LSTM分类代码pytorch

以下是一个简单的LSTM分类代码，使用PyTorch：

import torch
import torch.nn as nn

class LSTMClassifier(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.linear = nn.Linear(hidden_size, output_size)

    def forward(self, input):
        lstm_out, _ = self.lstm(input.view(len(input), 1, -1))
        linear_out = self.linear(lstm_out.view(len(input), -1))
        return linear_out[-1]

# 定义模型参数
input_size = 10  # 输入特征维度
hidden_size = 20  # LSTM隐藏层维度
output_size = 2  # 输出类别数

# 初始化模型
lstm_model = LSTMClassifier(input_size, hidden_size, output_size)

# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(lstm_model.parameters(), lr=0.01)

# 训练模型
for epoch in range(100):
    for input_data, label in training_data:
        lstm_model.zero_grad()
        lstm_output = lstm_model(input_data)
        loss = loss_fn(lstm_output, label)
        loss.backward()
        optimizer.step()

# 评估模型
lstm_model.eval()
correct = 0
total = 0
with torch.no_grad():
    for input_data, label in test_data:
        lstm_output = lstm_model(input_data)
        _, predicted = torch.max(lstm_output.data, 1)
        total += label.size(0)
        correct += (predicted == label).sum().item()
accuracy = 100 * correct / total
print('Accuracy of the LSTM classifier on the test set: {:.2f}%'.format(accuracy))

在这个示例中，我们使用一个LSTM模型对输入数据进行分类，并通过交叉熵损失函数进行训练。我们使用Adam优化器进行梯度下降，并在训练期间迭代地更新模型参数。在训练完成后，我们评估模型在测试集上的准确度，并输出结果。