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Now MLPPSoftmaxNet uses engine classes.
This commit is contained in:
parent
686d81a258
commit
999d55b667
@ -5,6 +5,7 @@
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//
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#include "softmax_net.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../data/data.h"
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@ -12,7 +13,8 @@
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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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#include "core/log/logger.h"
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#include <random>
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/*
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@ -62,11 +64,11 @@ void MLPPSoftmaxNet::set_alpha(const real_t val) {
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}
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*/
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std::vector<real_t> MLPPSoftmaxNet::model_test(std::vector<real_t> x) {
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Ref<MLPPVector> MLPPSoftmaxNet::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::model_set_test(std::vector<std::vector<real_t>> X) {
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Ref<MLPPMatrix> MLPPSoftmaxNet::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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@ -84,43 +86,41 @@ void MLPPSoftmaxNet::gradient_descent(real_t learning_rate, int max_epoch, bool
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cost_prev = cost(_y_hat, _output_set);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(_y_hat, _output_set);
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Ref<MLPPMatrix> error = alg.subtractionm(_y_hat, _output_set);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(_a2), error);
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Ref<MLPPMatrix> D2_1 = alg.matmultm(alg.transposem(_a2), error);
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// weights and bias updation for layer 2
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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_weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, D2_1));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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_bias2 = alg.subtract_matrix_rows(_bias2, alg.scalar_multiplym(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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Ref<MLPPMatrix> D1_1 = alg.matmultm(error, alg.transposem(_weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(_z2, true));
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Ref<MLPPMatrix> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivm(_z2));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(_input_set), D1_2);
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Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(_input_set), D1_2);
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// weight an bias updation for layer 1
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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_weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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_bias1 = alg.subtract_matrix_rows(_bias1, alg.scalar_multiplym(learning_rate, D1_2));
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forward_pass();
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// UI PORTION
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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 1:" << std::endl;
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MLPPUtilities::UI(_weights1, _bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(_weights2, _bias2);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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epoch++;
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@ -143,47 +143,64 @@ void MLPPSoftmaxNet::sgd(real_t learning_rate, int max_epoch, bool ui) {
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> output_set_row_tmp;
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output_set_row_tmp.instance();
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output_set_row_tmp->resize(_output_set->size().x);
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Ref<MLPPMatrix> y_hat_mat_tmp;
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y_hat_mat_tmp.instance();
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y_hat_mat_tmp->resize(Size2i(_bias1->size(), 1));
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Ref<MLPPMatrix> output_row_mat_tmp;
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output_row_mat_tmp.instance();
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output_row_mat_tmp->resize(Size2i(_output_set->size().x, 1));
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while (true) {
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int outputIndex = distribution(generator);
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int output_index = distribution(generator);
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std::vector<real_t> y_hat = evaluatev(_input_set[outputIndex]);
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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_output_set->get_row_into_mlpp_vector(output_index, output_set_row_tmp);
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output_row_mat_tmp->set_row_mlpp_vector(0, output_set_row_tmp);
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auto prop_res = propagatev(_input_set[outputIndex]);
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auto z2 = std::get<0>(prop_res);
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auto a2 = std::get<1>(prop_res);
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Ref<MLPPVector> y_hat = evaluatev(input_set_row_tmp);
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y_hat_mat_tmp->set_row_mlpp_vector(0, y_hat);
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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std::vector<real_t> error = alg.subtraction(y_hat, _output_set[outputIndex]);
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PropagateVResult prop_res = propagatev(input_set_row_tmp);
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cost_prev = cost(y_hat_mat_tmp, output_row_mat_tmp);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, output_set_row_tmp);
