利用pytorch写一个cnn与self-attention相结合的二分类代码
时间: 2023-05-14 13:05:40 浏览: 82
当然可以,以下是一个简单的示例代码:
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNNAttention(nn.Module):
def __init__(self, embedding_dim, num_filters, filter_sizes, hidden_dim, output_dim, dropout):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.convs = nn.ModuleList([
nn.Conv2d(in_channels=1, out_channels=num_filters, kernel_size=(fs, embedding_dim))
for fs in filter_sizes
])
self.attention = nn.Linear(num_filters, 1)
self.fc = nn.Linear(len(filter_sizes) * num_filters, hidden_dim)
self.dropout = nn.Dropout(dropout)
self.out = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
embedded = self.embedding(text)
embedded = embedded.unsqueeze(1)
conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]
pooled = torch.cat(pooled, dim=1)
attention_weights = F.softmax(self.attention(pooled), dim=0)
pooled = torch.mul(pooled, attention_weights)
x = self.fc(pooled)
x = F.relu(x)
x = self.dropout(x)
x = self.out(x)
return x
model = CNNAttention(100, 32, [3, 4, 5], 256, 2, 0.5)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 训练代码省略
这个模型将输入的文本通过 CNN 进行特征提取,然后使用 self-attention 机制对不同卷积核提取的特征进行加权,最后将加权后的特征输入到全连接层进行分类。