public static LabelWithConfidence LabelWithConfidence(NeuralNet model, NNInstrumentation instr, double[] datum, bool crop)
{
Vector <double> datum_v = DenseVector.OfArray(datum);
if (crop)
{
datum_v = model.CropMaybe(datum_v);
}
double[] outs = model.EvaluateNNConcretePostCrop(datum_v, instr);
// Console.WriteLine("Outs = {0}", DenseVector.OfArray(outs));
Tuple <double, int> max = UMath.Max(outs);
Tuple <double, int> secmax = UMath.MaxExcluding(max.Item2, outs);
UMath.SoftMax(outs);
var result = new LabelWithConfidence
{
datum = datum,
actualLabel = max.Item2,
secBestLabel = secmax.Item2,
softMaxValue = outs[max.Item2],
diffFromSecondBest = Math.Abs(outs[max.Item2] - outs[secmax.Item2])
};
return(result);
}
public static double[] RunWithSoftmax(NeuralNet model, double[] datum, bool crop)
{
Vector <double> datum_v = DenseVector.OfArray(datum);
if (crop)
{
datum_v = model.CropMaybe(datum_v);
}
double[] outs = model.EvaluateNNConcretePostCrop(datum_v, null);
UMath.SoftMax(outs);
return(outs);
}