Metadata-Version: 1.1
Name: pytorch-stateful-lstm
Version: 1.4.0
Summary: None
Home-page: https://github.com/cnt-dev/pytorch-stateful-lstm
Author: Hunt Zhan
Author-email: huntzhan.dev@gmail.com
License: MIT license
Description: =====================
        pytorch-stateful-lstm
        =====================
        
        
        .. image:: https://img.shields.io/pypi/v/pytorch_stateful_lstm.svg
                :target: https://pypi.python.org/pypi/pytorch_stateful_lstm
        
        .. image:: https://img.shields.io/travis/cnt-dev/pytorch-stateful-lstm.svg
                :target: https://travis-ci.org/cnt-dev/pytorch-stateful-lstm
        
        * Free software: MIT license
        
        Features
        --------
        
        Pytorch LSTM implementation powered by Libtorch, and with the support of:
        
        - Hidden/Cell Clip.
        - Skip Connections.
        - Variational Dropout & DropConnect.
        - Managed Initial State.
        - Built-in TBPTT.
        
        Benchmark: https://github.com/cnt-dev/pytorch-stateful-lstm/tree/master/benchmark
        
        Install
        -------
        
        Prerequisite: `torch>=1.0.0`, supported C++11 compiler (see here_). To install through pip::
        
            pip install pytorch-stateful-lstm
        
        .. _here: https://github.com/pytorch/pytorch/blob/0bf1383f0a6caa34945feaf19191986d18205251/torch/utils/cpp_extension.py#L169-L181
        
        Usage
        -----
        
        Example::
        
            import torch
            from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
            from pytorch_stateful_lstm import StatefulUnidirectionalLstm
        
            lstm = StatefulUnidirectionalLstm(
                    num_layers=2,
                    input_size=3,
                    hidden_size=5,
                    cell_size=7,
            )
        
            inputs = pack_padded_sequence(torch.rand(4, 5, 3), [5, 4, 2, 1], batch_first=True)
            raw_packed_outputs, lstm_state = lstm(
                    inputs.data,
                    inputs.batch_sizes
            )
            outputs = PackedSequence(raw_packed_outputs, inputs.batch_sizes)
        
        For the definition of parameters, see https://github.com/cnt-dev/pytorch-stateful-lstm/tree/master/extension.
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.0 (2019-01-03)
        ------------------
        
        * First release on PyPI.
        
Keywords: pytorch_stateful_lstm
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