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// Weight updation for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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Ref<MLPPMatrix> D2_1 = alg.outer_product(error, prop_res.a2);
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_weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, alg.transposem(D2_1)));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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// Bias updation for layer 2
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_bias2 = alg.subtraction(_bias2, alg.scalarMultiply(learning_rate, error));
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_bias2 = alg.subtractionnv(_bias2, alg.scalar_multiplynv(learning_rate, error));
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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.mat_vec_mult(_weights2, error);
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std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
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std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(_input_set[outputIndex], D1_2);
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Ref<MLPPVector> D1_1 = alg.mat_vec_multv(_weights2, error);
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Ref<MLPPVector> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivv(prop_res.z2));
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Ref<MLPPMatrix> D1_3 = alg.outer_product(input_set_row_tmp, D1_2);
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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_weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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// Bias updation for layer 1
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_bias1 = alg.subtraction(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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_bias1 = alg.subtractionnv(_bias1, alg.scalar_multiplynv(learning_rate, D1_2));
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y_hat = evaluatev(_input_set[outputIndex]);
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y_hat = evaluatev(input_set_row_tmp);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] }));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(_weights1, _bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(_weights2, _bias2);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_mat_tmp, output_row_mat_tmp));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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epoch++;
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@ -206,58 +223,53 @@ void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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MLPPUtilities::CreateMiniBatchMMBatch batches = MLPPUtilities::create_mini_batchesmm(_input_set, _output_set, n_mini_batch);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> y_hat = evaluatem(inputMiniBatches[i]);
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Ref<MLPPMatrix> current_input_mini_batch = batches.input_sets[i];
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Ref<MLPPMatrix> current_output_mini_batch = batches.output_sets[i];
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auto propagate_res = propagatem(inputMiniBatches[i]);
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auto z2 = std::get<0>(propagate_res);
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auto a2 = std::get<1>(propagate_res);
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Ref<MLPPMatrix> y_hat = evaluatem(current_input_mini_batch);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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PropagateMResult prop_res = propagatem(current_input_mini_batch);
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cost_prev = cost(y_hat, current_output_mini_batch);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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Ref<MLPPMatrix> error = alg.subtractionm(y_hat, current_output_mini_batch);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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Ref<MLPPMatrix> D2_1 = alg.matmultm(alg.transposem(prop_res.a2), error);
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// weights and bias updation for layser 2
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_weights2 = alg.subtraction(_weights2, alg.scalarMultiply(learning_rate, D2_1));
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//_reg
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_weights2 = regularization.regWeights(_weights2, _lambda, _alpha, "None");
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_weights2 = alg.subtractionm(_weights2, alg.scalar_multiplym(learning_rate, D2_1));
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_weights2 = regularization.reg_weightsm(_weights2, _lambda, _alpha, _reg);
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// Bias Updation for layer 2
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_bias2 = alg.subtractMatrixRows(_bias2, alg.scalarMultiply(learning_rate, error));
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_bias2 = alg.subtract_matrix_rows(_bias2, alg.scalar_multiplym(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(_weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
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Ref<MLPPMatrix> D1_1 = alg.matmultm(error, alg.transposem(_weights2));
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Ref<MLPPMatrix> D1_2 = alg.hadamard_productm(D1_1, avn.sigmoid_derivm(prop_res.z2));
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Ref<MLPPMatrix> D1_3 = alg.matmultm(alg.transposem(current_input_mini_batch), D1_2);
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// weight an bias updation for layer 1
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_weights1 = alg.subtraction(_weights1, alg.scalarMultiply(learning_rate, D1_3));
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//_reg
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_weights1 = regularization.regWeights(_weights1, _lambda, _alpha, "None");
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_weights1 = alg.subtractionm(_weights1, alg.scalar_multiplym(learning_rate, D1_3));
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_weights1 = regularization.reg_weightsm(_weights1, _lambda, _alpha, _reg);
