mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-30 16:07:12 +01:00
Reworked the api of MLPPTanhReg. It's now also inherited from Resource.
This commit is contained in:
parent
aaa236b14c
commit
28b7007bb7
@ -14,59 +14,122 @@
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#include <random>
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Ref<MLPPMatrix> MLPPTanhReg::get_input_set() {
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Ref<MLPPMatrix> MLPPTanhReg::get_input_set() const {
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return _input_set;
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}
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void MLPPTanhReg::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> MLPPTanhReg::get_output_set() {
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Ref<MLPPMatrix> MLPPTanhReg::get_output_set() const {
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return _output_set;
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}
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void MLPPTanhReg::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 MLPPTanhReg::get_reg() {
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MLPPReg::RegularizationType MLPPTanhReg::get_reg() const {
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return _reg;
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}
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void MLPPTanhReg::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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}
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real_t MLPPTanhReg::get_lambda() {
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real_t MLPPTanhReg::get_lambda() const {
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return _lambda;
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}
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void MLPPTanhReg::set_lambda(const real_t val) {
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_lambda = val;
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}
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real_t MLPPTanhReg::get_alpha() {
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real_t MLPPTanhReg::get_alpha() const {
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return _alpha;
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}
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void MLPPTanhReg::set_alpha(const real_t val) {
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_alpha = val;
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}
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Ref<MLPPVector> MLPPTanhReg::data_z_get() const {
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return _z;
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}
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void MLPPTanhReg::data_z_set(const Ref<MLPPVector> &val) {
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_z = val;
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}
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Ref<MLPPVector> MLPPTanhReg::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPTanhReg::data_y_hat_set(const Ref<MLPPVector> &val) {
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_y_hat = val;
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}
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Ref<MLPPVector> MLPPTanhReg::data_weights_get() const {
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return _weights;
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}
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void MLPPTanhReg::data_weights_set(const Ref<MLPPVector> &val) {
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_weights = val;
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}
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real_t MLPPTanhReg::data_bias_get() const {
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return _bias;
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}
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void MLPPTanhReg::data_bias_set(const real_t val) {
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_bias = val;
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}
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bool MLPPTanhReg::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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if (_y_hat->size() != n) {
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return true;
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}
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if (_weights->size() != k) {
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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 MLPPTanhReg::initialize() {
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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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_y_hat->resize(n);
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_weights->resize(k);
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MLPPUtilities utils;
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utils.weight_initializationv(_weights);
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_bias = utils.bias_initializationr();
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}
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Ref<MLPPVector> MLPPTanhReg::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_initialized, 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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real_t MLPPTanhReg::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_initialized, 0);
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ERR_FAIL_COND_V(needs_init(), 0);
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return evaluatev(x);
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}
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void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPTanhReg::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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@ -74,6 +137,8 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
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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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forward_pass();
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while (true) {
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@ -81,11 +146,11 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
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Ref<MLPPVector> error = _y_hat->subn(_output_set);
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_weights->sub(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.tanh_derivv(_z)))->scalar_multiplyn(learning_rate / _n));
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_weights->sub(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.tanh_derivv(_z)))->scalar_multiplyn(learning_rate / n));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / _n;
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_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / n;
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forward_pass();
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@ -103,8 +168,11 @@ void MLPPTanhReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
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}
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}
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void MLPPTanhReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPTanhReg::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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int n = _input_set->size().y;
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MLPPReg regularization;
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@ -113,7 +181,7 @@ void MLPPTanhReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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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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@ -165,8 +233,11 @@ void MLPPTanhReg::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 MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPTanhReg::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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int n = _input_set->size().y;
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MLPPActivation avn;
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MLPPReg regularization;
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@ -175,7 +246,7 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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int epoch = 1;
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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::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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while (true) {
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@ -191,11 +262,11 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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// Calculating the weight gradients
