This is an implementation of the LOSSGRAD optimization algorithm for PyTorch. The algorithm has been implemented in the paper: LOSSGRAD: automatic learning rate in gradient descent

Import the lossgrad module and simply wrap your SGD optimizer with LossgradOptimizer instance, e.g.:

optimizer = LossgradOptimizer(torch.optim.SGD(net.parameters(), lr=1e-4), net, criterion)

and pass the required additional arguments to the optimizer's step method:

optimizer.step(X, y, loss)

A simple example for MNIST dataset is shown in mnist_example.py.

A more complicated example for an autoencoder trained on Fashion-MNIST (with figures and image samples) is shown in autoencoder_fmnist_example.py.

If you use the algorithm you can cite our paper:

@article{wojcik2019lossgrad,
  title={LOSSGRAD: automatic learning rate in gradient descent},
  author={W{\'o}jcik, Bartosz and Maziarka, {\L}ukasz and Tabor, Jacek},
  journal={arXiv preprint arXiv:1902.07656},
  year={2019}
}