60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
import torch.nn as nn
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from torch.autograd import Variable
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class RNNModel(nn.Module):
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"""Container module with an encoder, a recurrent module, and a decoder."""
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def __init__(self, rnn_type, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weights=False):
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super(RNNModel, self).__init__()
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self.drop = nn.Dropout(dropout)
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self.encoder = nn.Embedding(ntoken, ninp)
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if rnn_type in ['LSTM', 'GRU']:
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self.rnn = getattr(nn, rnn_type)(ninp, nhid, nlayers, dropout=dropout)
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else:
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try:
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nonlinearity = {'RNN_TANH': 'tanh', 'RNN_RELU': 'relu'}[rnn_type]
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except KeyError:
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raise ValueError("""An invalid option for `--model` was supplied,
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options are ['LSTM', 'GRU', 'RNN_TANH' or 'RNN_RELU']""")
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self.rnn = nn.RNN(ninp, nhid, nlayers, nonlinearity=nonlinearity, dropout=dropout)
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self.decoder = nn.Linear(nhid, ntoken)
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# Optionally tie weights as in:
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# "Using the Output Embedding to Improve Language Models" (Press & Wolf 2016)
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# https://arxiv.org/abs/1608.05859
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# and
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# "Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling" (Inan et al. 2016)
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# https://arxiv.org/abs/1611.01462
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if tie_weights:
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if nhid != ninp:
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raise ValueError('When using the tied flag, nhid must be equal to emsize')
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self.decoder.weight = self.encoder.weight
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self.init_weights()
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self.rnn_type = rnn_type
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self.nhid = nhid
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self.nlayers = nlayers
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def init_weights(self):
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initrange = 0.1
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self.encoder.weight.data.uniform_(-initrange, initrange)
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self.decoder.bias.data.fill_(0)
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self.decoder.weight.data.uniform_(-initrange, initrange)
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def forward(self, input, hidden):
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emb = self.drop(self.encoder(input))
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output, hidden = self.rnn(emb, hidden)
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output = self.drop(output)
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decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2)))
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return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden
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def init_hidden(self, bsz):
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weight = next(self.parameters()).data
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if self.rnn_type == 'LSTM':
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return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),
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Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()))
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else:
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return Variable(weight.new(self.nlayers, bsz, self.nhid).zero_())
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