-val_acc:0.9046
Epoch 2708/5000
12s 152ms/step-loss:0.1244-acc:0.9868-val_loss:0.4528-val_acc:0.9066
Epoch 2709/5000
12s 153ms/step-loss:0.1208-acc:0.9874-val_loss:0.4600-val_acc:0.9013
Epoch 2710/5000
12s 153ms/step-loss:0.1251-acc:0.9856-val_loss:0.4551-val_acc:0.9039
Epoch 2711/5000
12s 153ms/step-loss:0.1242-acc:0.9872-val_loss:0.4457-val_acc:0.9073
Epoch 2712/5000
12s 153ms/step-loss:0.1269-acc:0.9859-val_loss:0.4577-val_acc:0.9027
Epoch 2713/5000
12s 153ms/step-loss:0.1295-acc:0.9846-val_loss:0.4609-val_acc:0.9039
Epoch 2714/5000
12s 153ms/step-loss:0.1244-acc:0.9870-val_loss:0.4614-val_acc:0.9019
Epoch 2715/5000
12s 153ms/step-loss:0.1213-acc:0.9877-val_loss:0.4560-val_acc:0.9046
Epoch 2716/5000
12s 152ms/step-loss:0.1252-acc:0.9862-val_loss:0.4501-val_acc:0.9059
Epoch 2717/5000
12s 153ms/step-loss:0.1257-acc:0.9860-val_loss:0.4686-val_acc:0.9015
Epoch 2718/5000
12s 153ms/step-loss:0.1233-acc:0.9870-val_loss:0.4636-val_acc:0.9022
Epoch 2719/5000
12s 153ms/step-loss:0.1242-acc:0.9864-val_loss:0.4403-val_acc:0.9086
Epoch 2720/5000
12s 153ms/step-loss:0.1268-acc:0.9858-val_loss:0.4516-val_acc:0.9050
Epoch 2721/5000
12s 152ms/step-loss:0.1222-acc:0.9876-val_loss:0.4555-val_acc:0.9055
Epoch 2722/5000
12s 152ms/step-loss:0.1192-acc:0.9883-val_loss:0.4387-val_acc:0.9076
Epoch 2723/5000
12s 152ms/step-loss:0.1235-acc:0.9868-val_loss:0.4663-val_acc:0.9059
Epoch 2724/5000
12s 152ms/step-loss:0.1246-acc:0.9862-val_loss:0.4729-val_acc:0.9028
Epoch 2725/5000
12s 152ms/step-loss:0.1291-acc:0.9844-val_loss:0.4582-val_acc:0.9037
Epoch 2726/5000
12s 152ms/step-loss:0.1228-acc:0.9872-val_loss:0.4613-val_acc:0.9028
Epoch 2727/5000
12s 152ms/step-loss:0.1225-acc:0.9870-val_loss:0.4545-val_acc:0.9074
Epoch 2728/5000
12s 152ms/step-loss:0.1225-acc:0.9873-val_loss:0.4643-val_acc:0.9047
Epoch 2729/5000
12s 152ms/step-loss:0.1240-acc:0.9872-val_loss:0.4518-val_acc:0.9052
Epoch 2730/5000
12s 152ms/step-loss:0.1248-acc:0.9867-val_loss:0.4580-val_acc:0.9045
Epoch 2731/5000
12s 152ms/step-loss:0.1258-acc:0.9863-val_loss:0.4620-val_acc:0.9028
Epoch 2732/5000
12s 152ms/step-loss:0.1273-acc:0.9862-val_loss:0.4536-val_acc:0.9053
Epoch 2733/5000
12s 152ms/step-loss:0.1273-acc:0.9862-val_loss:0.4440-val_acc:0.9074
Epoch 2734/5000
12s 152ms/step-loss:0.1257-acc:0.9856-val_loss:0.4456-val_acc:0.9043
Epoch 2735/5000
12s 151ms/step-loss:0.1231-acc:0.9876-val_loss:0.4559-val_acc:0.9051
Epoch 2736/5000
12s 152ms/step-loss:0.1254-acc:0.9858-val_loss:0.4470-val_acc:0.9077
Epoch 2737/5000
12s 152ms/step-loss:0.1246-acc:0.9866-val_loss:0.4549-val_acc:0.9048
Epoch 2738/5000
12s 152ms/step-loss:0.1223-acc:0.9874-val_loss:0.4676-val_acc:0.9047
Epoch 2739/5000
12s 151ms/step-loss:0.1228-acc:0.9871-val_loss:0.4466-val_acc:0.9072
Epoch 2740/5000
12s 152ms/step-loss:0.1236-acc:0.9869-val_loss:0.4514-val_acc:0.9045
Epoch 2741/5000
12s 151ms/step-loss:0.1271-acc:0.9853-val_loss:0.4638-val_acc:0.9020
Epoch 2742/5000
12s 152ms/step-loss:0.1256-acc:0.9860-val_loss:0.4513-val_acc:0.9084
Epoch 2743/5000
12s 152ms/step-loss:0.1241-acc:0.9868-val_loss:0.4537-val_acc:0.9090
Epoch 2744/5000
12s 152ms/step-loss:0.1242-acc:0.9866-val_loss:0.4572-val_acc:0.9058
Epoch 2745/5000
12s 152ms/step-loss:0.1251-acc:0.9858-val_loss:0.4705-val_acc:0.9030
Epoch 2746/5000
12s 151ms/step-loss:0.1258-acc:0.9858-val_loss:0.4691-val_acc:0.9034
Epoch 2747/5000
12s 151ms/step-loss:0.1255-acc:0.9865-val_loss:0.4597-val_acc:0.9013
Epoch 2748/5000
12s 151ms/step-loss:0.1255-acc:0.9862-val_loss:0.4440-val_acc:0.9070
Epoch 2749/5000
12s 152ms/step-loss:0.1256-acc:0.9856-val_loss:0.4690-val_acc:0.9029
Epoch 2750/5000
