Make your deep neural network Dynamically Isometric. Say farewell to vanishing and exploding gradients

• It purly makes your model weights 'w' tensor orthogonal (w.t()@w = Identity). This preserves the norm of the input tensor. Since the eigen-value of an orthogonal matrix is with norm equal to 1- gradients can easily seep even through a very deep network.
• Using SVD (NOT using QR Decomposition with Gram-Schmidt, that causes many issues). This ensures that even non-square matrices (MxN) will be orthogonalize, and also that the orthogonalization will be as close as possible to the current weights.

• import it:

from torchogonal import orthogonlize_module

• When initializing the model run:

orthogonlize_model(model)

IMPORTANT! This is an in-place function. This means the function does not return a new model, but it has changed the input model itself. So now 'model' weights are orthogonal.

• After each optimizer step, run this line again to re-orthogonalize the model:

orthogonlize_model(model)

The weights will be orthogonalize as close as possible to the changed weights (paper #2):

• Tested on Python 3.8 and Pytorch 1.10. The functions used in this repo are basic. This repo should easily work even older versions.

• Even though orthogonilization is extremely important in RNNs, they requiers a different approach of orthogonilzing (paper #3)

1. Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks (https://arxiv.org/pdf/1806.05393.pdf)
2. projUNN: efficient method for training deep networks with unitary matrices (https://arxiv.org/pdf/2203.05483.pdf)
3. Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks (https://arxiv.org/pdf/1806.05394.pdf)