神经网络自注意力机制python代码
时间: 2024-02-02 11:10:12 浏览: 30
以下是一个使用PyTorch实现神经网络自注意力机制的Python代码示例:
```python
import torch
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, input_dim):
super(SelfAttention, self).__init__()
self.query = nn.Linear(input_dim, input_dim)
self.key =.Linear(input_dim, input_dim)
self.value = nn.Linear(input_dim, input_dim)
self.softmax = nn.Softmax(dim=2)
def forward(self, x):
query = self.query(x)
key = self.key(x)
value = self.value(x)
scores = torch.matmul(query, key.transpose(1, 2))
attention_weights = self.softmax(scores)
attended_values = torch.matmul(attention_weights, value)
output = x + attended_values
return output
# 使用自注意力机制的神经网络示例
class NeuralNetwork(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(NeuralNetwork, self).__init__()
self.attention = SelfAttention(input_dim)
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.attention(x)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 创建一个输入张量
input_tensor = torch.randn(10, 20) # 假设输入维度为20,样本数为10
# 创建一个神经网络实例
model = NeuralNetwork(input_dim=20, hidden_dim=50, output_dim=10)
# 使用自注意力机制的神经网络进行前向传播
output = model(input_tensor)
print(output)
```