基于注意力机制的 BiLSTM 一维时序预测程序的例子，使用 PyTorch 实现

好的，以下是一个基于注意力机制的 BiLSTM 一维时序预测程序的例子，使用 PyTorch 实现:

import torch
import torch.nn as nn
import torch.optim as optim

class Attention(nn.Module):
    def __init__(self, hidden_size):
        super(Attention, self).__init__()
        self.hidden_size = hidden_size
        self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
        self.v = nn.Linear(hidden_size, 1, bias=False)

    def forward(self, hidden, encoder_outputs):
        seq_len = len(encoder_outputs)
        attn_energies = torch.zeros(seq_len)
        for i in range(seq_len):
            attn_energies[i] = self.score(hidden, encoder_outputs[i])
        return torch.softmax(attn_energies, dim=0)

    def score(self, hidden, encoder_output):
        energy = torch.tanh(self.attn(torch.cat((hidden, encoder_output), dim=1)))
        energy = self.v(energy)
        return energy

class BiLSTMWithAttention(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(BiLSTMWithAttention, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, bidirectional=True)
        self.attn = Attention(hidden_size)
        self.fc = nn.Linear(hidden_size * 2, 1)

    def forward(self, input):
        output, (hidden, cell) = self.lstm(input)
        attn_weights = self.attn(hidden[-1], output)
        context = torch.sum(attn_weights * output, dim=0)
        fc_output = self.fc(torch.cat((context, hidden[-1]), dim=1))
        return fc_output

input_size = 1
hidden_size = 64
lr = 0.01
epochs = 100

model = BiLSTMWithAttention(input_size, hidden_size)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# Generate sample data
data = torch.sin(torch.linspace(0, 10, 100)).unsqueeze(1)
labels = torch.sin(torch.linspace(10, 20, 100)).unsqueeze(1)

# Training loop
for epoch in range(epochs):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, labels)
    loss.backward()
    optimizer.step()

    if epoch % 10 == 0:
        print(f"Epoch: {epoch}, Loss: {loss.item()}")

# Test the model
test_data = torch.sin(torch.linspace(0, 10, 100)).unsqueeze(1)
test_labels = torch.sin(torch.linspace(10, 20, 100)).unsqueeze(1)

with torch.no_grad():
    test_output = model(test_data)
    test_loss = criterion(test_output, test_labels)
    print(f"Test Loss: {test_loss.item()}")

在这个例子中，我们定义了一个 BiLSTMWithAttention 类，该类将输入序列作为输入，并输出预测的下一个时间步。我们还定义了一个 Attention 类，该类实现了注意力机制。我们使用均方误差作为损失函数，并使用 Adam 作为优化算法。在训练过程中，我们使用正弦函数生成样本数据，并在每个 epoch 结束时输出损失。在测试过程中，我们再次使用正弦函数生成数据，并计算测试损失。