12s 152ms/step-loss:0.1253-acc:0.9864-val_loss:0.4515-val_acc:0.9037
Epoch 2751/5000
12s 151ms/step-loss:0.1230-acc:0.9869-val_loss:0.4741-val_acc:0.9035
Epoch 2752/5000
12s 151ms/step-loss:0.1289-acc:0.9846-val_loss:0.4739-val_acc:0.9010
Epoch 2753/5000
12s 151ms/step-loss:0.1281-acc:0.9854-val_loss:0.4494-val_acc:0.9033
Epoch 2754/5000
12s 151ms/step-loss:0.1258-acc:0.9863-val_loss:0.4558-val_acc:0.9058
Epoch 2755/5000
12s 151ms/step-loss:0.1261-acc:0.9859-val_loss:0.4617-val_acc:0.9045
Epoch 2756/5000
12s 151ms/step-loss:0.1250-acc:0.9864-val_loss:0.4554-val_acc:0.9052
Epoch 2757/5000
12s 151ms/step-loss:0.1262-acc:0.9859-val_loss:0.4478-val_acc:0.9060
Epoch 2758/5000
12s 152ms/step-loss:0.1225-acc:0.9872-val_loss:0.4455-val_acc:0.9047
Epoch 2759/5000
12s 151ms/step-loss:0.1234-acc:0.9868-val_loss:0.4477-val_acc:0.9067
Epoch 2760/5000
12s 152ms/step-loss:0.1271-acc:0.9859-val_loss:0.4535-val_acc:0.9032
Epoch 2761/5000
12s 151ms/step-loss:0.1238-acc:0.9867-val_loss:0.4691-val_acc:0.9012
Epoch 2762/5000
12s 151ms/step-loss:0.1233-acc:0.9868-val_loss:0.4584-val_acc:0.9029
Epoch 2763/5000
12s 151ms/step-loss:0.1244-acc:0.9871-val_loss:0.4508-val_acc:0.9016
Epoch 2764/5000
12s 151ms/step-loss:0.1233-acc:0.9865-val_loss:0.4672-val_acc:0.9027
Epoch 2765/5000
12s 151ms/step-loss:0.1264-acc:0.9863-val_loss:0.4467-val_acc:0.9066
Epoch 2766/5000
12s 151ms/step-loss:0.1244-acc:0.9866-val_loss:0.4622-val_acc:0.9018
Epoch 2767/5000
12s 151ms/step-loss:0.1231-acc:0.9872-val_loss:0.4463-val_acc:0.9054
Epoch 2768/5000
12s 151ms/step-loss:0.1241-acc:0.9867-val_loss:0.4526-val_acc:0.9055
Epoch 2769/5000
12s 151ms/step-loss:0.1271-acc:0.9854-val_loss:0.4525-val_acc:0.9023
Epoch 2770/5000
12s 152ms/step-loss:0.1225-acc:0.9878-val_loss:0.4537-val_acc:0.9046
Epoch 2771/5000
12s 151ms/step-loss:0.1248-acc:0.9865-val_loss:0.4617-val_acc:0.9050
Epoch 2772/5000
12s 151ms/step-loss:0.1251-acc:0.9861-val_loss:0.4598-val_acc:0.9050
Epoch 2773/5000
12s 151ms/step-loss:0.1268-acc:0.9859-val_loss:0.4630-val_acc:0.9044
Epoch 2774/5000
12s 152ms/step-loss:0.1231-acc:0.9874-val_loss:0.4568-val_acc:0.9015
Epoch 2775/5000
12s 152ms/step-loss:0.1254-acc:0.9861-val_loss:0.4578-val_acc:0.9038
Epoch 2776/5000
12s 151ms/step-loss:0.1225-acc:0.9873-val_loss:0.4647-val_acc:0.9030
Epoch 2777/5000
12s 151ms/step-loss:0.1227-acc:0.9874-val_loss:0.4515-val_acc:0.9047
Epoch 2778/5000
12s 151ms/step-loss:0.1261-acc:0.9858-val_loss:0.4580-val_acc:0.9032
Epoch 2779/5000
12s 151ms/step-loss:0.1240-acc:0.9866-val_loss:0.4722-val_acc:0.9035
Epoch 2780/5000
12s 151ms/step-loss:0.1236-acc:0.9871-val_loss:0.4720-val_acc:0.9014
Epoch 2781/5000
12s 151ms/step-loss:0.1264-acc:0.9859-val_loss:0.4523-val_acc:0.9031
Epoch 2782/5000
12s 152ms/step-loss:0.1261-acc:0.9859-val_loss:0.4556-val_acc:0.9046
Epoch 2783/5000
12s 151ms/step-loss:0.1266-acc:0.9859-val_loss:0.4390-val_acc:0.9088
Epoch 2784/5000
12s 151ms/step-loss:0.1244-acc:0.9862-val_loss:0.4533-val_acc:0.9033
Epoch 2785/5000
12s 152ms/step-loss:0.1227-acc:0.9871-val_loss:0.4548-val_acc:0.9038
Epoch 2786/5000
12s 152ms/step-loss:0.1229-acc:0.9870-val_loss:0.4468-val_acc:0.9066
Epoch 2787/5000
12s 152ms/step-loss:0.1220-acc:0.9870-val_loss:0.4466-val_acc:0.9060
Epoch 2788/5000
12s 152ms/step-loss:0.1294-acc:0.9849-val_loss:0.4455-val_acc:0.9025
Epoch 2789/5000
12s 151ms/step-loss:0.1251-acc:0.9866-val_loss:0.4618-val_acc:0.9024
Epoch 2790/5000
12s 151ms/step-loss:0.1235-acc:0.9871-val_loss:0.4551-val_acc:0.9035
Epoch 2791/5000
12s 152ms/step-loss:0.1260-acc:0.9858-val_loss:0.4594-val_acc:0.9025
Epoch 2792/5000
12s 151ms/step-loss:0.1207-acc:0.9879-val_loss:0.4490-val_acc:0.9024
