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:
The main features provided are:
- LISSOM’s activation
- Consisting of several layers following the
torch.nn.Moduleinterface. Found in pylissom.nn.modules.
- LISSOM’s hebbian learning mechanism and others
- Implemented following the
torch.optim.Optimizerinterface. Found in pylissom.optim.
- Configuration and model building tools
- Make it easy to track and change layer’s hyperparameters. Based in the
yamlconfig 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.Datasetinterface. Uses the popular
cv2libs. 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
- Found in pylissom.utils.training.pipeline module.