mirror of
https://github.com/Relintai/pmlpp.git
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933 lines
32 KiB
C++
933 lines
32 KiB
C++
//
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// ANN.cpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#include "ann.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 "core/log/logger.h"
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#include <random>
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Ref<MLPPVector> MLPPANN::model_set_test(const Ref<MLPPMatrix> &X) {
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if (!_network.empty()) {
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Ref<MLPPHiddenLayer> layer = _network[0];
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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->set_input(_network.write[_network.size() - 1]->get_a());
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} else {
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_output_layer->set_input(X);
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}
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_output_layer->forward_pass();
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return _output_layer->get_a();
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}
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real_t MLPPANN::model_test(const Ref<MLPPVector> &x) {
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if (!_network.empty()) {
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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.write[_network.size() - 1]->get_a_test());
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} else {
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_output_layer->test(x);
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}
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return _output_layer->get_a_test();
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}
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void MLPPANN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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forward_pass();
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real_t initial_learning_rate = learning_rate;
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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cost_prev = cost(_y_hat, _output_set);
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ComputeGradientsResult grads = compute_gradients(_y_hat, _output_set);
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grads.cumulative_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
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grads.output_w_grad->scalar_multiply(learning_rate / _n);
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update_parameters(grads.cumulative_hidden_layer_w_grad, grads.output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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forward_pass();
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if (ui) {
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print_ui(epoch, cost_prev, _y_hat, _output_set);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPANN::sgd(real_t learning_rate, int max_epoch, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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std::random_device rd;
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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> y_hat_row_tmp;
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y_hat_row_tmp.instance();
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y_hat_row_tmp->resize(1);
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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(1);
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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int output_index = distribution(generator);
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_input_set->row_get_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_element_set = _output_set->element_get(output_index);
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output_set_row_tmp->element_set(0, output_element_set);
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real_t y_hat = model_test(input_set_row_tmp);
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y_hat_row_tmp->element_set(0, y_hat);
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cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
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ComputeGradientsResult grads = compute_gradients(y_hat_row_tmp, output_set_row_tmp);
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grads.cumulative_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
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grads.output_w_grad->scalar_multiply(learning_rate / _n);
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update_parameters(grads.cumulative_hidden_layer_w_grad, grads.output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_test(input_set_row_tmp);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat_row_tmp, output_set_row_tmp);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPANN::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
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grads.cumulative_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
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grads.output_w_grad->scalar_multiply(learning_rate / _n);
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update_parameters(grads.cumulative_hidden_layer_w_grad, grads.output_w_grad, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(current_input_batch);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, current_output_batch);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPANN::momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool nag, bool ui) {
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class MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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// Initializing necessary components for Adam.
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Ref<MLPPTensor3> v_hidden;
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v_hidden.instance();
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Ref<MLPPVector> v_output;
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v_output.instance();
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
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if (!_network.empty() && v_hidden->size() == Size3i()) { // Initing our tensor
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v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
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}
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if (v_output->size() == 0) {
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v_output->resize(grads.output_w_grad->size());
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}
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if (nag) { // "Aposterori" calculation
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update_parameters(v_hidden, v_output, 0); // DON'T update bias.
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}
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v_hidden = v_hidden->scalar_multiplyn(gamma)->addn(grads.cumulative_hidden_layer_w_grad->scalar_multiplyn(learning_rate / _n));
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v_output = v_output->scalar_multiplyn(gamma)->addn(grads.output_w_grad->scalar_multiplyn(learning_rate / _n));
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update_parameters(v_hidden, v_output, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(current_input_batch);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, current_output_batch);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPANN::adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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// Initializing necessary components for Adam.
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Ref<MLPPTensor3> v_hidden;
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v_hidden.instance();
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Ref<MLPPVector> v_output;
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v_output.instance();
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
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if (!_network.empty() && v_hidden->size() == Size3i()) { // Initing our tensor
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v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
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}
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if (v_output->size() == 0) {
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v_output->resize(grads.output_w_grad->size());
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}
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v_hidden->add(grads.cumulative_hidden_layer_w_grad->exponentiaten(2));
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v_output->add(grads.output_w_grad->exponentiaten(2));
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Ref<MLPPTensor3> hidden_layer_updations = grads.cumulative_hidden_layer_w_grad->division_element_wisen(v_hidden->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
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Ref<MLPPVector> output_layer_updation = grads.output_w_grad->division_element_wisen(v_output->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
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update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(current_input_batch);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, current_output_batch);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPANN::adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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// Initializing necessary components for Adam.
