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30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import torch
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import torch.nn as nn
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class SentLevelRNN(nn.Module):
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def __init__(self, config):
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super().__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_weights = nn.Parameter(torch.rand(2 * sentence_num_hidden, 1))
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self.sentence_context_weights.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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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_weights)
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x = x.squeeze(dim=2)
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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.fc(x.squeeze(0))
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return x
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