PEPITA

Code to run the simulations of the paper:

Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass

Giorgia Dellaferrera, Gabriel Kreiman

Presented at ICML 2022: https://proceedings.mlr.press/v162/dellaferrera22a.html

Requirements

We run the experiments with the following:

Numpy framework (fully connected models): Python 3.9.5, Numpy 1.19.5, Keras 2.5.0

Pytorch framework (convolutional models): Python 3.7.10, Numpy 1.19.2, Pytorch 1.6.0

Experiments

Fully connected models - Pytorch version

The notebook Tutorial_PEPITA_FullyConnectedNets_CIFAR-10.ipynb provides a simple tutorial on how to implement and run the PEPITA training scheme for fully connected models. The entire framework is pytorch-based. The settings and results are the same as reported in the paper.

The training for 100 epochs takes approximately 1.5 hours on CPU.

Fully connected models - numpy version

The experiments are run through main.py, which uses functions in functions.py and utils.py. The entire framework is numpy-based and relies on the keras library to load the datasets.

For example, to run PEPITA with the standard settings on the MNIST dataset:

python main.py --exp_name Experiment1 \
    --learn_type ERIN --n_runs 1 --train_epochs 100 \
    --sample_passes 2 --n_samples all --eta 0.1 --dropout 0.9 \
    --eta_decay --mnist --validation --batch_size 64 \
    --update_type mom --w_init he_uniform \
    --build auto --struct uniform --start_size 1024 --n_hlayers 1 --act_hidden relu --act_out softmax

Note that the training scheme for PEPITA is denoted as ERIN (ERror-INput). If you train with PEPITA (ERIN), make sure to use the setting --sample_passes 2, to have for each input two forward passes.

Substitute --learn_type ERIN with --learn_type BP to train the network with backpropagation. Remember to set --sample_passes 1.

Convolutional models - Pytorch version

The experiments are run through main_pytorch.py, which uses functions in models.py. The entire framework is pytorch-based.

For example, to run PEPITA with the standard settings on the MNIST dataset:

python main_pytorch.py --exp_name Experiment2 \
    --learn_type ERIN --n_runs 1 --train_epochs 100 \
    --eta 0.01 --dropout 0.9 --Bstd 0.05 \
    --eta_decay --dataset mn --batch_size 50 \
    --update_type mom --w_init he_uniform \
    --model Net1conv1fcXL

The argument Bstd defines the standard deviation of the projection matrix. Here we use B instead of F (paper) to denote the projection matrix to avoid confusion with torch.nn.functional.

Convergence rate

The notebook plot_compute_slowness.ipynb contains the function to extract the convergence rate as "slowness" parameter.

Citation

@InProceedings{pmlr-v162-dellaferrera22a,
  title = 	 {Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass},
  author =       {Dellaferrera, Giorgia and Kreiman, Gabriel},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {4937--4955},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/dellaferrera22a/dellaferrera22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/dellaferrera22a.html},
}