将深度可分离卷积融入inception结构的代码实现

下面是一个将深度可分离卷积融入inception结构的代码实现，示例代码中使用的是Keras框架：

from keras.models import Model
from keras.layers import Input, Concatenate, Conv2D, SeparableConv2D, MaxPooling2D, Dropout

def inception_module(inputs, filters_1x1, filters_3x3_reduce, filters_3x3, filters_5x5_reduce, filters_5x5, filters_pool_proj):
    # 1x1卷积分支
    branch_1x1 = Conv2D(filters_1x1, (1, 1), padding='same', activation='relu')(inputs)
    
    # 3x3卷积分支
    branch_3x3 = Conv2D(filters_3x3_reduce, (1, 1), padding='same', activation='relu')(inputs)
    branch_3x3 = SeparableConv2D(filters_3x3, (3, 3), padding='same', activation='relu')(branch_3x3)
    
    # 5x5卷积分支
    branch_5x5 = Conv2D(filters_5x5_reduce, (1, 1), padding='same', activation='relu')(inputs)
    branch_5x5 = SeparableConv2D(filters_5x5, (5, 5), padding='same', activation='relu')(branch_5x5)
    
    # 池化分支
    branch_pool = MaxPooling2D((3, 3), strides=(1, 1), padding='same')(inputs)
    branch_pool = Conv2D(filters_pool_proj, (1, 1), padding='same', activation='relu')(branch_pool)
    
    # 合并分支
    output = Concatenate(axis=-1)([branch_1x1, branch_3x3, branch_5x5, branch_pool])
    return output

# 构建模型
inputs = Input(shape=(224, 224, 3))
x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu')(inputs)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = Conv2D(64, (1, 1), padding='same', activation='relu')(x)
x = Conv2D(192, (3, 3), padding='same', activation='relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = inception_module(x, 64, 96, 128, 16, 32, 32)
x = inception_module(x, 128, 128, 192, 32, 96, 64)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = inception_module(x, 192, 96, 208, 16, 48, 64)
x = inception_module(x, 160, 112, 224, 24, 64, 64)
x = inception_module(x, 128, 128, 256, 24, 64, 64)
x = inception_module(x, 112, 144, 288, 32, 64, 64)
x = inception_module(x, 256, 160, 320, 32, 128, 128)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = inception_module(x, 256, 160, 320, 32, 128, 128)
x = inception_module(x, 384, 192, 384, 48, 128, 128)
x = Dropout(0.4)(x)
x = Conv2D(1000, (1, 1), padding='same', activation='relu')(x)
x = GlobalAveragePooling2D()(x)
outputs = Activation('softmax')(x)

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