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WGAN cleanups.
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@ -10,7 +10,6 @@
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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@ -37,14 +36,11 @@ void MLPPWGAN::set_k(const int val) {
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}
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Ref<MLPPMatrix> MLPPWGAN::generate_example(int n) {
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MLPPLinAlg alg;
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return model_set_test_generator(alg.gaussian_noise(n, _k));
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return model_set_test_generator(MLPPMatrix::create_gaussian_noise(n, _k));
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}
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void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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//MLPPCost mlpp_cost;
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MLPPLinAlg alg;
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real_t cost_prev = 0;
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int epoch = 1;
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@ -53,7 +49,7 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter.
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while (true) {
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cost_prev = cost(_y_hat, alg.onevecnv(_n));
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cost_prev = cost(_y_hat, MLPPVector::create_vec_one(_n));
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Ref<MLPPMatrix> generator_input_set;
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Ref<MLPPMatrix> discriminator_input_set;
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@ -64,38 +60,38 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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// Training of the discriminator.
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for (int i = 0; i < CRITIC_INTERATIONS; i++) {
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generator_input_set = alg.gaussian_noise(_n, _k);
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generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k);
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discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_input_set));
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discriminator_input_set->rows_add_mlpp_matrix(_output_set); // Fake + real inputs.
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ly_hat = model_set_test_discriminator(discriminator_input_set);
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loutput_set = alg.scalar_multiplynv(-1, alg.onevecnv(_n)); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
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Ref<MLPPVector> output_set_real = alg.onevecnv(_n);
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loutput_set = MLPPVector::create_vec_one(_n)->scalar_multiplyn(-1); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
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Ref<MLPPVector> output_set_real = MLPPVector::create_vec_one(_n);
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loutput_set->append_mlpp_vector(output_set_real); // Fake + real output scores.
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DiscriminatorGradientResult discriminator_gradient_results = compute_discriminator_gradients(ly_hat, loutput_set);
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Vector<Ref<MLPPMatrix>> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad;
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Ref<MLPPTensor3> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad;
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Ref<MLPPVector> output_discriminator_w_grad = discriminator_gradient_results.output_w_grad;
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cumulative_discriminator_hidden_layer_w_grad = alg.scalar_multiplynvt(learning_rate / _n, cumulative_discriminator_hidden_layer_w_grad);
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output_discriminator_w_grad = alg.scalar_multiplynv(learning_rate / _n, output_discriminator_w_grad);
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cumulative_discriminator_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
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output_discriminator_w_grad->scalar_multiply(learning_rate / _n);
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update_discriminator_parameters(cumulative_discriminator_hidden_layer_w_grad, output_discriminator_w_grad, learning_rate);
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}
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// Training of the generator.
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generator_input_set = alg.gaussian_noise(_n, _k);
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generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k);
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discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_input_set));
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ly_hat = model_set_test_discriminator(discriminator_input_set);
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loutput_set = alg.onevecnv(_n);
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loutput_set = MLPPVector::create_vec_one(_n);
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Vector<Ref<MLPPMatrix>> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(_y_hat, loutput_set);
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cumulative_generator_hidden_layer_w_grad = alg.scalar_multiplynvt(learning_rate / _n, cumulative_generator_hidden_layer_w_grad);
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Ref<MLPPTensor3> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(_y_hat, loutput_set);
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cumulative_generator_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
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update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate);
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forward_pass();
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if (ui) {
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handle_ui(epoch, cost_prev, _y_hat, alg.onevecnv(_n));
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handle_ui(epoch, cost_prev, _y_hat, MLPPVector::create_vec_one(_n));
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}
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epoch++;
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@ -106,10 +102,9 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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}
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real_t MLPPWGAN::score() {
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MLPPLinAlg alg;
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MLPPUtilities util;
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forward_pass();
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return util.performance_vec(_y_hat, alg.onevecnv(_n));
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return util.performance_vec(_y_hat, MLPPVector::create_vec_one(_n));
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}
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void MLPPWGAN::save(const String &file_name) {
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@ -128,9 +123,7 @@ void MLPPWGAN::save(const String &file_name) {
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*/
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}
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void MLPPWGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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void MLPPWGAN::create_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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Ref<MLPPHiddenLayer> layer;
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layer.instance();
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@ -142,7 +135,7 @@ void MLPPWGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activa
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layer->set_alpha(alpha);
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if (_network.empty()) {
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layer->set_input(alg.gaussian_noise(_n, _k));
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layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
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} else {
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layer->set_input(_network.write[_network.size() - 1]->get_a());
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}
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@ -150,6 +143,33 @@ void MLPPWGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activa
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_network.push_back(layer);
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layer->forward_pass();
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}
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void MLPPWGAN::add_layer(Ref<MLPPHiddenLayer> layer) {
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if (!layer.is_valid()) {
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return;
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}
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if (_network.empty()) {
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layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
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} else {
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layer->set_input(_network.write[_network.size() - 1]->get_a());
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}
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_network.push_back(layer);
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layer->forward_pass();
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}
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Ref<MLPPHiddenLayer> MLPPWGAN::get_layer(const int index) {
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ERR_FAIL_INDEX_V(index, _network.size(), Ref<MLPPHiddenLayer>());
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return _network[index];
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}
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void MLPPWGAN::remove_layer(const int index) {
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ERR_FAIL_INDEX(index, _network.size());
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_network.remove(index);
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}
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int MLPPWGAN::get_layer_count() const {
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return _network.size();
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}
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void MLPPWGAN::add_output_layer(MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) {
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ERR_FAIL_COND(_network.empty());
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@ -236,12 +256,10 @@ real_t MLPPWGAN::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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}
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void MLPPWGAN::forward_pass() {
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MLPPLinAlg alg;
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if (!_network.empty()) {
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Ref<MLPPHiddenLayer> layer = _network[0];
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layer->set_input(alg.gaussian_noise(_n, _k));
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layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
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layer->forward_pass();
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for (int i = 1; i < _network.size(); i++) {
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@ -253,7 +271,7 @@ void MLPPWGAN::forward_pass() {
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_output_layer->set_input(_network.write[_network.size() - 1]->get_a());
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} else { // Should never happen, though.
