一维 transformer 代码
时间: 2023-08-29 17:05:30 浏览: 66
好的,我会尽力帮助您。一维 transformer 是一种序列模型,常用来处理序列数据,例如文本、语音等。下面是一个简单的一维 transformer 代码的示例:
```
import torch
import torch.nn as nn
class OneDimTransformer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, dropout):
super(OneDimTransformer, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.transformer_layers = nn.ModuleList([nn.TransformerEncoderLayer(hidden_dim, nhead=8, dim_feedforward=2048, dropout=dropout) for _ in range(num_layers)])
self.transformer_encoder = nn.TransformerEncoder(self.transformer_layers)
self.linear = nn.Linear(hidden_dim, output_dim)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, input):
# input: (batch_size, sequence_length)
input = self.embedding(input) # (batch_size, sequence_length, hidden_dim)
input = input.transpose(0, 1) # (sequence_length, batch_size, hidden_dim)
output = self.transformer_encoder(input) # (sequence_length, batch_size, hidden_dim)
output = output[-1, :, :] # (batch_size, hidden_dim)
output = self.linear(output) # (batch_size, output_dim)
output = self.softmax(output) # (batch_size, output_dim)
return output
```
这个模型首先将输入通过 embedding 层进行编码,然后将编码后的输入通过多层 transformer encoder 进行处理,最后通过一个线性层和 softmax 层得到输出。
希望这些信息对您有帮助。如果您有其他问题,欢迎随时追问。
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