Epoch 2793/5000
12s 151ms/step-loss:0.1224-acc:0.9874-val_loss:0.4498-val_acc:0.9037
Epoch 2794/5000
12s 151ms/step-loss:0.1236-acc:0.9874-val_loss:0.4533-val_acc:0.9008
Epoch 2795/5000
12s 152ms/step-loss:0.1224-acc:0.9874-val_loss:0.4410-val_acc:0.9074
Epoch 2796/5000
12s 152ms/step-loss:0.1237-acc:0.9867-val_loss:0.4506-val_acc:0.9057
Epoch 2797/5000
12s 151ms/step-loss:0.1237-acc:0.9868-val_loss:0.4451-val_acc:0.9042
Epoch 2798/5000
12s 151ms/step-loss:0.1247-acc:0.9862-val_loss:0.4678-val_acc:0.9009
Epoch 2799/5000
12s 151ms/step-loss:0.1262-acc:0.9864-val_loss:0.4639-val_acc:0.9024
Epoch 2800/5000
12s 151ms/step-loss:0.1269-acc:0.9857-val_loss:0.4550-val_acc:0.9029
Epoch 2801/5000
12s 151ms/step-loss:0.1273-acc:0.9857-val_loss:0.4514-val_acc:0.9036
Epoch 2802/5000
12s 152ms/step-loss:0.1240-acc:0.9869-val_loss:0.4525-val_acc:0.9031
Epoch 2803/5000
12s 151ms/step-loss:0.1244-acc:0.9865-val_loss:0.4652-val_acc:0.9018
Epoch 2804/5000
12s 151ms/step-loss:0.1265-acc:0.9864-val_loss:0.4765-val_acc:0.8992
Epoch 2805/5000
12s 151ms/step-loss:0.1260-acc:0.9855-val_loss:0.4589-val_acc:0.9025
Epoch 2806/5000
12s 151ms/step-loss:0.1244-acc:0.9870-val_loss:0.4605-val_acc:0.9039
Epoch 2807/5000
12s 151ms/step-loss:0.1243-acc:0.9864-val_loss:0.4580-val_acc:0.9028
Epoch 2808/5000
12s 152ms/step-loss:0.1213-acc:0.9874-val_loss:0.4514-val_acc:0.9060
Epoch 2809/5000
12s 151ms/step-loss:0.1213-acc:0.9876-val_loss:0.4663-val_acc:0.9008
Epoch 2810/5000
12s 152ms/step-loss:0.1249-acc:0.9870-val_loss:0.4634-val_acc:0.9025
Epoch 2811/5000
12s 151ms/step-loss:0.1252-acc:0.9865-val_loss:0.4576-val_acc:0.9057
Epoch 2812/5000
12s 151ms/step-loss:0.1250-acc:0.9861-val_loss:0.4713-val_acc:0.9003
Epoch 2813/5000
12s 151ms/step-loss:0.1257-acc:0.9859-val_loss:0.4511-val_acc:0.9059
Epoch 2814/5000
12s 152ms/step - loss:0.1257 - acc:0.9867 - val_loss:0.4700 - val_acc:0.9009
Epoch 2815/5000
12s 151ms/step - loss:0.1253 - acc:0.9860 - val_loss:0.4602 - val_acc:0.9046
Epoch 2816/5000
12s 151ms/step - loss:0.1262 - acc:0.9856 - val_loss:0.4570 - val_acc:0.9012
Epoch 2817/5000
12s 151ms/step - loss:0.1256 - acc:0.9861 - val_loss:0.4609 - val_acc:0.9020
Epoch 2818/5000
12s 151ms/step - loss:0.1262 - acc:0.9862 - val_loss:0.4482 - val_acc:0.9059
Epoch 2819/5000
12s 151ms/step - loss:0.1249 - acc:0.9865 - val_loss:0.4531 - val_acc:0.9058
Epoch 2820/5000
12s 151ms/step - loss:0.1225 - acc:0.9876 - val_loss:0.4457 - val_acc:0.9053
Epoch 2821/5000
12s 151ms/step - loss:0.1226 - acc:0.9871 - val_loss:0.4470 - val_acc:0.9061
Epoch 2822/5000
12s 152ms/step - loss:0.1253 - acc:0.9856 - val_loss:0.4415 - val_acc:0.9091
Epoch 2823/5000
12s 151ms/step - loss:0.1273 - acc:0.9849 - val_loss:0.4557 - val_acc:0.9026
Epoch 2824/5000
12s 151ms/step - loss:0.1238 - acc:0.9873 - val_loss:0.4350 - val_acc:0.9062
Epoch 2825/5000
12s 151ms/step - loss:0.1216 - acc:0.9875 - val_loss:0.4519 - val_acc:0.9055
Epoch 2826/5000
12s 151ms/step - loss:0.1245 - acc:0.9867 - val_loss:0.4502 - val_acc:0.9055
Epoch 2827/5000
12s 151ms/step - loss:0.1230 - acc:0.9872 - val_loss:0.4619 - val_acc:0.9049
Epoch 2828/5000
12s 151ms/step - loss:0.1238 - acc:0.9869 - val_loss:0.4563 - val_acc:0.9032
Epoch 2829/5000
12s 152ms/step - loss:0.1243 - acc:0.9863 - val_loss:0.4650 - val_acc:0.9017
Epoch 2830/5000
12s 152ms/step - loss:0.1241 - acc:0.9869 - val_loss:0.4628 - val_acc:0.9023
Epoch 2831/5000
12s 151ms/step - loss:0.1268 - acc:0.9857 - val_loss:0.4599 - val_acc:0.9058
Epoch 2832/5000
12s 151ms/step - loss:0.1234 - acc:0.9871 - val_loss:0.4551 - val_acc:0.9061
Epoch 2833/5000
