micrograd++

Are you tired of using predated hard-to-use C++ machine learning library???

Welcome to micrograd++. This repository is inspired by Andrej Karpathy's micrograd. micrograd++ is a pure C++ machine learning library designed to make machine learning accessible to everyone.

Overview

micrograd++ aims to provide a simple yet powerful framework for building and training machine learning models. By leveraging C++, it ensures performance efficiency and allows for deep integration with C++-based systems and applications.

Features

Pure C++: Entirely implemented in C++ for high performance.
Inspired by micrograd: Brings the simplicity and educational value of micrograd to the C++ ecosystem.
Accessible Machine Learning: Designed to be easy to use, even for those new to machine learning or C++.

Getting Started

Prerequisites

CMake: Version 3.15 or higher
C++ Compiler: Supports C++17 standard
opencv: For visualization

Building the Library

Clone the repository:

git clone https://github.com/gautam-sharma1/microgradpp.git
cd microgradpp

Create a build directory:
```
mkdir build
cd build
```
Configure the project with CMake:

Build microgradpp only
```
cmake ..
```
or

Builds example and tests
```
cmake .. -DBUILD_EXAMPLES=ON -DBUILD_TESTS=ON ..
```
or

To build a Release build
```
cmake .. -DCMAKE_BUILD_TYPE=Release
```
Build the project:
```
make
```

Using the Header only Library

Microgradpp is also available as a header only library.

Running the Example

After building, you can run the provided example by executing the m++ binary:

./m++

Project Structure

CMakeLists.txt: Build configuration for CMake.
include/: Header files for the library.
src/: Source files for the library.
main.cpp: Example usage of the library.

Usage

Here’s a brief example of how to use micrograd++:

#include <iostream>
#include "Value.h"

int main() {
    auto a = microgradpp::Value::create(2.0);
    auto b = microgradpp::Value::create(3.0);
    auto c = a * b;
    c->backProp();
    
    std::cout << "a->grad: " << a->grad << std::endl;
    std::cout << "b->grad: " << b->grad << std::endl;

    return 0;
}

Examples

Head over to the examples directory to play around with the examples. To build examples, configure cmake from the root directory as follows:

cd build
cmake -DBUILD_EXAMPLES=ON .. && make
cd examples
./example_mlp # to run mlp example

This will produce executables in the build folder

ML example

A simple multi layer perceptron is defined in mlp.cpp. Running it for 50 iterations gives the following result:

Computer Vision example

The example in images.cpp learns a cute german shephard puppy face. The output of it is as follows:

Contributing

We welcome contributions to micrograd++. Here are a few areas where you can help:

TODO

Modify CMakeLists to Add a Flag to Build Tests: Enhance the build configuration to optionally include tests.
Make a Tensor Class: Create a Tensor class to simplify data loading and manipulation.
Add an Activation Function Enum or Class: Implement a flexible way to handle different activation functions.
Make an Abstract Base Class for Layer and Value: Design abstract base classes to improve the architecture and extensibility.
CI/CD pipeline: Develop a pipeline using github actions to execute tests automatically on a commit.
Improve README: Add few examples on how to leverage this library.
Python Interface??: Probably make a python interface

License

This project is licensed under the GNU License - see the LICENSE file for details.

Acknowledgments

Andrej Karpathy: For the original micrograd library and inspiration.

Author

Gautam Sharma: gsharma

ggsharma/microgradpp