残差网络tensorflow代码

当谈到残差网络的实现，TensorFlow提供了一个方便的函数`tf.keras.layers.ResNet`。以下是一个简单的例子，展示了如何使用TensorFlow构建一个残差网络：

import tensorflow as tf
from tensorflow.keras import layers

# 定义残差块
class ResidualBlock(tf.keras.Model):
    def __init__(self, filters, strides=1, activation='relu', **kwargs):
        super(ResidualBlock, self).__init__(**kwargs)
        self.activation = tf.keras.activations.get(activation)
        self.main_layers = [
            layers.Conv2D(filters, 3, strides=strides, padding='same', use_bias=False),
            layers.BatchNormalization(),
            self.activation,
            layers.Conv2D(filters, 3, strides=1, padding='same', use_bias=False),
            layers.BatchNormalization()
        ]
        self.skip_layers = []
        if strides > 1:
            self.skip_layers = [
                layers.Conv2D(filters, 1, strides=strides, padding='same', use_bias=False),
                layers.BatchNormalization()
            ]

    def call(self, inputs):
        x = inputs
        for layer in self.main_layers:
            x = layer(x)
        skip_x = inputs
        for layer in self.skip_layers:
            skip_x = layer(skip_x)
        return self.activation(x + skip_x)

# 定义残差网络
class ResNet(tf.keras.Model):
    def __init__(self, num_classes, block_sizes=[2, 2, 2, 2], **kwargs):
        super(ResNet, self).__init__(**kwargs)
        self.conv1 = layers.Conv2D(64, 7, strides=2, padding='same', use_bias=False)
        self.bn1 = layers.BatchNormalization()
        self.activation = tf.keras.activations.relu
        self.max_pool = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')
        self.res_blocks = []
        for block_size in block_sizes:
            self.res_blocks.append(self.make_res_blocks(64, block_size))
            self.res_blocks.append(self.make_res_blocks(128, 1))
            self.res_blocks.append(self.make_res_blocks(256, 1))
            self.res_blocks.append(self.make_res_blocks(512, 1))
        self.global_avg_pool = layers.GlobalAveragePooling2D()
        self.fc = layers.Dense(num_classes)

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = self.activation(x)
        x = self.max_pool(x)
        for res_block in self.res_blocks:
            x = res_block(x)
        x = self.global_avg_pool(x)
        x = self.fc(x)
        return x

    def make_res_blocks(self, filters, num_blocks):
        res_blocks = tf.keras.Sequential()
        for _ in range(num_blocks):
            res_blocks.add(ResidualBlock(filters))
        return res_blocks

# 创建一个ResNet模型实例
model = ResNet(num_classes=10)

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

这只是一个简单的例子，你可以根据自己的需求进行修改和扩展。