12s 151ms/step - loss:0.1235 - acc:0.9865 - val_loss:0.4608 - val_acc:0.9055
Epoch 2834/5000
12s 151ms/step - loss:0.1257 - acc:0.9866 - val_loss:0.4463 - val_acc:0.9076
Epoch 2835/5000
12s 151ms/step - loss:0.1231 - acc:0.9869 - val_loss:0.4648 - val_acc:0.8993
Epoch 2836/5000
12s 151ms/step - loss:0.1246 - acc:0.9864 - val_loss:0.4587 - val_acc:0.9045
Epoch 2837/5000
12s 152ms/step - loss:0.1254 - acc:0.9865 - val_loss:0.4570 - val_acc:0.9009
Epoch 2838/5000
12s 151ms/step - loss:0.1257 - acc:0.9861 - val_loss:0.4606 - val_acc:0.9026
Epoch 2839/5000
12s 152ms/step - loss:0.1248 - acc:0.9864 - val_loss:0.4673 - val_acc:0.9034
Epoch 2840/5000
12s 151ms/step - loss:0.1253 - acc:0.9862 - val_loss:0.4600 - val_acc:0.9042
Epoch 2841/5000
12s 151ms/step - loss:0.1243 - acc:0.9866 - val_loss:0.4696 - val_acc:0.9013
Epoch 2842/5000
12s 150ms/step - loss:0.1240 - acc:0.9871 - val_loss:0.4504 - val_acc:0.9052
Epoch 2843/5000
12s 151ms/step - loss:0.1238 - acc:0.9865 - val_loss:0.4590 - val_acc:0.9025
Epoch 2844/5000
12s 151ms/step - loss:0.1246 - acc:0.9866 - val_loss:0.4587 - val_acc:0.9003
Epoch 2845/5000
12s 151ms/step - loss:0.1252 - acc:0.9865 - val_loss:0.4593 - val_acc:0.9022
Epoch 2846/5000
12s 152ms/step - loss:0.1225 - acc:0.9876 - val_loss:0.4584 - val_acc:0.9064
Epoch 2847/5000
12s 151ms/step - loss:0.1250 - acc:0.9866 - val_loss:0.4614 - val_acc:0.9063
Epoch 2848/5000
12s 152ms/step - loss:0.1252 - acc:0.9864 - val_loss:0.4774 - val_acc:0.9039
Epoch 2849/5000
12s 152ms/step - loss:0.1243 - acc:0.9868 - val_loss:0.4544 - val_acc:0.9066
Epoch 2850/5000
12s 152ms/step - loss:0.1255 - acc:0.9861 - val_loss:0.4497 - val_acc:0.9040
Epoch 2851/5000
12s 151ms/step - loss:0.1236 - acc:0.9867 - val_loss:0.4512 - val_acc:0.9018
Epoch 2852/5000
12s 152ms/step - loss:0.1258 - acc:0.9860 - val_loss:0.4568 - val_acc:0.9042
Epoch 2853/5000
12s 151ms/step - loss:0.1212 - acc:0.9871 - val_loss:0.4575 - val_acc:0.9030
Epoch 2854/5000
12s 151ms/step - loss:0.1240 - acc:0.9865 - val_loss:0.4592 - val_acc:0.9024
Epoch 2855/5000
12s 151ms/step - loss:0.1231 - acc:0.9869 - val_loss:0.4464 - val_acc:0.9079
Epoch 2856/5000
12s 152ms/step - loss:0.1229 - acc:0.9872 - val_loss:0.4571 - val_acc:0.9039
Epoch 2857/5000
12s 152ms/step - loss:0.1237 - acc:0.9871 - val_loss:0.4527 - val_acc:0.9056
Epoch 2858/5000
12s 152ms/step - loss:0.1224 - acc:0.9872 - val_loss:0.4403 - val_acc:0.9081
Epoch 2859/5000
12s 151ms/step - loss:0.1249 - acc:0.9859 - val_loss:0.4666 - val_acc:0.9017
Epoch 2860/5000
12s 151ms/step - loss:0.1259 - acc:0.9859 - val_loss:0.4420 - val_acc:0.9056
Epoch 2861/5000
12s 151ms/step - loss:0.1240 - acc:0.9865 - val_loss:0.4547 - val_acc:0.9039
Epoch 2862/5000
12s 151ms/step - loss:0.1240 - acc:0.9868 - val_loss:0.4583 - val_acc:0.9038
Epoch 2863/5000
12s 151ms/step - loss:0.1255 - acc:0.9856 - val_loss:0.4665 - val_acc:0.9044
Epoch 2864/5000
12s 152ms/step - loss:0.1264 - acc:0.9862 - val_loss:0.4568 - val_acc:0.9049
Epoch 2865/5000
12s 151ms/step - loss:0.1278 - acc:0.9852 - val_loss:0.4730 - val_acc:0.9007
Epoch 2866/5000
12s 152ms/step - loss:0.1257 - acc:0.9861 - val_loss:0.4602 - val_acc:0.9011
Epoch 2867/5000
12s 152ms/step - loss:0.1275 - acc:0.9862 - val_loss:0.4459 - val_acc:0.9055
Epoch 2868/5000
12s 152ms/step - loss:0.1265 - acc:0.9858 - val_loss:0.4441 - val_acc:0.9048
Epoch 2869/5000
12s 151ms/step - loss:0.1243 - acc:0.9874 - val_loss:0.4566 - val_acc:0.9034
Epoch 2870/5000
12s 151ms/step - loss:0.1261 - acc:0.9864 - val_loss:0.4653 - val_acc:0.9012
Epoch 2871/5000
12s 152ms/step - loss:0.1267 - acc:0.9860 - val_loss:0.4621 - val_acc:0.8996
