mirror of https://github.com/Relintai/pmlpp.git
113 lines
3.9 KiB
C++
113 lines
3.9 KiB
C++
//
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// HiddenLayer.cpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "hidden_layer.h"
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#include "../activation/activation.h"
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#include "../lin_alg/lin_alg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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MLPPHiddenLayer::MLPPHiddenLayer(int n_hidden, std::string activation, std::vector<std::vector<double>> input, std::string weightInit, std::string reg, double lambda, double alpha) :
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n_hidden(n_hidden), activation(activation), input(input), weightInit(weightInit), reg(reg), lambda(lambda), alpha(alpha) {
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weights = MLPPUtilities::weightInitialization(input[0].size(), n_hidden, weightInit);
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bias = MLPPUtilities::biasInitialization(n_hidden);
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activation_map["Linear"] = &MLPPActivation::linear;
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activationTest_map["Linear"] = &MLPPActivation::linear;
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activation_map["Sigmoid"] = &MLPPActivation::sigmoid;
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activationTest_map["Sigmoid"] = &MLPPActivation::sigmoid;
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activation_map["Swish"] = &MLPPActivation::swish;
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activationTest_map["Swish"] = &MLPPActivation::swish;
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activation_map["Mish"] = &MLPPActivation::mish;
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activationTest_map["Mish"] = &MLPPActivation::mish;
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activation_map["SinC"] = &MLPPActivation::sinc;
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activationTest_map["SinC"] = &MLPPActivation::sinc;
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activation_map["Softplus"] = &MLPPActivation::softplus;
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activationTest_map["Softplus"] = &MLPPActivation::softplus;
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activation_map["Softsign"] = &MLPPActivation::softsign;
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activationTest_map["Softsign"] = &MLPPActivation::softsign;
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activation_map["CLogLog"] = &MLPPActivation::cloglog;
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activationTest_map["CLogLog"] = &MLPPActivation::cloglog;
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activation_map["Logit"] = &MLPPActivation::logit;
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activationTest_map["Logit"] = &MLPPActivation::logit;
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activation_map["GaussianCDF"] = &MLPPActivation::gaussianCDF;
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activationTest_map["GaussianCDF"] = &MLPPActivation::gaussianCDF;
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activation_map["RELU"] = &MLPPActivation::RELU;
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activationTest_map["RELU"] = &MLPPActivation::RELU;
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activation_map["GELU"] = &MLPPActivation::GELU;
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activationTest_map["GELU"] = &MLPPActivation::GELU;
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activation_map["Sign"] = &MLPPActivation::sign;
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activationTest_map["Sign"] = &MLPPActivation::sign;
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activation_map["UnitStep"] = &MLPPActivation::unitStep;
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activationTest_map["UnitStep"] = &MLPPActivation::unitStep;
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activation_map["Sinh"] = &MLPPActivation::sinh;
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activationTest_map["Sinh"] = &MLPPActivation::sinh;
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activation_map["Cosh"] = &MLPPActivation::cosh;
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activationTest_map["Cosh"] = &MLPPActivation::cosh;
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activation_map["Tanh"] = &MLPPActivation::tanh;
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activationTest_map["Tanh"] = &MLPPActivation::tanh;
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activation_map["Csch"] = &MLPPActivation::csch;
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activationTest_map["Csch"] = &MLPPActivation::csch;
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activation_map["Sech"] = &MLPPActivation::sech;
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activationTest_map["Sech"] = &MLPPActivation::sech;
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activation_map["Coth"] = &MLPPActivation::coth;
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activationTest_map["Coth"] = &MLPPActivation::coth;
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activation_map["Arsinh"] = &MLPPActivation::arsinh;
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activationTest_map["Arsinh"] = &MLPPActivation::arsinh;
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activation_map["Arcosh"] = &MLPPActivation::arcosh;
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activationTest_map["Arcosh"] = &MLPPActivation::arcosh;
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activation_map["Artanh"] = &MLPPActivation::artanh;
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activationTest_map["Artanh"] = &MLPPActivation::artanh;
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activation_map["Arcsch"] = &MLPPActivation::arcsch;
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activationTest_map["Arcsch"] = &MLPPActivation::arcsch;
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activation_map["Arsech"] = &MLPPActivation::arsech;
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activationTest_map["Arsech"] = &MLPPActivation::arsech;
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activation_map["Arcoth"] = &MLPPActivation::arcoth;
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activationTest_map["Arcoth"] = &MLPPActivation::arcoth;
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}
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void MLPPHiddenLayer::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z = alg.mat_vec_add(alg.matmult(input, weights), bias);
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a = (avn.*activation_map[activation])(z, 0);
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}
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void MLPPHiddenLayer::Test(std::vector<double> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z_test = alg.addition(alg.mat_vec_mult(alg.transpose(weights), x), bias);
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a_test = (avn.*activationTest_map[activation])(z_test, 0);
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}
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