This is a PyTorch implementation of my tensorflow repository and is more complete due to the flexibility of PyTorch.
Lagrange Polynomial, Piecewise Lagrange Polynomial, Discontinuous Piecewise Lagrange Polynomial, Fourier Series, sum and product layers in PyTorch. The sparsity of using piecewise polynomial layers means that by adding new segments the representational power of your network increases, but the time to complete a forward step remains constant. Implementation includes simple fully connected layers, convolution layers and deconvolutional layers using these models. This is a PyTorch implementation of this Discontinuous Piecewise Polynomial Neural Networks including huge number of extensions including continuous, Fourier series and convolutional neural networks... and many applications with varrying degrees of success.
Using simple high order layers Simple function approximation
Using simple high order MLP 2d function approximation
The idea is extremely simple, instead of a weight at the synapse we have a function F(x) that can be arbitrarily complex. As a practical matter I implement this by using multiple weights corresponding to each link, these weight are used as parameters of the function, and to make sure there is still some GPU efficiency, these weights are just coefficients of the basis functions. In most of this work, the n-weights describe the value of a piecewise polynomial on a regular grid (in the case of a piecewise polynomial) each of the n-weights can be updated independently. A Lagrange polynomial and Gauss Lobatto points are used to minimize oscillations of the polynomial. The same approach can be applied to any "functional" synapse, and I also have Fourier series synapses in this repo as well. Because the non-linearity is applied on the link, the node is simply a summation
In the image below each "link" instead of being a single weight, is a function of both x and a set of weights. These functions can consist of an orthogonal basis functions for efficient approximation.
A small layer then looks like this, the values at the nodes are just summed.
A single neuron input output pair with a piecewise function is shown below. In the case where we use polynomials, Lagrange polynomials are being used so the values of the weights are identical to the value of the function at that point. The spacing is determined by chebyshev lobatto points, so there are always weights at the edge of each segment. In the case of discontinuous polynomial, the weights there are 2 weights for each interior segment edge.
The image below shows the function passing through the weights when using lagrange polynomials. Note that there is no derivative continuity at the boundaries.
Using higher order polynomial representations allow networks with much fewer total weights in certain cases. There is a well known phenomena in numerical modeling known as exponential convergence using spectral methods when using hp refinement, it's possible something like that can happen in neural networks as well.
Actually a single layer piecewise polynomial KAN (which is actually 2 layers) is a special case of a 2 layer piecewise polynomial network, which is used in this repo. Therefore, a piecewise polynomial layer is actually "Half a KAN" so it's actually simpler - Often all you need is a single polynomial layer at the input followed by a standard MLP so having the piecewise polynomial layer is important. Other names that have been used in the past Deep FLANN (functional link artificial neural network).
Lagrange polynomials are widely used in finite element analysis and have the advantage that the value of the weight is actually the value of the function at that point in space. By limiting the weights you are limiting the maximum value of the function (the function may be higher than the weights in between the nodes). Also, when you go beyond the range of definition [-1,1] the polynomial is still defined using the last (or first) polynomial in the sequence, whether you want it defined that way at high polynomial order is another question. I mention a paper at the bottom where they do a linear extension beyond the range [-1,1] so values do not rise too fast - but normalization works as well.
What about instabilities due to steep gradients? Seems like you can get around those with various approaches, polynomial refinement is one (start with piecewise linear and than increase the polynomial order after it converges), the lion optimizer helps a lot as well.
The biggest issues I've experienced though are that it's slower than dense networks and certain operations can take up more memory which can cause major issues with models that already push the limits of your gpu. Now that KANs are popular, hopefully there will be enough people to address all these issues.
In general, with enough effort, it seems I can make them "work" for any place the classic ReLU network works and in certain situations they clearly work much better. They also do a great job of overfitting, which just means, I need more data. For problems where your inputs are positional, x and y..., they seem to be far better.
Finally, I believe these methods actually will benefit much more from (approximate) second order optimizers. I used those in my original implementation. Although there are plenty of second order optimizers out there, to date, pytorch does not have a standard one except LBFGS which has its own issues.
All polynomials are Lagrange polynomials with Chebyshev interpolation points.
A helper function is provided in selecting and switching between these layers
from high_order_layers_torch.layers import *
layer1 = high_order_fc_layers(
layer_type=layer_type,
n=n,
in_features=784,
out_features=100,
segments=segments,
)where layer_type is one of
| layer_type | representation |
|---|---|
| continuous | piecewise polynomial using sum at the neuron |
| continuous_prod | piecewise polynomial using products at the neuron |
| discontinuous | discontinuous piecewise polynomial with sum at the neuron |
| discontinuous_prod | discontinous piecewise polynomial with product at the neuron |
| polynomial | single polynomial (non piecewise) with sum at the neuron |
| polynomial_prod | single polynomial (non piecewise) with product at the neuron |
| product | Product |
| fourier | fourier series with sum at the neuron |
n is the number of interpolation points per segment for polynomials or the number of frequencies for fourier series, segments is the number of segments for piecewise polynomials, alpha is used in product layers and when set to 1 keeps the linear part of the product, when set to 0 it subtracts the linear part from the product.
conv_layer = high_order_convolution_layers(layer_type=layer_type, n=n, in_channels=3, out_channels=6, kernel_size=5, segments=segments, rescale_output=rescale_output, periodicity=periodicity)All polynomials are Lagrange polynomials with Chebyshev interpolation points.
