model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

# credit to: https://github.com/Shawn1993/cnn-text-classification-pytorch/blob/master/model.py
class CNN_Text(nn.Module):
    
    def __init__(self, args):
        super(CNN_Text, self).__init__()
        self.args = args
        
        V = args.vocabs + 1
        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.convs1 = [nn.Conv2d(Ci, Co, (K, D)) for K in Ks]
        self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])
        '''
        self.conv13 = nn.Conv2d(Ci, Co, (3, D))
        self.conv14 = nn.Conv2d(Ci, Co, (4, D))
        self.conv15 = nn.Conv2d(Ci, Co, (5, D))
        '''
        self.dropout = nn.Dropout(args.dropout)
        self.fc1 = nn.Linear(len(Ks)*Co, C)

    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)  # (N, Co, W)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x

    def forward(self, x):
        x = self.embed(x)  # (N, W, D)
        if self.args.static:
            x = Variable(x)
        x = x.unsqueeze(1)  # (N, Ci, W, D)
        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1]  # [(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)

        '''
        x1 = self.conv_and_pool(x,self.conv13) #(N,Co)
        x2 = self.conv_and_pool(x,self.conv14) #(N,Co)
        x3 = self.conv_and_pool(x,self.conv15) #(N,Co)
        x = torch.cat((x1, x2, x3), 1) # (N,len(Ks)*Co)
        '''
        x = self.dropout(x)  # (N, len(Ks)*Co)
        logit = self.fc1(x)  # (N, C)
        return logit

    def cal_nlpos(self, logit, y):
        loss = F.cross_entropy(logit, y)
            
        for num, name in enumerate(self.state_dict()):
            param = self.state_dict()[name]
            loss += 0.5 * param.pow(2).sum()
        return(loss)