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1. 说明

在搭建网络结构中，为了保证搭建的网络具有高度扩展性和后续调试模型框架，在保证整体结构完整情况下，用nn.Identity 进行占位符处理。

2. pytorch 代码

• pytorch代码

import torch
import torch.nn as nnclass MyModel(nn.Module):def __init__(self, use_dropout=True):super(MyModel, self).__init__()self.features = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3, padding=1),nn.ReLU(),# 根据配置决定使用 Dropout 还是 Identitynn.Identity() if not use_dropout else nn.Dropout(p=0.5),nn.Conv2d(16, 32, kernel_size=3, padding=1),nn.ReLU(),)self.classifier = nn.Linear(32 * 32 * 32, 10)  # 假设输入图像尺寸为 32x32def forward(self, x):x = self.features(x)x = x.view(x.size(0), -1)return self.classifier(x)# 实例化模型时选择是否使用 Dropout
model_with_dropout = MyModel(use_dropout=True)
model_without_dropout = MyModel(use_dropout=False)# 测试模型输出形状
x = torch.randn(4, 3, 32, 32)
print("Output with dropout:", model_with_dropout(x).shape)
print("Output without dropout:", model_without_dropout(x).shape)

• result

Output with dropout: torch.Size([4, 10])
Output without dropout: torch.Size([4, 10])