Epoch 2872/5000
12s 151ms/step - loss:0.1240 - acc:0.9863 - val_loss:0.4517 - val_acc:0.9050
Epoch 2873/5000
12s 151ms/step - loss:0.1240 - acc:0.9867 - val_loss:0.4478 - val_acc:0.9058
Epoch 2874/5000
12s 151ms/step - loss:0.1241 - acc:0.9865 - val_loss:0.4507 - val_acc:0.9058
Epoch 2875/5000
12s 152ms/step - loss:0.1218 - acc:0.9879 - val_loss:0.4455 - val_acc:0.9055
Epoch 2876/5000
12s 151ms/step - loss:0.1252 - acc:0.9859 - val_loss:0.4639 - val_acc:0.9012
Epoch 2877/5000
12s 151ms/step - loss:0.1217 - acc:0.9872 - val_loss:0.4713 - val_acc:0.9009
Epoch 2878/5000
12s 152ms/step - loss:0.1227 - acc:0.9877 - val_loss:0.4590 - val_acc:0.9031
Epoch 2879/5000
12s 150ms/step - loss:0.1247 - acc:0.9863 - val_loss:0.4390 - val_acc:0.9054
Epoch 2880/5000
12s 152ms/step - loss:0.1251 - acc:0.9865 - val_loss:0.4582 - val_acc:0.9025
Epoch 2881/5000
12s 151ms/step - loss:0.1261 - acc:0.9859 - val_loss:0.4480 - val_acc:0.9053
Epoch 2882/5000
12s 152ms/step - loss:0.1228 - acc:0.9869 - val_loss:0.4430 - val_acc:0.9089
Epoch 2883/5000
12s 151ms/step - loss:0.1215 - acc:0.9869 - val_loss:0.4476 - val_acc:0.9061
Epoch 2884/5000
12s 151ms/step - loss:0.1272 - acc:0.9856 - val_loss:0.4586 - val_acc:0.9062
Epoch 2885/5000
12s 151ms/step - loss:0.1243 - acc:0.9861 - val_loss:0.4557 - val_acc:0.9021
Epoch 2886/5000
12s 151ms/step - loss:0.1240 - acc:0.9858 - val_loss:0.4631 - val_acc:0.9041
Epoch 2887/5000
12s 152ms/step - loss:0.1234 - acc:0.9870 - val_loss:0.4378 - val_acc:0.9074
Epoch 2888/5000
12s 152ms/step - loss:0.1245 - acc:0.9865 - val_loss:0.4415 - val_acc:0.9072
Epoch 2889/5000
12s 151ms/step - loss:0.1252 - acc:0.9865 - val_loss:0.4535 - val_acc:0.9072
Epoch 2890/5000
12s 151ms/step - loss:0.1218 - acc:0.9869 - val_loss:0.4449 - val_acc:0.9073
Epoch 2891/5000
12s 152ms/step - loss:0.1268 - acc:0.9852 - val_loss:0.4485 - val_acc:0.9015
Epoch 2892/5000
12s 152ms/step - loss:0.1248 - acc:0.9865 - val_loss:0.4578 - val_acc:0.9034
Epoch 2893/5000
12s 151ms/step - loss:0.1236 - acc:0.9870 - val_loss:0.4452 - val_acc:0.9067
Epoch 2894/5000
12s 151ms/step - loss:0.1251 - acc:0.9866 - val_loss:0.4537 - val_acc:0.9031
Epoch 2895/5000
12s 151ms/step - loss:0.1268 - acc:0.9859 - val_loss:0.4650 - val_acc:0.9049
Epoch 2896/5000
12s 151ms/step - loss:0.1245 - acc:0.9864 - val_loss:0.4558 - val_acc:0.9049
Epoch 2897/5000
12s 152ms/step - loss:0.1225 - acc:0.9867 - val_loss:0.4567 - val_acc:0.9062
Epoch 2898/5000
12s 151ms/step - loss:0.1227 - acc:0.9873 - val_loss:0.4518 - val_acc:0.9032
Epoch 2899/5000
12s 151ms/step - loss:0.1224 - acc:0.9877 - val_loss:0.4370 - val_acc:0.9065
Epoch 2900/5000
12s 151ms/step - loss:0.1232 - acc:0.9870 - val_loss:0.4514 - val_acc:0.9074
Epoch 2901/5000
12s 151ms/step - loss:0.1230 - acc:0.9867 - val_loss:0.4497 - val_acc:0.9035
Epoch 2902/5000
12s 152ms/step - loss:0.1242 - acc:0.9870 - val_loss:0.4468 - val_acc:0.9046
Epoch 2903/5000
12s 151ms/step - loss:0.1241 - acc:0.9866 - val_loss:0.4631 - val_acc:0.9030
Epoch 2904/5000
12s 152ms/step - loss:0.1232 - acc:0.9871 - val_loss:0.4451 - val_acc:0.9057
Epoch 2905/5000
12s 152ms/step - loss:0.1217 - acc:0.9869 - val_loss:0.4466 - val_acc:0.9079
Epoch 2906/5000
12s 150ms/step - loss:0.1249 - acc:0.9861 - val_loss:0.4484 - val_acc:0.9049
Epoch 2907/5000
12s 152ms/step - loss:0.1253 - acc:0.9865 - val_loss:0.4467 - val_acc:0.9079
Epoch 2908/5000
12s 152ms/step - loss:0.1250 - acc:0.9866 - val_loss:0.4523 - val_acc:0.9040
Epoch 2909/5000
12s 151ms/step - loss:0.1261 - acc:0.9855 - val_loss:0.4477 - val_acc:0.9075
Epoch 2910/5000
12s 152ms/step - loss:0.1247 - acc:0.9866 - val_loss:0.4358 - val_acc:0.9090
