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https://github.com/plandes/deeplearn

General deep learning tools
https://github.com/plandes/deeplearn

bilstm-crf convolutional-neural-networks crf deep-learning pypi pypi-link pytorch pytorch-cnn utility vectorized-features

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General deep learning tools

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README 

        
# Deep Zensols Deep Learning Framework



[![PyPI][pypi-badge]][pypi-link]

[![Python 3.10][python310-badge]][python310-link]

[![Python 3.11][python311-badge]][python311-link]

[![Build Status][build-badge]][build-link]



This deep learning library was designed to provide consistent and reproducible

results.



* See the [full documentation].

* See the [paper](https://aclanthology.org/2023.nlposs-1.16)



Features:

* Easy to configure and framework to allow for programmatic [debugging] of

  neural networks.

* [Reproducibility] of results

  * All [random seed state] is persisted in the trained model files.

  * Persisting of keys and key order across train, validation and test sets.

* Analysis of results with complete metrics available.

* A [vectorization] framework that allows for pickling tensors.

* Additional [layers]:

  * Full [BiLSTM-CRF] and stand-alone [CRF] implementation using easy to

    configure constituent layers.

  * Easy to configure *N* [deep convolution layer] with automatic

    dimensionality calculation and configurable pooling and batch centering.

  * [Convolutional layer factory] with dimensionality calculation.

  * [Recurrent layers] that abstracts RNN, GRU and LSTM.

  * *N* deep [linear layers].

  * Each layer's configurable with activation, dropout and batch normalization.

* [Pandas] integration to [data load], [easily manage] [vectorized features],

  and [report results].

* Multi-process for time consuming CPU feature [vectorization] requiring little

  to no coding.

* Resource and tensor deallocation with memory management.

* [Real-time performance] and loss metrics with plotting while training.

* Thorough [unit test] coverage.

* [Debugging] layers using easy to configure Python logging module and control

  points.

* A workflow and API to package and distribute models.  Then automatically

  download, install and inference with them in (optionally) two separate code

  bases.



Much of the code provides convenience functionality to [PyTorch].  However,

there is functionality that could be used for other deep learning APIs.



## Documentation



See the [full documentation].



## Obtaining



The easiest way to install the command line program is via the `pip` installer:

```bash

pip3 install zensols.deeplearn

```



Binaries are also available on [pypi].



## Workflow



This package provides a workflow for processing features, training and then

testing a model.  A high level outline of this process follows:

1. Container objects are used to represent and access data as features.

1. Instances of *data points* wrap the container objects.

1. Vectorize the features of each data point in to tensors.

1. Store the vectorized tensor features to disk so they can be retrieved

   quickly and frequently.

1. At train time, load the vectorized features in to memory and train.

1. Test the model and store the results to disk.



To jump right in, see the [examples](#examples) section.  However, it is better

to peruse the in depth explanation with the [Iris example] code follows:

* The initial [data processing], which includes data representation to batch

  creation.

* Creating and configuring the [model].

* Using a [facade] to train, validate and test the model.

* Analysis of [results], including training/validation loss graphs and

  performance metrics.



## Examples



The [Iris example] (also see the [Iris example configuration]) is the most

basic example of how to use this framework.  This example is detailed in the

[workflow](#workflow) documentation in detail.



There are also examples in the form of [Juypter] notebooks as well, which

include the:

* [Iris notebook] data set, which is a small data set of flower dimensions as a

  three label classification,

* [MNIST notebook] for the handwritten digit data set,

* [debugging notebook].



## Attribution



This project, or example code, uses:

* [PyTorch] as the underlying framework.

* Branched code from [Torch CRF](#torch-crf) for the [CRF] class.

* [pycuda] for Python integration with [CUDA].

* [scipy] for scientific utility.

* [Pandas] for prediction output.

* [matplotlib] for plotting loss curves.



Corpora used include:

* [Iris data set]

* [Adult data set]

* [MNIST data set]



### Torch CRF



The [CRF] class was taken and modified from Kemal Kurniawan's [pytorch_crf]

GitHub repository.  See the `README.md` module documentation for more

information.  This module was [forked pytorch_crf] with modifications.

However, the modifications were not merged and the project appears to be

inactive.



**Important**: This project will change to use it as a dependency pending

merging of the changes needed by this project.  Until then, it will remain as a

separate class in this project, which is easier to maintain as the only

class/code is the `CRF` class.



The [pytorch_crf] repository uses the same license as this repository, which

the [MIT License].  For this reason, there are no software/package tainting

issues.



## See Also



The [zensols deepnlp] project is a deep learning utility library for natural

language processing that aids in feature engineering and embedding layers that

builds on this project.



## Citation



If you use this project in your research please use the following BibTeX entry:



```bibtex

@inproceedings{landes-etal-2023-deepzensols,

    title = "{D}eep{Z}ensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility",

    author = "Landes, Paul  and

      Di Eugenio, Barbara  and

      Caragea, Cornelia",

    editor = "Tan, Liling  and

      Milajevs, Dmitrijs  and

      Chauhan, Geeticka  and

      Gwinnup, Jeremy  and

      Rippeth, Elijah",

    booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",

    month = dec,

    year = "2023",

    address = "Singapore, Singapore",

    publisher = "Association for Computational Linguistics",

    url = "https://aclanthology.org/2023.nlposs-1.16",

    pages = "141--146"

}

```



## Changelog



An extensive changelog is available [here](CHANGELOG.md).



