Christoph Heindl cheind
Tushar-N
/ pad_packed_demo.py
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October 27, 2024 15:17
How to use pad_packed_sequence in pytorch<1.1.0
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| import torch | |
| import torch.nn as nn | |
| from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
| seqs = ['gigantic_string','tiny_str','medium_str'] | |
| # make <pad> idx 0 | |
| vocab = ['<pad>'] + sorted(set(''.join(seqs))) | |
| # make model |
cheind
/ hmm_train_tf.py
Last active
December 5, 2022 07:20
HMM training based on gradient descent (Tensorflow version)
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| __author__ = 'Christoph Heindl' | |
| __copyright__ = 'Copyright 2017' | |
| __license__ = 'BSD' | |
| """Trains a HMM based on gradient descent optimization. | |
| The parameters (theta) of the model are transition and | |
| emission probabilities, as well as the initial state probabilities. | |
| Given a start solution, the negative log likelihood of data given the |