mirror of https://github.com/Relintai/pmlpp.git
323 lines
11 KiB
C++
323 lines
11 KiB
C++
/*************************************************************************/
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/* output_layer.cpp */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "output_layer.h"
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#include "../utilities/utilities.h"
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int MLPPOutputLayer::get_n_hidden() {
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return _n_hidden;
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}
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void MLPPOutputLayer::set_n_hidden(const int val) {
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_n_hidden = val;
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_initialized = false;
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}
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MLPPActivation::ActivationFunction MLPPOutputLayer::get_activation() {
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return _activation;
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}
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void MLPPOutputLayer::set_activation(const MLPPActivation::ActivationFunction val) {
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_activation = val;
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_initialized = false;
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}
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MLPPCost::CostTypes MLPPOutputLayer::get_cost() {
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return _cost;
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}
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void MLPPOutputLayer::set_cost(const MLPPCost::CostTypes val) {
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_cost = val;
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_initialized = false;
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}
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Ref<MLPPMatrix> MLPPOutputLayer::get_input() {
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return _input;
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}
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void MLPPOutputLayer::set_input(const Ref<MLPPMatrix> &val) {
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_input = val;
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_initialized = false;
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}
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Ref<MLPPVector> MLPPOutputLayer::get_weights() {
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return _weights;
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}
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void MLPPOutputLayer::set_weights(const Ref<MLPPVector> &val) {
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_weights = val;
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_initialized = false;
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}
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real_t MLPPOutputLayer::MLPPOutputLayer::get_bias() {
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return _bias;
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}
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void MLPPOutputLayer::set_bias(const real_t val) {
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_bias = val;
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_initialized = false;
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}
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Ref<MLPPVector> MLPPOutputLayer::get_z() {
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return _z;
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}
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void MLPPOutputLayer::set_z(const Ref<MLPPVector> &val) {
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_z = val;
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_initialized = false;
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}
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Ref<MLPPVector> MLPPOutputLayer::get_a() {
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return _a;
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}
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void MLPPOutputLayer::set_a(const Ref<MLPPVector> &val) {
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_a = val;
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_initialized = false;
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}
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real_t MLPPOutputLayer::get_z_test() {
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return _z_test;
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}
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void MLPPOutputLayer::set_z_test(const real_t val) {
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_z_test = val;
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_initialized = false;
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}
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real_t MLPPOutputLayer::get_a_test() {
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return _a_test;
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}
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void MLPPOutputLayer::set_a_test(const real_t val) {
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_a_test = val;
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_initialized = false;
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}
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Ref<MLPPVector> MLPPOutputLayer::get_delta() {
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return _delta;
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}
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void MLPPOutputLayer::set_delta(const Ref<MLPPVector> &val) {
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_delta = val;
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_initialized = false;
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}
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MLPPReg::RegularizationType MLPPOutputLayer::get_reg() {
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return _reg;
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}
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void MLPPOutputLayer::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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}
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real_t MLPPOutputLayer::get_lambda() {
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return _lambda;
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}
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void MLPPOutputLayer::set_lambda(const real_t val) {
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_lambda = val;
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_initialized = false;
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}
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real_t MLPPOutputLayer::get_alpha() {
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return _alpha;
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}
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void MLPPOutputLayer::set_alpha(const real_t val) {
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_alpha = val;
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_initialized = false;
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}
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MLPPUtilities::WeightDistributionType MLPPOutputLayer::get_weight_init() {
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return _weight_init;
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}
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void MLPPOutputLayer::set_weight_init(const MLPPUtilities::WeightDistributionType val) {
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_weight_init = val;
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_initialized = false;
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}
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bool MLPPOutputLayer::is_initialized() {
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return _initialized;
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}
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void MLPPOutputLayer::initialize() {
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if (_initialized) {
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return;
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}
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_weights->resize(_n_hidden);
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MLPPUtilities utils;
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utils.weight_initializationv(_weights, _weight_init);
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_bias = utils.bias_initializationr();
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_initialized = true;
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}
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void MLPPOutputLayer::forward_pass() {
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if (!_initialized) {
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initialize();
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}
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MLPPActivation avn;
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_z = _input->mult_vec(_weights)->scalar_addn(_bias);
