ResNet的代码实现

ResNet（残差网络）是一种深度卷积神经网络架构，特别设计来解决深度学习中训练深层模型时遇到的梯度消失问题。它的核心思想是在网络中引入了跨层连接（residual connection），使得信息可以直接从输入传递到更深的层次，简化了网络的学习过程。

在Python的Keras库中实现ResNet的一个基本示例如下：

from keras.layers import Conv2D, MaxPooling2D, Input, Add, Flatten, Dense
from keras.models import Model

def residual_block(x, filters, strides=1):
    shortcut = x
    # 卷积层
    x = Conv2D(filters=filters, kernel_size=3, padding='same', strides=strides)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # 再次卷积
    x = Conv2D(filters=filters, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)

    # 加上跳跃连接
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    
    return x

# 创建输入层
inputs = Input(shape=(height, width, channels))

# 拼接基础块和残差块
x = Conv2D(64, kernel_size=7, strides=2, padding='same')(inputs)
x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
x = residual_block(x, 64)   # 第一个残差块

# 构建剩余的残差块
num_blocks_per_stage = [3, 4, 6, 3]
for block_idx in range(len(num_blocks_per_stage)):
    for _ in range(num_blocks_per_stage[block_idx]):
        x = residual_block(x, 64 * (2 ** block_idx), strides=2 if block_idx == 0 else 1)

# 输出层
x = AveragePooling2D(pool_size=7)(x)
x = Flatten()(x)
outputs = Dense(num_classes, activation='softmax')(x)

# 定义完整的ResNet模型
model = Model(inputs=inputs, outputs=outputs)