5 releases
new 0.2.3 | Sep 25, 2024 |
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0.2.2 | Aug 17, 2024 |
0.2.1 | Aug 13, 2024 |
0.2.0 | Aug 12, 2024 |
0.1.0 | Aug 11, 2024 |
#235 in Machine learning
158 downloads per month
42KB
295 lines
Contains (Zip file, 29KB) bi_lstm_test.pt, (Zip file, 16KB) lstm_test.pt
Candle BiRNN
Implementing PyTorch LSTM inference using Candle, including the implementation of bidirectional LSTM inference.
Test Data
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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")
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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")
Dependencies
~10–20MB
~328K SLoC