使用keras框架写一个搭建mobilenetv1的代码

以下是使用Keras搭建MobileNetV1的代码：

from keras.layers import Input, Conv2D, DepthwiseConv2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dropout, Reshape, Activation
from keras.models import Model

def MobileNetV1(input_shape, num_classes):
    # 定义输入
    input_tensor = Input(shape=input_shape)

    # 定义卷积块
    def conv_block(inputs, filters, kernel_size, strides):
        x = Conv2D(filters, kernel_size, strides=strides, padding='same', use_bias=False)(inputs)
        x = BatchNormalization()(x)
        x = ReLU()(x)
        return x

    # 定义深度可分离卷积块
    def depthwise_conv_block(inputs, filters, kernel_size, strides):
        x = DepthwiseConv2D(kernel_size, strides=strides, padding='same', use_bias=False)(inputs)
        x = BatchNormalization()(x)
        x = ReLU()(x)
        x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', use_bias=False)(x)
        x = BatchNormalization()(x)
        x = ReLU()(x)
        return x

    # 定义MobileNetV1网络结构
    x = conv_block(input_tensor, 32, (3, 3), (2, 2))
    x = depthwise_conv_block(x, 64, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 128, (3, 3), (2, 2))
    x = depthwise_conv_block(x, 128, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 256, (3, 3), (2, 2))
    x = depthwise_conv_block(x, 256, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 512, (3, 3), (2, 2))
    x = depthwise_conv_block(x, 512, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 512, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 512, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 512, (3, 3), (1, 1))
    x = depthwise_conv_block(x, 1024, (3, 3), (2, 2))
    x = depthwise_conv_block(x, 1024, (3, 3), (1, 1))
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, 1024))(x)
    x = Dropout(0.001)(x)
    x = Conv2D(num_classes, (1, 1), padding='same')(x)
    x = Activation('softmax')(x)
    output_tensor = Reshape((num_classes,))(x)

    # 构建模型
    model = Model(inputs=input_tensor, outputs=output_tensor)

    return model

MobileNetV1是由卷积块和深度可分离卷积块组成的，使用了全局平均池化和1x1卷积来进行分类。其中，卷积块使用的是普通的卷积层，深度可分离卷积块使用的是深度可分离卷积层。这里的代码中使用了函数来定义卷积块和深度可分离卷积块，使得代码更加简洁易懂。