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Now MLPPMANN uses engine classes.
This commit is contained in:
parent
1224116e12
commit
f30e3a887d
@ -5,14 +5,15 @@
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//
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#include "mann.h"
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#include "core/log/logger.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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/*
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Ref<MLPPMatrix> MLPPMANN::get_input_set() {
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return input_set;
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@ -33,40 +34,54 @@ void MLPPMANN::set_output_set(const Ref<MLPPMatrix> &val) {
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}
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*/
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std::vector<std::vector<real_t>> MLPPMANN::model_set_test(std::vector<std::vector<real_t>> X) {
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ERR_FAIL_COND_V(!_initialized, std::vector<std::vector<real_t>>());
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Ref<MLPPMatrix> MLPPMANN::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_initialized, Ref<MLPPMatrix>());
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if (!_network.empty()) {
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_network[0].input = X;
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_network[0].forwardPass();
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Ref<MLPPHiddenLayer> layer = _network[0];
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].input = _network[i - 1].a;
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_network[i].forwardPass();
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layer->set_input(X);
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layer->forward_pass();
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for (int i = 1; i < _network.size(); i++) {
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layer = _network[i];
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Ref<MLPPHiddenLayer> prev_layer = _network[i - 1];
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layer->set_input(prev_layer->get_a());
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layer->forward_pass();
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}
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_output_layer->input = _network[_network.size() - 1].a;
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_output_layer->set_input(_network.write[_network.size() - 1]->get_a());
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} else {
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_output_layer->input = X;
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_output_layer->set_input(X);
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}
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_output_layer->forwardPass();
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_output_layer->forward_pass();
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return _output_layer->a;
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return _output_layer->get_a();
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}
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std::vector<real_t> MLPPMANN::model_test(std::vector<real_t> x) {
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ERR_FAIL_COND_V(!_initialized, std::vector<real_t>());
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Ref<MLPPVector> MLPPMANN::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
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if (!_network.empty()) {
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_network[0].Test(x);
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].Test(_network[i - 1].a_test);
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Ref<MLPPHiddenLayer> layer = _network[0];
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layer->test(x);
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for (int i = 1; i < _network.size(); i++) {
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layer = _network[i];
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Ref<MLPPHiddenLayer> prev_layer = _network[i - 1];
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layer->test(prev_layer->get_a_test());
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}
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_output_layer->Test(_network[_network.size() - 1].a_test);
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_output_layer->test(_network.write[_network.size() - 1]->get_a_test());
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} else {
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_output_layer->Test(x);
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_output_layer->test(x);
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}
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return _output_layer->a_test;
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return _output_layer->get_a_test();
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}
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void MLPPMANN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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@ -85,50 +100,57 @@ void MLPPMANN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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if (_output_layer->activation == "Softmax") {
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_output_layer->delta = alg.subtraction(_y_hat, _output_set);
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if (_output_layer->get_activation() == MLPPActivation::ACTIVATION_FUNCTION_SOFTMAX) {
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_output_layer->set_delta(alg.subtractionm(_y_hat, _output_set));
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} else {
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auto costDeriv = _output_layer->costDeriv_map[_output_layer->cost];
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auto outputAvn = _output_layer->activation_map[_output_layer->activation];
