MLask

A custom c++ deep learning library.
Explore the docs

View Demo · Report Bug >

About The Project

This is a custom c++ deep learning library. It was meant to be a challenge to understand how all the professional ML libraries work under the hood.

Features

• Fully connected layers
• Activation functions
• Backpropagation algorithm
• Confusion Matrixs
• Export to ONNX format

(back to top)

Built With

• CMake
• Eigen
• ONNX
• Protobuf

(back to top)

Getting Started

Prerequisites

• CMake 3.28 or higher
• c++23 compatible compiler

Installation

# add these to install faster, however protobuf is pretty big, so it will take a while. Be patient.
set(protobuf_BUILD_TESTS OFF CACHE BOOL "" FORCE)
set(protobuf_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
set(protobuf_BUILD_SHARED_LIBS OFF CACHE BOOL "" FORCE)
set(ONNX_BUILD_TESTS OFF CACHE BOOL "" FORCE)
set(EIGEN_BUILD_TESTING OFF CACHE BOOL "" FORCE)

include(FetchContent)
FetchContent_Declare(MLask
    GIT_REPOSITORY https://gitlab.com/Kamzie1/MLask.git
    GIT_TAG 1.0.3
    GIT_SHALLOW ON
    EXCLUDE_FROM_ALL
    SYSTEM)
FetchContent_MakeAvailable(MLask)

(back to top)

Usage

Defining a model

You need to create a model. Then you add layers.

Model model(1, 1);

model.addFullyConnectedLayer<1, 1>(); // Correct output->input flow is validated
model.addLayer<Relu>();
model.addLambdaActivationFunction([](float_t x){ return 0.5*x*x; }, [](float_t x){ return x; } ); // A layer that should be used only for prototyping.

Adding your own layer

You can define and add your own layer. All you need to do is create a class that derives from abstract Layer class.

class CustomLayer : public Layer{
public:
    // You need to override these functions. However if you want your layer to be exportable to ONNX for example, you also need to override tryConvertToONNX.
    vectorOut forward(vectorIn input) override;
    vectorIn backward(vectorOut error) override;
    void fit(float_t learning_rate) override {}
};

model.addLayer<CustomLayer>();// You can add your custom layer to the model like this.

To create activation function you can derive from ActivationFunction, which makes the implementation easier.

Training the model

  model.fit<errFunction>(trainX, trainY, 1000, 0.001);
  ConfusionMatrix conf = model.evaluate(testX, testY, interpret) // interpret is a ptr to the function that interprets the solution.
  std::cout<<conf;

Exporting the model

    std::cout<<model;
    model.exportToONNX("onnx_format.onnx", "One Neuron Neural Network");

For more examples, please refer to the Documentation

(back to top)