Main goal of this C++ library is to mimic the behavior of MLPRegressor and MLPClassifier (Multi-Layer Perceptron) of the Python Scikit-Learn library in C++. It contains all the underlying classes for representing a Multi-Layer Perceptron.
It also includes a very simple XML reader and MNIST file reader. MNIST is a database of handwritten digits that the main file uses to demonstrate the possibility of the library.

It uses only the C++ standard library. Compiled with C++14 compiler. No dependencies to other libraries.

• Gradient Descent Algorithms
  • Stochastic
  • Batch to be included (TODO)
  • Mini-batch to be included (TODO)
• Layer types included:
  • Dense
    • Hidden
    • Output classification (with a Cross-Entropy Error function)
    • Output regression (with Mean-Squared Error function)
  • Dropout
• Activation functions included:
  • Identity
  • Logistic
  • Tanh
  • ReLU
  • ISRLU
  • Other activation functions can be added easily in the ActivationFunction.h file.
• Momentum
• Adaptive and InvScaling learning rates
• Seed
• Serialization (save neural network to file / reload network from file)

• data Dataset used in main for demonstrating the possibility of the YANN-Library
  • MNIST handwritten digit database files
  • Iris flower dataset
• lib
  • include Header-only library that you can easily copy/paste into a project
    • mnist-reader Utility library for reading the MNIST handwritten digit database
    • neural-net The Yet Another Neural Network Library including MLPRegressor and MLPClassifier. See in subsequent section the structure of this folder.
    • xml-reader Very simple XML reader. YANN-Library can output a neural network structure to a file and read/load it back. At first I thought about using an XML format for such serialization, but finally ended up with a flat file structure. neural-net can thus be used without this XML reader
  • src Nothing as the library is currently a header-only library
• main
  • include & src Header and source files containing prediction examples using the YANN-Library
    • IrisClassification Example of usage of MLPClassifier to predict the type of iris flower.
    • MnistPrediction Example of usage of the neural-net classes to predict the MNIST handwritten digits.
    • XORPrediction Example of usage of MLPRegressor to predict the output of an XOR gate.
  • main.cpp The `main' which launches the 3 previous examples: iris classification, MNIST prediction, XOR prediction.
• test
  • expected Output of unit/regression tests compare to the result files contained in this folder
  • include/UnitTest.h Battery of unit/regression tests for the mnist-reader, neural-net and xml-reader. Many methods within the UnitTest class have the same name as the files in the expected folder: the output of such methods are compared to those files.
  • src Nothing as the unit/regression tests are all contained in the header files
  • test.cpp The `main' which launches the whole test battery.

classDiagram
MLP <|.. MLPRegressor
MLP <|.. MLPClassifier
MLP *-- "1..1" NeuralNetwork
NeuralNetwork *-- "0..*" NeuronLayer
NeuralNetwork *-- "1..1" Utils_SeedGenerator
Neuron *-- "1..1" ActivationFunction
NeuronLayer <|.. DenseLayer
NeuronLayer <|.. DropoutLayer
DenseLayer <|-- HiddenLayer
DenseLayer <|-- OutputClassificationLayer
DenseLayer <|-- OutputRegressionLayer
DenseLayer *-- "0..*" Neuron

class MLP {
    #unique_ptr<NeuralNetwork> net
    +fit(vect<vect<double> X_train, vect<double> y_train)
}
class MLPClassifier {
    +MLPClassifier(...)
    +predict(vect<double> X_test) size_t
}
class MLPRegressor {
    +MLPRegressor(...)
    +predict(vect<double> X_test) double
    +predict(vect<vect<double> X_test) vect_double
}
class NeuralNetwork {
    -vect<shared_ptr<NeuronLayer>> layers
    -double learningRate
    -double momentum
    +addHiddenLayer(vect<vect<double>> weights, ...)
    +addHiddenLayer(size_t inputSize, ...)
    +addOutputClassificationLayer(vect<vect<double>> weights, ...)
    +addOutputClassificationLayer(size_t inputSize, ...)
    +addOutputRegressionLayer(vect<vect<double>> weights, ...)
    +addOutputRegressionLayer(size_t inputSize, ...)
    +addDropoutLayer(double dropoutRate)
    +propagateForward(vect<double> input) output
    +propagateBackward(vect<double> expectedOutput)
    +saveToFile(string filepath) bool
    +loadFromFile(string filepath)$ NeuralNetwork
}
class DenseLayer {
    -vect<Neuron> neurons
    -double learningRate
    -double momentum
}
class DropoutLayer {
    -vect<bool> active_neurons
    -double dropoutRate
}
class Neuron {
    -vect<double> weights
    -shared_ptr<ActivationFunction> AFunc
    -double learningRate
    -double momentum
}
class ActivationFunction {
    +calc(double x) y
    +calcDerivate(double x) y
    +name() string
}
class Utils_SeedGenerator {
    +seed() : uint
}

You will find at the root of the project:

• Solution file for Visual Studio 2022
• Workspace file for Code::Blocks

If you would like to launch the main examples (iris classification, MNIST prediction, XOR prediction) set the main project as active/startup project (right click > Activate project in C::B, right click > Set as startup project in MS VS).

If you would like to launch the battery of unit/regression tests, set the test project as active/startup project.

It is also easy to copy/paste the files into other projects as the library relies only on the STL and the library files are header-only.

main.cpp within the main folder calls different examples which give examples on how to use the YANNL-Library

Globally MLPRegressor and MLPClassifier can be used approximately in the same way as the Python version.

It is also possible to build manually a neural network (NeuralNetwork class) and then add the layers (HiddenLayer, OutputClassificationLayer, OutputRegressionLayer, DropoutLayer). The input layer is not considered as a layer in the layer count. Its size is provided as input when building the NeuralNetwork. It should not be necessary to build manually layers and neurons as they are supposed to be built via the constructors and methods within NeuralNetwork.

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to run and update tests as appropriate.

• LGPL
• For closed-source software feel free to contact me

The MLPRegressor and MLPClassifier only use dense layers (hidden layers and output classification/regression layers).
Dropout layers are not used. Could be an improvement.
Further improvement would be to implement convolutional neural networks.

Mickael Deloison.

A Step by Step Back propagation Example
MLPRegressor specification
MLPClassifier specification
Dropout layer
Gradient Descent With Momentum from Scratch
Decoding the input shapes and output shapes of Multi Layered Perceptron model