|
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class CNN_text(nn.Module):
- def __init__(self, kernel_h=[3, 4, 5], kernel_num=100, embed_num=1000, embed_dim=300, num_classes=2, dropout=0.5,
- L2_constrain=3,
- pretrained_embeddings=None):
- super(CNN_text, self).__init__()
-
- self.embedding = nn.Embedding(embed_num, embed_dim)
- self.dropout = nn.Dropout(dropout)
- if pretrained_embeddings is not None:
- self.embedding.weight.data.copy_(torch.from_numpy(pretrained_embeddings))
-
- # the network structure
- # Conv2d: input- N,C,H,W output- (50,100,62,1)
- self.conv1 = nn.ModuleList([nn.Conv2d(1, kernel_num, (K, embed_dim)) for K in kernel_h])
- self.fc1 = nn.Linear(len(kernel_h) * kernel_num, num_classes)
-
- def max_pooling(self, x):
- x = F.relu(self.conv1(x)).squeeze(3) # N,C,L - (50,100,62)
- x = F.max_pool1d(x, x.size(2)).squeeze(2)
- # x.size(2)=62 squeeze: (50,100,1) -> (50,100)
- return x
-
- def forward(self, x):
- x = self.embedding(x) # output: (N,H,W) = (50,64,300)
- x = x.unsqueeze(1) # (N,C,H,W)
- x = [F.relu(conv(x)).squeeze(3) for conv in self.conv1] # [N, C, H(50,100,62),(50,100,61),(50,100,60)]
- x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [N,C(50,100),(50,100),(50,100)]
- x = torch.cat(x, 1)
- x = self.dropout(x)
- x = self.fc1(x)
- return x
-
-
- if __name__ == '__main__':
- model = CNN_text(kernel_h=[1, 2, 3, 4], embed_num=3, embed_dim=2)
- x = torch.LongTensor([[1, 2, 1, 2, 0]])
- print(model(x))
|
