mlx-kan 0.1.71

KAN: Kolmogorov–Arnold Networks on Apple silicon with MLX.

KAN: Kolmogorov–Arnold Networks in MLX for Apple silicon

This code contains an example implementation of training a Kolmogorov–Arnold Network (KAN) on the MNIST dataset using the MLX framework. This example demonstrates how to configure and train the model using various command-line arguments for flexibility.

Based on the paper

Table of Contents

• Installation
• Usage
  • Arguments
  • Examples
• Model Architecture
• Contributing
• License

Installation

Usage with PyPi

install the Package:

pip install mlx-kan

Example usage in Python:

from kan_mlx.kan import KAN

# Initialize and use KAN
kan_model = KAN()

python -m mlx-kan.quick_scripts.quick_train --help

python -m mlx-kan.quick_scripts.quick_train --dataset fashion_mnist --num-layers 3 --hidden-dim 128 --num-epochs 20 --batch-size 128 --learning-rate 0.0005 --seed 42

Clone this Repo

To run this example, you need to have Python and the necessary libraries installed. Follow these steps to set up your environment:

1. Clone the repository:

git clone https://github.com/Goekdeniz-Guelmez/mlx-kan.git
cd mlx-kan

2. Install the required packages:

pip install -r requirements.txt

Usage

You can run the script main.py to train the KAN model on the MNIST dataset. The script supports various command-line arguments for configuration.

Arguments

• --cpu: Use the Metal back-end.
• --use-kan-convolution: Use the Convolution KAN architecture. Will give a error because its not implemented yet.
• --dataset: The dataset to use (mnist or fashion_mnist). Default is mnist.
• --num_layers: Number of layers in the model. Default is 2.
• --in-features: Number input features. Default is 28.
• --out-features: Number output features. Default is 28.
• --num-classes: Number of output classes. Default is 10.
• --hidden_dim: Number of hidden units in each layer. Default is 64.
• --num_epochs: Number of epochs to train. Default is 10.
• --batch_size: Batch size for training. Default is 64.
• --learning_rate: Learning rate for the optimizer. Default is 1e-3.
• --weight-decay: Weight decay for the optimizer. Default is 1e-4.
• --eval-report-count: Number of epochs to report validations / test accuracy values. Default is 10.
• --save-path: Path with the model name where the trained KAN model will be saved. Default is traned_kan_model.safetensors.
• --train-batched: Use batch training instead of single epoch. Default is False.
• --seed: Random seed for reproducibility. Default is 0.

Examples

Find all Arguments wioth descriptions

python -m quick_scripts.quick_train --help

Basic Usage

Train the KAN model on the MNIST dataset with default settings:

python -m quick_scripts.quick_train --dataset mnist

Custom Configuration

Train the KAN model with a custom configuration:

python -m quick_scripts.quick_train --dataset fashion_mnist --num-layers 3 --hidden-dim 128 --num-epochs 20 --batch-size 128 --learning-rate 0.0005 --seed 42

Using CPU

Train the KAN model using the CPU backend:

python -m quick_scripts.quick_train --cpu --dataset mnist

Model Architecture

The KAN (Kolmogorov–Arnold Networks) class defines the model architecture. The network consists of multiple KANLinear layers, each defined by the provided parameters. The number of layers and the hidden dimension size can be configured via command-line arguments.

Example Model Initialization

layers_hidden = [28 * 28] + [hidden_dim] * (num_layers - 1) + [num_classes]
model = KAN(layers_hidden)

KAN Class

The KAN class initializes a sequence of KANLinear layers based on the provided hidden layers configuration. Each layer performs linear transformations with kernel attention mechanisms.

class KAN(nn.Module):
    def __init__(self, layers_hidden, grid_size=5, spline_order=3, scale_noise=0.1, scale_base=1.0, scale_spline=1.0, base_activation=nn.SiLU, grid_eps=0.02, grid_range=[-1, 1]):
        super().__init__()
        self.layers = []
        for in_features, out_features in zip(layers_hidden, layers_hidden[1:]):
            self.layers.append(
                KANLinear(
                    in_features, out_features, grid_size, spline_order, scale_noise, scale_base, scale_spline, base_activation, grid_eps, grid_range
                )
            )
    def __call__(self, x, update_grid=False):
        for layer in self.layers:
            if update_grid:
                layer.update_grid(x)
            x = layer(x)
        return x
    
    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):
        return mx.add(*(
            layer.regularization_loss(regularize_activation, regularize_entropy) 
            for layer in self.layers
        ))

KanConvolutional Class

The KanConvolutional class defines the convolutional model architecture. The network consists of multiple KANConv layers, each defined by the provided parameters. This class is used for models that require convolutional layers.

class KanConvolutional(nn.Module):
    def __init__(self, layers_hidden, grid_size=5, spline_order=3, scale_noise=0.1, scale_base=1.0, scale_spline=1.0, base_activation=nn.SiLU, grid_eps=0.02, grid_range=[-1, 1]):
        super().__init__()
        self.layers = []
        for in_channels, out_channels in zip(layers_hidden, layers_hidden[1:]):
            self.layers.append(
                KANConv(
                    in_channels, out_channels, grid_size, spline_order, scale_noise, scale_base, scale_spline, base_activation, grid_eps, grid_range
                )
            )
    def __call__(self, x, update_grid=False):
        for layer in self.layers:
            if update_grid:
                layer.update_grid(x)
            x = layer(x)
        return x
    
    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):
        return mx.add(*(
            layer.regularization_loss(regularize_activation, regularize_entropy) 
            for layer in self.layers
        ))

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

Made with love by Gökdeniz Gülmez.