Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2024-11-08 13:12:09 +01:00
Code Issues Packages Projects Releases Wiki Activity

More cleanups to WGAN.

Browse Source
This commit is contained in:
Relintai 2023-05-01 10:44:40 +02:00
parent 04e8f6c02e
commit 1498a17ec6
2 changed files with 36 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

61
mlpp/wgan/wgan.cpp
View File

@ -20,12 +20,6 @@ Ref<MLPPMatrix> MLPPWGAN::get_output_set() {
}
void MLPPWGAN::set_output_set(const Ref<MLPPMatrix> &val) {
_output_set = val;
_n = 0;
if (_output_set.is_valid()) {
_n = _output_set->size().y;
}
}
int MLPPWGAN::get_k() const {
@ -43,13 +37,14 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
//MLPPCost mlpp_cost;
real_t cost_prev = 0;
int epoch = 1;
int n = _output_set->size().y;
forward_pass();
const int CRITIC_INTERATIONS = 5; // Wasserstein GAN specific parameter.
while (true) {
cost_prev = cost(_y_hat, MLPPVector::create_vec_one(_n));
cost_prev = cost(_y_hat, MLPPVector::create_vec_one(n));
Ref<MLPPMatrix> generator_input_set;
Ref<MLPPMatrix> discriminator_input_set;
@ -60,38 +55,38 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
// Training of the discriminator.
for (int i = 0; i < CRITIC_INTERATIONS; i++) {
generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k);
generator_input_set = MLPPMatrix::create_gaussian_noise(n, _k);
discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_input_set));
discriminator_input_set->rows_add_mlpp_matrix(_output_set); // Fake + real inputs.
ly_hat = model_set_test_discriminator(discriminator_input_set);
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
Ref<MLPPVector> output_set_real = MLPPVector::create_vec_one(_n);
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
Ref<MLPPVector> output_set_real = MLPPVector::create_vec_one(n);
loutput_set->append_mlpp_vector(output_set_real); // Fake + real output scores.
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;
Ref<MLPPVector> output_discriminator_w_grad = discriminator_gradient_results.output_w_grad;
cumulative_discriminator_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
output_discriminator_w_grad->scalar_multiply(learning_rate / _n);
cumulative_discriminator_hidden_layer_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);
}
// Training of the generator.
generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k);
generator_input_set = MLPPMatrix::create_gaussian_noise(n, _k);
discriminator_input_set->set_from_mlpp_matrix(model_set_test_generator(generator_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);
cumulative_generator_hidden_layer_w_grad->scalar_multiply(learning_rate / _n);
cumulative_generator_hidden_layer_w_grad->scalar_multiply(learning_rate / n);
update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate);
forward_pass();
if (ui) {
handle_ui(epoch, cost_prev, _y_hat, MLPPVector::create_vec_one(_n));
handle_ui(epoch, cost_prev, _y_hat, MLPPVector::create_vec_one(n));
}
epoch++;
@ -104,7 +99,9 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
real_t MLPPWGAN::score() {
MLPPUtilities util;
forward_pass();
return util.performance_vec(_y_hat, MLPPVector::create_vec_one(_n));
int n = _output_set->size().y;
return util.performance_vec(_y_hat, MLPPVector::create_vec_one(n));
}
void MLPPWGAN::save(const String &file_name) {
@ -134,8 +131,10 @@ void MLPPWGAN::create_layer(int n_hidden, MLPPActivation::ActivationFunction act
layer->set_lambda(lambda);
layer->set_alpha(alpha);
int n = _output_set->size().y;
if (_network.empty()) {
layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
layer->set_input(MLPPMatrix::create_gaussian_noise(n, _k));
} else {
layer->set_input(_network.write[_network.size() - 1]->get_a());
}
@ -149,7 +148,9 @@ void MLPPWGAN::add_layer(Ref<MLPPHiddenLayer> layer) {
}
if (_network.empty()) {
layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
int n = _output_set->size().y;
layer->set_input(MLPPMatrix::create_gaussian_noise(n, _k));
} else {
layer->set_input(_network.write[_network.size() - 1]->get_a());
}
@ -187,16 +188,14 @@ void MLPPWGAN::add_output_layer(MLPPUtilities::WeightDistributionType weight_ini
_output_layer->set_alpha(alpha);
}
MLPPWGAN::MLPPWGAN(real_t p_k, const Ref<MLPPMatrix> &p_output_set) {
MLPPWGAN::MLPPWGAN(int p_k, const Ref<MLPPMatrix> &p_output_set) {
_output_set = p_output_set;
_n = p_output_set->size().y;
_k = p_k;
_y_hat.instance();
}
MLPPWGAN::MLPPWGAN() {
_n = 0;
_k = 0;
_y_hat.instance();
@ -256,10 +255,12 @@ real_t MLPPWGAN::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
}
void MLPPWGAN::forward_pass() {
int n = _output_set->size().y;
if (!_network.empty()) {
Ref<MLPPHiddenLayer> layer = _network[0];
layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
layer->set_input(MLPPMatrix::create_gaussian_noise(n, _k));
layer->forward_pass();
for (int i = 1; i < _network.size(); i++) {
@ -271,7 +272,7 @@ void MLPPWGAN::forward_pass() {
_output_layer->set_input(_network.write[_network.size() - 1]->get_a());
} else { // Should never happen, though.
_output_layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k));
_output_layer->set_input(MLPPMatrix::create_gaussian_noise(n, _k));
}
_output_layer->forward_pass();
@ -280,8 +281,10 @@ void MLPPWGAN::forward_pass() {
}
void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_updations, const Ref<MLPPVector> &output_layer_updation, real_t learning_rate) {
int n = _output_set->size().y;
_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);
_output_layer->set_bias(_output_layer->get_bias() - learning_rate * _output_layer->get_delta()->sum_elements() / n);
if (!_network.empty()) {
Ref<MLPPHiddenLayer> layer = _network[_network.size() - 1];
@ -292,7 +295,7 @@ void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_upd
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)));
layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / n)));
for (int i = _network.size() - 2; i > _network.size() / 2; i--) {
layer = _network[i];
@ -300,13 +303,15 @@ void MLPPWGAN::update_discriminator_parameters(Ref<MLPPTensor3> hidden_layer_upd
hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, 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)));
}
}
}
void MLPPWGAN::update_generator_parameters(Ref<MLPPTensor3> hidden_layer_updations, real_t learning_rate) {
if (!_network.empty()) {
int n = _output_set->size().y;
Ref<MLPPMatrix> slice;
slice.instance();
@ -318,7 +323,7 @@ void MLPPWGAN::update_generator_parameters(Ref<MLPPTensor3> hidden_layer_updatio
//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;
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)));
}
}
}

7
mlpp/wgan/wgan.h
View File

@ -49,7 +49,7 @@ public:
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);
MLPPWGAN(int k, const Ref<MLPPMatrix> &output_set);
MLPPWGAN();
~MLPPWGAN();
@ -82,13 +82,12 @@ protected:
static void _bind_methods();
Ref<MLPPMatrix> _output_set;
Ref<MLPPVector> _y_hat;
int _k;
Vector<Ref<MLPPHiddenLayer>> _network;
Ref<MLPPOutputLayer> _output_layer;
int _n;
int _k;
Ref<MLPPVector> _y_hat;
};
#endif /* WGAN_hpp */