Epoch 2911/5000
12s 150ms/step - loss:0.1222 - acc:0.9876 - val_loss:0.4622 - val_acc:0.9016
Epoch 2912/5000
12s 152ms/step - loss:0.1246 - acc:0.9863 - val_loss:0.4487 - val_acc:0.9069
Epoch 2913/5000
12s 152ms/step - loss:0.1231 - acc:0.9873 - val_loss:0.4466 - val_acc:0.9051
Epoch 2914/5000
12s 151ms/step - loss:0.1240 - acc:0.9866 - val_loss:0.4554 - val_acc:0.9051
Epoch 2915/5000
12s 152ms/step - loss:0.1232 - acc:0.9872 - val_loss:0.4558 - val_acc:0.9063
Epoch 2916/5000
12s 151ms/step - loss:0.1217 - acc:0.9870 - val_loss:0.4533 - val_acc:0.9055
Epoch 2917/5000
12s 151ms/step - loss:0.1242 - acc:0.9866 - val_loss:0.4584 - val_acc:0.9017
Epoch 2918/5000
12s 152ms/step - loss:0.1233 - acc:0.9871 - val_loss:0.4594 - val_acc:0.9025
Epoch 2919/5000
12s 151ms/step - loss:0.1213 - acc:0.9876 - val_loss:0.4582 - val_acc:0.9034
Epoch 2920/5000
12s 151ms/step - loss:0.1226 - acc:0.9872 - val_loss:0.4494 - val_acc:0.9047
Epoch 2921/5000
12s 151ms/step - loss:0.1218 - acc:0.9872 - val_loss:0.4576 - val_acc:0.9066
Epoch 2922/5000
12s 151ms/step - loss:0.1243 - acc:0.9863 - val_loss:0.4597 - val_acc:0.9055
Epoch 2923/5000
12s 152ms/step - loss:0.1269 - acc:0.9852 - val_loss:0.4563 - val_acc:0.9054
Epoch 2924/5000
12s 151ms/step - loss:0.1242 - acc:0.9865 - val_loss:0.4465 - val_acc:0.9038
Epoch 2925/5000
12s 152ms/step - loss:0.1218 - acc:0.9872 - val_loss:0.4531 - val_acc:0.9027
Epoch 2926/5000
12s 151ms/step - loss:0.1247 - acc:0.9863 - val_loss:0.4551 - val_acc:0.9046
Epoch 2927/5000
12s 151ms/step - loss:0.1242 - acc:0.9866 - val_loss:0.4591 - val_acc:0.9019
Epoch 2928/5000
12s 151ms/step - loss:0.1232 - acc:0.9867 - val_loss:0.4550 - val_acc:0.9037
Epoch 2929/5000
12s 151ms/step - loss:0.1216 - acc:0.9879 - val_loss:0.4495 - val_acc:0.9054
Epoch 2930/5000
12s 152ms/step - loss:0.1228 - acc:0.9871 - val_loss:0.4478 - val_acc:0.9043
Epoch 2931/5000
12s 152ms/step - loss:0.1243 - acc:0.9859 - val_loss:0.4601 - val_acc:0.9025
Epoch 2932/5000
12s 152ms/step - loss:0.1238 - acc:0.9865 - val_loss:0.4561 - val_acc:0.9050
Epoch 2933/5000
12s 152ms/step - loss:0.1233 - acc:0.9873 - val_loss:0.4625 - val_acc:0.9024
Epoch 2934/5000
12s 152ms/step - loss:0.1245 - acc:0.9859 - val_loss:0.4558 - val_acc:0.9025
Epoch 2935/5000
12s 152ms/step - loss:0.1252 - acc:0.9862 - val_loss:0.4648 - val_acc:0.9030
Epoch 2936/5000
12s 151ms/step - loss:0.1229 - acc:0.9872 - val_loss:0.4648 - val_acc:0.9024
Epoch 2937/5000
12s 152ms/step - loss:0.1233 - acc:0.9867 - val_loss:0.4577 - val_acc:0.9015
Epoch 2938/5000
12s 151ms/step - loss:0.1266 - acc:0.9854 - val_loss:0.4721 - val_acc:0.9011
Epoch 2939/5000
12s 151ms/step - loss:0.1236 - acc:0.9868 - val_loss:0.4562 - val_acc:0.9050
Epoch 2940/5000
12s 152ms/step - loss:0.1221 - acc:0.9868 - val_loss:0.4583 - val_acc:0.9046
Epoch 2941/5000
12s 152ms/step - loss:0.1244 - acc:0.9863 - val_loss:0.4601 - val_acc:0.9039
Epoch 2942/5000
12s 151ms/step - loss:0.1234 - acc:0.9873 - val_loss:0.4710 - val_acc:0.9021
Epoch 2943/5000
12s 152ms/step - loss:0.1227 - acc:0.9869 - val_loss:0.4574 - val_acc:0.9057
Epoch 2944/5000
12s 151ms/step - loss:0.1245 - acc:0.9862 - val_loss:0.4752 - val_acc:0.9023
Epoch 2945/5000
12s 151ms/step - loss:0.1228 - acc:0.9866 - val_loss:0.4870 - val_acc:0.8995
Epoch 2946/5000
12s 151ms/step - loss:0.1230 - acc:0.9870 - val_loss:0.4680 - val_acc:0.9030
Epoch 2947/5000
12s 151ms/step - loss:0.1240 - acc:0.9870 - val_loss:0.4631 - val_acc:0.9043
Epoch 2948/5000
12s 151ms/step - loss:0.1244 - acc:0.9863 - val_loss:0.4531 - val_acc:0.9046
Epoch 2949/5000
