残差块工厂

### 残差块的概念及其重要性 残差块（Residual Block），作为深度学习领域中的一个重要组件，在构建深层神经网络时解决了梯度消失问题并提高了训练效率。该结构首次被引入到ResNet架构中，使得非常深的网络可以成功收敛和优化[^1]。 ### 使用PyTorch实现残差块 在PyTorch中定义一个简单的残差单元可以通过继承`torch.nn.Module`类完成： ```python import torch.nn as nn class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.downsample = downsample def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out ``` 这段代码展示了如何创建一个基本版本的残差模块，其中包含了两层卷积操作以及跳跃连接机制[^2]。 ### 使用TensorFlow实现残差块 而在TensorFlow里，则可通过Keras API轻松搭建相同的结构： ```python from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Add, Input from tensorflow.keras.models import Model def residual_block(input_tensor, filters, strides=(1, 1)): shortcut = input_tensor # 主路径 x = Conv2D(filters=filters, kernel_size=(3, 3), strides=strides, padding='same')(input_tensor) x = BatchNormalization()(x) x = Activation('relu')(x) x = Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(x) x = BatchNormalization()(x) # 如果步幅不为1或者输入输出通道数不同则调整shortcut分支 if strides != (1, 1) or int(input_tensor.shape[-1]) != filters: shortcut = Conv2D(filters=filters, kernel_size=(1, 1), strides=strides)(input_tensor) shortcut = BatchNormalization()(shortcut) x = Add()([x, shortcut]) output = Activation('relu')(x) return output inputs = Input(shape=(None, None, 64)) output = residual_block(inputs, 64) model = Model(inputs=[inputs], outputs=output) ``` 此部分演示了利用函数式API来组装带有跳接特性的残差单元的方法。