keras搬砖系列-残差网络的实现

ResNet-50图

#coding=utf-8
from keras.models import Model
from keras.layers import Input,Dense,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,ZeroPadding2D
from keras.layers import add,Flatten
#from keras.layers.convolutional import Conv2D,MaxPooling2D,AveragePooling2D
from keras.optimizers import SGD
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[0],kernel_size=(1,1),strides=strides,padding='same')
    x = Conv2d_BN(x, nb_filter=nb_filter[1], kernel_size=(3,3), padding='same')
    x = Conv2d_BN(x, nb_filter=nb_filter[2], kernel_size=(1,1), padding='same')
    if with_conv_shortcut:
        shortcut = Conv2d_BN(inpt,nb_filter=nb_filter[2],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)

x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3),strides=(1,1),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[64,64,256],kernel_size=(3,3))

x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[128,128,512],kernel_size=(3,3))

x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[256,256,1024],kernel_size=(3,3))

x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3),strides=(2,2),with_conv_shortcut=True)
x = Conv_Block(x,nb_filter=[512,512,2048],kernel_size=(3,3))
x = Conv_Block(x,nb_filter=[512,512,2048],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)
sgd = SGD(decay=0.0001,momentum=0.9)
model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
model.summary()
           

参考大佬连接：

http://blog.csdn.net/wmy199216/article/details/71171401

http://blog.csdn.net/ljp1919/article/details/78996146

他们实在太强啦