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deepsplines-1.0.0
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مشاهده بیشترتوضیحات
A framework to train the activation functions of a neural network
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepsplines-1.0.0 |
| نام | deepsplines |
| نسخه کتابخانه | 1.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Joaquim Campos |
| ایمیل نویسنده | joaquimcampos15@hotmail.com |
| آدرس صفحه اصلی | https://github.com/joaquimcampos/DeepSplines |
| آدرس اینترنتی | https://pypi.org/project/deepsplines/ |
| مجوز | MIT |
.. image:: https://user-images.githubusercontent.com/26142730/128066373-a42476b4-6694-4810-8397-d6e1fa2638a8.png
:width: 50 %
:align: center
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5158821.svg
:target: https://doi.org/10.5281/zenodo.5158821
*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
Requirements
============
* python >= 3.7
* numpy >= 1.10
* pytorch >= 1.5.1
* torchvision >= 0.2.2
* matplotlib >= 3.3.1
* (optional) CUDA
The code was developed and tested on a x86_64 Linux system.
Installation
============
To install the package, we first create an environment with python 3.7 (or greater):
.. code-block:: bash
>> conda create -y -n deepsplines python=3.7
>> source activate deepsplines
Quick Install
--------------
*DeepSplines* is available on `Pypi <https://pypi.org/project/deepsplines/>`_.
Therefore, you can install the package via the command:
.. code-block:: bash
>> pip install deepsplines
.. role:: bash(code)
:language: bash
For NVIDIA GPU compatibility, you need to additionally install :bash:`cudatoolkit`
(via :bash:`conda install -c anaconda cudatoolkit`)
Developper Install
------------------
It is also possible to install *DeepSplines* from the source for developpers:
.. code-block:: bash
>> git clone https://github.com/joaquimcampos/DeepSplines
>> cd <repository_dir>/
>> pip install -e .
Usage
=====
Here we show an 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 :bash:`--help` to the commands above.
Developers
==========
*DeepSplines* is developed by the `Biomedical Imaging Group <http://bigwww.epfl.ch/>`_,
`École Polytéchnique Fédérale de Lausanne <https://www.epfl.ch/en/>`_, Switzerland.
For citing this package, please see: http://doi.org/10.5281/zenodo.5156042
Original authors:
- **Joaquim Campos** (joaquimcampos15@hotmail.com)
- **Pakshal Bohra** (pakshal.bohra@epfl.ch)
Contributors:
- **Harshit Gupta**
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.
License
=======
The code is released under the terms of the `MIT License <https://github.com/joaquimcampos/DeepSplines/blob/master/LICENSE>`_
Acknowledgements
================
This work was supported in part by the Swiss National Science Foundation under Grant 200020_184646 / 1 and in part by the European Research Council (ERC)
under Grant 692726-GlobalBioIm.
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.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.10 | numpy |
| >=1.5.1 | torch |
| >=0.2.2 | torchvision |
| >=3.3.1 | matplotlib |
| - | autopep8 |
| - | flake8 |
| - | yapf |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl deepsplines-1.0.0:
pip install deepsplines-1.0.0.whl
نصب پکیج tar.gz deepsplines-1.0.0:
pip install deepsplines-1.0.0.tar.gz