在PyTorch中使用LSTM加速训练随机神经网络

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 假设有一个名为dataset的数据集，包含输入数据x和对应的标签y
dataset = ...
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

input_size = ...
hidden_size = ...
num_layers = ...
output_size = ...
model = LSTMModel(input_size, hidden_size, num_layers, output_size)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

total_step = len(dataloader)
for epoch in range(num_epochs):
    for i, (x, y) in enumerate(dataloader):
        x = x.to(device)
        y = y.to(device)

        # 前向传播
        outputs = model(x)
        loss = criterion(outputs, y)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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