pytorch lstm条件文本生成完整代码
时间: 2024-09-16 20:05:16 浏览: 44
在PyTorch中,实现LSTM条件文本生成通常涉及序列到序列模型(seq2seq),结合注意力机制。以下是使用PyTorch构建一个基本的LSTM条件语言模型的简化版代码示例:
```python
import torch
import torch.nn as nn
class LSTMConditionalGenerator(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers=1, dropout=0.5):
super(LSTMConditionalGenerator, self).__init__()
self.hidden_size = hidden_size
self.n_layers = n_layers
self.dropout = dropout
# LSTM层
self.lstm = nn.LSTM(input_size, hidden_size, n_layers, batch_first=True, dropout=dropout)
# 输出线性层和softmax激活
self.fc_out = nn.Linear(hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=-1)
def forward(self, input, hidden):
out, hidden = self.lstm(input, hidden)
out = self.fc_out(out[:, -1, :]) # 取最后一个时间步的隐藏状态作为输出
out = self.softmax(out) # 应用softmax
return out, hidden
def init_hidden(self, batch_size):
weight = next(self.parameters()).data
return (weight.new(self.n_layers, batch_size, self.hidden_size).zero_(),
weight.new(self.n_layers, batch_size, self.hidden_size).zero_())
# 使用例子
input_size = vocab_size # 假设vocab_size是词汇表大小
hidden_size = 256
output_size = vocab_size
model = LSTMConditionalGenerator(input_size, hidden_size, output_size)
# 假设我们有一个batch的数据,例如batch_input和batch_hidden
batch_size = len(batch_input)
hidden = model.init_hidden(batch_size)
for input in batch_input:
output, hidden = model(input.unsqueeze(0), hidden)
```
注意,这只是一个简化的版本,实际应用中还需要处理输入和目标之间的对齐,以及训练过程中的teacher forcing(用真实下一个词作为输入而不是模型预测)等步骤。此外,为了完成整个任务,你需要准备数据集、构建单词嵌入以及设置适当的损失函数(如交叉熵)和优化器。