| layer_type | representation |
|---|---|
| continuous(1d,2d) | piecewise continuous polynomial |
| discontinuous(1d,2d) | piecewise discontinuous polynomial |
| polynomial(1d,2d) | single polynomial |
| fourier(1d,2d) | fourier series convolution |
For non convolutional layers I've found that initializing the polynomials to continuous line across all segments, works better then a random wiggly polynomial. I don't have similar functions implemented for convolutional layers. Here is a function that does this initialization (it can be found in networks.py)
def initialize_network_polynomial_layers(
network: nn.Module,
max_slope: float,
max_offset: float,
scale_slope: Callable[[float], float] = lambda input_size: 1,
)
p refinement is taking an existing network and increasing the polynomial order of that network without changing the network output. This allow the user to train a network at low polynomial order and then use that same network to initialize a network with higher polynomial order. This is particularly useful since a high order polynomial network will often converge poorly without the right initialization, the lower order network provides a good initial solution. The function for changing the order of a network is
from high_order_layers_torch.networks import interpolate_high_order_mlp
interpolate_high_order_mlp(
network_in: HighOrderMLP, network_out: HighOrderMLP
current implementation only works with high order MLPs, not with convnets. A similar function exists for h refinement. h refinement is refining the number of segments in a layer, and is used for similar reasoning. Layers with lots of segments may be slow to converge so the user starts with a small number of segments (1 or 2) and then increases the number of segments (h) using the lower initialization. The following function currently only works for high order MLPs, not with convnets
from high_order_layers_torch.network import hp_refine_high_order_mlp
hp_refine_high_order_mlp(
network_in: HighOrderMLP, network_out: HighOrderMLP
)
This repo uses poetry, so run
poetry install
and then
poetry shell
pip install high-order-layers-torchor
poetry add high-order-layers-torch
Approximating a simple function using a single input and single output (single layer) with no hidden layers to approximate a function using continuous and discontinuous piecewise polynomials (with 5 pieces) and simple polynomials and fourier series. The standard approach using ReLU is non competitive. To see more complex see the implicit representation page here.
python examples/function_example.pySimple XOR problem using the standard network structure (2 inputs 2 hidden 1 output) this will also work with no hidden layers. The function is discontinuous along the axis and we try and fit that function. Using piecewise discontinuous layers the model can match the function exactly.
With piecewise continuous it doesn't work quite as well.
Polynomial doesn't work well at all (expected).
python examples/mnist.py max_epochs=1 train_fraction=0.1 layer_type=continuous2d n=4 segments=2python examples/cifar100.py -m max_epochs=20 train_fraction=1.0 layer_type=polynomial segments=2 n=7 nonlinearity=False rescale_output=False periodicity=2.0 lr=0.001 linear_output=False
python examples/autoencoder.py -m max_epochs=300 train_fraction=1.0
Still a WIP. Does work, but needs improvement.
python examples/variational_autoencoder.py -m max_epochs=300 train_fraction=1.0
run with nevergrad for parameter tuning
python examples/variational_autoencoder.py -m
Without polynomial refinement
python examples/invariant_mnist.py max_epochs=100 train_fraction=1 mlp.layer_type=continuous mlp.n=5 mlp.p_refine=False mlp.hidden.layers=4with polynomial refinement (p-refinement)
python examples/invariant_mnist.py max_epochs=100 train_fraction=1 layer_type=mlp.continuous mlp.n=2 mlp.target_n=5 mlp.p_refine=True
I've also added hp refinement, but it needs a lot of testing.
An example of implicit representation for image compression, language generation can be found here. I intend to explore generative models in natural language further here
An example using implicit representation to solve hyperbolic (nonlinear) wave equations can be found here
Examples using these networks for natural language generation can be found here
No real progress here here
After installing and running
poetry shell
run
pytest
for coverage, run
coverage run -m pytest
and then
coverage report
The layers used here do not require additional activation functions and use a simple sum or product in place of the activation. I almost always use sum units, but product units are performed in this manner
The 1 is added to each function output to as each of the sub products is also computed. The linear part is controlled by the alpha parameter.
The Lion optimizer seems to be the best choice since it performs better than Adam in general, but seems to work especially well for the case of polynomials.
Although you can use batchnorm, layernorm etc... work better, I've found that you can actually just use the infinity norm ("max_abs" norm) which has no parameters for this formulation (same approach seems not to work very well for standard relu networks - but need to investigate this further). The max_abs normalization is defined this way
normalized_x = x/(max(abs(x))+eps)
where the normalization is done per sample (as opposed to per batch). The way the layers are formulated, we don't want the neuron values to extend beyond [-1, 1] as the polynomial values grow rapidly beyond that range. You can also use mirror periodicity to keep the values within from growing rapidly. We want the values to cover the entire range [-1, 1] of the polynomials as the weights are packed towards the edges of each segment (though using even number of segments means you'll have a lot of weights near the origin).
You can refer to this repo here
@misc{Loverich2020,
author = {Loverich, John},
title = {High Order Layers Torch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jloveric/high-order-layers-torch}},
}
and there is an old paper which was originally written in c++ and doesn't cover nearly as much as this repo and all the dependent repos, which you can also refer to
@article{loverich2015discontinuous,
title={Discontinuous Piecewise Polynomial Neural Networks},
author={Loverich, John},
journal={arXiv preprint arXiv:1505.04211},
year={2015}
}
KAN: Kolmogorov–Arnold Networks 2024 was published (9 years after the original implementation of the technique in this repo), where B-splines were used on the grid. Looking at that repo, the real difference seems to be B-splines vs lagrange polynomials.
Variations on the Chebyshev-Lagrange Activation Function implements a linear extension to the values beyond [-1,1] which would solve the problem of polynomial growth outside that range.
KAN: Kolmogorov–Arnold Networks: A review 2024 A review of KANs with respect to other types of networks, especially spline networks
Learning Activation Functions in Deep (Spline) Neural Networks 2020 using B splines
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