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_bias1 = alg.subtractMatrixRows(_bias1, alg.scalarMultiply(learning_rate, D1_2));
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_bias1 = alg.subtract_matrix_rows(_bias1, alg.scalar_multiplym(learning_rate, D1_2));
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y_hat = evaluatem(inputMiniBatches[i]);
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y_hat = evaluatem(current_input_mini_batch);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(_weights1, _bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(_weights2, _bias2);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_mini_batch));
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PLOG_MSG("Layer 1:");
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MLPPUtilities::print_ui_mb(_weights1, _bias1);
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PLOG_MSG("Layer 2:");
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MLPPUtilities::print_ui_mb(_weights2, _bias2);
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}
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}
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@ -274,17 +286,17 @@ void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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real_t MLPPSoftmaxNet::score() {
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MLPPUtilities util;
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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 MLPPSoftmaxNet::save(std::string fileName) {
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void MLPPSoftmaxNet::save(const String &file_name) {
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MLPPUtilities util;
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util.saveParameters(fileName, _weights1, _bias1, false, 1);
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util.saveParameters(fileName, _weights2, _bias2, true, 2);
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//util.saveParameters(fileName, _weights1, _bias1, false, 1);
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//util.saveParameters(fileName, _weights2, _bias2, true, 2);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::get_embeddings() {
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_embeddings() {
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return _weights1;
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}
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@ -301,23 +313,37 @@ void MLPPSoftmaxNet::initialize() {
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_initialized = true;
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}
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MLPPSoftmaxNet::MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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MLPPSoftmaxNet::MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = p_input_set.size();
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_k = p_input_set[0].size();
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_n = p_input_set->size().y;
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_k = p_input_set->size().x;
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_n_hidden = p_n_hidden;
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_n_class = p_output_set[0].size();
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_n_class = p_output_set->size().x;
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.resize(_n);
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_y_hat.instance();
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_y_hat->resize(Size2i(0, _n));
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_weights1 = MLPPUtilities::weightInitialization(_k, _n_hidden);
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_weights2 = MLPPUtilities::weightInitialization(_n_hidden, _n_class);
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_bias1 = MLPPUtilities::biasInitialization(_n_hidden);
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_bias2 = MLPPUtilities::biasInitialization(_n_class);
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MLPPUtilities utils;
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_weights1.instance();
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_weights1->resize(Size2i(_n_hidden, _k));
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utils.weight_initializationm(_weights1);
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_weights2.instance();
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_weights2->resize(Size2i(_n_class, _n_hidden));
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utils.weight_initializationm(_weights2);
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_bias1.instance();
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_bias1->resize(_n_hidden);
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utils.bias_initializationv(_bias1);
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_bias2.instance();
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_bias2->resize(_n_class);
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utils.bias_initializationv(_bias2);
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_initialized = true;
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}
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@ -328,62 +354,65 @@ MLPPSoftmaxNet::MLPPSoftmaxNet() {
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MLPPSoftmaxNet::~MLPPSoftmaxNet() {
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}
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real_t MLPPSoftmaxNet::cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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real_t MLPPSoftmaxNet::cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
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MLPPReg regularization;
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MLPPData data;
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class MLPPCost cost;
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MLPPCost mlpp_cost;
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//_reg
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return cost.CrossEntropy(y_hat, y) + regularization.regTerm(_weights1, _lambda, _alpha, "None") + regularization.regTerm(_weights2, _lambda, _alpha, "None");
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return mlpp_cost.cross_entropym(y_hat, y) + regularization.reg_termm(_weights1, _lambda, _alpha, _reg) + regularization.reg_termm(_weights2, _lambda, _alpha, _reg);
