将深度可分离卷积融入inception结构的代码实现

以下是使用深度可分离卷积融入inception结构的代码实现：

from keras.layers import Input, Conv2D, DepthwiseConv2D, Concatenate

def inception_block(x, filters):
    tower_1 = Conv2D(filters=filters[0], kernel_size=(1, 1), padding='same', activation='relu')(x)

    tower_2 = Conv2D(filters=filters[1], kernel_size=(1, 1), padding='same', activation='relu')(x)
    tower_2 = DepthwiseConv2D(kernel_size=(3, 3), padding='same', activation='relu')(tower_2)
    tower_2 = Conv2D(filters=filters[1], kernel_size=(1, 1), padding='same', activation='relu')(tower_2)

    tower_3 = Conv2D(filters=filters[2], kernel_size=(1, 1), padding='same', activation='relu')(x)
    tower_3 = DepthwiseConv2D(kernel_size=(5, 5), padding='same', activation='relu')(tower_3)
    tower_3 = Conv2D(filters=filters[2], kernel_size=(1, 1), padding='same', activation='relu')(tower_3)

    tower_4 = DepthwiseConv2D(kernel_size=(3, 3), padding='same', activation='relu')(x)
    tower_4 = Conv2D(filters=filters[3], kernel_size=(1, 1), padding='same', activation='relu')(tower_4)

    output = Concatenate(axis=-1)([tower_1, tower_2, tower_3, tower_4])
    return output

这里的`inception_block`函数实现了一个inception结构，其中包含四个分支，分别是一个1x1的卷积层、一个1x1卷积接一个3x3深度可分离卷积、一个1x1卷积接一个5x5深度可分离卷积和一个3x3深度可分离卷积。最后将四个分支的输出在通道维度上拼接起来。