用pytorch写一段用于碳交易价预测的代码,输入的特征有8个,模型包含lstm
时间: 2023-03-29 11:03:56 浏览: 90
很高兴回答你的问题。以下是用 PyTorch 编写的碳交易价格预测模型的代码,其中包含 LSTM 神经网络:
```python
import torch
import torch.nn as nn
import numpy as np
# 定义 LSTM 模型
class LSTMModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(LSTMModel, self).__init__()
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, input):
lstm_out, _ = self.lstm(input.view(len(input), 1, -1))
output = self.fc(lstm_out[-1])
return output
# 定义训练函数
def train(model, optimizer, loss_fn, train_data, train_labels):
model.train()
optimizer.zero_grad()
output = model(train_data)
loss = loss_fn(output, train_labels)
loss.backward()
optimizer.step()
return loss.item()
# 定义测试函数
def test(model, loss_fn, test_data, test_labels):
model.eval()
with torch.no_grad():
output = model(test_data)
test_loss = loss_fn(output, test_labels)
pred = output.data.max(1, keepdim=True)[1]
correct = pred.eq(test_labels.data.view_as(pred)).sum()
return test_loss.item(), correct.item() / len(test_labels)
# 定义主函数
def main():
# 加载数据
data = np.load('carbon_data.npy')
labels = np.load('carbon_labels.npy')
# 划分训练集和测试集
train_data = torch.from_numpy(data[:800]).float()
train_labels = torch.from_numpy(labels[:800]).long()
test_data = torch.from_numpy(data[800:]).float()
test_labels = torch.from_numpy(labels[800:]).long()
# 定义模型和优化器
model = LSTMModel(8, 16, 2)
optimizer = torch.optim.Adam(model.parameters(), lr=.01)
loss_fn = nn.CrossEntropyLoss()
# 训练模型
for epoch in range(100):
train_loss = train(model, optimizer, loss_fn, train_data, train_labels)
test_loss, test_acc = test(model, loss_fn, test_data, test_labels)
print('Epoch [{}/{}], Train Loss: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.2f}%'
.format(epoch+1, 100, train_loss, test_loss, test_acc*100))
if __name__ == '__main__':
main()
```
以上是用 PyTorch 编写的碳交易价格预测模型的代码,其中包含 LSTM 神经网络。希望对你有所帮助。