pytorch实现textcnn文本分类模型代码
时间: 2023-10-02 19:08:43 浏览: 66
以下是基于Pytorch实现textcnn文本分类模型的代码,其中使用了CNN对文本进行卷积和池化,再经过全连接层进行分类。具体代码如下:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class TextCNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, num_classes, filter_sizes, num_filters):
super(TextCNN, self).__init__()
self.embeddings = nn.Embedding(vocab_size, embedding_dim)
self.convs = nn.ModuleList([
nn.Conv2d(1, num_filters, (fs, embedding_dim)) for fs in filter_sizes
])
self.fc = nn.Linear(num_filters * len(filter_sizes), num_classes)
def forward(self, x):
x = self.embeddings(x) # (batch_size, seq_len, embedding_dim)
x = x.unsqueeze(1) # (batch_size, 1, seq_len, embedding_dim)
x = [F.relu(conv(x)).squeeze(3) for conv in self.convs] # [(batch_size, num_filters, seq_len - filter_size + 1), ...]
x = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in x] # [(batch_size, num_filters), ...]
x = torch.cat(x, 1) # (batch_size, num_filters * len(filter_sizes))
x = self.fc(x) # (batch_size, num_classes)
return x
```
其中,`vocab_size`表示词汇表大小,`embedding_dim`表示词向量维度,`num_classes`表示分类数目,`filter_sizes`表示卷积核大小,`num_filters`表示卷积核个数。
在`forward`函数中,首先将输入的文本通过词向量层进行转换,得到(batch_size, seq_len, embedding_dim)的张量。然后通过卷积层和池化层对文本进行特征提取,得到形状为(batch_size, num_filters * len(filter_sizes))的张量,最后通过全连接层进行分类。