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New initial OutputLayer implementation.
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@ -8,7 +8,6 @@
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "core/containers/hash_map.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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@ -11,6 +11,238 @@
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#include <iostream>
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#include <random>
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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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}
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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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}
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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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}
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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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}
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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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}
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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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}
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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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}
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Ref<MLPPVector> 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 Ref<MLPPVector> &val) {
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z_test = val;
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}
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Ref<MLPPVector> 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 Ref<MLPPVector> &val) {
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a_test = val;
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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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}
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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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}
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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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}
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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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}
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void MLPPOutputLayer::forward_pass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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//z = alg.mat_vec_addv(alg.matmultm(input, weights), bias);
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//a = avn.run_activation_norm_matrix(activation, z);
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}
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void MLPPOutputLayer::test(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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//z_test = alg.additionm(alg.mat_vec_multv(alg.transposem(weights), x), bias);
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//a_test = avn.run_activation_norm_matrix(activation, z_test);
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}
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MLPPOutputLayer::MLPPOutputLayer(int p_n_hidden, MLPPActivation::ActivationFunction p_activation, 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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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.instance();
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a_test.instance();
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delta.instance();
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weights.instance();
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bias = 0;
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//weights->resize(Size2i(input->size().x, n_hidden));
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//bias->resize(n_hidden);
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//MLPPUtilities utils;
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//utils.weight_initializationm(weights, weight_init);
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//utils.bias_initializationv(bias);
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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.instance();
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a_test.instance();
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delta.instance();
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weights.instance();
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bias = 0;
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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_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::OBJECT, "z_test", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "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::OBJECT, "a_test", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "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("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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MLPPOldOutputLayer::MLPPOldOutputLayer(int n_hidden, std::string activation, std::string cost, std::vector<std::vector<real_t>> input, std::string weightInit, std::string reg, real_t lambda, real_t alpha) :
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n_hidden(n_hidden), activation(activation), cost(cost), input(input), weightInit(weightInit), reg(reg), lambda(lambda), alpha(alpha) {
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@ -9,14 +9,106 @@
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//
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/object/reference.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include <map>
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#include <string>
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#include <vector>
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class MLPPOutputLayer : public Reference {
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GDCLASS(MLPPOutputLayer, Reference);
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public:
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int get_n_hidden();
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void set_n_hidden(const int val);
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MLPPActivation::ActivationFunction get_activation();
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void set_activation(const MLPPActivation::ActivationFunction val);
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Ref<MLPPMatrix> get_input();
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void set_input(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_weights();
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void set_weights(const Ref<MLPPVector> &val);
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real_t get_bias();
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void set_bias(const real_t val);
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Ref<MLPPVector> get_z();
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void set_z(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_a();
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void set_a(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_z_test();
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void set_z_test(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_a_test();
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void set_a_test(const Ref<MLPPVector> &val);
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Ref<MLPPVector> get_delta();
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void set_delta(const Ref<MLPPVector> &val);
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MLPPReg::RegularizationType get_reg();
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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void set_lambda(const real_t val);
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real_t get_alpha();
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void set_alpha(const real_t val);
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MLPPUtilities::WeightDistributionType get_weight_init();
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void set_weight_init(const MLPPUtilities::WeightDistributionType val);
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void forward_pass();
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void test(const Ref<MLPPVector> &x);
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MLPPOutputLayer(int p_n_hidden, MLPPActivation::ActivationFunction p_activation, 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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MLPPOutputLayer();
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~MLPPOutputLayer();
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protected:
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static void _bind_methods();
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int n_hidden;
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MLPPActivation::ActivationFunction activation;
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std::string cost;
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Ref<MLPPMatrix> input;
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Ref<MLPPVector> weights;
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real_t bias;
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Ref<MLPPVector> z;
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Ref<MLPPVector> a;
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Ref<MLPPVector> z_test;
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Ref<MLPPVector> a_test;
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Ref<MLPPVector> delta;
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// Regularization Params
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MLPPReg::RegularizationType reg;
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real_t lambda; /* Regularization Parameter */
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real_t alpha; /* This is the controlling param for Elastic Net*/
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MLPPUtilities::WeightDistributionType weight_init;
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//std::map<std::string, real_t (MLPPCost::*)(std::vector<real_t>, std::vector<real_t>)> cost_map;
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//std::map<std::string, std::vector<real_t> (MLPPCost::*)(std::vector<real_t>, std::vector<real_t>)> costDeriv_map;
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};
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class MLPPOldOutputLayer {
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public:
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@ -55,5 +147,4 @@ public:
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void Test(std::vector<real_t> x);
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};
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#endif /* OutputLayer_hpp */
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