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Ref<MLPPTensor3> v_hidden;
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v_hidden.instance();
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Ref<MLPPVector> v_output;
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v_output.instance();
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
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if (!_network.empty() && v_hidden->size() == Size3i()) { // Initing our tensor
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v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
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}
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if (v_output->size() == 0) {
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v_output->resize(grads.output_w_grad->size());
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}
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v_hidden = v_hidden->scalar_multiplyn(1 - b1)->addn(grads.cumulative_hidden_layer_w_grad->exponentiaten(2)->scalar_multiplyn(b1));
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v_output->add(grads.output_w_grad->exponentiaten(2));
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Ref<MLPPTensor3> hidden_layer_updations = grads.cumulative_hidden_layer_w_grad->division_element_wisen(v_hidden->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
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Ref<MLPPVector> output_layer_updation = grads.output_w_grad->division_element_wisen(v_output->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
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update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
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y_hat = model_set_test(current_input_batch);
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if (ui) {
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print_ui(epoch, cost_prev, y_hat, current_output_batch);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forward_pass();
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}
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void MLPPANN::adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
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MLPPCost mlpp_cost;
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real_t cost_prev = 0;
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int epoch = 1;
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real_t initial_learning_rate = learning_rate;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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// Initializing necessary components for Adam.
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Ref<MLPPTensor3> m_hidden;
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Ref<MLPPTensor3> v_hidden;
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m_hidden.instance();
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v_hidden.instance();
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Ref<MLPPVector> m_output;
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Ref<MLPPVector> v_output;
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m_output.instance();
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v_output.instance();
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while (true) {
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learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
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for (int i = 0; i < n_mini_batch; i++) {
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Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch = batches.output_sets[i];
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Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
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if (!_network.empty() && m_hidden->size() == Size3i() && v_hidden->size() == Size3i()) { // Initing our tensor
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m_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
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v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
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}
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if (m_output->size() == 0 && v_output->size()) {
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m_output->resize(grads.output_w_grad->size());
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v_output->resize(grads.output_w_grad->size());
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}
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m_hidden = m_hidden->scalar_multiplyn(b1)->addn(grads.cumulative_hidden_layer_w_grad->scalar_multiplyn(1 - b1));
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v_hidden = v_hidden->scalar_multiplyn(b2)->addn(grads.cumulative_hidden_layer_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
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m_output = m_output->scalar_multiplyn(b1)->addn(grads.output_w_grad->scalar_multiplyn(1 - b1));
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v_output = v_output->scalar_multiplyn(b2)->addn(grads.output_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
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Ref<MLPPTensor3> m_hidden_hat = m_hidden->scalar_multiplyn(1 / (1 - Math::pow(b1, epoch)));
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Ref<MLPPTensor3> v_hidden_hat = v_hidden->scalar_multiplyn(1 / (1 - Math::pow(b2, epoch)));
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Ref<MLPPVector> m_output_hat = m_output->scalar_multiplyn(1 / (1 - Math::pow(b1, epoch)));
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Ref<MLPPVector> v_output_hat = v_output->scalar_multiplyn(1 / (1 - Math::pow(b2, epoch)));
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Ref<MLPPTensor3> hidden_layer_updations = m_hidden_hat->division_element_wisen(v_hidden_hat->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
Ref<MLPPVector> output_layer_updation = m_output_hat->division_element_wisen(v_output_hat->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
|
|
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = model_set_test(current_input_batch);
|
|
|
|
if (ui) {
|
|
print_ui(epoch, cost_prev, y_hat, current_output_batch);
|
|
}
|
|
}
|
|
|
|
epoch++;
|
|
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
forward_pass();
|
|
}
|
|
|
|
void MLPPANN::adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
|
MLPPCost mlpp_cost;
|
|
|
|
real_t cost_prev = 0;
|
|
int epoch = 1;
|
|
real_t initial_learning_rate = learning_rate;
|
|
|
|
// Creating the mini-batches
|
|
int n_mini_batch = _n / mini_batch_size;
|
|
// always evaluate the result
|
|
// always do forward pass only ONCE at end.
|
|
|
|
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
|
|
|
// Initializing necessary components for Adam.
