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Cleaned up TanhReg.

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Relintai 2023-04-28 18:36:21 +02:00
parent 40d2f95a57
commit 2c0e20dd8b
2 changed files with 44 additions and 49 deletions
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91
mlpp/tanh_reg/tanh_reg.cpp
View File

@ -14,7 +14,6 @@
#include <random>
/*
Ref<MLPPMatrix> MLPPTanhReg::get_input_set() {
return _input_set;
}
@ -38,8 +37,6 @@ MLPPReg::RegularizationType MLPPTanhReg::get_reg() {
}
void MLPPTanhReg::set_reg(const MLPPReg::RegularizationType val) {
_reg = val;
_initialized = false;
}
real_t MLPPTanhReg::get_lambda() {
@ -47,8 +44,6 @@ real_t MLPPTanhReg::get_lambda() {
}
void MLPPTanhReg::set_lambda(const real_t val) {
_lambda = val;
_initialized = false;
}
real_t MLPPTanhReg::get_alpha() {
@ -56,25 +51,24 @@ real_t MLPPTanhReg::get_alpha() {
}
void MLPPTanhReg::set_alpha(const real_t val) {
_alpha = val;
_initialized = false;
}
*/
// Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
// real_t model_test(const Ref<MLPPVector> &x);
Ref<MLPPVector> MLPPTanhReg::model_set_test(const Ref<MLPPMatrix> &X) {
ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
return evaluatem(X);
}
real_t MLPPTanhReg::model_test(const Ref<MLPPVector> &x) {
ERR_FAIL_COND_V(!_initialized, 0);
return evaluatev(x);
}
void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
@ -85,13 +79,13 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
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);
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multnv(alg.transposenm(_input_set), alg.hadamard_productnv(error, avn.tanh_derivv(_z)))));
_weights->sub(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.tanh_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.tanh_derivv(_z))) / _n;
_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / _n;
forward_pass();
@ -110,7 +104,8 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
}
void MLPPTanhReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
MLPPLinAlg alg;
ERR_FAIL_COND(!_initialized);
MLPPReg regularization;
real_t cost_prev = 0;
@ -147,7 +142,7 @@ void MLPPTanhReg::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 - y_hat * y_hat), input_set_row_tmp));
_weights->subn(input_set_row_tmp->scalar_multiplyn(learning_rate * error * (1 - y_hat * y_hat)));
_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
// Bias updation
@ -171,8 +166,9 @@ void MLPPTanhReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
}
void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
ERR_FAIL_COND(!_initialized);
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
@ -191,14 +187,15 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
Ref<MLPPVector> z = propagatem(current_input_batch_entry);
cost_prev = cost(y_hat, current_output_batch_entry);
Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_batch_entry);
Ref<MLPPVector> error = y_hat->subn(current_output_batch_entry);
// Calculating the weight gradients
_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multnv(alg.transposenm(current_input_batch_entry), alg.hadamard_productnv(error, avn.tanh_derivv(z)))));
_weights->sub(current_input_batch_entry->transposen()->mult_vec(error->hadamard_productn(avn.tanh_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.tanh_derivv(_z))) / _n;
_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / _n;
forward_pass();
@ -221,6 +218,8 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
}
real_t MLPPTanhReg::score() {
ERR_FAIL_COND_V(!_initialized, 0);
MLPPUtilities util;
return util.performance_vec(_y_hat, _output_set);
@ -240,28 +239,16 @@ void MLPPTanhReg::initialize() {
return;
}
//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
_initialized = true;
}
MLPPTanhReg::MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
_input_set = p_input_set;
_output_set = p_output_set;
_n = _input_set->size().y;
_k = _input_set->size().x;
_reg = p_reg;
_lambda = p_lambda;
_alpha = p_alpha;
_y_hat.instance();
_y_hat->resize(_n);
_weights->resize(_k);
MLPPUtilities utils;
_weights.instance();
_weights->resize(_k);
utils.weight_initializationv(_weights);
_bias = utils.bias_initializationr();
@ -269,8 +256,26 @@ MLPPTanhReg::MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVecto
_initialized = true;
}
MLPPTanhReg::MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
_input_set = p_input_set;
_output_set = p_output_set;
_reg = p_reg;
_lambda = p_lambda;
_alpha = p_alpha;
_y_hat.instance();
_weights.instance();
_initialized = false;
initialize();
}
MLPPTanhReg::MLPPTanhReg() {
_initialized = false;
_y_hat.instance();
_weights.instance();
}
MLPPTanhReg::~MLPPTanhReg() {
}
@ -283,29 +288,23 @@ real_t MLPPTanhReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y)
}
real_t MLPPTanhReg::evaluatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.tanh_normr(alg.dotnv(_weights, x) + _bias);
return avn.tanh_normr(_weights->dot(x) + _bias);
}
real_t MLPPTanhReg::propagatev(const Ref<MLPPVector> &x) {
MLPPLinAlg alg;
return alg.dotnv(_weights, x) + _bias;
return _weights->dot(x) + _bias;
}
Ref<MLPPVector> MLPPTanhReg::evaluatem(const Ref<MLPPMatrix> &X) {
MLPPLinAlg alg;
MLPPActivation avn;
return avn.tanh_normv(alg.scalar_addnv(_bias, alg.mat_vec_multnv(X, _weights)));
return avn.tanh_normv(X->mult_vec(_weights)->scalar_addn(_bias));
}
Ref<MLPPVector> MLPPTanhReg::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);
}
// Tanh ( wTx + b )
@ -317,7 +316,6 @@ void MLPPTanhReg::forward_pass() {
}
void MLPPTanhReg::_bind_methods() {
/*
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPTanhReg::get_input_set);
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPTanhReg::set_input_set);
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
@ -351,5 +349,4 @@ void MLPPTanhReg::_bind_methods() {
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPTanhReg::is_initialized);
ClassDB::bind_method(D_METHOD("initialize"), &MLPPTanhReg::initialize);
*/
}

2
mlpp/tanh_reg/tanh_reg.h
View File

@ -21,7 +21,6 @@ class MLPPTanhReg : public Reference {
GDCLASS(MLPPTanhReg, Reference);
public:
/*
Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
@ -36,7 +35,6 @@ public:
real_t get_alpha();
void set_alpha(const real_t val);
*/
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
real_t model_test(const Ref<MLPPVector> &x);