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：

导入所需的库

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

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
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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。

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
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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。

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()))
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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。