|
|
Ref<MLPPTensor3> m_hidden;
|
|
Ref<MLPPTensor3> u_hidden;
|
|
m_hidden.instance();
|
|
u_hidden.instance();
|
|
|
|
Ref<MLPPVector> m_output;
|
|
Ref<MLPPVector> u_output;
|
|
m_output.instance();
|
|
u_output.instance();
|
|
|
|
while (true) {
|
|
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
|
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
|
|
Ref<MLPPVector> current_output_batch = batches.output_sets[i];
|
|
|
|
Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
|
|
cost_prev = cost(y_hat, current_output_batch);
|
|
|
|
ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
|
|
|
|
if (!_network.empty() && m_hidden->size() == Size3i() && u_hidden->size() == Size3i()) { // Initing our tensor
|
|
m_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
u_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
}
|
|
|
|
if (m_output->size() == 0 && u_output->size() == 0) {
|
|
m_output->resize(grads.output_w_grad->size());
|
|
u_output->resize(grads.output_w_grad->size());
|
|
}
|
|
|
|
m_hidden->addb(m_hidden->scalar_multiplyn(b1), grads.cumulative_hidden_layer_w_grad->scalar_multiplyn(1 - b1));
|
|
u_hidden->maxb(u_hidden->scalar_multiplyn(b2), grads.cumulative_hidden_layer_w_grad->absn());
|
|
|
|
m_output->addb(m_output->scalar_multiplyn(b1), grads.output_w_grad->scalar_multiplyn(1 - b1));
|
|
u_output->maxb(u_output->scalar_multiplyn(b2), grads.output_w_grad->absn());
|
|
|
|
Ref<MLPPTensor3> m_hidden_hat = m_hidden->scalar_multiplyn(1 / (1 - Math::pow(b1, epoch)));
|
|
Ref<MLPPVector> m_output_hat = m_output->scalar_multiplyn(1 / (1 - Math::pow(b1, epoch)));
|
|
|
|
Ref<MLPPTensor3> hidden_layer_updations = m_hidden_hat->division_element_wisen(u_hidden->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
Ref<MLPPVector> output_layer_updation = m_output_hat->division_element_wisen(u_output->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
|
|
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = model_set_test(current_input_batch);
|
|
|
|
if (ui) {
|
|
print_ui(epoch, cost_prev, y_hat, current_output_batch);
|
|
}
|
|
}
|
|
|
|
epoch++;
|
|
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
forward_pass();
|
|
}
|
|
|
|
void MLPPANN::nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
|
MLPPCost mlpp_cost;
|
|
|
|
real_t cost_prev = 0;
|
|
int epoch = 1;
|
|
real_t initial_learning_rate = learning_rate;
|
|
|
|
// Creating the mini-batches
|
|
int n_mini_batch = _n / mini_batch_size;
|
|
// always evaluate the result
|
|
// always do forward pass only ONCE at end.
|
|
|
|
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
|
|
|
// Initializing necessary components for Adam.
|
|
Ref<MLPPTensor3> m_hidden;
|
|
Ref<MLPPTensor3> v_hidden;
|
|
m_hidden.instance();
|
|
v_hidden.instance();
|
|
|
|
Ref<MLPPVector> m_output;
|
|
Ref<MLPPVector> v_output;
|
|
m_output.instance();
|
|
v_output.instance();
|
|
|
|
while (true) {
|
|
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
|
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
|
|
Ref<MLPPVector> current_output_batch = batches.output_sets[i];
|
|
|
|
Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
|
|
cost_prev = cost(y_hat, current_output_batch);
|
|
|
|
ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
|
|
|
|
if (!_network.empty() && m_hidden->size() == Size3i() && v_hidden->size() == Size3i()) { // Initing our tensor
|
|
m_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
}
|
|
|
|
if (m_output->size() == 0 && v_output->size() == 0) {
|
|
m_output->resize(grads.output_w_grad->size());
|
|
v_output->resize(grads.output_w_grad->size());
|
|
}
|
|
|
|
m_hidden->addb(m_hidden->scalar_multiplyn(b1), grads.cumulative_hidden_layer_w_grad->scalar_multiplyn(1 - b1));
|
|
v_hidden->addb(v_hidden->scalar_multiplyn(b2), grads.cumulative_hidden_layer_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
|
|
|
|
m_output->addb(m_output->scalar_multiplyn(b1), grads.output_w_grad->scalar_multiplyn(1 - b1));
|
|
v_output->addb(v_output->scalar_multiplyn(b2), grads.output_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
|
|
|
|
Ref<MLPPTensor3> m_hidden_hat = m_hidden->scalar_multiplyn(1 / (1.0 - Math::pow(b1, epoch)));
