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); }