tensorflow 实现 Xception

Xception是一种深度学习模型架构，它是由Google提出的，基于Inception架构的改进版本。下面是使用TensorFlow实现Xception模型的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.layers import SeparableConv2D, Add, Dense
from tensorflow.keras.models import Model

def separable_conv_block(inputs, filters, kernel_size, strides):
    x = SeparableConv2D(filters, kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    return x

def entry_flow(inputs):
    x = Conv2D(32, 3, strides=2, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = tf.keras.activations.relu(x)

    x = Conv2D(64, 3, padding='same')(x)
    x = BatchNormalization()(x)
    x = tf.keras.activations.relu(x)

    residual = Conv2D(128, 1, strides=2, padding='same')(x)
    residual = BatchNormalization()(residual)

    x = separable_conv_block(x, 128, 3, strides=1)
    x = separable_conv_block(x, 128, 3, strides=1)
    x = MaxPooling2D(3, strides=2, padding='same')(x)
    x = Add()([x, residual])

    residual = Conv2D(256, 1, strides=2, padding='same')(x)
    residual = BatchNormalization()(residual)

    x = separable_conv_block(x, 256, 3, strides=1)
    x = separable_conv_block(x, 256, 3, strides=1)
    x = MaxPooling2D(3, strides=2, padding='same')(x)
    x = Add()([x, residual])

    residual = Conv2D(728, 1, strides=2, padding='same')(x)
    residual = BatchNormalization()(residual)

    x = separable_conv_block(x, 728, 3, strides=1)
    x = separable_conv_block(x, 728, 3, strides=1)
    x = MaxPooling2D(3, strides=2, padding='same')(x)
    x = Add()([x, residual])
    
    return x

def middle_flow(inputs):
    for _ in range(8):
        residual = inputs

        x = separable_conv_block(inputs, 728, 3, strides=1)
        x = separable_conv_block(x, 728, 3, strides=1)
        x = separable_conv_block(x, 728, 3, strides=1)

        inputs = Add()([inputs, x])

    return inputs

def exit_flow(inputs):
    residual = Conv2D(1024, 1, strides=2, padding='same')(inputs)
    residual = BatchNormalization()(residual)

    x = separable_conv_block(inputs, 728, 3, strides=1)
    x = separable_conv_block(x, 1024, 3, strides=1)
    x = MaxPooling2D(3, strides=2, padding='same')(x)
    x = Add()([x, residual])

    x = separable_conv_block(x, 1536, 3, strides=1)
    x = separable_conv_block(x, 2048, 3, strides=1)
    x = GlobalAveragePooling2D()(x)

    x = Dense(1000, activation='softmax')(x)

    return x

def Xception(input_shape=(299, 299, 3), classes=1000):
    inputs = tf.keras.Input(shape=input_shape)
    
    x = entry_flow(inputs)
    x = middle_flow(x)
    outputs = exit_flow(x)
    
    model = Model(inputs=inputs, outputs=outputs)
    
    return model

# 创建Xception模型
model = Xception()

# 打印模型结构
model.summary()

这段代码定义了Xception模型的各个层和模型结构，并使用TensorFlow的Keras API构建了一个Xception模型。你可以根据自己的需求调整模型的输入形状和输出类别数。