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More test cleanups.

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Relintai 2023-12-28 00:49:34 +01:00
parent 4350722185
commit 8c01fbf060
2 changed files with 3 additions and 57 deletions
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16
test/mlpp_tests.cpp
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@ -562,7 +562,7 @@ void MLPPTests::test_soft_max_network(bool ui) {
Ref<MLPPDataComplex> dt = data.load_wine(_wine_data_path); Ref<MLPPDataComplex> dt = data.load_wine(_wine_data_path);
MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1); MLPPSoftmaxNet model(dt->get_input(), dt->get_output(), 1);
model.train_gradient_descent(0.01, 100000, ui); model.train_gradient_descent(0.000001, 300, ui);
PLOG_MSG(model.model_set_test(dt->get_input())->to_string()); PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl; std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
} }
@ -602,30 +602,20 @@ void MLPPTests::test_dynamically_sized_ann(bool ui) {
output_set->set_from_std_vector(outputSet); output_set->set_from_std_vector(outputSet);
MLPPANN ann(algn.transposenm(input_set), output_set); MLPPANN ann(algn.transposenm(input_set), output_set);
ann.add_layer(2, MLPPActivation::ACTIVATION_FUNCTION_COSH); ann.add_layer(2, MLPPActivation::ACTIVATION_FUNCTION_COSH);
ann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS); ann.add_output_layer(MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS);
ann.amsgrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, ui); ann.amsgrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, ui);
ann.adadelta(1, 1000, 2, 0.9, 0.000001, ui); ann.adadelta(1, 1000, 2, 0.9, 0.000001, ui);
ann.momentum(0.1, 8000, 2, 0.9, true, ui); ann.momentum(0.1, 8000, 2, 0.9, true, ui);
ann.set_learning_rate_scheduler_drop(MLPPANN::SCHEDULER_TYPE_STEP, 0.5, 1000); ann.set_learning_rate_scheduler_drop(MLPPANN::SCHEDULER_TYPE_STEP, 0.5, 1000);
ann.gradient_descent(0.01, 30000); ann.gradient_descent(0.01, 30000);
PLOG_MSG(ann.model_set_test(algn.transposenm(input_set))->to_string()); PLOG_MSG(ann.model_set_test(algn.transposenm(input_set))->to_string());
PLOG_MSG("ACCURACY: " + String::num(100 * ann.score()) + "%"); PLOG_MSG("ACCURACY: " + String::num(100 * ann.score()) + "%");
} }
void MLPPTests::test_wgan_old(bool ui) { void MLPPTests::test_wgan_old(bool ui) {
//MLPPStat stat;
MLPPLinAlg alg;
//MLPPActivation avn;
//MLPPCost cost;
//MLPPData data;
//MLPPConvolutions conv;
std::vector<std::vector<real_t>> outputSet = {
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 },
{ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40 }
};
} }
void MLPPTests::test_wgan(bool ui) { void MLPPTests::test_wgan(bool ui) {
//MLPPStat stat; //MLPPStat stat;

44
test/mlpp_tests_old.cpp
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@ -121,54 +121,10 @@ void MLPPTestsOld::test_support_vector_classification(bool ui) {
void MLPPTestsOld::test_mlp(bool ui) { void MLPPTestsOld::test_mlp(bool ui) {
} }
void MLPPTestsOld::test_soft_max_network(bool ui) { void MLPPTestsOld::test_soft_max_network(bool ui) {
MLPPLinAlgOld alg;
MLPPData data;
// SOFTMAX NETWORK
Ref<MLPPDataComplex> dt = data.load_wine(_wine_data_path);
MLPPSoftmaxNetOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1);
model_old.gradientDescent(0.01, 100000, ui);
alg.printMatrix(model_old.modelSetTest(dt->get_input()->to_std_vector()));
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
} }
void MLPPTestsOld::test_autoencoder(bool ui) { void MLPPTestsOld::test_autoencoder(bool ui) {
MLPPLinAlgOld alg;
std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, { 3, 5, 9, 12, 15, 18, 21, 24, 27, 30 } };
// AUTOENCODER
MLPPAutoEncoderOld model_old(alg.transpose(inputSet), 5);
model_old.SGD(0.001, 300000, ui);
alg.printMatrix(model_old.modelSetTest(alg.transpose(inputSet)));
std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl;
Ref<MLPPMatrix> input_set;
input_set.instance();
input_set->set_from_std_vectors(inputSet);
} }
void MLPPTestsOld::test_dynamically_sized_ann(bool ui) { void MLPPTestsOld::test_dynamically_sized_ann(bool ui) {
MLPPLinAlgOld alg;
// DYNAMICALLY SIZED ANN
// Possible Weight Init Methods: Default, Uniform, HeNormal, HeUniform, XavierNormal, XavierUniform
// Possible Activations: Linear, Sigmoid, Swish, Softplus, Softsign, CLogLog, Ar{Sinh, Cosh, Tanh, Csch, Sech, Coth}, GaussianCDF, GELU, UnitStep
// Possible Loss Functions: MSE, RMSE, MBE, LogLoss, CrossEntropy, HingeLoss
std::vector<std::vector<real_t>> inputSet = { { 0, 0, 1, 1 }, { 0, 1, 0, 1 } };
std::vector<real_t> outputSet = { 0, 1, 1, 0 };
MLPPANNOld ann_old(alg.transpose(inputSet), outputSet);
ann_old.addLayer(2, "Cosh");
ann_old.addOutputLayer("Sigmoid", "LogLoss");
ann_old.AMSGrad(0.1, 10000, 1, 0.9, 0.999, 0.000001, ui);
ann_old.Adadelta(1, 1000, 2, 0.9, 0.000001, ui);
ann_old.Momentum(0.1, 8000, 2, 0.9, true, ui);
ann_old.setLearningRateScheduler("Step", 0.5, 1000);
ann_old.gradientDescent(0.01, 30000);
alg.printVector(ann_old.modelSetTest(alg.transpose(inputSet)));
std::cout << "ACCURACY: " << 100 * ann_old.score() << "%" << std::endl;
} }
void MLPPTestsOld::test_wgan_old(bool ui) { void MLPPTestsOld::test_wgan_old(bool ui) {
//MLPPStat stat; //MLPPStat stat;