基于pytorch实现多头注意力机制的LSTM网络模型

实现多头注意力机制的LSTM网络模型可以分为以下几个步骤：

1. 导入所需的库

import torch
import torch.nn as nn
import torch.nn.functional as F

2. 定义多头注意力机制的类

class MultiHeadAttention(nn.Module):
    def __init__(self, n_heads, d_model, dropout):
        super(MultiHeadAttention, self).__init__()

        self.n_heads = n_heads
        self.d_k = d_model // n_heads

        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.out = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        bs = q.size(0)

        # perform linear operation and split into N heads
        k = self.k_linear(k).view(bs, -1, self.n_heads, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.n_heads, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.n_heads, self.d_k)

        # transpose to get dimensions bs * N * sl * d_model
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)

        # calculate attention using function we will define next
        scores = self.attention(q, k, v, self.d_k, mask, self.dropout)
        
        # concatenate heads and put through final linear layer
        concat = scores.transpose(1,2).contiguous().view(bs, -1, self.n_heads*self.d_k)

        output = self.out(concat)

        return output

在构建多头注意力机制的类时，我们首先需要定义每个头的数量、模型维度和丢失率。在构造函数中，我们定义了线性层，以将输入线性映射到查询、键和值空间。我们还使用了`nn.Dropout`来减少过拟合。在`forward`函数中，我们首先对输入进行线性变换，并将输出重塑为多头矩阵。然后我们执行一个自定义的`attention`函数，该函数将计算注意力权重，并将结果与值矩阵相乘。最后，我们将多头矩阵重新连接，并通过一个线性层输出。

3. 定义自定义的注意力函数

def attention(q, k, v, d_k, mask=None, dropout=None):

    scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(d_k)
    
    if mask is not None:
        mask = mask.unsqueeze(1)
        scores = scores.masked_fill(mask == 0, -1e9)

    scores = F.softmax(scores, dim=-1)

    if dropout is not None:
        scores = dropout(scores)

    output = torch.matmul(scores, v)

    return output

在自定义的注意力函数中，我们首先通过将查询矩阵和键矩阵相乘并除以`sqrt(d_k)`来计算得分。然后，我们可以选择应用掩码来避免将注意力权重分配给无关的值。接下来，我们对得分进行softmax操作，并在需要时应用dropout。最后，我们将注意力权重乘以值矩阵，以获得最终的输出。

4. 定义LSTM网络模型

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, n_layers, n_heads, dropout):
        super(LSTMModel, self).__init__()

        self.hidden_dim = hidden_dim
        self.n_layers = n_layers

        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True, bidirectional=True)
        self.attention = MultiHeadAttention(n_heads, hidden_dim*2, dropout)
        self.fc = nn.Linear(hidden_dim*2, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.n_layers*2, x.size(0), self.hidden_dim).to(device)
        c0 = torch.zeros(self.n_layers*2, x.size(0), self.hidden_dim).to(device)

        output, (hidden, cell) = self.lstm(x, (h0, c0))

        # Apply attention
        attention_output = self.attention(output, output, output)

        # Concatenate hidden states from last layer
        hidden = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)

        out = self.fc(hidden)

        return out

在构建LSTM网络模型时，我们首先定义了输入维度、隐藏维度、输出维度、层数、多头数和丢失率。在构造函数中，我们定义了一个双向LSTM层和一个多头注意力层。在`forward`函数中，我们首先将输入通过LSTM层，并获取隐藏状态。然后，我们将LSTM的输出输入多头注意力层。接下来，我们将最后一层的隐藏状态连接起来，并通过一个线性层输出。

5. 实例化模型并训练

# 定义超参数
input_dim = 10
hidden_dim = 32
output_dim = 1
n_layers = 2
n_heads = 4
dropout = 0.2
learning_rate = 0.001
num_epochs = 10

# 实例化模型
model = LSTMModel(input_dim, hidden_dim, output_dim, n_layers, n_heads, dropout).to(device)

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

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, targets) in enumerate(train_loader):
        inputs, targets = inputs.to(device), targets.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        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()))

在实例化模型并定义损失函数和优化器之后，我们可以开始训练模型。在每个epoch中，我们通过迭代训练数据集中的每个批次来更新模型。最后，我们可以使用训练好的模型进行预测。