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MLPPSoftmaxNet rework.
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cd1f5a2805
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@ -16,62 +16,115 @@
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#include <random>
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/*
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_input_set() {
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_input_set() const {
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return _input_set;
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}
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void MLPPSoftmaxNet::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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_initialized = false;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_output_set() {
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_output_set() const {
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return _output_set;
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}
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void MLPPSoftmaxNet::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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_initialized = false;
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}
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MLPPReg::RegularizationType MLPPSoftmaxNet::get_reg() {
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int MLPPSoftmaxNet::get_n_hidden() const {
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return _n_hidden;
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}
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void MLPPSoftmaxNet::set_n_hidden(const int val) {
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_n_hidden = val;
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}
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MLPPReg::RegularizationType MLPPSoftmaxNet::get_reg() const {
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return _reg;
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}
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void MLPPSoftmaxNet::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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_initialized = false;
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}
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real_t MLPPSoftmaxNet::get_lambda() {
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real_t MLPPSoftmaxNet::get_lambda() const {
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return _lambda;
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}
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void MLPPSoftmaxNet::set_lambda(const real_t val) {
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_lambda = val;
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_initialized = false;
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}
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real_t MLPPSoftmaxNet::get_alpha() {
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real_t MLPPSoftmaxNet::get_alpha() const {
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return _alpha;
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}
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void MLPPSoftmaxNet::set_alpha(const real_t val) {
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_alpha = val;
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_initialized = false;
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}
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*/
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPSoftmaxNet::data_y_hat_set(const Ref<MLPPMatrix> &val) {
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_y_hat = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_weights1_get() const {
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return _weights1;
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}
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void MLPPSoftmaxNet::data_weights1_set(const Ref<MLPPMatrix> &val) {
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_weights1 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_weights2_get() const {
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return _weights2;
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}
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void MLPPSoftmaxNet::data_weights2_set(const Ref<MLPPMatrix> &val) {
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_weights2 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::data_bias1_get() const {
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return _bias1;
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}
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void MLPPSoftmaxNet::data_bias1_set(const Ref<MLPPVector> &val) {
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_bias1 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::data_bias2_get() const {
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return _bias2;
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}
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void MLPPSoftmaxNet::data_bias2_set(const Ref<MLPPVector> &val) {
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_bias2 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_z2_get() const {
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return _z2;
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}
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void MLPPSoftmaxNet::data_z2_set(const Ref<MLPPMatrix> &val) {
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_z2 = val;
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::data_a2_get() const {
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return _a2;
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}
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void MLPPSoftmaxNet::data_a2_set(const Ref<MLPPMatrix> &val) {
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_a2 = val;
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}
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Ref<MLPPVector> MLPPSoftmaxNet::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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return evaluatev(x);
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), Ref<MLPPVector>());
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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return evaluatem(X);
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}
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void MLPPSoftmaxNet::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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void MLPPSoftmaxNet::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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@ -125,16 +178,21 @@ void MLPPSoftmaxNet::gradient_descent(real_t learning_rate, int max_epoch, bool
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}
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}
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void MLPPSoftmaxNet::sgd(real_t learning_rate, int max_epoch, bool ui) {
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void MLPPSoftmaxNet::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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int n = _input_set->size().y;
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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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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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@ -209,14 +267,19 @@ void MLPPSoftmaxNet::sgd(real_t learning_rate, int max_epoch, bool ui) {
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forward_pass();
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}
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void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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void MLPPSoftmaxNet::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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int n = _input_set->size().y;
