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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class CNN_Text(nn.Module):
def __init__(self, args):
super(CNN_Text, self).__init__()
self.args = args
V = args.embed_num
D = args.embed_dim
C = args.class_num
Ci = 1
Co = args.kernel_num
Ks = args.kernel_sizes
self.embed = nn.Embedding(V, D)
self.convs = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])
self.dropout = nn.Dropout(args.dropout)
self.fc1 = nn.Linear(len(Ks) * Co, C)
if self.args.static:
self.embed.weight.requires_grad = False
def forward(self, x):
x = self.embed(x) # (N, W, D)
x = x.unsqueeze(1) # (N, Ci, W, D)
x = [F.relu(conv(x)).squeeze(3) for conv in self.convs] # [(N, Co, W), ...]*len(Ks)
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # [(N, Co), ...]*len(Ks)
x = torch.cat(x, 1)
x = self.dropout(x) # (N, len(Ks)*Co)
logit = self.fc1(x) # (N, C)
return logit