12s 151ms/step - loss:0.1265 - acc:0.9857 - val_loss:0.4524 - val_acc:0.9047
Epoch 2950/5000
12s 151ms/step - loss:0.1242 - acc:0.9870 - val_loss:0.4612 - val_acc:0.9021
Epoch 2951/5000
12s 152ms/step - loss:0.1252 - acc:0.9861 - val_loss:0.4594 - val_acc:0.9056
Epoch 2952/5000
12s 151ms/step - loss:0.1238 - acc:0.9865 - val_loss:0.4678 - val_acc:0.9039
Epoch 2953/5000
12s 151ms/step - loss:0.1195 - acc:0.9883 - val_loss:0.4595 - val_acc:0.9059
Epoch 2954/5000
12s 151ms/step - loss:0.1219 - acc:0.9870 - val_loss:0.4533 - val_acc:0.9056
Epoch 2955/5000
12s 152ms/step - loss:0.1266 - acc:0.9854 - val_loss:0.4631 - val_acc:0.9023
Epoch 2956/5000
12s 151ms/step - loss:0.1252 - acc:0.9856 - val_loss:0.4567 - val_acc:0.9050
Epoch 2957/5000
12s 151ms/step - loss:0.1263 - acc:0.9862 - val_loss:0.4424 - val_acc:0.9104
Epoch 2958/5000
12s 151ms/step - loss:0.1221 - acc:0.9871 - val_loss:0.4534 - val_acc:0.9059
Epoch 2959/5000
12s 152ms/step - loss:0.1227 - acc:0.9869 - val_loss:0.4523 - val_acc:0.9097
Epoch 2960/5000
12s 150ms/step - loss:0.1237 - acc:0.9874 - val_loss:0.4554 - val_acc:0.9057
Epoch 2961/5000
12s 151ms/step - loss:0.1246 - acc:0.9860 - val_loss:0.4488 - val_acc:0.9077
Epoch 2962/5000
12s 152ms/step - loss:0.1235 - acc:0.9872 - val_loss:0.4559 - val_acc:0.9021
Epoch 2963/5000
12s 151ms/step - loss:0.1226 - acc:0.9873 - val_loss:0.4650 - val_acc:0.9019
Epoch 2964/5000
12s 151ms/step - loss:0.1259 - acc:0.9858 - val_loss:0.4653 - val_acc:0.9009
Epoch 2965/5000
12s 151ms/step - loss:0.1259 - acc:0.9861 - val_loss:0.4566 - val_acc:0.9026
Epoch 2966/5000
12s 151ms/step - loss:0.1221 - acc:0.9873 - val_loss:0.4626 - val_acc:0.9038
Epoch 2967/5000
12s 152ms/step - loss:0.1251 - acc:0.9861 - val_loss:0.4591 - val_acc:0.9049
Epoch 2968/5000
12s 151ms/step - loss:0.1242 - acc:0.9861 - val_loss:0.4526 - val_acc:0.9073
Epoch 2969/5000
12s 152ms/step - loss:0.1235 - acc:0.9865 - val_loss:0.4467 - val_acc:0.9064
Epoch 2970/5000
12s 151ms/step - loss:0.1272 - acc:0.9854 - val_loss:0.4561 - val_acc:0.9058
Epoch 2971/5000
12s 152ms/step - loss:0.1233 - acc:0.9869 - val_loss:0.4655 - val_acc:0.9031
Epoch 2972/5000
12s 152ms/step - loss:0.1221 - acc:0.9871 - val_loss:0.4414 - val_acc:0.9078
Epoch 2973/5000
12s 151ms/step - loss:0.1232 - acc:0.9867 - val_loss:0.4539 - val_acc:0.9062
Epoch 2974/5000
12s 152ms/step - loss:0.1253 - acc:0.9860 - val_loss:0.4650 - val_acc:0.9040
Epoch 2975/5000
12s 151ms/step - loss:0.1249 - acc:0.9865 - val_loss:0.4454 - val_acc:0.9089
Epoch 2976/5000
12s 151ms/step - loss:0.1219 - acc:0.9878 - val_loss:0.4514 - val_acc:0.9044
Epoch 2977/5000
12s 152ms/step - loss:0.1255 - acc:0.9853 - val_loss:0.4585 - val_acc:0.9056
Epoch 2978/5000
12s 151ms/step - loss:0.1242 - acc:0.9862 - val_loss:0.4536 - val_acc:0.9058
Epoch 2979/5000
12s 152ms/step - loss:0.1248 - acc:0.9864 - val_loss:0.4638 - val_acc:0.9048
Epoch 2980/5000
12s 150ms/step - loss:0.1275 - acc:0.9854 - val_loss:0.4498 - val_acc:0.9050
Epoch 2981/5000
12s 152ms/step - loss:0.1260 - acc:0.9856 - val_loss:0.4647 - val_acc:0.9033
Epoch 2982/5000
12s 151ms/step - loss:0.1245 - acc:0.9865 - val_loss:0.4649 - val_acc:0.9033
Epoch 2983/5000
12s 151ms/step - loss:0.1265 - acc:0.9857 - val_loss:0.4429 - val_acc:0.9103
Epoch 2984/5000
12s 151ms/step - loss:0.1242 - acc:0.9866 - val_loss:0.4585 - val_acc:0.9023
Epoch 2985/5000
12s 152ms/step - loss:0.1257 - acc:0.9858 - val_loss:0.4490 - val_acc:0.9038
Epoch 2986/5000
12s 151ms/step - loss:0.1252 - acc:0.9863 - val_loss:0.4498 - val_acc:0.9043
Epoch 2987/5000
12s 151ms/step - loss:0.1248 - acc:0.9864 - val_loss:0.4459 - val_acc:0.9058
Epoch 2988/5000