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_output_layer->delta = alg.hadamard_product((mlpp_cost.*costDeriv)(_y_hat, _output_set), (avn.*outputAvn)(_output_layer->z, 1));
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_output_layer->set_delta(alg.hadamard_productm(mlpp_cost.run_cost_deriv_matrix(_output_layer->get_cost(), _y_hat, _output_set), avn.run_activation_deriv_matrix(_output_layer->get_activation(), _output_layer->get_z())));
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}
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std::vector<std::vector<real_t>> outputWGrad = alg.matmult(alg.transpose(_output_layer->input), _output_layer->delta);
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Ref<MLPPMatrix> output_w_grad = alg.matmultm(alg.transposem(_output_layer->get_input()), _output_layer->get_delta());
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_output_layer->weights = alg.subtraction(_output_layer->weights, alg.scalarMultiply(learning_rate / _n, outputWGrad));
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_output_layer->weights = regularization.regWeights(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg);
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_output_layer->bias = alg.subtractMatrixRows(_output_layer->bias, alg.scalarMultiply(learning_rate / _n, _output_layer->delta));
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_output_layer->set_weights(alg.subtractionm(_output_layer->get_weights(), alg.scalar_multiplym(learning_rate / _n, output_w_grad)));
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_output_layer->set_weights(regularization.reg_weightsm(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg()));
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_output_layer->set_bias(alg.subtract_matrix_rows(_output_layer->get_bias(), alg.scalar_multiplym(learning_rate / _n, _output_layer->get_delta())));
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if (!_network.empty()) {
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auto hiddenLayerAvn = _network[_network.size() - 1].activation_map[_network[_network.size() - 1].activation];
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_network[_network.size() - 1].delta = alg.hadamard_product(alg.matmult(_output_layer->delta, alg.transpose(_output_layer->weights)), (avn.*hiddenLayerAvn)(_network[_network.size() - 1].z, true));
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std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(_network[_network.size() - 1].input), _network[_network.size() - 1].delta);
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Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
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_network[_network.size() - 1].weights = alg.subtraction(_network[_network.size() - 1].weights, alg.scalarMultiply(learning_rate / _n, hiddenLayerWGrad));
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_network[_network.size() - 1].weights = regularization.regWeights(_network[_network.size() - 1].weights, _network[_network.size() - 1].lambda, _network[_network.size() - 1].alpha, _network[_network.size() - 1].reg);
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_network[_network.size() - 1].bias = alg.subtractMatrixRows(_network[_network.size() - 1].bias, alg.scalarMultiply(learning_rate / _n, _network[_network.size() - 1].delta));
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//auto hiddenLayerAvn = layer.activation_map[layer.activation];
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layer->set_delta(alg.hadamard_productm(alg.matmultm(_output_layer->get_delta(), alg.transposem(_output_layer->get_weights())), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
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layer->set_weights(alg.subtractionm(layer->get_weights(), alg.scalar_multiplym(learning_rate / _n, hidden_layer_w_grad)));
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layer->set_weights(regularization.reg_weightsm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()));
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layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / _n, layer->get_delta())));
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for (int i = _network.size() - 2; i >= 0; i--) {
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hiddenLayerAvn = _network[i].activation_map[_network[i].activation];
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_network[i].delta = alg.hadamard_product(alg.matmult(_network[i + 1].delta, _network[i + 1].weights), (avn.*hiddenLayerAvn)(_network[i].z, true));
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hiddenLayerWGrad = alg.matmult(alg.transpose(_network[i].input), _network[i].delta);
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_network[i].weights = alg.subtraction(_network[i].weights, alg.scalarMultiply(learning_rate / _n, hiddenLayerWGrad));
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_network[i].weights = regularization.regWeights(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg);
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_network[i].bias = alg.subtractMatrixRows(_network[i].bias, alg.scalarMultiply(learning_rate / _n, _network[i].delta));
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layer = _network[i];
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Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
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//hiddenLayerAvn = layer.activation_map[layer.activation];
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layer->set_delta(alg.hadamard_productm(alg.matmultm(next_layer->get_delta(), next_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
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layer->set_weights(alg.subtractionm(layer->get_weights(), alg.scalar_multiplym(learning_rate / _n, hidden_layer_w_grad)));
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layer->set_weights(regularization.reg_weightsm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()));
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layer->set_bias(alg.subtract_matrix_rows(layer->get_bias(), alg.scalar_multiplym(learning_rate / _n, layer->get_delta())));
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}
