Getting Started

There are Jupyter notebooks with tutorials at the github’s page of the project. If Github is not rendering them, we leave these links at your disposal:

Linear modules

Lissom modules

Optimizers

Orientation Maps and pylissom tools

The main features provided are:

LISSOM’s activation

Consisting of several layers following the torch.nn.Module interface. Found in pylissom.nn.modules.

LISSOM’s hebbian learning mechanism and others

Implemented following the torch.optim.Optimizer interface. Found in pylissom.optim.

Configuration and model building tools

Make it easy to track and change layer’s hyperparameters. Based in the configobj and yaml config libraries. Examples of config files and code can be found in pylissom.models and pylissom.utils.config.

Common Guassian stimuli for LISSOM experiments

Following the torch.utils.data.Dataset interface. Uses the popular scikit-image and cv2 libs. Found in pylissom.datasets and pylissom.utils.stimuli module.

Plotting helpers

For displaying LISSOM layers weights and activations mainly in Jupyter Notebooks. Uses matplotlib. Found in pylissom.utils.plotting.

Training objects for simplifying torch boilerplate.

Found in pylissom.utils.training.pipeline module.