Metadata-Version: 2.1
Name: deepsplines
Version: 0.0.5
Summary: A framework to train the activation functions of a neural network
Home-page: https://github.com/joaquimcampos/DeepSplines
Author: Joaquim Campos
Author-email: joaquimcampos15@hotmail.com
License: MIT
Project-URL: Bug Tracker, https://github.com/joaquimcampos/DeepSplines/issues
Keywords: activation functions,B-splines,deep learning,regularization,sparsity
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: dev
License-File: LICENSE


.. image:: https://user-images.githubusercontent.com/26142730/128066373-a42476b4-6694-4810-8397-d6e1fa2638a8.png
  :width: 40 %
  :align: center

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5156042.svg
   :target: https://doi.org/10.5281/zenodo.5156042

Deep Spline Neural Networks
===========================

*DeepSplines* is a framework to train the activation functions of a neural network.

The aim of this repository is to:

* Facilitate the reproduction of the results reported in the research papers:

  * `Learning Activation Functions in Deep (Spline) Neural Networks <http://bigwww.epfl.ch/publications/bohra2003.html>`_;

  * `Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant <http://bigwww.epfl.ch/publications/aziznejad2001.html>`_.

* Enable a seamless integration of deep spline activation functions in a custom neural network.


The proposed scheme is based on the theoretical work of
`M.Unser <http://bigwww.epfl.ch/publications/unser1901.html>`_.


.. contents:: **Table of Contents**
    :depth: 2


Installation
============

A minimal installation requires:

* numpy >= 1.10
* pytorch >= 1.5.1
* torchvision >= 0.2.2
* matplotlib >= 3.3.1

You can install the package via the commands:

.. code-block:: bash

    >> conda create -y -n deepsplines python=3.7
    >> source activate deepsplines
    >> python3 -m pip install deepsplines

.. role:: bash(code)
   :language: bash

For GPU compatibility, you need to additionally install :bash:`cudatoolkit`
(*e.g.* via :bash:`conda install -c anaconda cudatoolkit`)

Usage
=====

Example on how to adapt the `PyTorch CIFAR-10 tutorial <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_
to use DeepBSpline activations.

.. code-block:: python

    from deepsplines.ds_modules import dsnn


    class DSNet(dsnn.DSModule):
        def __init__(self):

            super().__init__()

            self.conv_ds = nn.ModuleList()
            self.fc_ds = nn.ModuleList()

            # deepspline parameters
            opt_params = {
                'size': 51,
                'range_': 4,
                'init': 'leaky_relu',
                'save_memory': False
            }

            # convolutional layer with 6 output channels
            self.conv1 = nn.Conv2d(3, 6, 5)
            self.conv_ds.append(dsnn.DeepBSpline('conv', 6, **opt_params))
            self.pool = nn.MaxPool2d(2, 2)
            self.conv2 = nn.Conv2d(6, 16, 5)
            self.conv_ds.append(dsnn.DeepBSpline('conv', 16, **opt_params))

            # fully-connected layer with 120 output units
            self.fc1 = nn.Linear(16 * 5 * 5, 120)
            self.fc_ds.append(dsnn.DeepBSpline('fc', 120, **opt_params))
            self.fc2 = nn.Linear(120, 84)
            self.fc_ds.append(dsnn.DeepBSpline('fc', 84, **opt_params))
            self.fc3 = nn.Linear(84, 10)

        def forward(self, x):

            x = self.pool(self.conv_ds[0](self.conv1(x)))
            x = self.pool(self.conv_ds[1](self.conv2(x)))
            x = torch.flatten(x, 1)  # flatten all dimensions except batch
            x = self.fc_ds[0](self.fc1(x))
            x = self.fc_ds[1](self.fc2(x))
            x = self.fc3(x)

            return x

    dsnet = DSNet()
    dsnet.to(device)

    main_optimizer = optim.SGD(dsnet.parameters_no_deepspline(),
                               lr=0.001,
                               momentum=0.9)
    aux_optimizer = optim.Adam(dsnet.parameters_deepspline())

    lmbda = 1e-4 # regularization weight
    lipschitz = False # lipschitz control

    for epoch in range(2):

        for i, data in enumerate(trainloader):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data[0].to(device), data[1].to(device)

            # zero the parameter gradients
            main_optimizer.zero_grad()
            aux_optimizer.zero_grad()

            outputs = dsnet(inputs)
            loss = criterion(outputs, labels)

            # add regularization loss
            if lipschitz is True:
                loss = loss + lmbda * dsnet.BV2()
            else:
                loss = loss + lmbda * dsnet.TV2()

            loss.backward()
            main_optimizer.step()
            aux_optimizer.step()


For full details, please consult `scripts/deepsplines_tutorial.py <https://github.com/joaquimcampos/DeepSplines/blob/master/scripts/deepsplines_tutorial.py>`_.

Reproducing results
-------------------

To reproduce the results shown in the research papers [Bohra-Campos2020]_ and [Aziznejad2020]_ one can run the following scripts:

.. code-block:: bash

    >> ./scripts/run_resnet32_cifar.py
    >> ./scripts/run_nin_cifar.py
    >> ./scripts/run_twoDnet.py

To see the running options, please add ``--help`` to the commands above.

Authors
=======

DeepSplines is developed by the Biomedical Imaging Group at BIG. Original authors:

-   **Joaquim Campos**
-   **Pakshal Bohra**

Contributor:
-   **Harshit Gupta**

For citing this package, please see: http://doi.org/10.5281/zenodo.5156042

References
==========

.. [Bohra-Campos2020] P. Bohra, J. Campos, H. Gupta, S. Aziznejad, M. Unser, "Learning Activation Functions in Deep (Spline) Neural Networks," IEEE Open Journal of Signal Processing, vol. 1, pp.295-309, November 19, 2020.

.. [Aziznejad2020] S. Aziznejad, H. Gupta, J. Campos, M. Unser, "Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant," IEEE Transactions on Signal Processing, vol. 68, pp. 4688-4699, August 10, 2020.

MIT License

Copyright (c) 2021 Joaquim Campos, Pakshal Bohra

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


