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MLPPProbitReg cleanup.

This commit is contained in:
Relintai 2023-04-29 17:59:27 +02:00
parent f9eabcfcdd
commit 27d197bf5d
2 changed files with 19 additions and 30 deletions
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40
mlpp/probit_reg/probit_reg.cpp
View File

@ -8,7 +8,6 @@
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
@ -71,7 +70,6 @@ void MLPPProbitReg::gradient_descent(real_t learning_rate, int max_epoch, bool u
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
@ -81,14 +79,14 @@ void MLPPProbitReg::gradient_descent(real_t learning_rate, int max_epoch, bool u
while (true) {
cost_prev = cost(_y_hat, _output_set);
Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
Ref<MLPPVector> error = _y_hat->subn(_output_set);
// Calculating the weight gradients
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multnv(alg.transposenm(_input_set), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z)))));
_weights->sub(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(_z)))->scalar_multiplyn(learning_rate / _n));
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
// Calculating the bias gradients
_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z))) / _n;
_bias -= learning_rate * error->hadamard_productn(avn.gaussian_cdf_derivv(_z))->sum_elements() / _n;
forward_pass();
@ -109,7 +107,6 @@ void MLPPProbitReg::mle(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
@ -119,14 +116,14 @@ void MLPPProbitReg::mle(real_t learning_rate, int max_epoch, bool ui) {
while (true) {
cost_prev = cost(_y_hat, _output_set);
Ref<MLPPVector> error = alg.subtractionnv(_output_set, _y_hat);
Ref<MLPPVector> error = _output_set->subn(_y_hat);
// Calculating the weight gradients
_weights = alg.additionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multnv(alg.transposenm(_input_set), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z)))));
_weights->add(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(_z)))->scalar_multiplyn(learning_rate / _n));
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
// Calculating the bias gradients
_bias += learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z))) / _n;
_bias += learning_rate * error->hadamard_productn(avn.gaussian_cdf_derivv(_z))->sum_elements() / _n;
forward_pass();
@ -148,7 +145,6 @@ void MLPPProbitReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
// NOTE: ∂y_hat/∂z is sparse
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
@ -186,7 +182,7 @@ void MLPPProbitReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
real_t error = y_hat - output_set_entry;
// Weight Updation
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * error * ((1 / Math::sqrt(2 * Math_PI)) * Math::exp(-z * z / 2)), input_set_row_tmp));
_weights->sub(input_set_row_tmp->scalar_multiplyn(learning_rate * error * ((1 / Math::sqrt(2 * Math_PI)) * Math::exp(-z * z / 2))));
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
// Bias updation
@ -213,7 +209,6 @@ void MLPPProbitReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_siz
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
@ -239,14 +234,15 @@ void MLPPProbitReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_siz
cost_prev = cost(y_hat, current_output);
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output);
Ref<MLPPVector> error = y_hat->subn(current_output);
// Calculating the weight gradients
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / batches.input_sets.size(), alg.mat_vec_multnv(alg.transposenm(current_input), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(z_tmp)))));
_weights->sub(current_input->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(z_tmp)))->scalar_multiplyn(learning_rate / batches.input_sets.size()));
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
// Calculating the bias gradients
_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(z_tmp))) / batches.input_sets.size();
_bias -= learning_rate * error->hadamard_productn(avn.gaussian_cdf_derivv(z_tmp))->sum_elements() / batches.input_sets.size();
y_hat = evaluatev(current_input);
if (ui) {
@ -361,29 +357,23 @@ real_t MLPPProbitReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &
}
Ref<MLPPVector> MLPPProbitReg::evaluatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.gaussian_cdf_normv(alg.scalar_addnv(_bias, alg.mat_vec_multnv(X, _weights)));
return avn.gaussian_cdf_normv(X->mult_vec(_weights)->scalar_addn(_bias));
}
Ref<MLPPVector> MLPPProbitReg::propagatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
return alg.scalar_addnv(_bias, alg.mat_vec_multnv(X, _weights));
return X->mult_vec(_weights)->scalar_addn(_bias);
}
real_t MLPPProbitReg::evaluatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.gaussian_cdf_normr(alg.dotnv(_weights, x) + _bias);
return avn.gaussian_cdf_normr(_weights->dot(x) + _bias);
}
real_t MLPPProbitReg::propagatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
return alg.dotnv(_weights, x) + _bias;
return _weights->dot(x) + _bias;
}
// gaussianCDF ( wTx + b )

9
mlpp/probit_reg/probit_reg.h
View File

@ -71,6 +71,10 @@ protected:
Ref<MLPPMatrix> _input_set;
Ref<MLPPVector> _output_set;
// Regularization Params
MLPPReg::RegularizationType _reg;
real_t _lambda;
real_t _alpha; /* This is the controlling param for Elastic Net*/
Ref<MLPPVector> _z;
Ref<MLPPVector> _y_hat;
@ -80,11 +84,6 @@ protected:
int _n;
int _k;
// Regularization Params
MLPPReg::RegularizationType _reg;
real_t _lambda;
real_t _alpha; /* This is the controlling param for Elastic Net*/
bool _initialized;
};