循环神经网络LSTM Pytorch（直接可用版）_lstm神经网络代码python-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_41682402/article/details/135997581

本文介绍了如何使用Python的PyTorch库构建一个LSTM模型，用于处理时间序列数据的预测。它详细展示了数据预处理、模型定义、损失函数选择、优化器配置以及模型训练和新数据预测的过程。

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import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 生成一些样例数据
torch.manual_seed(0)
sequence_length = 10
num_samples = 1000

X_train = torch.rand(num_samples, sequence_length, 1)
y_train = torch.sum(X_train, dim=1) + 0.1 * torch.randn(num_samples, 1)

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

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[:, -1, :])
        return output

# 初始化模型、损失函数和优化器
input_size = 1
hidden_size = 50
output_size = 1
model = LSTMModel(input_size, hidden_size, output_size)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 将数据转换为 PyTorch 的 Tensor
X_train = X_train.float()
y_train = y_train.float()

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(X_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 预测新数据
X_new = torch.rand(5, sequence_length, 1).float()
predictions = model(X_new)

print("Predictions:")
print(predictions.detach().numpy())