Matcha

What it is

Matcha is a framework for optimized tensor arithmetic and machine learning. It features a very intuitive interface inspired by Numpy and Keras. Matcha brings all this to C++. It also provides a way to accelerate itself by Just-In-Time inspecting and modifying the structure of given tensor functions and compiling them into a set of instructions. On top of that, Matcha delivers a seamless dataset pipeline system, automatic differentiation system, and neural networks framework.

#include <iostream>
#include <matcha>

using namespace matcha;

int main() {
  Net net {
    nn::flatten,                             // flatten the inputs
    nn::Fc{300, "relu,batchnorm"},           // hidden layer
    nn::Fc{10, "softmax"}                    // output layer
  };

  Dataset mnist = load("mnist_train.csv");   // load the MNIST dataset
  net.loss = nn::Nll{};                      // use the negative log likelihood loss
  net.fit(mnist.batch(64));                  // fit the model

  tensor digit = load("digit.png");          // load a single digit image
  tensor probabilities = net(digit);         // make a prediction
  tensor result = argmax(probabilities);     // voila

  std::cout << "it is " << result << " with "
            << probabilities[result] * 100 << "% probability" << std::endl;
}

For more, check out tutorials.

License

Matcha is open source. It is available under the MIT license. It may be freely used and distributed.

Plans (and what's currently missing)

More thorough CMake integration
Python interface, Java/Kotlin interface
Differentiable and JIT-compilable conditions and loops
GPU acceleration (OpenCL, then CUDA)

Troubleshooting

Please, open an issue under the appropriate package on GitHub.

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