non-uniform-piecewise-layers 0.1.0

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Dynamic Non-Uniform Piecewise Linear Layers

Example adding nodes

Example moving nodes (square wave)

Example moving nodes (circle)

Layer weights for the circle

A PyTorch implementation of non-uniform piecewise linear layers. These layers can learn arbitrary continuous piecewise linear functions, where both the positions (x-coordinates) and values (y-coordinates) of the control points are learned parameters.

I typically run these in either dynamic mode, i.e, adding nodes or adaptively where the nodes are moved, but conserving total number of nodes.

this is a work in progress

Example moving nodes implicit representation

Using 40 neurons in a single hidden layer

Function Approximation Example

See examples/sine_fitting.py for a complete example of approximating a complex function using the non-uniform piecewise linear layer.

Square Wave

Non default example

python examples/dynamic_square_wave.py training.adapt=move model.num_points=20 training.refine_every_n_epochs=10 data.num_points=100

MNIST

Running with and moving nodes with varrying number of points. You can run with larger learning_rate, to get faster results

python examples/mnist_classification.py -m model_type=adaptive epochs=100 move_nodes=True,False num_points=10 learning_rate=1e-4

Shakespeare

Approaching good results with things like this

python examples/shakespeare_generation.py -m training.learning_rate=1e-3 training.num_epochs=20 training.move_every_n_batches=200 model.hidden_size=32,64 model.num_points=32 training.batch_size=128

small memory machine

python examples/shakespeare_generation.py -m training.learning_rate=1e-3 training.num_epochs=20 training.move_every_n_batches=50 model.hidden_size=16 model.num_points=32 training.batch_size=64 training.adapt=move

2D Implicit Representation

uv run python examples/implicit_image.py model.normalization=noop training.num_epochs=20

3D Implicit Representation

This one is pretty solid

python examples/implicit_3d.py mesh_resolution=100 learning_rate=1e-5 hidden_layers=[40, 40]

rendering after run

python examples/implicit_3d.py render_only=true model_path=/path/to/model.pt high_res_resolution=256

different output name

python examples/implicit_3d.py render_high_res=true render_output_file="my_render.png"

Running visualization tests

use the -v to write data to file

pytest tests/test_visualization.py -v

Interesting Papers

Deep Networks Always Grok and Here's Why

Papers below are not currently used in this project, but would be interesting to investigate in the future Oja’s plasticity rule overcomes several challenges of training neural networks under biological constraints

Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective