不要使用nn.LSTM,用底层写
时间: 2023-07-20 12:25:47 浏览: 43
好的,以下是一个使用底层操作实现 LSTM 的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class CustomLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers):
super(CustomLSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
# 定义 LSTM 层所需的参数
self.W_ii = nn.Parameter(torch.Tensor(input_size, hidden_size))
self.W_hi = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
self.b_i = nn.Parameter(torch.Tensor(hidden_size))
self.W_if = nn.Parameter(torch.Tensor(input_size, hidden_size))
self.W_hf = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
self.b_f = nn.Parameter(torch.Tensor(hidden_size))
self.W_ig = nn.Parameter(torch.Tensor(input_size, hidden_size))
self.W_hg = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
self.b_g = nn.Parameter(torch.Tensor(hidden_size))
self.W_io = nn.Parameter(torch.Tensor(input_size, hidden_size))
self.W_ho = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
self.b_o = nn.Parameter(torch.Tensor(hidden_size))
# 初始化参数
self.reset_parameters()
def reset_parameters(self):
"""初始化参数"""
std = 1.0 / (self.hidden_size ** 0.5)
for weight in self.parameters():
nn.init.uniform_(weight, -std, std)
def forward(self, x, h, c):
# x: (batch_size, seq_length, input_size)
# h: (num_layers, batch_size, hidden_size)
# c: (num_layers, batch_size, hidden_size)
batch_size, seq_length, input_size = x.size()
h_prev = h[0] # 取出上一层的隐状态
# 初始化当前层的隐状态和细胞状态
h_t = torch.zeros(batch_size, self.hidden_size).to(x.device)
c_t = torch.zeros(batch_size, self.hidden_size).to(x.device)
# 对序列的每个时间步进行计算
for t in range(seq_length):
x_t = x[:, t, :]
# 输入门计算过程
i_t = torch.sigmoid(torch.matmul(x_t, self.W_ii) + torch.matmul(h_prev, self.W_hi) + self.b_i)
# 遗忘门计算过程
f_t = torch.sigmoid(torch.matmul(x_t, self.W_if) + torch.matmul(h_prev, self.W_hf) + self.b_f)
# 计算细胞状态更新
g_t = torch.tanh(torch.matmul(x_t, self.W_ig) + torch.matmul(h_prev, self.W_hg) + self.b_g)
c_t = f_t * c_t + i_t * g_t
# 输出门计算过程
o_t = torch.sigmoid(torch.matmul(x_t, self.W_io) + torch.matmul(h_prev, self.W_ho) + self.b_o)
# 计算隐状态更新
h_t = o_t * torch.tanh(c_t)
h_prev = h_t # 更新上一层的隐状态
h_out = h_t.unsqueeze(0) # 将输出的隐状态添加一个维度
c_out = c_t.unsqueeze(0) # 将输出的细胞状态添加一个维度
return h_out, (h_out, c_out)
```
这个实现使用了四个参数矩阵和四个偏置向量来计算输入门、遗忘门、输出门和细胞状态。在 `forward` 方法中,我们对序列中的每个时间步进行计算,并更新隐状态和细胞状态。最后,我们返回最后一个时间步的隐状态和更新后的隐状态和细胞状态。需要注意的是,这个实现是单向的,只考虑了正向传播的情况。