cortical-team / neko

Neko: a Library for Exploring Neuromorphic Learning Rules

Paper

https://arxiv.org/abs/2105.00324

Installation

git clone https://github.com/cortical-team/neko.git
cd neko
pip install -e .

Code Example

Train a RSNN with ALIF neurons with e-prop on MNIST:

from neko.backend import pytorch_backend as backend
from neko.datasets import MNIST
from neko.evaluator import Evaluator
from neko.layers import ALIFRNNModel
from neko.learning_rules import Eprop
from neko.trainers import Trainer

x_train, y_train, x_test, y_test = MNIST().load()
x_train, x_test = x_train / 255., x_test / 255.

model = ALIFRNNModel(128, 10, backend=backend, task_type='classification', return_sequence=False)
evaluated_model = Evaluator(model=model, loss='categorical_crossentropy', metrics=['accuracy', 'firing_rate'])
algo = Eprop(evaluated_model, mode='symmetric')
trainer = Trainer(algo)
trainer.train(x_train, y_train, epochs=30)

Example Scripts

Learning with e-prop

Training on the MNIST dataset with the same setting above, but more options available. For example, you can learn with BPTT and three variations of e-prop.

python examples/mnist.py

Training on the TIMIT dataset. You need to place the timit_processed folder the same place as the script containing the processed dataset produced by a script from the original authors of e-prop.

python examples/timit.py

Regularization enabled:

python timit.py --reg --eprop_mode symmetric --reg_coeff 5e-7
# Test: {'loss': 0.8918977379798889, 'accuracy': 0.7501428091397849, 'firing_rate': 12.973159790039062}

Faster training (~7.5X, 28s per epoch with RTX3090) with regularization enabled:

python timit.py --reg --eprop_mode symmetric --reg_coeff 3e-8 --batch_size 256  --learning_rate 0.01
# Test: {'loss': 0.8605409860610962, 'accuracy': 0.7542506720430108, 'firing_rate': 13.105131149291992}

Probabilistic learning with HMC

Training on the MNIST-1D dataset with HMC:

python examples/mnist_1d_hmc.py

Analogue Neural Network Training with Manhattan Rule

Training on the MNIST dataset with the simple Manhattan rule or Mahattan material rule:

python examples/mnist_manhattan.py

Gradient Comparison Tool

Compare the gradients from BPTT with the three varients of e-prop:

python examples/mnist_gradcompare.py

This is a visualization from the results of the script above.