BioPyTorch

PyTorch implementation of the learning rule proposed in "Unsupervised learning by competing hidden units", D. Krotov, J. J. Hopfield, 2019, https://www.pnas.org/content/116/16/7723

Installation

Install with pip:

pip install biopytorch

Usage

The package provides two layers, BioLinear and BioConv2d, that respectively mirror the features of nn.Linear and nn.Conv2d from PyTorch, with the added support of training with the alternative rule proposed by Krotov & Hopfield.

They share all the same parameters of their analogues (except for BioConv2d, which currently does not support the use of bias). To execute a single update step, call the method training_step.

See the example notebook in notebooks for more details.

Other files

• dev/bioconv2d_dev.py contains an alternative implementation of BioConv2d using F.unfold. The performance is significantly worse (especially for memory), so it should not be used in practice. However, the algorithm is easier to follow, and can be used to get a better understanding of the Krotov learning rule.
• slides contains a few explanatory slides
• notebooks: examples

Benchmark

Hyperparameters ($p$, $k$, $\Delta$ - for their meaning, see the slides, or the docstrings) are optimized with respect to the validation accuracy of classification on the CIFAR-10 dataset, using the Optuna library.

Specifically, the architecture (taken from [2]) is as follows:

The (Classifier) segment is inserted in different positions - after (1), (2), ... - such that the change in performance given by deeper layers may be measured.

Depending on the number of hebbian layers preceding the (Classifier), the performance obtained with the best hyperparameters found is as follows:

$p$, $k$ and $\Delta$ are the same for all the BioConv2d layers. When the full architecture is trained, different hyperparameters are used for the BioLinear layer. However, for the best run, they are exactly equal to the ones already used for the previous BioConv2d, which are reported in the table.

Note that performance is slightly better than the results obtained in [2], here reported for reference:

A full report on the hyperparameter optimization is available on wandb.

Sources

[1] Krotov, Hopfield, "Unsupervised learning by competing hidden units", 2019

[2] Amato et al., "Hebbian Learning Meets Deep Convolutional Neural Networks", 2019