使用keras框架写一个搭建mobilenetv2的代码

以下是使用Keras框架搭建MobileNetV2的代码： ``` from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout, Dense from keras.layers import DepthwiseConv2D, BatchNormalization, Activation, Add, Reshape from keras.models import Model def relu6(x): return K.relu(x, max_value=6) def _conv_block(inputs, filters, kernel, strides): x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs) x = BatchNormalization()(x) return Activation(relu6)(x) def _bottleneck(inputs, filters, kernel, t, s, r=False): tchannel = K.int_shape(inputs)[-1] * t x = _conv_block(inputs, tchannel, (1, 1), (1, 1)) x = DepthwiseConv2D(kernel, strides=(s, s), padding='same', depth_multiplier=1)(x) x = BatchNormalization()(x) x = Activation(relu6)(x) x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same')(x) x = BatchNormalization()(x) if r: x = Add()([x, inputs]) return x def _inverted_residual_block(inputs, filters, kernel, t, strides, n): x = _bottleneck(inputs, filters, kernel, t, strides) for i in range(1, n): x = _bottleneck(x, filters, kernel, t, 1, True) return x def MobileNetV2(input_shape, k): inputs = Input(shape=input_shape) x = _conv_block(inputs, 32, (3, 3), strides=(2, 2)) x = _inverted_residual_block(x, 16, (3, 3), t=1, strides=1, n=1) x = _inverted_residual_block(x, 24, (3, 3), t=6, strides=2, n=2) x = _inverted_residual_block(x, 32, (3, 3), t=6, strides=2, n=3) x = _inverted_residual_block(x, 64, (3, 3), t=6, strides=2, n=4) x = _inverted_residual_block(x, 96, (3, 3), t=6, strides=1, n=3) x = _inverted_residual_block(x, 160, (3, 3), t=6, strides=2, n=3) x = _inverted_residual_block(x, 320, (3, 3), t=6, strides=1, n=1) x = _conv_block(x, 1280, (1, 1), strides=(1, 1)) x = GlobalAveragePooling2D()(x) x = Reshape((1, 1, 1280))(x) x = Dropout(0.3, name='Dropout')(x) x = Conv2D(k, (1, 1), padding='same')(x) x = Activation('softmax', name='softmax')(x) output = Reshape((k,))(x) model = Model(inputs, output) return model ``` 这个代码定义了一个名为MobileNetV2的函数，该函数接受输入形状和类别数，并返回一个Keras模型。该模型使用MobileNetV2网络架构。MobileNetV2是一种轻量级的卷积神经网络，可以用于图像分类、对象检测和语义分割等任务。该网络使用深度可分离卷积层和残差连接来减少参数和计算量，从而实现更高效的计算。