|
|
Ref<MLPPTensor3> v_hidden_hat = v_hidden->scalar_multiplyn(1 / (1.0 - Math::pow(b2, epoch)));
|
|
Ref<MLPPTensor3> m_hidden_final = m_hidden_hat->scalar_multiplyn(b1)->addn(grads.cumulative_hidden_layer_w_grad->scalar_multiplyn((1 - b1) / (1 - Math::pow(b1, epoch))));
|
|
|
|
Ref<MLPPVector> m_output_hat = m_output->scalar_multiplyn(1 / (1.0 - Math::pow(b1, epoch)));
|
|
Ref<MLPPVector> v_output_hat = v_output->scalar_multiplyn(1 / (1.0 - Math::pow(b2, epoch)));
|
|
Ref<MLPPVector> m_output_final = m_output_hat->scalar_multiplyn(b1)->addn(grads.output_w_grad->scalar_multiplyn((1 - b1) / (1.0 - Math::pow(b1, epoch))));
|
|
|
|
Ref<MLPPTensor3> hidden_layer_updations = m_hidden_final->division_element_wisen(v_hidden_hat->sqrtn()->scalar_multiplyn(e))->scalar_multiplyn(learning_rate / _n);
|
|
Ref<MLPPVector> output_layer_updation = m_output_final->division_element_wisen(v_output_hat->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
|
|
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
|
|
y_hat = model_set_test(current_input_batch);
|
|
|
|
if (ui) {
|
|
print_ui(epoch, cost_prev, y_hat, current_output_batch);
|
|
}
|
|
}
|
|
|
|
epoch++;
|
|
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
forward_pass();
|
|
}
|
|
|
|
void MLPPANN::amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
|
MLPPCost mlpp_cost;
|
|
|
|
real_t cost_prev = 0;
|
|
int epoch = 1;
|
|
real_t initial_learning_rate = learning_rate;
|
|
|
|
// Creating the mini-batches
|
|
int n_mini_batch = _n / mini_batch_size;
|
|
// always evaluate the result
|
|
// always do forward pass only ONCE at end.
|
|
|
|
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
|
|
|
// Initializing necessary components for Adam.
|
|
Ref<MLPPTensor3> m_hidden;
|
|
Ref<MLPPTensor3> v_hidden;
|
|
m_hidden.instance();
|
|
v_hidden.instance();
|
|
|
|
Ref<MLPPTensor3> v_hidden_hat;
|
|
v_hidden_hat.instance();
|
|
|
|
Ref<MLPPVector> m_output;
|
|
Ref<MLPPVector> v_output;
|
|
m_output.instance();
|
|
v_output.instance();
|
|
|
|
Ref<MLPPVector> v_output_hat;
|
|
v_output_hat.instance();
|
|
|
|
while (true) {
|
|
learning_rate = apply_learning_rate_scheduler(initial_learning_rate, _decay_constant, epoch, _drop_rate);
|
|
|
|
for (int i = 0; i < n_mini_batch; i++) {
|
|
Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
|
|
Ref<MLPPVector> current_output_batch = batches.output_sets[i];
|
|
|
|
Ref<MLPPVector> y_hat = model_set_test(current_input_batch);
|
|
cost_prev = cost(y_hat, current_output_batch);
|
|
|
|
ComputeGradientsResult grads = compute_gradients(y_hat, current_output_batch);
|
|
|
|
if (!_network.empty() && m_hidden->size() == Size3i() && v_hidden->size() == Size3i()) { // Initing our tensor
|
|
|
|
m_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
v_hidden->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
v_hidden_hat->resize(grads.cumulative_hidden_layer_w_grad->size());
|
|
}
|
|
|
|
if (m_output->size() == 0 && v_output->size() == 0) {
|
|
m_output->resize(grads.output_w_grad->size());
|
|
v_output->resize(grads.output_w_grad->size());
|
|
v_output_hat->resize(grads.output_w_grad->size());
|
|
}
|
|
|
|
m_hidden->addb(m_hidden->scalar_multiplyn(b1), grads.cumulative_hidden_layer_w_grad->scalar_multiplyn(1 - b1));
|
|
v_hidden->addb(v_hidden->scalar_multiplyn(b2), grads.cumulative_hidden_layer_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
|
|
|
|
m_output->addb(m_output->scalar_multiplyn(b1), grads.output_w_grad->scalar_multiplyn(1 - b1));
|
|
v_output->addb(v_output->scalar_multiplyn(b2), grads.output_w_grad->exponentiaten(2)->scalar_multiplyn(1 - b2));
|
|
|
|
v_hidden_hat->max(v_hidden);
|
|
v_output_hat->max(v_output);
|
|
|
|
Ref<MLPPTensor3> hidden_layer_updations = m_hidden->division_element_wisen(v_hidden_hat->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
Ref<MLPPVector> output_layer_updation = m_output->division_element_wisen(v_output_hat->sqrtn()->scalar_addn(e))->scalar_multiplyn(learning_rate / _n);
|
|
|
|
update_parameters(hidden_layer_updations, output_layer_updation, learning_rate); // subject to change. may want bias to have this matrix too.