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}
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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std::cout << "Layer " << _network.size() + 1 << ": " << std::endl;
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MLPPUtilities::UI(_output_layer->weights, _output_layer->bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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PLOG_MSG("Layer " + itos(_network.size() + 1) + ": ");
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MLPPUtilities::print_ui_mb(_output_layer->get_weights(), _output_layer->get_bias());
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if (!_network.empty()) {
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std::cout << "Layer " << _network.size() << ": " << std::endl;
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for (int i = _network.size() - 1; i >= 0; i--) {
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std::cout << "Layer " << i + 1 << ": " << std::endl;
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MLPPUtilities::UI(_network[i].weights, _network[i].bias);
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PLOG_MSG("Layer " + itos(i + 1) + ": ");
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Ref<MLPPHiddenLayer> layer = _network[i];
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MLPPUtilities::print_ui_mb(layer->get_weights(), layer->get_bias());
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}
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}
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}
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@ -148,39 +170,41 @@ real_t MLPPMANN::score() {
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forward_pass();
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return util.performance(_y_hat, _output_set);
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return util.performance_mat(_y_hat, _output_set);
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}
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void MLPPMANN::save(std::string fileName) {
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void MLPPMANN::save(const String &file_name) {
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ERR_FAIL_COND(!_initialized);
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/*
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MLPPUtilities util;
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if (!_network.empty()) {
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util.saveParameters(fileName, _network[0].weights, _network[0].bias, false, 1);
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util.saveParameters(file_name, _network[0].weights, _network[0].bias, false, 1);
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for (uint32_t i = 1; i < _network.size(); i++) {
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util.saveParameters(fileName, _network[i].weights, _network[i].bias, true, i + 1);
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util.saveParameters(file_name, _network[i].weights, _network[i].bias, true, i + 1);
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}
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util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
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util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
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} else {
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util.saveParameters(fileName, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
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util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
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}
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*/
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}
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void MLPPMANN::add_layer(int n_hidden, std::string activation, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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void MLPPMANN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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if (_network.empty()) {
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_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, _input_set, weightInit, reg, lambda, alpha));
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_network[0].forwardPass();
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_network.push_back(Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, _input_set, weight_init, reg, lambda, alpha))));
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_network.write[0]->forward_pass();
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} else {
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_network.push_back(MLPPOldHiddenLayer(n_hidden, activation, _network[_network.size() - 1].a, weightInit, reg, lambda, alpha));
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_network[_network.size() - 1].forwardPass();
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_network.push_back(Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha))));
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_network.write[_network.size() - 1]->forward_pass();
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}
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}
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void MLPPMANN::add_output_layer(std::string activation, std::string loss, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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void MLPPMANN::add_output_layer(MLPPActivation::ActivationFunction activation, MLPPCost::CostTypes loss, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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if (!_network.empty()) {
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_output_layer = new MLPPOldMultiOutputLayer(_n_output, _network[0].n_hidden, activation, loss, _network[_network.size() - 1].a, weightInit, reg, lambda, alpha);
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_output_layer = Ref<MLPPMultiOutputLayer>(memnew(MLPPMultiOutputLayer(_n_output, _network.write[_network.size() - 1]->get_n_hidden(), activation, loss, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha)));
