lstm pytorch代码

以下是一个简单的 LSTM 模型的 PyTorch 代码示例：

import torch
import torch.nn as nn

# 定义 LSTM 模型类
class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_dim).requires_grad_()
        c0 = torch.zeros(1, x.size(0), self.hidden_dim).requires_grad_()
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

# 定义训练函数
def train(model, train_loader, learning_rate, num_epochs):
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(num_epochs):
        for i, (inputs, labels) in enumerate(train_loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

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

# 定义测试函数
def test(model, test_loader):
    with torch.no_grad():
        correct = 0
        total = 0
        for inputs, labels in test_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        print('Test Accuracy of the model on the test images: {} %'.format(100 * correct / total))

# 主函数
if __name__ == '__main__':
    # 定义模型参数
    input_dim = 28
    hidden_dim = 100
    output_dim = 10
    num_epochs = 5
    learning_rate = 0.001

    # 加载数据
    train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
    test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)

    # 实例化模型并训练
    model = LSTMModel(input_dim, hidden_dim, output_dim)
    train(model, train_loader, learning_rate, num_epochs)

    # 测试模型
    test(model, test_loader)

以上代码中实现了一个简单的 LSTM 模型，并使用 PyTorch 中的 DataLoader 加载了 MNIST 数据集进行训练和测试。