import torch
import torch.nn as nn
# 定义Transformer模型
class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers):
super(Transformer, self).__init__()
# 编码器和解码器的初始化
self.encoder = nn.TransformerEncoderLayer(input_dim, hidden_dim, num_layers)
self.decoder = nn.TransformerDecoderLayer(output_dim, hidden_dim, num_layers)
def forward(self, src, tgt):
# 编码器的前向传播
enc_output = self.encoder(src)
# 解码器的前向传播
dec_output = self.decoder(tgt, enc_output)
return dec_output
# 创建Transformer模型实例
input_dim = 100
output_dim = 200
hidden_dim = 256
num_layers = 4
model = Transformer(input_dim, output_dim, hidden_dim, num_layers)
# 定义输入和目标数据
src = torch.randn(50, input_dim)
tgt = torch.randn(60, output_dim)
# 进行前向传播
output = model(src, tgt)
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