public void Deep_NN_generic_jason_simple_rmse()
{
Init_dataset_jason_linear_regression();
BuildGenericDeepNN build =
new BuildGenericDeepNN();
build.SetParameters(0, 2);
//Use Default Parameters
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < .61 && value > 0);
}
public void NN_backpropagation_generic_rprop_no_hidden_pythagoras_rmse()
{
Init_dataset_pythagoras();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(0, 0, .3, 1);
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < 10);
}
public void NN_backpropagation_generic_rprop_jason_simple_rmse()
{
Init_dataset_jason_linear_regression();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(0, 1, .01, 10000);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < .61 && value > 0);
}
public void NN_backpropagation_2L_jason_simple_rmse()
{
Init_dataset_jason_linear_regression();
Build2LBackPropagation build =
new Build2LBackPropagation();
build.SetParameters(0, .01, 3000, .05);
build.SetActivationFunction(0, new Dasmic.MLLib.Algorithms.NeuralNetwork.Support.ActivationFunction.Sigmoid());
build.SetActivationFunction(1, new Dasmic.MLLib.Algorithms.NeuralNetwork.Support.ActivationFunction.Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < 1.0);
}
public void NN_backpropagation_generic_std_no_hidden_pythagoras_rmse()
{
Init_dataset_pythagoras();
BuildGenericBackPropagationStandard build =
new BuildGenericBackPropagationStandard();
build.SetParameters(0, 0, .3, 1);
//build.SetNumberOfHiddenLayers(0);
//build.AddHiddenLayer(0, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value > 100000);
}