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// Creating the mini-batches
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int n_mini_batch = _n / mini_batch_size;
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int n_mini_batch = n / mini_batch_size;
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MLPPUtilities::CreateMiniBatchMMBatch batches = MLPPUtilities::create_mini_batchesmm(_input_set, _output_set, n_mini_batch);
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@ -278,72 +341,117 @@ void MLPPSoftmaxNet::mbgd(real_t learning_rate, int max_epoch, int mini_batch_si
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}
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real_t MLPPSoftmaxNet::score() {
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ERR_FAIL_COND_V(!_input_set.is_valid() || !_output_set.is_valid(), 0);
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ERR_FAIL_COND_V(needs_init(), 0);
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MLPPUtilities util;
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return util.performance_mat(_y_hat, _output_set);
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}
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void MLPPSoftmaxNet::save(const String &file_name) {
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MLPPUtilities util;
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//util.saveParameters(fileName, _weights1, _bias1, false, 1);
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//util.saveParameters(fileName, _weights2, _bias2, true, 2);
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}
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Ref<MLPPMatrix> MLPPSoftmaxNet::get_embeddings() {
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return _weights1;
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}
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bool MLPPSoftmaxNet::is_initialized() {
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return _initialized;
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bool MLPPSoftmaxNet::needs_init() const {
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if (!_input_set.is_valid()) {
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return true;
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}
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if (!_output_set.is_valid()) {
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return true;
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}
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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int n_class = _output_set->size().x;
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if (_y_hat->size().y != n) {
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return true;
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}
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if (_weights1->size() != Size2i(_n_hidden, k)) {
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return true;
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}
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if (_weights2->size() != Size2i(n_class, _n_hidden)) {
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return true;
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}
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if (_bias1->size() != _n_hidden) {
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return true;
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}
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if (_bias2->size() != n_class) {
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return true;
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}
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return false;
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}
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void MLPPSoftmaxNet::initialize() {
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if (_initialized) {
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return;
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}
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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int n_class = _output_set->size().x;
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_initialized = true;
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_y_hat->resize(Size2i(0, n));
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MLPPUtilities utils;
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_weights1->resize(Size2i(_n_hidden, k));
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utils.weight_initializationm(_weights1);
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_weights2->resize(Size2i(n_class, _n_hidden));
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utils.weight_initializationm(_weights2);
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_bias1->resize(_n_hidden);
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utils.bias_initializationv(_bias1);
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_bias2->resize(n_class);
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utils.bias_initializationv(_bias2);
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}
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MLPPSoftmaxNet::MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, 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().y;
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_k = p_input_set->size().x;
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_n_hidden = p_n_hidden;
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_n_class = p_output_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.instance();
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_y_hat->resize(Size2i(0, _n));
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MLPPUtilities utils;
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_weights1.instance();
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_weights1->resize(Size2i(_n_hidden, _k));
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utils.weight_initializationm(_weights1);
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_weights2.instance();
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_weights2->resize(Size2i(_n_class, _n_hidden));
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utils.weight_initializationm(_weights2);
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_bias1.instance();
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_bias1->resize(_n_hidden);
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utils.bias_initializationv(_bias1);
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_bias2.instance();
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_bias2->resize(_n_class);
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utils.bias_initializationv(_bias2);
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_initialized = true;
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_z2.instance();
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_a2.instance();
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initialize();
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}
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MLPPSoftmaxNet::MLPPSoftmaxNet() {
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_initialized = false;
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_n_hidden = 0;
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_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
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_lambda = 0;
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_alpha = 0;
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_y_hat.instance();
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_weights1.instance();
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_weights2.instance();
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_bias1.instance();
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_bias2.instance();
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_z2.instance();
