mogrifier lstm的python代码
时间: 2024-01-25 09:04:48 浏览: 34
以下是一个使用Mogrifier LSTM的简单示例代码:
```python
import torch
import torch.nn as nn
class MogrifierLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers):
super(MogrifierLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.input_gate = nn.Linear(input_size, hidden_size)
self.forget_gate = nn.Linear(input_size, hidden_size)
self.output_gate = nn.Linear(input_size, hidden_size)
self.cell_gate = nn.Linear(input_size, hidden_size)
self.mogrify_input = nn.Linear(hidden_size, hidden_size)
self.mogrify_forget = nn.Linear(hidden_size, hidden_size)
self.mogrify_output = nn.Linear(hidden_size, hidden_size)
self.mogrify_cell = nn.Linear(hidden_size, hidden_size)
def forward(self, input):
batch_size = input.size(0)
hidden = (torch.zeros(self.num_layers, batch_size, self.hidden_size),
torch.zeros(self.num_layers, batch_size, self.hidden_size))
for i in range(self.num_layers):
hx, cx = hidden
ix = self.input_gate(input) + self.mogrify_input(cx[i])
fx = self.forget_gate(input) + self.mogrify_forget(cx[i])
ox = self.output_gate(input) + self.mogrify_output(cx[i])
gx = self.cell_gate(input) + self.mogrify_cell(cx[i])
input_gate = torch.sigmoid(ix)
forget_gate = torch.sigmoid(fx)
output_gate = torch.sigmoid(ox)
cell_gate = torch.tanh(gx)
cy = (forget_gate * cx[i]) + (input_gate * cell_gate)
hy = output_gate * torch.tanh(cy)
input = hy
hidden = (hy.unsqueeze(0), cy.unsqueeze(0))
return hidden
# 使用示例
input_size = 10
hidden_size = 20
num_layers = 2
seq_length = 5
batch_size = 3
# 创建Mogrifier LSTM模型
model = MogrifierLSTM(input_size, hidden_size, num_layers)
# 创建随机输入张量
input = torch.randn(seq_length, batch_size, input_size)
# 前向传播
output = model(input)
print(output)
```
请注意,这只是一个简单的示例代码,实际应用中可能需要进行更多的调整和优化。
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