OPENNN 预测结果总是1.0
neural_network = new NeuralNetwork(4, 6, 1);
ModelSelection model_selection;
TrainingStrategy training_strategy;
LossIndex loss_index;
DataSet data_set;
data_set.set_data_file_name("D://iris_plant1.csv");
data_set.set_separator("Comma");
data_set.load_data();
OpenNN::Variables * variables_pointer = data_set.get_variables_pointer();
variables_pointer->set_name(0, "sepal_length");
variables_pointer->set_units(0, "centimeters");
variables_pointer->set_use(0, Variables::Input);
variables_pointer->set_name(1, "sepal_width");
variables_pointer->set_units(1, "centimeters");
variables_pointer->set_use(1, Variables::Input);
variables_pointer->set_name(2, "petal_length");
variables_pointer->set_units(2, "centimeters");
variables_pointer->set_use(2, Variables::Input);
variables_pointer->set_name(3, "petal_width");
variables_pointer->set_units(3, "centimeters");
variables_pointer->set_use(3, Variables::Input);
variables_pointer->set_name(4, "iris_setosa");
variables_pointer->set_use(4, Variables::Target);
const Matrix<std::string> inputs_information = variables_pointer->arrange_inputs_information();
const Matrix<std::string> targets_information = variables_pointer->arrange_targets_information();
Instances* instances_pointer = data_set.get_instances_pointer();
instances_pointer->split_random_indices();
const Vector< Statistics<double> > inputs_statistics = data_set.scale_inputs_minimum_maximum();
Inputs* inputs_pointer = neural_network->get_inputs_pointer();
inputs_pointer->set_information(inputs_information);
Outputs* outputs_pointer = neural_network->get_outputs_pointer();
outputs_pointer->set_information(targets_information);
neural_network->construct_scaling_layer();
ScalingLayer* scaling_layer_pointer = neural_network->get_scaling_layer_pointer();
//scaling_layer_pointer->set_scaling_method(ScalingLayer::MinimumMaximum);
scaling_layer_pointer->set_scaling_method(ScalingLayer::NoScaling);
neural_network->construct_unscaling_layer();
UnscalingLayer* unscaling_layer_pointer = neural_network->get_unscaling_layer_pointer();
unscaling_layer_pointer->set_unscaling_method(UnscalingLayer::NoUnscaling);
neural_network->construct_probabilistic_layer();
//neural_network->set_bounding_layer_pointer(true);
ProbabilisticLayer* probabilistic_layer_pointer = neural_network->get_probabilistic_layer_pointer();
probabilistic_layer_pointer->set_probabilistic_method(ProbabilisticLayer::Softmax);
// Loss index
loss_index.set_data_set_pointer(&data_set);
loss_index.set_neural_network_pointer(neural_network);
training_strategy.set(&loss_index);
training_strategy.set_main_type(TrainingStrategy::QUASI_NEWTON_METHOD);
QuasiNewtonMethod* quasi_Newton_method_pointer = training_strategy.get_quasi_Newton_method_pointer();
quasi_Newton_method_pointer->set_minimum_loss_increase(1.0e-6);
training_strategy.set_display(false);
TrainingStrategy::Results results = training_strategy.perform_training();
// Model selection
model_selection.set_training_strategy_pointer(&training_strategy);
model_selection.set_order_selection_type(ModelSelection::GOLDEN_SECTION);
GoldenSectionOrder* golden_section_order_pointer = model_selection.get_golden_section_order_pointer();
golden_section_order_pointer->set_tolerance(1.0e-7);
// Testing analysis
TestingAnalysis testing_analysis(neural_network, &data_set);
const Matrix<size_t> confusion = testing_analysis.calculate_confusion();
// Save results
data_set.save("data_set.xml");
neural_network->save("neural_network.xml");
neural_network->save_expression("expression.txt");
training_strategy.save("training_strategy.xml");
model_selection.save("model_selection.xml");
使用calculate_outputs预测结果，输出总是1.