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_output_layer->set_input(alg.gaussian_noise(_n, _k));
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_output_layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
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}
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_output_layer->forward_pass();
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@ -261,38 +279,46 @@ void MLPPWGAN::forward_pass() {
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_y_hat->set_from_mlpp_vector(_output_layer->get_a());
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}
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void MLPPWGAN::update_discriminator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
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MLPPLinAlg alg;
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_output_layer->set_weights(alg.subtractionnv(_output_layer->get_weights(), output_layer_updation));
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_output_layer->set_bias(_output_layer->get_bias() - learning_rate * alg.sum_elementsv(_output_layer->get_delta()) / _n);
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void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
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_output_layer->set_weights(_output_layer->get_weights()->subn(output_layer_updation));
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_output_layer->set_bias(_output_layer->get_bias() - learning_rate * _output_layer->get_delta()->sum_elements() / _n);
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if (!_network.empty()) {
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Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
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layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[0]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta())));
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Ref<MLPPMatrix> slice;
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slice.instance();
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hidden_layer_updations->z_slice_get_into_mlpp_matrix(0, 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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for (int i = _network.size() - 2; i > _network.size() / 2; i--) {
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layer = _network[i];
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layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta())));
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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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void MLPPWGAN::update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate) {
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MLPPLinAlg alg;
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void MLPPWGAN::update_generator_parameters(Ref<MLPPTensor3> hidden_layer_updations, real_t learning_rate) {
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if (!_network.empty()) {
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Ref<MLPPMatrix> slice;
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slice.instance();
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for (int i = _network.size() / 2; i >= 0; i--) {
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Ref<MLPPHiddenLayer> 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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//std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl;
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//std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl;
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layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1]));
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layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta())));
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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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@ -300,24 +326,23 @@ void MLPPWGAN::update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_
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MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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DiscriminatorGradientResult data;
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_output_layer->set_delta(alg.hadamard_productnv(mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, output_set), avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z())));
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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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data.output_w_grad = alg.mat_vec_multnv(alg.transposenm(_output_layer->get_input()), _output_layer->get_delta());
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data.output_w_grad = alg.additionnv(data.output_w_grad, regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg()));
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data.output_w_grad = _output_layer->get_input()->transposen()->mult_vec(_output_layer->get_delta());
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data.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(alg.hadamard_productnm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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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 = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, 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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data.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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//std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl;
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//std::cout << "WEIGHTS SECOND:" << layer.weights.size() << "x" << layer.weights[0].size() << std::endl;
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@ -326,41 +351,39 @@ MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(
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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(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
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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 = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
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data.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, 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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hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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data.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 data;
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}
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Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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Ref<MLPPTensor3> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
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class MLPPCost cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
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Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
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Ref<MLPPVector> cost_deriv_vector = cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, output_set);
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Ref<MLPPVector> activation_deriv_vector = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z());
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_output_layer->set_delta(alg.hadamard_productnv(cost_deriv_vector, activation_deriv_vector));
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_output_layer->set_delta(cost_deriv_vector->hadamard_productn(activation_deriv_vector));
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Ref<MLPPVector> output_w_grad = alg.mat_vec_multnv(alg.transposenm(_output_layer->get_input()), _output_layer->get_delta());
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output_w_grad = alg.additionnv(output_w_grad, regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg()));
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Ref<MLPPVector> output_w_grad = _output_layer->get_input()->transposen()->mult_vec(_output_layer->get_delta());
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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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Ref<MLPPMatrix> activation_deriv_matrix = avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z());
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layer->set_delta(alg.hadamard_productnm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), activation_deriv_matrix));
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layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(activation_deriv_matrix));
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|
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Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
|
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cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, 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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Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
|
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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.
