残差神经网络python代码

以下是一个用Python编写的残差神经网络（Residual Neural Network）的代码示例：

import tensorflow as tf
from tensorflow.keras import layers

def resnet_block(input_data, filters, kernel_size, strides):
    # 第一个卷积层
    x = layers.Conv2D(filters, kernel_size, strides=strides, padding='same')(input_data)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    
    # 第二个卷积层
    x = layers.Conv2D(filters, kernel_size, padding='same')(x)
    x = layers.BatchNormalization()(x)
    
    # 将输入数据与输出进行相加
    residual = layers.Conv2D(filters, kernel_size=1, strides=strides, padding='same')(input_data)
    x = layers.add([x, residual])
    x = layers.Activation('relu')(x)
    
    return x

def build_resnet(input_shape, num_classes):
    input_data = tf.keras.Input(shape=input_shape)
    
    # 第一个卷积层
    x = layers.Conv2D(64, 7, strides=2, padding='same')(input_data)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    
    # 残差块
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    x = resnet_block(x, filters=64, kernel_size=3, strides=1)
    
    x = resnet_block(x, filters=128, kernel_size=3, strides=2)
    x = resnet_block(x, filters=128, kernel_size=3, strides=1)
    
    x = resnet_block(x, filters=256, kernel_size=3, strides=2)
    x = resnet_block(x, filters=256, kernel_size=3, strides=1)
    
    x = resnet_block(x, filters=512, kernel_size=3, strides=2)
    x = resnet_block(x, filters=512, kernel_size=3, strides=1)
    
    # 全局平均池化层
    x = layers.GlobalAveragePooling2D()(x)
    
    # 全连接层
    x = layers.Dense(256)(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Dense(num_classes, activation='softmax')(x)
    
    model = tf.keras.Model(input_data, x)
    
    return model

这段代码定义了一个基本的残差神经网络结构，通过调用`build_resnet`函数，可以构建一个具有指定输入形状和类别数的残差神经网络模型。你可以根据自己的需求进行修改和扩展。