栈编码器结合LSTM参数预测案例代码 pytorch

以下是一个使用栈编码器和LSTM进行参数预测的简单示例代码，使用PyTorch实现：

import torch
import torch.nn as nn

class StackLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(StackLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.stack_lstm = nn.LSTM(input_size, hidden_size, num_layers)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, h, c):
        out, (h, c) = self.stack_lstm(x, (h, c))
        out = self.fc(out[:, -1, :])
        return out, (h, c)

input_size = 1
hidden_size = 64
num_layers = 2
output_size = 1
seq_len = 20
batch_size = 1
learning_rate = 0.001
num_epochs = 1000

x = torch.randn(seq_len, batch_size, input_size)
y = torch.randn(batch_size, output_size)

model = StackLSTM(input_size, hidden_size, num_layers, output_size)

criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    h = torch.zeros(num_layers, batch_size, hidden_size)
    c = torch.zeros(num_layers, batch_size, hidden_size)

    outputs = []
    for i in range(seq_len):
        out, (h, c) = model(x[i], h, c)
        outputs.append(out)
    
    loss = criterion(outputs[-1], y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if epoch % 100 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Test the model
with torch.no_grad():
    h = torch.zeros(num_layers, batch_size, hidden_size)
    c = torch.zeros(num_layers, batch_size, hidden_size)
    test_input = torch.randn(seq_len, batch_size, input_size)
    test_output = torch.randn(batch_size, output_size)
    predicted_output = []
    for i in range(seq_len):
        out, (h, c) = model(test_input[i], h, c)
        predicted_output.append(out)

    test_loss = criterion(predicted_output[-1], test_output)
    print(f'Test Loss: {test_loss.item():.4f}')

在这个示例中，我们使用一个具有2个LSTM层的栈编码器来预测一个具有1个输入和1个输出的简单序列。我们使用均方误差损失函数和Adam优化器进行训练，并在每个epoch结束后输出训练损失。在测试阶段，我们生成一个新的序列来测试模型，并计算测试损失。