This is an implementation of Olshausen and Field's sparse coding paper in PyTorch. Iterative Shrinkage/Thresholding Algorithm (ISTA) is used to fit neuronal responses for the input. Gradients for receptive fields are calculated through PyTorh's autograd feature.
To run the program:
cd src/scripts
python train.py To see a list of available hyperparameters to change:
python train.py -hA checkpoint of the model is saved every 10 epochs to trained_models. To see the tensorboard logs:
tensorboard --logdir=runs- Fast-ISTA
- Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609. https://doi.org/10.1038/381607a0
- IMAGES.mat is downloaded from Olshausen's original Matlab implementation website: http://www.rctn.org/bruno/sparsenet/