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Fixed Wgan.

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Relintai 2023-12-27 23:23:50 +01:00
parent aa010e891c
commit c227786c40
2 changed files with 31 additions and 24 deletions
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46
mlpp/wgan/wgan.cpp
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@ -65,10 +65,15 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
loutput_set->append_mlpp_vector(output_set_real); // Fake + real output scores. loutput_set->append_mlpp_vector(output_set_real); // Fake + real output scores.
DiscriminatorGradientResult discriminator_gradient_results = compute_discriminator_gradients(ly_hat, loutput_set); DiscriminatorGradientResult discriminator_gradient_results = compute_discriminator_gradients(ly_hat, loutput_set);
Ref<MLPPTensor3> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad; Vector<Ref<MLPPMatrix>> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad;
Ref<MLPPVector> output_discriminator_w_grad = discriminator_gradient_results.output_w_grad; Ref<MLPPVector> output_discriminator_w_grad = discriminator_gradient_results.output_w_grad;
cumulative_discriminator_hidden_layer_w_grad->scalar_multiply(learning_rate / n); real_t lrpn = learning_rate / n;
for (int j = 0; j < cumulative_discriminator_hidden_layer_w_grad.size(); ++j) {
cumulative_discriminator_hidden_layer_w_grad.write[j]->scalar_multiply(lrpn);
}
output_discriminator_w_grad->scalar_multiply(learning_rate / n); output_discriminator_w_grad->scalar_multiply(learning_rate / n);
update_discriminator_parameters(cumulative_discriminator_hidden_layer_w_grad, output_discriminator_w_grad, learning_rate); update_discriminator_parameters(cumulative_discriminator_hidden_layer_w_grad, output_discriminator_w_grad, learning_rate);
} }
@ -79,8 +84,14 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
ly_hat = model_set_test_discriminator(discriminator_input_set); ly_hat = model_set_test_discriminator(discriminator_input_set);
loutput_set = MLPPVector::create_vec_one(n); loutput_set = MLPPVector::create_vec_one(n);
Ref<MLPPTensor3> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(_y_hat, loutput_set); Vector<Ref<MLPPMatrix>> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(_y_hat, loutput_set);
cumulative_generator_hidden_layer_w_grad->scalar_multiply(learning_rate / n);
real_t lrpn = learning_rate / n;
for (int i = 0; i < cumulative_generator_hidden_layer_w_grad.size(); ++i) {
cumulative_generator_hidden_layer_w_grad.write[i]->scalar_multiply(lrpn);
}
update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate); update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate);
forward_pass(); forward_pass();
@ -280,7 +291,7 @@ void MLPPWGAN::forward_pass() {
_y_hat->set_from_mlpp_vector(_output_layer->get_a()); _y_hat->set_from_mlpp_vector(_output_layer->get_a());
} }
void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) { void MLPPWGAN::update_discriminator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
int n = _output_set->size().y; int n = _output_set->size().y;
_output_layer->set_weights(_output_layer->get_weights()->subn(output_layer_updation)); _output_layer->set_weights(_output_layer->get_weights()->subn(output_layer_updation));
@ -289,10 +300,7 @@ void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_upd
if (!_network.empty()) { if (!_network.empty()) {
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1]; Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
Ref<MLPPMatrix> slice; Ref<MLPPMatrix> slice = hidden_layer_updations[0];
slice.instance();
hidden_layer_updations->z_slice_get_into_mlpp_matrix(0, slice);
layer->set_weights(layer->get_weights()->subn(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))); layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / n)));
@ -300,7 +308,7 @@ void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_upd
for (int i = _network.size() - 2; i > _network.size() / 2; i--) { for (int i = _network.size() - 2; i > _network.size() / 2; i--) {
layer = _network[i]; layer = _network[i];
hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, slice); slice = hidden_layer_updations[(_network.size() - 2) - i + 1];
layer->set_weights(layer->get_weights()->subn(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))); layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / n)));
@ -308,17 +316,16 @@ void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_upd
} }
} }
void MLPPWGAN::update_generator_parameters(Ref<MLPPTensor3> hidden_layer_updations, real_t learning_rate) { void MLPPWGAN::update_generator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, real_t learning_rate) {
if (!_network.empty()) { if (!_network.empty()) {
int n = _output_set->size().y; int n = _output_set->size().y;
Ref<MLPPMatrix> slice; Ref<MLPPMatrix> slice;
slice.instance();
for (int i = _network.size() / 2; i >= 0; i--) { for (int i = _network.size() / 2; i >= 0; i--) {
Ref<MLPPHiddenLayer> layer = _network[i]; Ref<MLPPHiddenLayer> layer = _network[i];
hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, slice); slice = hidden_layer_updations[(_network.size() - 2) - i + 1];
//std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl; //std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl;
//std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl; //std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl;
@ -347,7 +354,7 @@ MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(
Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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. data.cumulative_hidden_layer_w_grad.push_back(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.
//std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl; //std::cout << "HIDDENLAYER FIRST:" << hiddenLayerWGrad.size() << "x" << hiddenLayerWGrad[0].size() << std::endl;
//std::cout << "WEIGHTS SECOND:" << layer.weights.size() << "x" << layer.weights[0].size() << std::endl; //std::cout << "WEIGHTS SECOND:" << layer.weights.size() << "x" << layer.weights[0].size() << std::endl;
@ -359,19 +366,19 @@ MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(
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()))); 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())));
hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta());
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. data.cumulative_hidden_layer_w_grad.push_back(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.
} }
} }
return data; return data;
} }
Ref<MLPPTensor3> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) { Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
class MLPPCost cost; class MLPPCost cost;
MLPPActivation avn; MLPPActivation avn;
MLPPReg regularization; MLPPReg regularization;
Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
Ref<MLPPVector> cost_deriv_vector = cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, output_set); Ref<MLPPVector> cost_deriv_vector = cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, output_set);
Ref<MLPPVector> activation_deriv_vector = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z()); Ref<MLPPVector> activation_deriv_vector = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z());
@ -388,7 +395,8 @@ Ref<MLPPTensor3> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_
layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(activation_deriv_matrix)); layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(activation_deriv_matrix));
Ref<MLPPMatrix> hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); Ref<MLPPMatrix> 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.
cumulative_hidden_layer_w_grad.push_back(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--) { for (int i = _network.size() - 2; i >= 0; i--) {
layer = _network[i]; layer = _network[i];
@ -399,7 +407,7 @@ Ref<MLPPTensor3> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVector> &y_
layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(activation_deriv_matrix)); 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()); 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. cumulative_hidden_layer_w_grad.push_back(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.
} }
} }

9
mlpp/wgan/wgan.h
View File

@ -61,21 +61,20 @@ protected:
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y); real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
void forward_pass(); void forward_pass();
void update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate); void update_discriminator_parameters(const Vector<Ref<MLPPMatrix>> &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); void update_generator_parameters(const Vector<Ref<MLPPMatrix>> &hidden_layer_updations, real_t learning_rate);
struct DiscriminatorGradientResult { struct DiscriminatorGradientResult {
Ref<MLPPTensor3> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. Vector<Ref<MLPPMatrix>> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads.
Ref<MLPPVector> output_w_grad; Ref<MLPPVector> output_w_grad;
DiscriminatorGradientResult() { DiscriminatorGradientResult() {
cumulative_hidden_layer_w_grad.instance();
output_w_grad.instance(); output_w_grad.instance();
} }
}; };
DiscriminatorGradientResult compute_discriminator_gradients(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set); DiscriminatorGradientResult compute_discriminator_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); Vector<Ref<MLPPMatrix>> 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); void handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set);