12s 152ms/step - loss:0.1226 - acc:0.9868 - val_loss:0.4559 - val_acc:0.9032
Epoch 2989/5000
12s 151ms/step - loss:0.1237 - acc:0.9868 - val_loss:0.4560 - val_acc:0.9051
Epoch 2990/5000
12s 151ms/step - loss:0.1242 - acc:0.9870 - val_loss:0.4648 - val_acc:0.9033
Epoch 2991/5000
12s 152ms/step - loss:0.1245 - acc:0.9863 - val_loss:0.4604 - val_acc:0.9018
Epoch 2992/5000
12s 151ms/step - loss:0.1240 - acc:0.9865 - val_loss:0.4575 - val_acc:0.9036
Epoch 2993/5000
13s 157ms/step - loss:0.1226 - acc:0.9872 - val_loss:0.4626 - val_acc:0.9028
Epoch 2994/5000
12s 152ms/step - loss:0.1255 - acc:0.9860 - val_loss:0.4777 - val_acc:0.8998
Epoch 2995/5000
12s 151ms/step - loss:0.1256 - acc:0.9860 - val_loss:0.4574 - val_acc:0.9023
Epoch 2996/5000
12s 151ms/step - loss:0.1232 - acc:0.9866 - val_loss:0.4663 - val_acc:0.9024
Epoch 2997/5000
12s 151ms/step - loss:0.1210 - acc:0.9881 - val_loss:0.4663 - val_acc:0.9046
Epoch 2998/5000
12s 152ms/step - loss:0.1201 - acc:0.9876 - val_loss:0.4492 - val_acc:0.9065
Epoch 2999/5000
12s 152ms/step - loss:0.1260 - acc:0.9861 - val_loss:0.4677 - val_acc:0.9031
Epoch 3000/5000
12s 151ms/step - loss:0.1256 - acc:0.9861 - val_loss:0.4517 - val_acc:0.9044
Epoch 3001/5000
lr changed to 0.0009999999776482583
12s 151ms/step - loss:0.1226 - acc:0.9877 - val_loss:0.4332 - val_acc:0.9071
Epoch 3002/5000
12s 151ms/step - loss:0.1123 - acc:0.9911 - val_loss:0.4282 - val_acc:0.9088
Epoch 3003/5000
12s 152ms/step - loss:0.1072 - acc:0.9926 - val_loss:0.4277 - val_acc:0.9110
Epoch 3004/5000
12s 152ms/step - loss:0.1051 - acc:0.9938 - val_loss:0.4253 - val_acc:0.9108
Epoch 3005/5000
12s 152ms/step - loss:0.1041 - acc:0.9941 - val_loss:0.4242 - val_acc:0.9101
Epoch 3006/5000
12s 151ms/step - loss:0.1021 - acc:0.9945 - val_loss:0.4259 - val_acc:0.9098
Epoch 3007/5000
12s 151ms/step - loss:0.1034 - acc:0.9940 - val_loss:0.4255 - val_acc:0.9100
Epoch 3008/5000
12s 152ms/step - loss:0.1018 - acc:0.9949 - val_loss:0.4252 - val_acc:0.9100
Epoch 3009/5000
12s 152ms/step - loss:0.1029 - acc:0.9945 - val_loss:0.4276 - val_acc:0.9103
Epoch 3010/5000
12s 151ms/step - loss:0.1018 - acc:0.9947 - val_loss:0.4275 - val_acc:0.9102
Epoch 3011/5000
12s 152ms/step - loss:0.1004 - acc:0.9951 - val_loss:0.4237 - val_acc:0.9106
Epoch 3012/5000
12s 152ms/step - loss:0.0996 - acc:0.9954 - val_loss:0.4213 - val_acc:0.9120
Epoch 3013/5000
12s 151ms/step - loss:0.0997 - acc:0.9953 - val_loss:0.4247 - val_acc:0.9112
Epoch 3014/5000
12s 151ms/step - loss:0.0998 - acc:0.9956 - val_loss:0.4249 - val_acc:0.9111
Epoch 3015/5000
12s 152ms/step - loss:0.0999 - acc:0.9953 - val_loss:0.4261 - val_acc:0.9103
Epoch 3016/5000
12s 151ms/step - loss:0.0984 - acc:0.9958 - val_loss:0.4285 - val_acc:0.9102
Epoch 3017/5000
12s 151ms/step - loss:0.0999 - acc:0.9954 - val_loss:0.4284 - val_acc:0.9098
Epoch 3018/5000
12s 154ms/step - loss:0.0997 - acc:0.9952 - val_loss:0.4290 - val_acc:0.9105
Epoch 3019/5000
12s 152ms/step - loss:0.0992 - acc:0.9955 - val_loss:0.4273 - val_acc:0.9118
Epoch 3020/5000
12s 151ms/step - loss:0.0988 - acc:0.9953 - val_loss:0.4270 - val_acc:0.9110
Epoch 3021/5000
12s 152ms/step - loss:0.0988 - acc:0.9957 - val_loss:0.4298 - val_acc:0.9104
Epoch 3022/5000
12s 151ms/step - loss:0.0984 - acc:0.9957 - val_loss:0.4317 - val_acc:0.9103
Epoch 3023/5000
12s 151ms/step - loss:0.0976 - acc:0.9960 - val_loss:0.4282 - val_acc:0.9107
Epoch 3024/5000
12s 152ms/step - loss:0.0984 - acc:0.9959 - val_loss:0.4283 - val_acc:0.9111
Epoch 3025/5000
12s 152ms/step - loss:0.0969 - acc:0.9960 - val_loss:0.4288 - val_acc:0.9090

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...