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_weights->sub(current_input_batch_entry->transposen()->mult_vec(error->hadamard_productn(avn.tanh_derivv(z)))->scalar_multiplyn(learning_rate / _n));
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_weights->sub(current_input_batch_entry->transposen()->mult_vec(error->hadamard_productn(avn.tanh_derivv(z)))->scalar_multiplyn(learning_rate / n));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / _n;
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_bias -= learning_rate * error->hadamard_productn(avn.tanh_derivv(_z))->sum_elements() / n;
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forward_pass();
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@ -218,44 +289,14 @@ void MLPPTanhReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
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}
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real_t MLPPTanhReg::score() {
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ERR_FAIL_COND_V(!_initialized, 0);
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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_vec(_y_hat, _output_set);
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}
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void MLPPTanhReg::save(const String &file_name) {
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//MLPPUtilities util;
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//util.saveParameters(file_name, _weights, _bias);
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}
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bool MLPPTanhReg::is_initialized() {
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return _initialized;
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}
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void MLPPTanhReg::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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_n = _input_set->size().y;
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_k = _input_set->size().x;
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_y_hat->resize(_n);
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_weights->resize(_k);
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MLPPUtilities utils;
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utils.weight_initializationv(_weights);
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_bias = utils.bias_initializationr();
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_initialized = true;
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}
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MLPPTanhReg::MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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@ -263,17 +304,22 @@ MLPPTanhReg::MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVecto
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_lambda = p_lambda;
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_alpha = p_alpha;
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_bias = 0;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_initialized = false;
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initialize();
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}
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MLPPTanhReg::MLPPTanhReg() {
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_initialized = false;
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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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_bias = 0;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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}
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@ -336,17 +382,31 @@ void MLPPTanhReg::_bind_methods() {
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPTanhReg::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ClassDB::bind_method(D_METHOD("data_z_get"), &MLPPTanhReg::data_z_get);
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ClassDB::bind_method(D_METHOD("data_z_set", "val"), &MLPPTanhReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_z_set", "data_z_get");
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ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPTanhReg::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPTanhReg::data_y_hat_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_y_hat_set", "data_y_hat_get");
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ClassDB::bind_method(D_METHOD("data_weights_get"), &MLPPTanhReg::data_weights_get);
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ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPTanhReg::data_weights_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_weights_set", "data_weights_get");
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ClassDB::bind_method(D_METHOD("data_bias_get"), &MLPPTanhReg::data_bias_get);
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ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPTanhReg::data_bias_set);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "data_bias"), "data_bias_set", "data_bias_get");
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPTanhReg::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPTanhReg::initialize);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPTanhReg::model_test);
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPTanhReg::model_set_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPTanhReg::mbgd, false);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::train_gradient_descent, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::train_sgd, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPTanhReg::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPTanhReg::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPTanhReg::save);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPTanhReg::is_initialized);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPTanhReg::initialize);
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}
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@ -10,46 +10,56 @@
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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 MLPPTanhReg : public Reference {
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GDCLASS(MLPPTanhReg, Reference);
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class MLPPTanhReg : public Resource {
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GDCLASS(MLPPTanhReg, Resource);
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public:
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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);
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MLPPReg::RegularizationType get_reg();
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MLPPReg::RegularizationType get_reg() const;
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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real_t get_lambda() const;
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void set_lambda(const real_t val);
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real_t get_alpha();
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real_t get_alpha() const;
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void set_alpha(const real_t val);
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Ref<MLPPVector> data_z_get() const;
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void data_z_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_y_hat_get() const;
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void data_y_hat_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_weights_get() const;
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void data_weights_set(const Ref<MLPPVector> &val);
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real_t data_bias_get() const;
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void data_bias_set(const real_t val);
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bool needs_init() const;
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void initialize();
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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void save(const String &file_name);
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bool is_initialized();
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void initialize();
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MLPPTanhReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPTanhReg();
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@ -70,20 +80,16 @@ protected:
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _y_hat;
|
||||
Ref<MLPPVector> _weights;
|
||||
real_t _bias;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
// Regularization Params
|
||||
MLPPReg::RegularizationType _reg;
|
||||
real_t _lambda;
|
||||
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
bool _initialized;
|
||||
Ref<MLPPVector> _z;
|
||||
Ref<MLPPVector> _y_hat;
|
||||
Ref<MLPPVector> _weights;
|
||||
real_t _bias;
|
||||
};
|
||||
|
||||
#endif /* TanhReg_hpp */
|
||||
|
||||
Loading…
Reference in New Issue
Block a user