GitHub - dev-null321/RacoGrad: Deep learning library

RacoGrad: A Deep Learning Framework in Racket

RacoGrad is a deep learning framework implemented in Racket, a dialect of the Lisp/Scheme family of programming languages. It provides a comprehensive set of tools for creating, training, and evaluating neural networks, with a focus on both educational value and practical functionality.

Features

• Tensor Operations: Comprehensive set of tensor operations with shape checking and broadcasting
• Device-Aware Computation: Support for CPU, GPU (via OpenCL), and MLX (Apple Silicon)
• Automatic Differentiation: Backpropagation for gradient computation
• CNN Support: Implementation of convolutional neural networks with LeNet-5 architecture
• MNIST Dataset Handling: Tools for loading, preprocessing, and training on MNIST
• Early Stopping and Validation: Techniques to prevent overfitting during training

Installation

1. Clone this repository:

   git clone https://github.com/dev-null321/RacoGrad.git
   cd RacoGrad

2. Download the MNIST dataset:

   mkdir -p mnist-data
   cd mnist-data

   Download the following files and place them in the mnist-data directory:

   Note I used Kaggle do download the dataset. Download it and place it in your directory.

   Note: Remember to gunzip these files after downloading.

3. Compile the C extensions:

Usage

Training a Logistic Regression Model on MNIST

#lang racket
(require "mnist.rkt")

;; The mnist.rkt module will automatically load and train a logistic regression model
;; on the MNIST dataset when required

Training a CNN on MNIST

#lang racket
(require "CNN.rkt")

;; To train a CNN on the default device (MLX if available):
(train-cnn)

;; To specify the device:
(train-cnn 'cpu)  ; Use CPU
(train-cnn 'mlx)  ; Use MLX (Apple Silicon)
(train-cnn 'gpu)  ; Use GPU (via OpenCL)

;; To specify training parameters:
(train-cnn 'cpu 10 64)  ; 10 epochs, batch size 64

Tensor Operations

(require "tensor.rkt")

;; Create a tensor
(define t (t:create '(2 3) #(1 2 3 4 5 6)))

;; Basic operations
(t:add t1 t2)      ; Add two tensors
(t:mul t1 t2)      ; Matrix multiplication
(t:scale t 2.0)    ; Scalar multiplication
(t:transpose t)    ; Transpose tensor

;; Device-aware tensors
(require "tensor_device.rkt")
(require "device.rkt")

;; Create a device tensor on CPU
(define dt (dt:create '(2 3) #(1 2 3 4 5 6) (cpu)))

;; Move to GPU if available
(dt:to dt (gpu))

;; Operations automatically use the appropriate device
(dt:add dt1 dt2)

Documentation

For detailed information about the implementation and usage, refer to the following documents:

• User Guide: Basic usage guide for users
• Implementation Details: Technical details of the implementation
• Optimization Strategy: Performance optimization strategies
• GPU Acceleration: Details on GPU acceleration

New Additions

This updated version of RacoGrad includes several major enhancements:

• Convolutional Neural Networks: Full implementation of CNN with backpropagation
• Device-Aware Computing: Abstraction layer for running on different hardware
• Hardware Acceleration: Support for GPU via OpenCL and Apple Silicon via MLX
• Improved MNIST Training: Added validation splits and early stopping
• Better Documentation: Comprehensive documentation of the implementation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.