基于pytorch使用TCN用于多输入多输出的预测的完整代码

这里是一个基于 PyTorch 的 TCN（Temporal Convolutional Network）的多输入多输出预测的完整代码示例。在这个示例中，我们使用了一个包含两个输入和两个输出的时间序列数据集。我们将使用 TCN 模型来预测这些输出。

首先，我们需要导入必要的库和设置一些超参数：

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler

# 设置超参数
BATCH_SIZE = 32
EPOCHS = 100
LR = 0.001
NUM_INPUTS = 2
NUM_OUTPUTS = 2
SEQ_LEN = 30
NUM_CHANNELS = [32, 32, 32]
KERNEL_SIZE = 3
DROPOUT = 0.2

接下来，我们将定义我们的数据集类：

# 定义我们的数据集类
class TimeSeriesDataset(Dataset):
    def __init__(self, data, seq_len):
        self.data = data
        self.seq_len = seq_len
        
    def __len__(self):
        return len(self.data) - self.seq_len
    
    def __getitem__(self, idx):
        x = self.data[idx:idx+self.seq_len, :-NUM_OUTPUTS]
        y = self.data[idx:idx+self.seq_len, -NUM_OUTPUTS:]
        return x, y

然后，我们将加载和处理我们的数据集。在这个示例中，我们使用了一个包含 1000 个时间步和 4 个特征的随机数据集。

# 加载和处理数据集
data = np.random.randn(1000, NUM_INPUTS+NUM_OUTPUTS)
scaler = StandardScaler()
data = scaler.fit_transform(data)

train_data = data[:800]
test_data = data[800:]

train_dataset = TimeSeriesDataset(train_data, SEQ_LEN)
test_dataset = TimeSeriesDataset(test_data, SEQ_LEN)

train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)

接下来，我们将定义我们的 TCN 模型：

# 定义 TCN 模型
class TCN(nn.Module):
    def __init__(self, num_inputs, num_outputs, seq_len, num_channels, kernel_size, dropout):
        super().__init__()
        self.num_inputs = num_inputs
        self.num_outputs = num_outputs
        self.seq_len = seq_len
        self.num_channels = num_channels
        self.kernel_size = kernel_size
        self.dropout = dropout
        
        self.conv_layers = nn.ModuleList()
        in_channels = num_inputs
        for num_channels in num_channels:
            self.conv_layers.append(nn.Conv1d(in_channels, num_channels, kernel_size))
            in_channels = num_channels
        
        self.fc_layers = nn.ModuleList()
        self.fc_layers.append(nn.Linear(num_channels * seq_len, num_outputs))
        
    def forward(self, x):
        # 输入形状：(batch_size, num_inputs, seq_len)
        x = x.permute(0, 2, 1)
        # 输入形状：(batch_size, seq_len, num_inputs)
        
        for conv_layer in self.conv_layers:
            x = conv_layer(x)
            x = nn.functional.relu(x)
            x = nn.functional.dropout(x, p=self.dropout, training=self.training)
        # 输出形状：(batch_size, num_channels[-1], seq_len-kernel_size+1)
        
        x = x.view(-1, self.num_channels[-1] * (self.seq_len - self.kernel_size + 1))
        # 输出形状：(batch_size, num_channels[-1]*(seq_len-kernel_size+1))
        
        for fc_layer in self.fc_layers:
            x = fc_layer(x)
        # 输出形状：(batch_size, num_outputs)
        
        return x

在训练模型之前，我们需要定义损失函数和优化器：

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LR)

最后，我们将训练和测试我们的模型：

# 训练和测试模型
model = TCN(NUM_INPUTS, NUM_OUTPUTS, SEQ_LEN, NUM_CHANNELS, KERNEL_SIZE, DROPOUT)

for epoch in range(EPOCHS):
    # 训练模型
    model.train()
    train_loss = 0
    for x, y in train_loader:
        optimizer.zero_grad()
        y_pred = model(x.float())
        loss = criterion(y_pred, y.float())
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    train_loss /= len(train_loader)
    
    # 测试模型
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for x, y in test_loader:
            y_pred = model(x.float())
            loss = criterion(y_pred, y.float())
            test_loss += loss.item()
        test_loss /= len(test_loader)
    
    # 打印损失
    print(f"Epoch {epoch+1}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}")

这就是一个基于 PyTorch 的 TCN 的多输入多输出预测的完整代码示例。