全连接层加dropout代码pytorch

以下是全连接层加dropout的PyTorch代码：

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        
        self.fc1 = nn.Linear(784, 512)
        self.dropout1 = nn.Dropout(p=0.5)
        
        self.fc2 = nn.Linear(512, 256)
        self.dropout2 = nn.Dropout(p=0.5)
        
        self.output = nn.Linear(256, 10)
        
    def forward(self, x):
        x = x.view(-1, 784)
        
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout1(x)
        
        x = self.fc2(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        
        x = self.output(x)
        return x

在上面的代码中，我们创建一个包含两个全连接层和两个dropout层的神经网络。dropout层的概率为0.5，这意味着每个输入元素有0.5的概率被随机清除。这有助于防止过拟合，提高训练的泛化能力。