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} else {
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_output_layer = new MLPPOldMultiOutputLayer(_n_output, _k, activation, loss, _input_set, weightInit, reg, lambda, alpha);
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_output_layer = Ref<MLPPMultiOutputLayer>(memnew(MLPPMultiOutputLayer(_n_output, _k, activation, loss, _input_set, weight_init, reg, lambda, alpha)));
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}
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}
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@ -198,12 +222,12 @@ void MLPPMANN::initialize() {
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_initialized = true;
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}
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MLPPMANN::MLPPMANN(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set) {
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MLPPMANN::MLPPMANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = _input_set.size();
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_k = _input_set[0].size();
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_n_output = _output_set[0].size();
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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_n_output = _output_set->size().x;
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_initialized = true;
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}
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@ -213,39 +237,48 @@ MLPPMANN::MLPPMANN() {
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}
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MLPPMANN::~MLPPMANN() {
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delete _output_layer;
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}
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real_t MLPPMANN::cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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real_t MLPPMANN::cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
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MLPPReg regularization;
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class MLPPCost cost;
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real_t totalRegTerm = 0;
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MLPPCost mlpp_cost;
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real_t total_reg_term = 0;
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auto cost_function = _output_layer->cost_map[_output_layer->cost];
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if (!_network.empty()) {
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for (uint32_t i = 0; i < _network.size() - 1; i++) {
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totalRegTerm += regularization.regTerm(_network[i].weights, _network[i].lambda, _network[i].alpha, _network[i].reg);
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for (int i = 0; i < _network.size() - 1; i++) {
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Ref<MLPPHiddenLayer> layer = _network[i];
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total_reg_term += regularization.reg_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg());
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}
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}
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return (cost.*cost_function)(y_hat, y) + totalRegTerm + regularization.regTerm(_output_layer->weights, _output_layer->lambda, _output_layer->alpha, _output_layer->reg);
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return mlpp_cost.run_cost_norm_matrix(_output_layer->get_cost(), y_hat, y) + total_reg_term + regularization.reg_termm(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg());
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}
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void MLPPMANN::forward_pass() {
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if (!_network.empty()) {
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_network[0].input = _input_set;
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_network[0].forwardPass();
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Ref<MLPPHiddenLayer> layer = _network[0];
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for (uint32_t i = 1; i < _network.size(); i++) {
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_network[i].input = _network[i - 1].a;
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_network[i].forwardPass();
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layer->set_input(_input_set);
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layer->forward_pass();
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for (int i = 1; i < _network.size(); i++) {
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layer = _network[i];
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Ref<MLPPHiddenLayer> prev_layer = _network[i - 1];
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layer->set_input(prev_layer->get_a());
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layer->forward_pass();
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}
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_output_layer->input = _network[_network.size() - 1].a;
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_output_layer->set_input(_network.write[_network.size() - 1]->get_a());
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} else {
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_output_layer->input = _input_set;
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_output_layer->set_input(_input_set);
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}
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_output_layer->forwardPass();
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_y_hat = _output_layer->a;
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_output_layer->forward_pass();
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_y_hat = _output_layer->get_a();
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}