|
||||
|
||||
for (int i = _network.size() - 2; i >= 0; i--) {
|
||||
layer = _network[i];
|
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@ -368,9 +391,10 @@ Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVect
|
||||
|
||||
activation_deriv_matrix = avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z());
|
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|
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layer->set_delta(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), activation_deriv_matrix));
|
||||
hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta());
|
||||
cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, 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.
|
||||
layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(activation_deriv_matrix));
|
||||
hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
|
||||
|
||||
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.
|
||||
}
|
||||
}
|
||||
|
||||
@ -409,6 +433,11 @@ void MLPPWGAN::_bind_methods() {
|
||||
ClassDB::bind_method(D_METHOD("score"), &MLPPWGAN::score);
|
||||
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPWGAN::save);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("add_layer", "activation", "weight_init", "reg", "lambda", "alpha"), &MLPPWGAN::add_layer, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::REGULARIZATION_TYPE_NONE, 0.5, 0.5);
|
||||
ClassDB::bind_method(D_METHOD("create_layer", "activation", "weight_init", "reg", "lambda", "alpha"), &MLPPWGAN::create_layer, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::REGULARIZATION_TYPE_NONE, 0.5, 0.5);
|
||||
ClassDB::bind_method(D_METHOD("add_layer", "layer"), &MLPPWGAN::add_layer);
|
||||
ClassDB::bind_method(D_METHOD("get_layer", "index"), &MLPPWGAN::get_layer);
|
||||
ClassDB::bind_method(D_METHOD("remove_layer", "index"), &MLPPWGAN::remove_layer);
|
||||
ClassDB::bind_method(D_METHOD("get_layer_count"), &MLPPWGAN::score);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("add_output_layer", "weight_init", "reg", "lambda", "alpha"), &MLPPWGAN::add_output_layer, MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::REGULARIZATION_TYPE_NONE, 0.5, 0.5);
|
||||
}
|
||||
|
||||
@ -15,6 +15,7 @@
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_tensor3.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include "../hidden_layer/hidden_layer.h"
|
||||
@ -40,7 +41,12 @@ public:
|
||||
real_t score();
|
||||
void save(const String &file_name);
|
||||
|
||||
void add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void create_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
void add_layer(Ref<MLPPHiddenLayer> layer);
|
||||
Ref<MLPPHiddenLayer> get_layer(const int index);
|
||||
void remove_layer(const int index);
|
||||
int get_layer_count() const;
|
||||
|
||||
void add_output_layer(MLPPUtilities::WeightDistributionType weight_init = MLPPUtilities::WEIGHT_DISTRIBUTION_TYPE_DEFAULT, MLPPReg::RegularizationType reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
|
||||
MLPPWGAN(real_t k, const Ref<MLPPMatrix> &output_set);
|
||||
@ -55,16 +61,21 @@ protected:
|
||||
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
||||
|
||||
void forward_pass();
|
||||
void update_discriminator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
||||
void update_generator_parameters(Vector<Ref<MLPPMatrix>> hidden_layer_updations, real_t learning_rate);
|
||||
void update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate);
|
||||
void update_generator_parameters(Ref<MLPPTensor3> hidden_layer_updations, real_t learning_rate);
|
||||
|
||||
struct DiscriminatorGradientResult {
|
||||
Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
|
||||
Ref<MLPPVector> output_w_grad;
|
||||
|
||||
DiscriminatorGradientResult() {
|
||||
cumulative_hidden_layer_w_grad.instance();
|
||||
output_w_grad.instance();
|
||||
}
|
||||
};
|
||||
|
||||
DiscriminatorGradientResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
Vector<Ref<MLPPMatrix>> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
Ref<MLPPTensor3> compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
|
||||
void handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);
|
||||
|
||||
|
||||
@ -499,9 +499,9 @@ void MLPPTests::test_wgan(bool ui) {
|
||||
output_set = output_set->transposen();
|
||||
|
||||
MLPPWGAN gan(2, output_set); // our gan is a wasserstein gan (wgan)
|
||||
gan.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
|
||||
gan.add_layer(2, MLPPActivation::ACTIVATION_FUNCTION_RELU);
|
||||
gan.add_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
|
||||
gan.create_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
|
||||
gan.create_layer(2, MLPPActivation::ACTIVATION_FUNCTION_RELU);
|
||||
gan.create_layer(5, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID);
|
||||
gan.add_output_layer(); // User can specify weight init- if necessary.
|
||||
gan.gradient_descent(0.1, 55000, ui);
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user