mirror of https://github.com/Relintai/pmlpp.git
136 lines
4.6 KiB
C++
136 lines
4.6 KiB
C++
//
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// OutputLayer.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 "output_layer_old.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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MLPPOldOutputLayer::MLPPOldOutputLayer(int p_n_hidden, std::string p_activation, std::string p_cost, std::vector<std::vector<real_t>> p_input, std::string p_weightInit, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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n_hidden = p_n_hidden;
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activation = p_activation;
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cost = p_cost;
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input = p_input;
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weightInit = p_weightInit;
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reg = p_reg;
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lambda = p_lambda;
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alpha = p_alpha;
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weights = MLPPUtilities::weightInitialization(n_hidden, weightInit);
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bias = MLPPUtilities::biasInitialization();
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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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costDeriv_map["MSE"] = &MLPPCost::MSEDeriv;
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cost_map["MSE"] = &MLPPCost::MSE;
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costDeriv_map["RMSE"] = &MLPPCost::RMSEDeriv;
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cost_map["RMSE"] = &MLPPCost::RMSE;
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costDeriv_map["MAE"] = &MLPPCost::MAEDeriv;
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cost_map["MAE"] = &MLPPCost::MAE;
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costDeriv_map["MBE"] = &MLPPCost::MBEDeriv;
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cost_map["MBE"] = &MLPPCost::MBE;
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costDeriv_map["LogLoss"] = &MLPPCost::LogLossDeriv;
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cost_map["LogLoss"] = &MLPPCost::LogLoss;
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costDeriv_map["CrossEntropy"] = &MLPPCost::CrossEntropyDeriv;
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cost_map["CrossEntropy"] = &MLPPCost::CrossEntropy;
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costDeriv_map["HingeLoss"] = &MLPPCost::HingeLossDeriv;
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cost_map["HingeLoss"] = &MLPPCost::HingeLoss;
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costDeriv_map["WassersteinLoss"] = &MLPPCost::HingeLossDeriv;
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cost_map["WassersteinLoss"] = &MLPPCost::HingeLoss;
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}
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void MLPPOldOutputLayer::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z = alg.scalarAdd(bias, alg.mat_vec_mult(input, weights));
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a = (avn.*activation_map[activation])(z, false);
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}
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void MLPPOldOutputLayer::Test(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z_test = alg.dot(weights, x) + bias;
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a_test = (avn.*activationTest_map[activation])(z_test, false);
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}
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