pytorch实现卷积神经网络-双向长短期记忆网络(1DCNN-BILSTM-Attention)的多输入单输出回归预测。

首先，我们需要导入必要的库和模块，包括PyTorch、NumPy、Pandas等。同时，我们也需要定义模型的超参数，包括卷积层、双向LSTM层、注意力机制等参数。

import torch
import torch.nn as nn
import numpy as np
import pandas as pd

# 定义超参数
num_epochs = 100
batch_size = 32
learning_rate = 0.001
input_size = 10
hidden_size = 64
num_layers = 2
num_classes = 1
num_filters = 32
kernel_size = 3
dropout_rate = 0.5
attention_size = 32

接下来，我们需要定义模型的结构。我们采用了1D卷积层和双向LSTM层，并在最后添加了一个注意力机制。

class CNN_BiLSTM_Attention(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes, num_filters, kernel_size, dropout_rate, attention_size):
        super(CNN_BiLSTM_Attention, self).__init__()
        
        self.conv = nn.Conv1d(in_channels=input_size, out_channels=num_filters, kernel_size=kernel_size)
        self.relu = nn.ReLU()
        
        self.lstm = nn.LSTM(input_size=num_filters, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True, dropout=dropout_rate)
        
        self.attention = nn.Sequential(
            nn.Linear(in_features=hidden_size*2, out_features=attention_size),
            nn.Tanh(),
            nn.Linear(in_features=attention_size, out_features=1)
        )
        
        self.dropout = nn.Dropout(p=dropout_rate)
        self.fc = nn.Linear(in_features=hidden_size*2, out_features=num_classes)
        
    def forward(self, x):
        # 卷积层
        x = self.conv(x.transpose(1, 2)).transpose(1, 2)
        x = self.relu(x)
        
        # 双向LSTM层
        h0 = torch.zeros(num_layers*2, x.size(0), hidden_size).to(device)
        c0 = torch.zeros(num_layers*2, x.size(0), hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        
        # 注意力机制
        attention_weights = self.attention(out)
        attention_weights = torch.softmax(attention_weights, dim=1)
        out = out * attention_weights
        out = out.sum(dim=1)
        
        # 输出层
        out = self.dropout(out)
        out = self.fc(out)
        
        return out

接下来，我们需要加载数据集并进行预处理。在本例中，我们使用了一个包含10个特征和1个目标变量的数据集。我们将数据集分为训练集和测试集，同时将数据转换为PyTorch张量。

# 加载数据集
data = pd.read_csv('data.csv')
x_data = data.iloc[:, :-1].values
y_data = data.iloc[:, -1:].values

# 划分数据集
train_size = int(0.8 * len(x_data))
train_x = torch.from_numpy(x_data[:train_size]).float()
train_y = torch.from_numpy(y_data[:train_size]).float()
test_x = torch.from_numpy(x_data[train_size:]).float()
test_y = torch.from_numpy(y_data[train_size:]).float()

# 创建数据集迭代器
train_dataset = torch.utils.data.TensorDataset(train_x, train_y)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = torch.utils.data.TensorDataset(test_x, test_y)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

接下来，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=learning_rate)

最后，我们可以开始模型的训练和测试。

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, targets) in enumerate(train_loader):
        inputs = inputs.to(device)
        targets = targets.to(device)
        
        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, targets in test_loader:
        inputs = inputs.to(device)
        targets = targets.to(device)
        outputs = model(inputs)
        total += targets.size(0)
        correct += (outputs == targets).sum().item()

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

完整代码如下：