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_a = avn.run_activation_norm_vector(_activation, _z);
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}
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void MLPPOutputLayer::test(const Ref<MLPPVector> &x) {
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if (!_initialized) {
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initialize();
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}
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MLPPActivation avn;
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_z_test = _weights->dot(x) + _bias;
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_a_test = avn.run_activation_norm_real(_activation, _z_test);
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}
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MLPPOutputLayer::MLPPOutputLayer(int p_n_hidden, MLPPActivation::ActivationFunction p_activation, MLPPCost::CostTypes p_cost, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType 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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// Regularization Params
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_reg = p_reg;
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_lambda = p_lambda; /* Regularization Parameter */
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_alpha = p_alpha; /* This is the controlling param for Elastic Net*/
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_weight_init = p_weight_init;
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_z.instance();
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_a.instance();
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_z_test = 0;
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_a_test = 0;
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_delta.instance();
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_weights.instance();
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_bias = 0;
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_weights->resize(_n_hidden);
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MLPPUtilities utils;
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utils.weight_initializationv(_weights, _weight_init);
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_bias = utils.bias_initializationr();
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_initialized = true;
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}
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MLPPOutputLayer::MLPPOutputLayer() {
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_n_hidden = 0;
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_activation = MLPPActivation::ACTIVATION_FUNCTION_LINEAR;
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// Regularization Params
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//reg = 0;
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_lambda = 0; /* Regularization Parameter */
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_alpha = 0; /* This is the controlling param for Elastic Net*/
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_weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT;
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_z.instance();
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_a.instance();
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_z_test = 0;
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_a_test = 0;
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_delta.instance();
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_weights.instance();
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_bias = 0;
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_initialized = false;
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}
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MLPPOutputLayer::~MLPPOutputLayer() {
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}
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void MLPPOutputLayer::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_n_hidden"), &MLPPOutputLayer::get_n_hidden);
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ClassDB::bind_method(D_METHOD("set_n_hidden", "val"), &MLPPOutputLayer::set_n_hidden);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "n_hidden"), "set_n_hidden", "get_n_hidden");
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ClassDB::bind_method(D_METHOD("get_activation"), &MLPPOutputLayer::get_activation);
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ClassDB::bind_method(D_METHOD("set_activation", "val"), &MLPPOutputLayer::set_activation);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "activation"), "set_activation", "get_activation");
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ClassDB::bind_method(D_METHOD("get_cost"), &MLPPOutputLayer::get_cost);
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ClassDB::bind_method(D_METHOD("set_cost", "val"), &MLPPOutputLayer::set_cost);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "cost"), "set_cost", "get_cost");
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ClassDB::bind_method(D_METHOD("get_input"), &MLPPOutputLayer::get_input);
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ClassDB::bind_method(D_METHOD("set_input", "val"), &MLPPOutputLayer::set_input);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input", "get_input");
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ClassDB::bind_method(D_METHOD("get_weights"), &MLPPOutputLayer::get_weights);
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ClassDB::bind_method(D_METHOD("set_weights", "val"), &MLPPOutputLayer::set_weights);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_weights", "get_weights");
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ClassDB::bind_method(D_METHOD("get_bias"), &MLPPOutputLayer::get_bias);
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ClassDB::bind_method(D_METHOD("set_bias", "val"), &MLPPOutputLayer::set_bias);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "bias"), "set_bias", "get_bias");
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ClassDB::bind_method(D_METHOD("get_z"), &MLPPOutputLayer::get_z);
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ClassDB::bind_method(D_METHOD("set_z", "val"), &MLPPOutputLayer::set_z);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "z", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_z", "get_z");
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ClassDB::bind_method(D_METHOD("get_a"), &MLPPOutputLayer::get_a);
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ClassDB::bind_method(D_METHOD("set_a", "val"), &MLPPOutputLayer::set_a);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "a", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_a", "get_a");
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ClassDB::bind_method(D_METHOD("get_z_test"), &MLPPOutputLayer::get_z_test);
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ClassDB::bind_method(D_METHOD("set_z_test", "val"), &MLPPOutputLayer::set_z_test);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "z_test"), "set_z_test", "get_z_test");
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ClassDB::bind_method(D_METHOD("get_a_test"), &MLPPOutputLayer::get_a_test);
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ClassDB::bind_method(D_METHOD("set_a_test", "val"), &MLPPOutputLayer::set_a_test);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "a_test"), "set_a_test", "get_a_test");
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ClassDB::bind_method(D_METHOD("get_delta"), &MLPPOutputLayer::get_delta);
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ClassDB::bind_method(D_METHOD("set_delta", "val"), &MLPPOutputLayer::set_delta);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "delta", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_delta", "get_delta");
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ClassDB::bind_method(D_METHOD("get_reg"), &MLPPOutputLayer::get_reg);
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ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPOutputLayer::set_reg);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
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ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPOutputLayer::get_lambda);
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ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPOutputLayer::set_lambda);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
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ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPOutputLayer::get_alpha);
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPOutputLayer::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ClassDB::bind_method(D_METHOD("get_weight_init"), &MLPPOutputLayer::get_weight_init);
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ClassDB::bind_method(D_METHOD("set_weight_init", "val"), &MLPPOutputLayer::set_weight_init);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "set_weight_init"), "set_weight_init", "get_weight_init");
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPOutputLayer::is_initialized);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPOutputLayer::initialize);
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ClassDB::bind_method(D_METHOD("forward_pass"), &MLPPOutputLayer::forward_pass);
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ClassDB::bind_method(D_METHOD("test", "x"), &MLPPOutputLayer::test);
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}
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