Deeplearning framework for PyTorch
FOS is a Python framework that makes it easy to develop neural network models in PyTorch. Some of its main features include:
- 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.
You can install FOS using pip:
pip install fos
Or alternatively from the source:
python setup.py install
Fos requires Python 3.6 or higher.
Training a model, requires just a few lines of code. First create the model, optimizer and loss function that you want to use, using normal PyTorch code:
model = resnet18() optim = Adam(model.parameters()) loss = F.binary_cross_entropy_with_logits
Then create the FOS workout that will take care of the training and output:
workout = Workout(net, loss, optim)
And we are ready to start the training:
workout.fit(train_data, valid_data, epochs=5)
You can find several example Jupyter notebooks here
You can also run them on Google Colab directly:
- Basic https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/basic_fos.ipynb
- MINST https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/mnist_fos.ipynb
- Inputs https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/inputs_fos.ipynb
- Tensorboard https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/tensorboard_fos.ipynb
If you want to help out, we appreciate all contributions. Please see the contribution guidelines for more information.
As always, PRs are welcome :)=
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