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Now MLPPExpReg uses engine classes.
This commit is contained in:
parent
737b34f53d
commit
17486baae9
@ -14,11 +14,11 @@
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#include <iostream>
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#include <random>
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std::vector<real_t> MLPPExpReg::model_set_test(std::vector<std::vector<real_t>> X) {
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Ref<MLPPVector> MLPPExpReg::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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real_t MLPPExpReg::model_test(std::vector<real_t> x) {
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real_t MLPPExpReg::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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@ -34,35 +34,35 @@ void MLPPExpReg::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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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (int j = 0; j < _n; j++) {
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sum += error[j] * _input_set[j][i] * std::pow(_weights[i], _input_set[j][i] - 1);
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sum += error->get_element(j) * _input_set->get_element(j, i) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i) - 1);
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}
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real_t w_gradient = sum / _n;
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (int j = 0; j < _n; j++) {
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sum2 += error[j] * std::pow(_weights[i], _input_set[j][i]);
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sum2 += error->get_element(j) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i));
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}
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real_t i_gradient = sum2 / _n;
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// Weight/initial updation
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_weights[i] -= learning_rate * w_gradient;
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_initial[i] -= learning_rate * i_gradient;
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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real_t sum = 0;
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for (int j = 0; j < _n; j++) {
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sum += (_y_hat[j] - _output_set[j]);
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sum += (_y_hat->get_element(j) - _output_set->get_element(j));
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}
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real_t b_gradient = sum / _n;
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@ -72,8 +72,8 @@ void MLPPExpReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
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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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MLPPUtilities::UI(_weights, _bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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@ -94,35 +94,53 @@ void MLPPExpReg::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> 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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int output_index = distribution(generator);
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real_t y_hat = evaluatev(_input_set[output_index]);
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cost_prev = cost({ y_hat }, { _output_set[output_index] });
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_set_element = _output_set->get_element(output_index);
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output_set_row_tmp->set_element(0, output_set_element);
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real_t y_hat = evaluatev(input_set_row_tmp);
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y_hat_row_tmp->set_element(0, y_hat);
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cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradients
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real_t w_gradient = (y_hat - _output_set[output_index]) * _input_set[output_index][i] * std::pow(_weights[i], _input_set[output_index][i] - 1);
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real_t i_gradient = (y_hat - _output_set[output_index]) * std::pow(_weights[i], _input_set[output_index][i]);
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real_t w_gradient = (y_hat - output_set_element) * input_set_row_tmp->get_element(i) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i) - 1);
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real_t i_gradient = (y_hat - output_set_element) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i));
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// Weight/initial updation
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_weights[i] -= learning_rate * w_gradient;
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_initial[i] -= learning_rate * i_gradient;
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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real_t b_gradient = (y_hat - _output_set[output_index]);
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real_t b_gradient = (y_hat - output_set_element);
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// Bias updation
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_bias -= learning_rate * b_gradient;
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y_hat = evaluatev(_input_set[output_index]);
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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[output_index] }));
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MLPPUtilities::UI(_weights, _bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_row_tmp, output_set_row_tmp));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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@ -144,51 +162,52 @@ void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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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 input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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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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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[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 = evaluatem(current_input_batch);
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cost_prev = cost(y_hat, current_output_batch);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_batch);
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for (int j = 0; j < _k; j++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
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sum += error[k] * input_mini_batches[i][k][j] * std::pow(_weights[j], input_mini_batches[i][k][j] - 1);
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for (int k = 0; k < current_output_batch->size(); k++) {
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sum += error->get_element(k) * current_input_batch->get_element(k, j) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j) - 1);
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}
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real_t w_gradient = sum / output_mini_batches[i].size();
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real_t w_gradient = sum / current_output_batch->size();
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
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sum2 += error[k] * std::pow(_weights[j], input_mini_batches[i][k][j]);
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for (int k = 0; k < current_output_batch->size(); k++) {
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sum2 += error->get_element(k) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j));
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}
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real_t i_gradient = sum2 / output_mini_batches[i].size();
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real_t i_gradient = sum2 / current_output_batch->size();
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// Weight/initial updation
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_weights[j] -= learning_rate * w_gradient;
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_initial[j] -= learning_rate * i_gradient;
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_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
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_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
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}
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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real_t sum = 0;
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for (uint32_t j = 0; j < output_mini_batches[i].size(); j++) {
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sum += (y_hat[j] - output_mini_batches[i][j]);
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for (int j = 0; j < current_output_batch->size(); j++) {
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sum += (y_hat->get_element(j) - current_output_batch->get_element(j));
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}
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//real_t b_gradient = sum / output_mini_batches[i].size();
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y_hat = evaluatem(input_mini_batches[i]);
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y_hat = evaluatem(current_input_batch);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
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MLPPUtilities::UI(_weights, _bias);
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MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_batch));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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@ -205,28 +224,40 @@ void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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real_t MLPPExpReg::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_vec(_y_hat, _output_set);