|
|
y_hat = model_set_test(current_input_batch);
|
|
|
|
if (ui) {
|
|
print_ui(epoch, cost_prev, y_hat, current_output_batch);
|
|
}
|
|
}
|
|
|
|
epoch++;
|
|
|
|
if (epoch > max_epoch) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
forward_pass();
|
|
}
|
|
|
|
real_t MLPPANN::score() {
|
|
MLPPUtilities util;
|
|
|
|
forward_pass();
|
|
|
|
return util.performance_vec(_y_hat, _output_set);
|
|
}
|
|
|
|
void MLPPANN::save(const String &file_name) {
|
|
MLPPUtilities util;
|
|
|
|
/*
|
|
if (!_network.empty()) {
|
|
util.saveParameters(file_name, _network[0].weights, _network[0].bias, false, 1);
|
|
for (uint32_t i = 1; i < _network.size(); i++) {
|
|
util.saveParameters(file_name, _network[i].weights, _network[i].bias, true, i + 1);
|
|
}
|
|
util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, true, _network.size() + 1);
|
|
} else {
|
|
util.saveParameters(file_name, _output_layer->weights, _output_layer->bias, false, _network.size() + 1);
|
|
}
|
|
*/
|
|
}
|
|
|
|
void MLPPANN::set_learning_rate_scheduler(SchedulerType type, real_t decay_constant) {
|
|
_lr_scheduler = type;
|
|
_decay_constant = decay_constant;
|
|
}
|
|
|
|
void MLPPANN::set_learning_rate_scheduler_drop(SchedulerType type, real_t decay_constant, real_t drop_rate) {
|
|
_lr_scheduler = type;
|
|
_decay_constant = decay_constant;
|
|
_drop_rate = drop_rate;
|
|
}
|
|
|
|
void MLPPANN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
|
|
if (_network.empty()) {
|
|
_network.push_back(Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, _input_set, weight_init, reg, lambda, alpha))));
|
|
_network.write[0]->forward_pass();
|
|
} else {
|
|
_network.push_back(Ref<MLPPHiddenLayer>(memnew(MLPPHiddenLayer(n_hidden, activation, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha))));
|
|
_network.write[_network.size() - 1]->forward_pass();
|
|
}
|
|
}
|
|
|
|
void MLPPANN::add_output_layer(MLPPActivation::ActivationFunction activation, MLPPCost::CostTypes loss, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
|
|
if (!_network.empty()) {
|
|
_output_layer = Ref<MLPPOutputLayer>(memnew(MLPPOutputLayer(_network.write[_network.size() - 1]->get_n_hidden(), activation, loss, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha)));
|
|
} else {
|
|
_output_layer = Ref<MLPPOutputLayer>(memnew(MLPPOutputLayer(_k, activation, loss, _input_set, weight_init, reg, lambda, alpha)));
|
|
}
|
|
}
|
|
|
|
MLPPANN::MLPPANN(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set) {
|
|
_input_set = p_input_set;
|
|
_output_set = p_output_set;
|
|
|
|
_n = _input_set->size().y;
|
|
_k = _input_set->size().x;
|
|
_lr_scheduler = SCHEDULER_TYPE_NONE;
|
|
_decay_constant = 0;
|
|
_drop_rate = 0;
|
|
}
|
|
|
|
MLPPANN::MLPPANN() {
|
|
}
|
|
|
|
MLPPANN::~MLPPANN() {
|
|
}
|
|
|
|
// https://en.wikipedia.org/wiki/Learning_rate
|
|
// Learning Rate Decay (C2W2L09) - Andrew Ng - Deep Learning Specialization
|
|
real_t MLPPANN::apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate) {
|
|
if (_lr_scheduler == SCHEDULER_TYPE_TIME) {
|
|
return learning_rate / (1 + decay_constant * epoch);
|
|
} else if (_lr_scheduler == SCHEDULER_TYPE_EPOCH) {
|
|
return learning_rate * (decay_constant / std::sqrt(epoch));
|
|
} else if (_lr_scheduler == SCHEDULER_TYPE_STEP) {
|
|
return learning_rate * Math::pow(decay_constant, int((1 + epoch) / drop_rate)); // Utilizing an explicit int conversion implicitly takes the floor.
