pyrfd 1.0.1

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pyrfd

Pytorch implementation of RFD (see arXiv)

Covariance model

Provides an implementation of the SquaredExponential covariance model with an auto_fit function, which requires only

1. A model_factory which returns the same but randomly initialized model every time it is called
2. A loss function e.g. torch.nn.functional.nll_loss which accepts a prediction and a true value
3. data, which can be passed to torch.utils.DataLoader with different batch size parameters such that it returns (x,y) tuples when iterated on
4. a csv filename which acts as the cache for the covariance model ofthis unique (model, data, loss) combination.

Implementation of RFD

Such a covariance model can then be passed to RFD which implements the pytorch optimizer interface. The end result can be used like torch.optim.Adam

Example usage

from benchmaking.classification.mnist.models.cnn3 import CNN3

import torch
import torchvision as tv

from pyrfd import RFD, SquaredExponential

cov_model = SquaredExponential()
cov_model.auto_fit(
    model_factory=CNN3,
    loss=torch.nn.functional.nll_loss,
    data= tv.datasets.MNIST(
        root="mnistSimpleCNN/data",
        train=True,
        transform=tv.transforms.ToTensor()
    ),
    cache="cache/CNN3_mnist.csv",
    # should be unique for (models, data, loss)
)
rfd = RFD(
    CNN3().parameters(),
    covariance_model=cov_model
)

How to cite

@inproceedings{benningRandomFunctionDescent2024,
  title = {Random {{Function Descent}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Benning, Felix and D{\"o}ring, Leif},
  year = {2024},
  month = dec,
  volume = {37},
  primaryclass = {cs, math, stat},
  publisher = {Curran Associates, Inc.},
  address = {Vancouver, Canada},
}