keras实现VGG16CIFAR10数据集方式_vgg16+finetune - CSDN文库

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keras实现实现VGG16 CIFAR10数据集方式数据集方式

主要介绍了keras实现VGG16 CIFAR10数据集方式，具有很好的参考价值，希望对大家有所帮助。一起跟随小

编过来看看吧

我就废话不多说了，大家还是直接看代码吧！

import keras

from keras.datasets import cifar10

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation, Flatten

from keras.layers import Conv2D, MaxPooling2D, BatchNormalization

from keras import optimizers

import numpy as np

from keras.layers.core import Lambda

from keras import backend as K

from keras.optimizers import SGD

from keras import regularizers

#import data

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train = x_train.astype('float32')

x_test = x_test.astype('float32')

y_train = keras.utils.to_categorical(y_train, 10)

y_test = keras.utils.to_categorical(y_test, 10)

weight_decay = 0.0005

nb_epoch=100

batch_size=32

#layer1 32*32*3

model = Sequential()

model.add(Conv2D(64, (3, 3), padding='same',

input_shape=(32,32,3),kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.3))

#layer2 32*32*64

model.add(Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

#layer3 16*16*64

model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.4))

#layer4 16*16*128

model.add(Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

#layer5 8*8*128

model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.4))

#layer6 8*8*256

model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.4))

#layer7 8*8*256

model.add(Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

#layer8 4*4*256

model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.4))

#layer9 4*4*512

model.add(Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))

model.add(Activation('relu'))

model.add(BatchNormalization())

model.add(Dropout(0.4))

#layer10 4*4*512

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