#coding=utf-8  
from tensorflow.keras.models import Model  
from tensorflow.keras.layers import Input,Dense,Dropout,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D,concatenate  
from tensorflow.keras.layers 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()