基于Tensorflow生成Mobielnetv3的代码

以下是一个基于Tensorflow生成Mobilenetv3的代码示例：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, DepthwiseConv2D, GlobalAveragePooling2D, Reshape, Dropout, Activation, BatchNormalization, add, Dense

def relu6(x):
    return tf.keras.backend.relu(x, max_value=6.0)

def hard_swish(x):
    return x * tf.keras.backend.relu(x + 3.0, max_value=6.0) / 6.0

def conv_block(inputs, filters, kernel, strides):
    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1
    x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)
    x = BatchNormalization(axis=channel_axis)(x)
    return Activation(relu6)(x)

def bottleneck(inputs, filters, kernel, t, s, r=False):
    channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1
    tchannel = tf.keras.backend.int_shape(inputs)[channel_axis] * t
    x = conv_block(inputs, tchannel, (1, 1), (1, 1))
    x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation(relu6)(x)
    x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)
    if r:
        x = add([x, inputs])
    return x

def MobileNetV3_large(shape=(224, 224, 3), classes=1000):
    inputs = Input(shape)
    x = conv_block(inputs, 16, (3, 3), strides=(2, 2))

    x = bottleneck(x, 16, (3, 3), t=1, s=1, r=True)
    x = bottleneck(x, 24, (3, 3), t=4, s=2)
    x = bottleneck(x, 24, (3, 3), t=3, s=1, r=True)
    x = bottleneck(x, 40, (5, 5), t=3, s=2)
    x = bottleneck(x, 40, (5, 5), t=3, s=1, r=True)
    x = bottleneck(x, 40, (5, 5), t=3, s=1, r=True)
    x = bottleneck(x, 80, (3, 3), t=6, s=2)
    x = bottleneck(x, 80, (3, 3), t=2.5, s=1, r=True)
    x = bottleneck(x, 80, (3, 3), t=2.3, s=1, r=True)
    x = bottleneck(x, 80, (3, 3), t=2.3, s=1, r=True)
    x = bottleneck(x, 112, (5, 5), t=6, s=1, r=True)
    x = bottleneck(x, 112, (5, 5), t=6, s=1, r=True)
    x = bottleneck(x, 160, (5, 5), t=6, s=2, r=True)
    x = bottleneck(x, 160, (5, 5), t=6, s=1, r=True)
    x = bottleneck(x, 160, (5, 5), t=6, s=1, r=True)

    x = conv_block(x, 960, (1, 1), strides=(1, 1))
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, 960))(x)
    x = Dropout(0.2)(x)
    x = Conv2D(classes, (1, 1), padding='same')(x)
    x = Activation('softmax')(x)
    x = Reshape((classes,))(x)

    model = Model(inputs, x)
    return model

这个代码示例定义了一个名为`MobileNetV3_large`的函数，用于创建一个大型的Mobilenetv3模型。在函数中，我们定义了`conv_block`和`bottleneck`两个函数，用于构建模型的基础组件。然后，我们按照Mobilenetv3的结构，使用这些组件构建了一个大型的模型。最后，我们将模型编译并返回。