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}
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void MLPPExpReg::save(std::string file_name) {
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void MLPPExpReg::save(const String &file_name) {
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MLPPUtilities util;
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util.saveParameters(file_name, _weights, _initial, _bias);
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//util.saveParameters(file_name, _weights, _initial, _bias);
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}
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MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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MLPPExpReg::MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, 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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_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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_weights = MLPPUtilities::weightInitialization(_k);
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_initial = MLPPUtilities::weightInitialization(_k);
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_bias = MLPPUtilities::biasInitialization();
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_y_hat.instance();
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_y_hat->resize(_n);
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MLPPUtilities util;
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_weights.instance();
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_weights->resize(_k);
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util.weight_initializationv(_weights);
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_initial.instance();
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_initial->resize(_k);
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util.weight_initializationv(_initial);
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_bias = util.bias_initializationr();
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}
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MLPPExpReg::MLPPExpReg() {
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@ -234,33 +265,38 @@ MLPPExpReg::MLPPExpReg() {
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MLPPExpReg::~MLPPExpReg() {
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}
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real_t MLPPExpReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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real_t MLPPExpReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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MLPPReg regularization;
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MLPPCost mlpp_cost;
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return mlpp_cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
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return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
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}
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real_t MLPPExpReg::evaluatev(std::vector<real_t> x) {
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real_t MLPPExpReg::evaluatev(const Ref<MLPPVector> &x) {
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real_t y_hat = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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y_hat += _initial[i] * std::pow(_weights[i], x[i]);
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for (int i = 0; i < x->size(); i++) {
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y_hat += _initial->get_element(i) * Math::pow(_weights->get_element(i), x->get_element(i));
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}
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return y_hat + _bias;
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}
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std::vector<real_t> MLPPExpReg::evaluatem(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> y_hat;
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y_hat.resize(X.size());
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Ref<MLPPVector> MLPPExpReg::evaluatem(const Ref<MLPPMatrix> &X) {
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Ref<MLPPVector> y_hat;
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y_hat.instance();
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y_hat->resize(X->size().y);
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for (uint32_t i = 0; i < X.size(); i++) {
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y_hat[i] = 0;
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for (uint32_t j = 0; j < X[i].size(); j++) {
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y_hat[i] += _initial[j] * std::pow(_weights[j], X[i][j]);
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for (int i = 0; i < X->size().y; i++) {
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real_t y;
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for (int j = 0; j < X->size().x; j++) {
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y += _initial->get_element(j) * Math::pow(_weights->get_element(j), X->get_element(i, j));
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}
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y_hat[i] += _bias;
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y += _bias;
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y_hat->set_element(i, y);
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}
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return y_hat;
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@ -12,15 +12,17 @@
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#include "core/object/reference.h"
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#include <string>
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#include <vector>
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#include "../regularization/reg.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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class MLPPExpReg : public Reference {
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GDCLASS(MLPPExpReg, Reference);
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public:
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std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
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real_t model_test(std::vector<real_t> x);
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t 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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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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@ -28,35 +30,35 @@ public:
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real_t score();
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void save(std::string file_name);
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void save(const String &file_name);
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MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPExpReg();
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~MLPPExpReg();
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protected:
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real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t evaluatev(std::vector<real_t> x);
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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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real_t evaluatev(const Ref<MLPPVector> &x);
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Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
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static void _bind_methods();
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std::vector<std::vector<real_t>> _input_set;
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std::vector<real_t> _output_set;
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std::vector<real_t> _y_hat;
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std::vector<real_t> _weights;
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std::vector<real_t> _initial;
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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Ref<MLPPVector> _y_hat;
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Ref<MLPPVector> _weights;
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Ref<MLPPVector> _initial;
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real_t _bias;
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int _n;
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int _k;
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// Regularization Params
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std::string _reg;
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MLPPReg::RegularizationType _reg;
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real_t _lambda;
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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};
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@ -447,10 +447,18 @@ void MLPPTests::test_exp_reg_regression(bool ui) {
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alg.printVector(model_old.modelSetTest(alg.transpose(inputSet)));
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std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPExpReg model(alg.transpose(inputSet), outputSet);
|
||||
Ref<MLPPMatrix> input_set;
|
||||
input_set.instance();
|
||||
input_set->set_from_std_vectors(inputSet);
|
||||
|
||||
Ref<MLPPVector> output_set;
|
||||
output_set.instance();
|
||||
output_set->set_from_std_vector(outputSet);
|
||||
|
||||
MLPPExpReg model(alg.transposem(input_set), output_set);
|
||||
model.sgd(0.001, 10000, ui);
|
||||
alg.printVector(model.model_set_test(alg.transpose(inputSet)));
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
PLOG_MSG(model.model_set_test(alg.transposem(input_set))->to_string());
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
|
||||
}
|
||||
void MLPPTests::test_tanh_regression(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user