biotorch 0.0.1

BioTorch is a PyTorch framework specializing in biologically plausible learning algorithms.

BioTorch is a PyTorch framework specializing in biologically plausible learning algorithms

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BioTorch Provides:

🧠 Implementations of layers, models and biologically plausible learning algorithms. It allows to load existing state-of-the-art models, easy creation of custom models and automatic conversion of any existing model.

🧠 A framework to train, evaluate and benchmark different biologically plausible learning algorithms in a selection of datasets. It is focused on the principles of PyTorch design and research reproducibility. Configuration files that include the choice of a fixed seed and deterministic math and CUDA operations are provided.

🧠 A place of colaboration, ideas sharing and discussion.

Methods Supported

Feedback Alignment

Name	Mode	Official Implementations
Feedback Alignment	'fa'	N/A
Direct Feedback Alignment	'dfa'	[Torch]
Sign Symmetry	['usf', 'brsf', 'frsf']	[PyTorch]

Metrics Supported

Layer Weight Alignment	Layer Weight Norm Ratio

Quick Tour

Create a Feedback Aligment (FA) ResNet-18 model

from biotorch.models.fa import resnet18
model = resnet18()

Create a custom model with uSF layers

import torch.nn.functional as F
from biotorch.layers.usf import Conv2d, Linear

class Model(nn.Module):
  def __init__(self):
    super(Model, self).__init__()
    self.conv1 = Conv2d(in_channels=64, out_channels=128, kernel_size=3)
    self.fc = Linear(in_features=256, out_features=10)

  def forward(self, x):
    out = F.relu(self.conv1(x))
    out = F.avg_pool2d(out, out.size()[3])
    return self.fc(out)

model = Model()

Automatically convert AlexNet to use the "frSF" algorithm

from torchvision.models import alexnet
from biotorch.module.biomodule import BioModule

model = BioModule(module=alexnet(), mode='frsf')

Run an experiment on the command line

python benchmark.py --config benchmark_configs/mnist/fa.yaml

If you want the experiment to be reproducible, check that you have specified a seed and the parameter deterministicis set to True in the configuration file yaml. That will apply all the PyTorch reproducibility steps. If you are running your experiment on GPU add the extra environment variable CUBLAS_WORKSPACE_CONFIG.

CUBLAS_WORKSPACE_CONFIG=:4096:8 python benchmark.py --config benchmark_configs/mnist/fa.yaml

Click here to learn more about the configuration file API.

Run an experiment on a Jupyter Notebook

Installation

We are hosted in PyPI, you can install the library using pip:

pip install biotorch

Or from source:

python setup.py install

Benchmarks

MNIST

CIFAR-10

ImageNet

Contributing

If you want to contribute to the project please read the CONTRIBUTING section. If you found any bug please don't hesitate to comment in the Issues section.