From eb63b090fb825d1261fa1fbbfe90a13f2b095435 Mon Sep 17 00:00:00 2001 From: jloveric Date: Sat, 22 Jun 2024 20:24:06 -0700 Subject: [PATCH] Update readme --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8ebc266..cbb3261 100644 --- a/README.md +++ b/README.md @@ -292,7 +292,7 @@ The polynomial last layer is the same as that used in the convolution. |9 | 0.990 | 0.989 |10| 0.990 | 0.990 -## MNIST Block 3D polynomial +## MNIST Block 3D polynomial / block piecewise polynomial Basics implemented, needs to be made more efficient and model needs to change a bit. Currently uses a single NxNxN polynomial with 3 inputs (grayscale color, x, y) and 10 outputs to solve mnist. Not performant, but will work on it. ``` @@ -300,7 +300,11 @@ python3 examples/block_mnist.py accelerator='cuda' batch_size=64 optimizer.lr=1e ``` and ``` -python examples/block_mnist.py optimizer=lion optimizer.lr=1e-3 batch_size=16 max_epochs=100 n=28 +python examples/block_mnist.py optimizer=lion optimizer.lr=1e-3 batch_size=16 max_epochs=100 n=28 layer_type=polynomial_3d +``` +and then for block piecewise polynomial +``` +python examples/block_mnist.py optimizer=lion optimizer.lr=1e-3 batch_size=16 max_epochs=100 n=3 segments=10 layer_type=continuous_nd ``` ## CIFAR100 (convolutional)