static float QuadraticWholeCost(NeuralNet neuralNet, LearningExample[] learningExamples)
{
float wholeCost = 0;
for (int i = 0; i < learningExamples.Length; i++)
{
wholeCost += QuadraticSingleCost(learningExamples[i].Output, neuralNet.Compute(learningExamples[i].Input));
}
return(wholeCost / learningExamples.Length);
}
static float CrossEntropyWholeCost(NeuralNet neuralNet, LearningExample[] learningExamples)
{
float wholeCost = 0;
for (int i = 0; i < learningExamples.Length; i++)
{
float singleCost = CrossEntropySingleCost(learningExamples[i].Output, neuralNet.Compute(learningExamples[i].Input));
wholeCost += singleCost;
}
return(-wholeCost / learningExamples.Length);
}
public override void OnInspectorGUI()
{
base.OnInspectorGUI();
NeuralNet neuralNet = target as NeuralNet;
if (neuralNet.Initialized)
{
EditorGUILayout.HelpBox(string.Format("InputSize: {0}\nCost funtion: {1}", neuralNet.InputSize, neuralNet.CostFunction), MessageType.Info, true);
}
else
{
EditorGUILayout.HelpBox("Not initialized", MessageType.Info, true);
}
}
static float L1Cost(NeuralNet neuralNet, int numberOfTests)
{
float l1Cost = 0;
for (int layer = 0; layer < neuralNet.NumberOfLayers; layer++)
{
for (int node = 0; node < neuralNet.NodesInLayer(layer); node++)
{
for (int weight = 0; weight < neuralNet.WeightsOfNodeInLayer(layer); weight++)
{
l1Cost += Mathf.Abs(neuralNet.WeightAccessor[layer, node, weight]);
}
}
}
return(l1Cost * neuralNet.RegularizationRate / numberOfTests);
}
public static float ComputeCost(NeuralNet neuralNet, LearningExample[] learningExamples)
{
float cost = 0;
// C0
switch (neuralNet.CostFunction)
{
case NeuralNet.CostFuntionKind.Quadratic:
cost = QuadraticWholeCost(neuralNet, learningExamples);
break;
case NeuralNet.CostFuntionKind.CrossEntropy:
cost = CrossEntropyWholeCost(neuralNet, learningExamples);
break;
default:
throw new System.NotImplementedException();
}
// Regularization
switch (neuralNet.RegularizationMethod)
{
case NeuralNet.RegularizationMethodKind.None:
break;
case NeuralNet.RegularizationMethodKind.L2:
cost += L2Cost(neuralNet, learningExamples.Length);
break;
case NeuralNet.RegularizationMethodKind.L1:
cost += L1Cost(neuralNet, learningExamples.Length);
break;
default:
throw new System.NotImplementedException();
}
//
return(cost);
}