## Community



Please star the project and let me know how and where you use this API.

Contributions as pull requests, feedback and any input is welcome.



## License



[MIT License]



Copyright (c) 2020 - 2023 Paul Landes



[pypi]: https://pypi.org/project/zensols.deeplearn/

[pypi-link]: https://pypi.python.org/pypi/zensols.deeplearn

[pypi-badge]: https://img.shields.io/pypi/v/zensols.deeplearn.svg

[python310-badge]: https://img.shields.io/badge/python-3.10-blue.svg

[python310-link]: https://www.python.org/downloads/release/python-3100

[python311-badge]: https://img.shields.io/badge/python-3.11-blue.svg

[python311-link]: https://www.python.org/downloads/release/python-3110

[build-badge]: https://github.com/plandes/util/workflows/CI/badge.svg

[build-link]: https://github.com/plandes/deeplearn/actions



[MIT License]: LICENSE.md

[PyTorch]: https://pytorch.org

[Juypter]: https://jupyter.org

[pycuda]: https://pypi.org/project/pycuda/

[CUDA]: https://developer.nvidia.com/cuda-toolkit

[scipy]: https://www.scipy.org

[Pandas]: https://pandas.pydata.org

[matplotlib]: https://matplotlib.org



[pytorch_crf]: https://github.com/kmkurn/pytorch-crf

[forked pytorch_crf]: https://github.com/plandes/pytorch-crf

[zensols.deeplearn.layer.CRF]: api/zensols.deeplearn.layer.html#zensols.deeplearn.layer.crf.CRF

[zensols deepnlp]: https://plandes.github.io/deepnlp



[full documentation]: https://plandes.github.io/deeplearn/index.html

[Iris notebook]: https://github.com/plandes/deeplearn/tree/master/notebook/iris.ipynb

[MNIST notebook]: https://github.com/plandes/deeplearn/tree/master/notebook/mnist.ipynb

[debugging notebook]: https://github.com/plandes/deeplearn/tree/master/notebook/debug.ipynb



[model]: https://plandes.github.io/deeplearn/doc/model.html

[facade]: https://plandes.github.io/deeplearn/doc/facade.html

[results]: https://plandes.github.io/deeplearn/doc/results.html

[data processing]: https://plandes.github.io/deeplearn/doc/preprocess.html

[layers]: https://plandes.github.io/deeplearn/doc/layers.html

[reproducibility]: https://plandes.github.io/deeplearn/doc/results.html#reproducibility

[debugging]: https://plandes.github.io/deeplearn/doc/facade.html#debugging-the-model

[random seed state]: api/zensols.deeplearn.html#zensols.deeplearn.torchconfig.TorchConfig.set_random_seed

[Real-time performance]: https://plandes.github.io/deeplearn/doc/results.html#plotting-loss

[Debugging]: https://plandes.github.io/deeplearn/doc/model.html#debugging

[unit test]: https://github.com/plandes/deeplearn/tree/master/test/python

[vectorization]: https://plandes.github.io/deeplearn/doc/preprocess.html#vectorizers

[Iris example]: https://github.com/plandes/deeplearn/blob/master/test/python/iris/model.py

[Iris example configuration]: https://github.com/plandes/deeplearn/blob/master/test-resources/iris



[Iris data set]: https://archive.ics.uci.edu/ml/datasets/iris

[Adult data set]: http://archive.ics.uci.edu/ml/datasets/Adult

[MNIST data set]: http://yann.lecun.com/exdb/mnist/



[data load]: https://plandes.github.io/deeplearn/api/zensols.dataframe.html?highlight=dataframestash#zensols.dataframe.stash.DataframeStash

[easily manage]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.dataframe.html?highlight=dataframefeaturevectorizermanager#zensols.deeplearn.dataframe.vectorize.DataframeFeatureVectorizerManager

[vectorized features]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.vectorize.html?highlight=seriesencodablefeaturevectorizer#zensols.deeplearn.vectorize.vectorizers.OneHotEncodedEncodableFeatureVectorizer

[report results]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.result.html?highlight=modelresultreporter#zensols.deeplearn.result.report.ModelResultReporter



[Convolutional layer factory]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.layer.html#zensols.deeplearn.layer.conv.ConvolutionLayerFactory

[CRF]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.layer.html#zensols.deeplearn.layer.crf.CRF

[BiLSTM-CRF]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.layer.html?highlight=recurrentcrf#zensols.deeplearn.layer.recurcrf.RecurrentCRF

[Recurrent layers]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.layer.html#zensols.deeplearn.layer.recur.RecurrentAggregation

[linear layers]: https://plandes.github.io/deeplearn/api/zensols.deeplearn.layer.html#zensols.deeplearn.layer.linear.DeepLinear