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Ashutosh-Adhikari
650882fb6e
* Add Reuters option in common.dataset * Add Reuters option in common.dataset * Add HAN model * Add XML-CNN * Add HAN * Add Hierarchical tokenization for Reuters * Add README for HAN * Add XML Readme * Update HAN Readme
33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.nn.functional as F
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class SentLevelRNN(nn.Module):
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def __init__(self, config):
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super(SentLevelRNN, self).__init__()
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dataset = config.dataset
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sentence_num_hidden = config.sentence_num_hidden
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word_num_hidden = config.word_num_hidden
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target_class = config.target_class
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self.sentence_context_wghts = nn.Parameter(torch.rand(2*sentence_num_hidden, 1))
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self.sentence_context_wghts.data.uniform_(-0.1, 0.1)
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self.sentence_GRU = nn.GRU(2*word_num_hidden, sentence_num_hidden, bidirectional = True)
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self.sentence_linear = nn.Linear(2*sentence_num_hidden, 2*sentence_num_hidden, bias = True)
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self.fc = nn.Linear(2*sentence_num_hidden , target_class)
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self.soft_sent = nn.Softmax()
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self.final_log_soft = F.log_softmax
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def forward(self,x):
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sentence_h,_ = self.sentence_GRU(x)
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x = torch.tanh(self.sentence_linear(sentence_h))
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x = torch.matmul(x, self.sentence_context_wghts)
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x = x.squeeze()
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x = self.soft_sent(x.transpose(1,0))
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x = torch.mul(sentence_h.permute(2,0,1), x.transpose(1,0))
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x = torch.sum(x,dim = 1).transpose(1,0).unsqueeze(0)
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#x = self.final_log_soft(self.fc(x.squeeze(0)))
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x = self.fc(x.squeeze(0))
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return x
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