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Deeplearning framework for PyTorch

Project description

Introduction

FOS is a Python framework that makes it easy to develop neural network models in PyTorch. Some of its main features are:

  • Less boilerplate code required, see also the example below.
  • Lightweight and no magic under the hood that might get in the way.
  • You can extend Fos using common OO patterns.
  • Get the insights you need into the performance of the model.

Installation

You can install FOS using pip:

pip install fos

Or alternatively from the source:

python setup.py install

Fos requires Python 3.5 or higher.

Usage

Training a model, requires just a few lines of code. First create the model, optimizer and loss function that you want to use, creating plain PyTorch objects:

net   = resnet18()
optim = Adam(predictor.parameters())
loss  = F.binary_cross_entropy_with_logits

Then create the FOS objects that will take care of the training and output:

workout   = Workout(predictor, loss, optim)

And we are ready to start the training:

trainer.fit(train_data, valid_data, epochs=5)

Examples

You can find several example Jupyter notebooks here, or even more convenient try them directly in a Google Colab environment:

  1. Basic Example
  2. MNIST example

Contribution

If you want to help out, we appreciate all contributions. Please see the [contribution guidelines]() for more information.

As always, PRs are welcome :)=

Project details


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