kigichang / candle-birnn

Candle BiRNN

Implementing PyTorch LSTM inference using Candle, including the implementation of bidirectional LSTM inference.

Test Data

1. lstm_test.pt: Results generated using a PyTorch demo program. The code is as follows:

   import torch
   import torch.nn as nn
   
   rnn = nn.LSTM(10, 20, 1)
   input = torch.randn(5, 3, 10)
   output, (hn, cn) = rnn(input)
   
   state_dict = rnn.state_dict()
   state_dict['input'] = input
   state_dict['output'] = output
   state_dict['hn'] = hn
   state_dict['cn'] = cn
   torch.save(state_dict, "lstm_test.pt")

2. bi_lstm_test.pt: Results generated using a PyTorch demo program. The code is as follows:

   import torch
   import torch.nn as nn
   
   rnn = nn.LSTM(10, 20, 1, bidirectional=True)
   input = torch.randn(5, 3, 10)
   output, (hn, cn) = rnn(input)
   
   state_dict = rnn.state_dict()
   state_dict['input'] = input
   state_dict['output'] = output
   state_dict['hn'] = hn
   state_dict['cn'] = cn
   torch.save(state_dict, "bi_lstm_test.pt")