Enola

Library for perform some tensor operation for specific project, built with C++17

currently including:

• Neural Network
• Function:
  • Sigmoid
  • Activation Function:
    • Binary Step
    • Exponential Linear Unit (ELU)
    • Rectified Linear Unit (RELU)
    • Softplus
    • Squareplus
    • Swish
• Operation:
  • Deep Copy
• Score:
  • Mean Absolute Error
  • Mean Square Error
• Tensor:
  • Operation
  • Storage ( Currently Support on CPU Process )
  • Tensor View

basic usage

Simple neural Network

#include "<enola/nn.hpp>"
#include <exception>
#include <iostream>

int main() {
  try {
    // define the arch of neural network
    // each value in the vector representing number of neuron in layer
    // - first value is size of input layer
    // - intermediate value representing hidden layers
    // last value is size of the output layer
    std::vector<size_t> layer_size = {
        2,  // inputs: 2 neuron (two features)
        3,  // hidden layer: 3 neuron
        1,  // output: 1 neuron (binary classification or regression)
    };

    // initialize neural network with specific architecture
    enola::neural::NeuralNetwork<double> nn(layer_size);

    // prepare input data for neural network
    // this example use simple input vector with two values
    std::vector<double>                  input  = {0.5, 0.8};

    // perform forward propagation to computing the output of neural network
    std::vector<double>                  output = nn.forward_propagation(input);

    // print output of the neural network
    std::cout << "output: ";
    for (double val : output) {
      std::cout << val << " "; // print each output
    }
    std::cout << std::endl;
  } catch (const std::exception &error) {
    std::cerr << "error: " << error.what() << std::endl;
  }

  return 0;
}

for more example check on example folder.