Keras - GoogLeNet

 Keras - GoogLeNet

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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#coding=utf-8

from keras.models import Model

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

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, padding='same',strides=(1,1),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 Inception(x,nb_filter):

    branch1x1 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

 

    branch3x3 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

    branch3x3 = Conv2d_BN(branch3x3,nb_filter,(3,3), padding='same',strides=(1,1),name=None)

 

    branch5x5 = Conv2d_BN(x,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

    branch5x5 = Conv2d_BN(branch5x5,nb_filter,(1,1), padding='same',strides=(1,1),name=None)

 

    branchpool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same')(x)

    branchpool = Conv2d_BN(branchpool,nb_filter,(1,1),padding='same',strides=(1,1),name=None)

 

    x = concatenate([branch1x1,branch3x3,branch5x5,branchpool],axis=3)

 

    return x

 

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

#padding = 'same'，填充为(步长-1）/2,还可以用ZeroPadding2D((3,3))

x = Conv2d_BN(inpt,64,(7,7),strides=(2,2),padding='same')

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

x = Conv2d_BN(x,192,(3,3),strides=(1,1),padding='same')

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

x = Inception(x,64)#256

x = Inception(x,120)#480

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

x = Inception(x,128)#512

x = Inception(x,128)

x = Inception(x,128)

x = Inception(x,132)#528

x = Inception(x,208)#832

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

x = Inception(x,208)

x = Inception(x,256)#1024

x = AveragePooling2D(pool_size=(7,7),strides=(7,7),padding='same')(x)

x = Dropout(0.4)(x)

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

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

model = Model(inpt,x,name='inception')

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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