分层残差块用pytorch代码实现

Sure, here's an example implementation of a residual block with layer normalization in PyTorch:

import torch
import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, dim, dropout=0.1):
        super(ResidualBlock, self).__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.dropout1 = nn.Dropout(dropout)
        self.fc1 = nn.Linear(dim, dim)
        self.norm2 = nn.LayerNorm(dim)
        self.dropout2 = nn.Dropout(dropout)
        self.fc2 = nn.Linear(dim, dim)

    def forward(self, x):
        # First layer
        residual = x
        x = self.norm1(x)
        x = nn.functional.relu(x)
        x = self.dropout1(x)
        x = self.fc1(x)

        # Second layer
        x = self.norm2(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)

        # Residual connection
        x += residual

        return x

I hope this helps!