|
|
} else if (_lr_scheduler == SCHEDULER_TYPE_EXPONENTIAL) {
|
|
return learning_rate * Math::exp(-decay_constant * epoch);
|
|
}
|
|
|
|
return learning_rate;
|
|
}
|
|
|
|
real_t MLPPANN::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPReg regularization;
|
|
MLPPCost mlpp_cost;
|
|
|
|
real_t total_reg_term = 0;
|
|
|
|
if (!_network.empty()) {
|
|
for (int i = 0; i < _network.size() - 1; i++) {
|
|
Ref<MLPPHiddenLayer> layer = _network[i];
|
|
|
|
total_reg_term += regularization.reg_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg());
|
|
}
|
|
}
|
|
|
|
return mlpp_cost.run_cost_norm_vector(_output_layer->get_cost(), y_hat, y) + total_reg_term + regularization.reg_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg());
|
|
}
|
|
|
|
void MLPPANN::forward_pass() {
|
|
if (!_network.empty()) {
|
|
Ref<MLPPHiddenLayer> layer = _network[0];
|
|
|
|
layer->set_input(_input_set);
|
|
layer->forward_pass();
|
|
|
|
for (int i = 1; i < _network.size(); i++) {
|
|
layer = _network[i];
|
|
Ref<MLPPHiddenLayer> prev_layer = _network[i - 1];
|
|
|
|
layer->set_input(prev_layer->get_a());
|
|
layer->forward_pass();
|
|
}
|
|
|
|
_output_layer->set_input(_network.write[_network.size() - 1]->get_a());
|
|
} else {
|
|
_output_layer->set_input(_input_set);
|
|
}
|
|
|
|
_output_layer->forward_pass();
|
|
|
|
_y_hat = _output_layer->get_a();
|
|
}
|
|
|
|
void MLPPANN::update_parameters(const Ref<MLPPTensor3> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
|
|
_output_layer->set_weights(_output_layer->get_weights()->subn(output_layer_updation));
|
|
_output_layer->set_bias(_output_layer->get_bias() - learning_rate * _output_layer->get_delta()->sum_elements() / _n);
|
|
|
|
Ref<MLPPMatrix> slice;
|
|
slice.instance();
|
|
|
|
if (!_network.empty()) {
|
|
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
|
|
|
|
hidden_layer_updations->z_slice_get_into_mlpp_matrix(0, slice);
|
|
|
|
layer->set_weights(layer->get_weights()->subn(slice));
|
|
layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / _n)));
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for (int i = _network.size() - 2; i >= 0; i--) {
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layer = _network[i];
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hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, slice);
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layer->set_weights(layer->get_weights()->subn(slice));
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layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / _n)));
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}
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}
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}
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MLPPANN::ComputeGradientsResult MLPPANN::compute_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &_output_set) {
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// std::cout << "BEGIN" << std::endl;
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPReg regularization;
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ComputeGradientsResult res;
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_output_layer->set_delta(mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, _output_set)->hadamard_productn(avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z())));
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res.output_w_grad = _output_layer->get_input()->transposen()->mult_vec(_output_layer->get_delta());
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res.output_w_grad->add(regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg()));
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if (!_network.empty()) {
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Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
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layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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// TODO Are these always uniform in size?
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res.cumulative_hidden_layer_w_grad->resize(Size3i(hidden_layer_w_grad->size().x, hidden_layer_w_grad->size().y, 0));
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// Adding to our cumulative hidden layer grads. Maintain reg terms as well.
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res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg())));
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for (int i = _network.size() - 2; i >= 0; i--) {
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layer = _network[i];
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Ref<MLPPHiddenLayer> next_layer = _network[i + 1];
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layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
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}
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}
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return res;
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}
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void MLPPANN::print_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &p_output_set) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, p_output_set));
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PLOG_MSG("Layer " + itos(_network.size() + 1) + ": ");
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MLPPUtilities::print_ui_vb(_output_layer->get_weights(), _output_layer->get_bias());
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if (!_network.empty()) {
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for (int i = _network.size() - 1; i >= 0; i--) {
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Ref<MLPPHiddenLayer> layer = _network[i];
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PLOG_MSG("Layer " + itos(i + 1) + ": ");
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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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void MLPPANN::_bind_methods() {
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}
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