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}
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std::vector<real_t> MLPPSoftmaxNet::evaluatev(std::vector<real_t> x) {
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Ref<MLPPVector> MLPPSoftmaxNet::evaluatev(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
|
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std::vector<real_t> a2 = avn.sigmoid(z2);
|
||||
Ref<MLPPVector> z2 = alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights1), x), _bias1);
|
||||
Ref<MLPPVector> a2 = avn.sigmoid_normv(z2);
|
||||
|
||||
return avn.adjSoftmax(alg.addition(alg.mat_vec_mult(alg.transpose(_weights2), a2), _bias2));
|
||||
return avn.adj_softmax_normv(alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights2), a2), _bias2));
|
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}
|
||||
|
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPSoftmaxNet::propagatev(std::vector<real_t> x) {
|
||||
MLPPSoftmaxNet::PropagateVResult MLPPSoftmaxNet::propagatev(const Ref<MLPPVector> &x) {
|
||||
MLPPLinAlg alg;
|
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MLPPActivation avn;
|
||||
|
||||
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(_weights1), x), _bias1);
|
||||
std::vector<real_t> a2 = avn.sigmoid(z2);
|
||||
PropagateVResult res;
|
||||
|
||||
return { z2, a2 };
|
||||
res.z2 = alg.additionnv(alg.mat_vec_multv(alg.transposem(_weights1), x), _bias1);
|
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res.a2 = avn.sigmoid_normv(res.z2);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
std::vector<std::vector<real_t>> MLPPSoftmaxNet::evaluatem(std::vector<std::vector<real_t>> X) {
|
||||
Ref<MLPPMatrix> MLPPSoftmaxNet::evaluatem(const Ref<MLPPMatrix> &X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
|
||||
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
|
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
||||
Ref<MLPPMatrix> z2 = alg.mat_vec_addv(alg.matmultm(X, _weights1), _bias1);
|
||||
Ref<MLPPMatrix> a2 = avn.sigmoid_normm(z2);
|
||||
|
||||
return avn.adjSoftmax(alg.mat_vec_add(alg.matmult(a2, _weights2), _bias2));
|
||||
return avn.adj_softmax_normm(alg.mat_vec_addv(alg.matmultm(a2, _weights2), _bias2));
|
||||
}
|
||||
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPSoftmaxNet::propagatem(std::vector<std::vector<real_t>> X) {
|
||||
MLPPSoftmaxNet::PropagateMResult MLPPSoftmaxNet::propagatem(const Ref<MLPPMatrix> &X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
|
||||
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, _weights1), _bias1);
|
||||
std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
|
||||
MLPPSoftmaxNet::PropagateMResult res;
|
||||
|
||||
return { z2, a2 };
|
||||
res.z2 = alg.mat_vec_addv(alg.matmultm(X, _weights1), _bias1);
|
||||
res.a2 = avn.sigmoid_normm(res.z2);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
void MLPPSoftmaxNet::forward_pass() {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
|
||||
_z2 = alg.mat_vec_add(alg.matmult(_input_set, _weights1), _bias1);
|
||||
_a2 = avn.sigmoid(_z2);
|
||||
_y_hat = avn.adjSoftmax(alg.mat_vec_add(alg.matmult(_a2, _weights2), _bias2));
|
||||
_z2 = alg.mat_vec_addv(alg.matmultm(_input_set, _weights1), _bias1);
|
||||
_a2 = avn.sigmoid_normm(_z2);
|
||||
_y_hat = avn.adj_softmax_normm(alg.mat_vec_addv(alg.matmultm(_a2, _weights2), _bias2));
|
||||
}
|
||||
|
||||
void MLPPSoftmaxNet::_bind_methods() {
|
||||
|
||||
@ -16,9 +16,6 @@
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class MLPPSoftmaxNet : public Reference {
|
||||
GDCLASS(MLPPSoftmaxNet, Reference);
|
||||
|
||||
@ -40,8 +37,8 @@ public:
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
std::vector<real_t> model_test(std::vector<real_t> x);
|
||||
std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
|
||||
Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
|
||||
Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
|
||||
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
@ -49,44 +46,55 @@ public:
|
||||
|
||||
real_t score();
|
||||
|
||||
void save(std::string fileName);
|
||||
void save(const String &file_name);
|
||||
|
||||
std::vector<std::vector<real_t>> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
|
||||
Ref<MLPPMatrix> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
|
||||
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
//MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
|
||||
MLPPSoftmaxNet();
|
||||
~MLPPSoftmaxNet();
|
||||
|
||||
protected:
|
||||
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);
|
||||
|
||||
std::vector<real_t> evaluatev(std::vector<real_t> x);
|
||||
std::tuple<std::vector<real_t>, std::vector<real_t>> propagatev(std::vector<real_t> x);
|
||||
Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
|
||||
|
||||
std::vector<std::vector<real_t>> evaluatem(std::vector<std::vector<real_t>> X);
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagatem(std::vector<std::vector<real_t>> X);
|
||||
struct PropagateVResult {
|
||||
Ref<MLPPVector> z2;
|
||||
Ref<MLPPVector> a2;
|
||||
};
|
||||
|
||||
PropagateVResult propagatev(const Ref<MLPPVector> &x);
|
||||
|
||||
Ref<MLPPMatrix> evaluatem(const Ref<MLPPMatrix> &X);
|
||||
|
||||
struct PropagateMResult {
|
||||
Ref<MLPPMatrix> z2;
|
||||
Ref<MLPPMatrix> a2;
|
||||
};
|
||||
|
||||
PropagateMResult propagatem(const Ref<MLPPMatrix> &X);
|
||||
|
||||
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<std::vector<real_t>> _weights1;
|
||||
std::vector<std::vector<real_t>> _weights2;
|
||||
Ref<MLPPMatrix> _weights1;
|
||||
Ref<MLPPMatrix> _weights2;
|
||||
|
||||
std::vector<real_t> _bias1;
|
||||
std::vector<real_t> _bias2;
|
||||
Ref<MLPPVector> _bias1;
|
||||
Ref<MLPPVector> _bias2;
|
||||
|
||||
std::vector<std::vector<real_t>> _z2;
|
||||
std::vector<std::vector<real_t>> _a2;
|
||||
Ref<MLPPMatrix> _z2;
|
||||
Ref<MLPPMatrix> _a2;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
@ -562,9 +562,9 @@ void MLPPTests::test_soft_max_network(bool ui) {
|
||||
alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPSoftmaxNet model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1);
|
||||
MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1);
|
||||
model.gradient_descent(0.01, 100000, ui);
|
||||
alg.printMatrix(model.model_set_test(dt->get_input()->to_std_vector()));
|
||||
PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
}
|
||||
void MLPPTests::test_autoencoder(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user