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Initial HiddenLayer rework.

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Relintai 2023-01-31 01:22:13 +01:00
parent ea4978f535
commit a44d2c98a1
2 changed files with 147 additions and 1 deletions
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101
mlpp/hidden_layer/hidden_layer.cpp
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@ -12,6 +12,107 @@
#include <iostream>
#include <random>
/*
void MLPPHiddenLayer::forward_pass() {
MLPPLinAlg alg;
MLPPActivation avn;
z = alg.mat_vec_add(alg.matmult(input, weights), bias);
a = (avn.*activation_map[activation])(z, false);
}
void MLPPHiddenLayer::test(std::vector<real_t> x) {
MLPPLinAlg alg;
MLPPActivation avn;
z_test = alg.addition(alg.mat_vec_mult(alg.transpose(weights), x), bias);
a_test = (avn.*activationTest_map[activation])(z_test, 0);
}
MLPPHiddenLayer::MLPPHiddenLayer(int n_hidden, std::string activation, std::vector<std::vector<real_t>> input, std::string weightInit, std::string reg, real_t lambda, real_t alpha) :
n_hidden(n_hidden), activation(activation), input(input), weightInit(weightInit), reg(reg), lambda(lambda), alpha(alpha) {
weights = MLPPUtilities::weightInitialization(input[0].size(), n_hidden, weightInit);
bias = MLPPUtilities::biasInitialization(n_hidden);
activation_map["Linear"] = &MLPPActivation::linear;
activationTest_map["Linear"] = &MLPPActivation::linear;
activation_map["Sigmoid"] = &MLPPActivation::sigmoid;
activationTest_map["Sigmoid"] = &MLPPActivation::sigmoid;
activation_map["Swish"] = &MLPPActivation::swish;
activationTest_map["Swish"] = &MLPPActivation::swish;
activation_map["Mish"] = &MLPPActivation::mish;
activationTest_map["Mish"] = &MLPPActivation::mish;
activation_map["SinC"] = &MLPPActivation::sinc;
activationTest_map["SinC"] = &MLPPActivation::sinc;
activation_map["Softplus"] = &MLPPActivation::softplus;
activationTest_map["Softplus"] = &MLPPActivation::softplus;
activation_map["Softsign"] = &MLPPActivation::softsign;
activationTest_map["Softsign"] = &MLPPActivation::softsign;
activation_map["CLogLog"] = &MLPPActivation::cloglog;
activationTest_map["CLogLog"] = &MLPPActivation::cloglog;
activation_map["Logit"] = &MLPPActivation::logit;
activationTest_map["Logit"] = &MLPPActivation::logit;
activation_map["GaussianCDF"] = &MLPPActivation::gaussianCDF;
activationTest_map["GaussianCDF"] = &MLPPActivation::gaussianCDF;
activation_map["RELU"] = &MLPPActivation::RELU;
activationTest_map["RELU"] = &MLPPActivation::RELU;
activation_map["GELU"] = &MLPPActivation::GELU;
activationTest_map["GELU"] = &MLPPActivation::GELU;
activation_map["Sign"] = &MLPPActivation::sign;
activationTest_map["Sign"] = &MLPPActivation::sign;
activation_map["UnitStep"] = &MLPPActivation::unitStep;
activationTest_map["UnitStep"] = &MLPPActivation::unitStep;
activation_map["Sinh"] = &MLPPActivation::sinh;
activationTest_map["Sinh"] = &MLPPActivation::sinh;
activation_map["Cosh"] = &MLPPActivation::cosh;
activationTest_map["Cosh"] = &MLPPActivation::cosh;
activation_map["Tanh"] = &MLPPActivation::tanh;
activationTest_map["Tanh"] = &MLPPActivation::tanh;
activation_map["Csch"] = &MLPPActivation::csch;
activationTest_map["Csch"] = &MLPPActivation::csch;
activation_map["Sech"] = &MLPPActivation::sech;
activationTest_map["Sech"] = &MLPPActivation::sech;
activation_map["Coth"] = &MLPPActivation::coth;
activationTest_map["Coth"] = &MLPPActivation::coth;
activation_map["Arsinh"] = &MLPPActivation::arsinh;
activationTest_map["Arsinh"] = &MLPPActivation::arsinh;
activation_map["Arcosh"] = &MLPPActivation::arcosh;
activationTest_map["Arcosh"] = &MLPPActivation::arcosh;
activation_map["Artanh"] = &MLPPActivation::artanh;
activationTest_map["Artanh"] = &MLPPActivation::artanh;
activation_map["Arcsch"] = &MLPPActivation::arcsch;
activationTest_map["Arcsch"] = &MLPPActivation::arcsch;
activation_map["Arsech"] = &MLPPActivation::arsech;
activationTest_map["Arsech"] = &MLPPActivation::arsech;
activation_map["Arcoth"] = &MLPPActivation::arcoth;
activationTest_map["Arcoth"] = &MLPPActivation::arcoth;
}
*/
MLPPOldHiddenLayer::MLPPOldHiddenLayer(int n_hidden, std::string activation, std::vector<std::vector<real_t>> input, std::string weightInit, std::string reg, real_t lambda, real_t alpha) :
n_hidden(n_hidden), activation(activation), input(input), weightInit(weightInit), reg(reg), lambda(lambda), alpha(alpha) {

47
mlpp/hidden_layer/hidden_layer.h
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@ -8,14 +8,60 @@
// Created by Marc Melikyan on 11/4/20.
//
#include "core/containers/hash_map.h"
#include "core/math/math_defs.h"
#include "core/string/ustring.h"
#include "core/object/reference.h"
#include "../activation/activation.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <map>
#include <string>
#include <vector>
class MLPPHiddenLayer : public Reference {
GDCLASS(MLPPHiddenLayer, Reference);
public:
int n_hidden;
int activation;
Ref<MLPPMatrix> input;
Ref<MLPPMatrix> weights;
Ref<MLPPVector> bias;
Ref<MLPPMatrix> z;
Ref<MLPPMatrix> a;
HashMap<int, Ref<MLPPMatrix> (MLPPActivation::*)(const Ref<MLPPMatrix> &, bool)> activation_map;
HashMap<int, Ref<MLPPVector> (MLPPActivation::*)(const Ref<MLPPVector> &, bool)> activation_test_map;
Ref<MLPPVector> z_test;
Ref<MLPPVector> a_test;
Ref<MLPPMatrix> delta;
// Regularization Params
String reg;
real_t lambda; /* Regularization Parameter */
real_t alpha; /* This is the controlling param for Elastic Net*/
String weight_init;
void forward_pass();
void test(const Ref<MLPPVector> &x);
MLPPHiddenLayer(int p_n_hidden, int p_activation, Ref<MLPPMatrix> p_input, String p_weight_init, String p_reg, real_t p_lambda, real_t p_alpha);
MLPPHiddenLayer();
~MLPPHiddenLayer();
};
class MLPPOldHiddenLayer {
public:
@ -51,5 +97,4 @@ public:
void Test(std::vector<real_t> x);
};
#endif /* HiddenLayer_hpp */