Keras - ResNet

 Keras - ResNet

2021/03/21

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說明：

# 為本來的註解

## 為新增的註解

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## ResNet - 34

#coding=utf-8

from keras.models import Model

from keras.layers import Input,Dense,Dropout,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,concatenate,Activation,ZeroPadding2D

from keras.layers import add,Flatten

#from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D

import numpy as np

seed = 7

np.random.seed(seed)

 

def Conv2d_BN(x, nb_filter,kernel_size, strides=(1,1), padding='same',name=None):

    if name is not None:

        bn_name = name + '_bn'

        conv_name = name + '_conv'

    else:

        bn_name = None

        conv_name = None

 

    x = Conv2D(nb_filter,kernel_size,padding=padding,strides=strides,activation='relu',name=conv_name)(x)

    x = BatchNormalization(axis=3,name=bn_name)(x)

    return x

 

def Conv_Block(inpt,nb_filter,kernel_size,strides=(1,1), with_conv_shortcut=False):

    x = Conv2d_BN(inpt,nb_filter=nb_filter,kernel_size=kernel_size,strides=strides,padding='same')

    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size,padding='same')

    if with_conv_shortcut:

        shortcut = Conv2d_BN(inpt,nb_filter=nb_filter,strides=strides,kernel_size=kernel_size)

        x = add([x,shortcut])

        return x

    else:

        x = add([x,inpt])

        return x

 

inpt = Input(shape=(224,224,3))

x = ZeroPadding2D((3,3))(inpt)

x = Conv2d_BN(x,nb_filter=64,kernel_size=(7,7),strides=(2,2),padding='valid')

x = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='same')(x)

#(56,56,64)

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=64,kernel_size=(3,3))

#(28,28,128)

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=128,kernel_size=(3,3))

#(14,14,256)

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=256,kernel_size=(3,3))

#(7,7,512)

x = Conv_Block(x,nb_filter=512,kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)

x = Conv_Block(x,nb_filter=512,kernel_size=(3,3))

x = Conv_Block(x,nb_filter=512,kernel_size=(3,3))

x = AveragePooling2D(pool_size=(7,7))(x)

x = Flatten()(x)

x = Dense(1000,activation='softmax')(x)

 

model = Model(inputs=inpt,outputs=x)

model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])

model.summary()

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版权声明：本文为CSDN博主「wmy199216」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。

原文链接：https://blog.csdn.net/wmy199216/article/details/71171401

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