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_a2.instance();
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}
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MLPPSoftmaxNet::~MLPPSoftmaxNet() {
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}
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@ -406,7 +514,6 @@ void MLPPSoftmaxNet::forward_pass() {
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}
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void MLPPSoftmaxNet::_bind_methods() {
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/*
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSoftmaxNet::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSoftmaxNet::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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@ -427,18 +534,46 @@ void MLPPSoftmaxNet::_bind_methods() {
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPSoftmaxNet::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ADD_GROUP("Data", "data");
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ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPSoftmaxNet::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSoftmaxNet::data_y_hat_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_y_hat_set", "data_y_hat_get");
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ClassDB::bind_method(D_METHOD("data_weights1_get"), &MLPPSoftmaxNet::data_weights1_get);
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ClassDB::bind_method(D_METHOD("data_weights1_set", "val"), &MLPPSoftmaxNet::data_weights1_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights1", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_weights1_set", "data_weights1_get");
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ClassDB::bind_method(D_METHOD("data_weights2_get"), &MLPPSoftmaxNet::data_weights2_get);
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ClassDB::bind_method(D_METHOD("data_weights2_set", "val"), &MLPPSoftmaxNet::data_weights2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_weights2_set", "data_weights2_get");
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ClassDB::bind_method(D_METHOD("data_bias1_get"), &MLPPSoftmaxNet::data_bias1_get);
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ClassDB::bind_method(D_METHOD("data_bias1_set", "val"), &MLPPSoftmaxNet::data_bias1_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_bias1", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_bias1_set", "data_bias1_get");
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ClassDB::bind_method(D_METHOD("data_bias2_get"), &MLPPSoftmaxNet::data_bias2_get);
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ClassDB::bind_method(D_METHOD("data_bias2_set", "val"), &MLPPSoftmaxNet::data_bias2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_bias2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_bias2_set", "data_bias2_get");
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ClassDB::bind_method(D_METHOD("data_z2_get"), &MLPPSoftmaxNet::data_z2_get);
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ClassDB::bind_method(D_METHOD("data_z2_set", "val"), &MLPPSoftmaxNet::data_z2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_z2_set", "data_z2_get");
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ClassDB::bind_method(D_METHOD("data_a2_get"), &MLPPSoftmaxNet::data_a2_get);
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ClassDB::bind_method(D_METHOD("data_a2_set", "val"), &MLPPSoftmaxNet::data_a2_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_a2", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "data_a2_set", "data_a2_get");
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSoftmaxNet::model_test);
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSoftmaxNet::model_set_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxNet::mbgd, false);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::train_gradient_descent, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSoftmaxNet::train_sgd, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSoftmaxNet::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPSoftmaxNet::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSoftmaxNet::save);
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ClassDB::bind_method(D_METHOD("get_embeddings"), &MLPPSoftmaxNet::get_embeddings);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSoftmaxNet::is_initialized);
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSoftmaxNet::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPSoftmaxNet::initialize);
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*/
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}
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@ -9,48 +9,68 @@
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "core/object/resource.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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#include "../regularization/reg.h"
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class MLPPSoftmaxNet : public Reference {
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GDCLASS(MLPPSoftmaxNet, Reference);
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class MLPPSoftmaxNet : public Resource {
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GDCLASS(MLPPSoftmaxNet, Resource);
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public:
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/*
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Ref<MLPPMatrix> get_input_set();
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Ref<MLPPMatrix> get_input_set() const;
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_output_set();
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Ref<MLPPMatrix> get_output_set() const;
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void set_output_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
MLPPReg::RegularizationType get_reg();
|
||||
int get_n_hidden() const;
|
||||
void set_n_hidden(const int val);
|
||||
|
||||
MLPPReg::RegularizationType get_reg() const;
|
||||
void set_reg(const MLPPReg::RegularizationType val);
|
||||
|
||||
real_t get_lambda();
|
||||
real_t get_lambda() const;
|
||||
void set_lambda(const real_t val);
|
||||
|
||||
real_t get_alpha();
|
||||
real_t get_alpha() const;
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
Ref<MLPPMatrix> data_y_hat_get() const;
|
||||
void data_y_hat_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPMatrix> data_weights1_get() const;
|
||||
void data_weights1_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPMatrix> data_weights2_get() const;
|
||||
void data_weights2_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPVector> data_bias1_get() const;
|
||||
void data_bias1_set(const Ref<MLPPVector> &val);
|
||||
|
||||
Ref<MLPPVector> data_bias2_get() const;
|
||||
void data_bias2_set(const Ref<MLPPVector> &val);
|
||||
|
||||
Ref<MLPPMatrix> data_z2_get() const;
|
||||
void data_z2_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPMatrix> data_a2_get() const;
|
||||
void data_a2_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
|
||||
Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
|
||||
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
|
||||
real_t score();
|
||||
|
||||
void save(const String &file_name);
|
||||
|
||||
Ref<MLPPMatrix> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
|
||||
|
||||
bool is_initialized();
|
||||
bool needs_init() const;
|
||||
void initialize();
|
||||
|
||||
MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
@ -101,12 +121,6 @@ protected:
|
||||
|
||||
Ref<MLPPMatrix> _z2;
|
||||
Ref<MLPPMatrix> _a2;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
int _n_class;
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* SoftmaxNet_hpp */
|
||||
|
||||
@ -415,7 +415,7 @@ void MLPPTests::test_soft_max_network(bool ui) {
|
||||
Ref<MLPPDataComplex> dt = data.load_wine(_wine_data_path);
|
||||
|
||||
MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1);
|
||||
model.gradient_descent(0.01, 100000, ui);
|
||||
model.train_gradient_descent(0.01, 100000, ui);
|
||||
PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user