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void MLPPMANN::_bind_methods() {
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@ -20,12 +20,6 @@
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#include "../hidden_layer/hidden_layer.h"
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#include "../multi_output_layer/multi_output_layer.h"
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#include "../hidden_layer/hidden_layer_old.h"
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#include "../multi_output_layer/multi_output_layer_old.h"
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#include <string>
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#include <vector>
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class MLPPMANN : public Reference {
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GDCLASS(MLPPMANN, Reference);
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@ -38,38 +32,38 @@ public:
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void set_output_set(const Ref<MLPPMatrix> &val);
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*/
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std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
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std::vector<real_t> model_test(std::vector<real_t> x);
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Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
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Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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real_t score();
|
||||
|
||||
void save(std::string file_name);
|
||||
void save(const String &file_name);
|
||||
|
||||
void add_layer(int n_hidden, std::string activation, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(std::string activation, std::string loss, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_output_layer(MLPPActivation::ActivationFunction activation, MLPPCost::CostTypes loss, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
MLPPMANN(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set);
|
||||
MLPPMANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set);
|
||||
|
||||
MLPPMANN();
|
||||
~MLPPMANN();
|
||||
|
||||
private:
|
||||
real_t cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
|
||||
real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<std::vector<real_t>> _output_set;
|
||||
std::vector<std::vector<real_t>> _y_hat;
|
||||
Ref<MLPPMatrix> _input_set;
|
||||
Ref<MLPPMatrix> _output_set;
|
||||
Ref<MLPPMatrix> _y_hat;
|
||||
|
||||
std::vector<MLPPOldHiddenLayer> _network;
|
||||
MLPPOldMultiOutputLayer *_output_layer;
|
||||
Vector<Ref<MLPPHiddenLayer>> _network;
|
||||
Ref<MLPPMultiOutputLayer> _output_layer;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
@ -136,9 +136,11 @@ void MLPPMultiOutputLayer::test(const Ref<MLPPVector> &x) {
|
||||
_a_test = avn.run_activation_norm_vector(_activation, _z_test);
|
||||
}
|
||||
|
||||
MLPPMultiOutputLayer::MLPPMultiOutputLayer(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) {
|
||||
MLPPMultiOutputLayer::MLPPMultiOutputLayer(int n_output, int p_n_hidden, MLPPActivation::ActivationFunction p_activation, MLPPCost::CostTypes cost, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
|
||||
_n_output = n_output;
|
||||
_n_hidden = p_n_hidden;
|
||||
_activation = p_activation;
|
||||
_cost = cost;
|
||||
|
||||
_input = p_input;
|
||||
|
||||
|
||||
@ -76,7 +76,7 @@ public:
|
||||
void forward_pass();
|
||||
void test(const Ref<MLPPVector> &x);
|
||||
|
||||
MLPPMultiOutputLayer(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);
|
||||
MLPPMultiOutputLayer(int n_output, int p_n_hidden, MLPPActivation::ActivationFunction p_activation, MLPPCost::CostTypes cost, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha);
|
||||
|
||||
MLPPMultiOutputLayer();
|
||||
~MLPPMultiOutputLayer();
|
||||
|
||||
@ -745,11 +745,19 @@ void MLPPTests::test_dynamically_sized_mann(bool ui) {
|
||||
alg.printMatrix(mann_old.modelSetTest(inputSet));
|
||||
std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPMANN mann(inputSet, outputSet);
|
||||
mann.add_output_layer("Linear", "MSE");
|
||||
Ref<MLPPMatrix> input_set;
|
||||
input_set.instance();
|
||||
input_set->set_from_std_vectors(inputSet);
|
||||
|
||||
Ref<MLPPMatrix> output_set;
|
||||
output_set.instance();
|
||||
output_set->set_from_std_vectors(outputSet);
|
||||
|
||||
MLPPMANN mann(input_set, output_set);
|
||||
mann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_LINEAR, MLPPCost::COST_TYPE_MSE);
|
||||
mann.gradient_descent(0.001, 80000, false);
|
||||
alg.printMatrix(mann.model_set_test(inputSet));
|
||||
std::cout << "ACCURACY: " << 100 * mann.score() << "%" << std::endl;
|
||||
PLOG_MSG(mann.model_set_test(input_set)->to_string());
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * mann.score()) + "%");
|
||||
}
|
||||
void MLPPTests::test_train_test_split_mann(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
@ -787,12 +795,12 @@ void MLPPTests::test_train_test_split_mann(bool ui) {
|
||||
alg.printMatrix(mann_old.modelSetTest(split_data.test->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPMANN mann(split_data.train->get_input()->to_std_vector(), split_data.train->get_output()->to_std_vector());
|
||||
mann.add_layer(100, "RELU", "XavierNormal");
|
||||
mann.add_output_layer("Softmax", "CrossEntropy", "XavierNormal");
|
||||
MLPPMANN mann(split_data.train->get_input(), split_data.train->get_output());
|
||||
mann.add_layer(100, MLPPActivation::ACTIVATION_FUNCTION_RELU, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL);
|
||||
mann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SOFTMAX, MLPPCost::COST_TYPE_CROSS_ENTROPY, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_XAVIER_NORMAL);
|
||||
mann.gradient_descent(0.1, 80000, ui);
|
||||
alg.printMatrix(mann.model_set_test(split_data.test->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * mann.score() << "%" << std::endl;
|
||||
PLOG_MSG(mann.model_set_test(split_data.test->get_input())->to_string());
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * mann.score()) + "%");
|
||||
}
|
||||
|
||||
void MLPPTests::test_naive_bayes() {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user