public static bool VerifyLossFuncDerivative(LossFunc func, Tensor targetOutput, bool onlyPositiveOutput = false, int batchSize = 1, float tolerance = 0.01f)
{
Tensor.SetOpMode(Tensor.OpMode.CPU);
var output = new Tensor(new Shape(3, 3, 3, batchSize));
output.FillWithRand(10, onlyPositiveOutput ? 0 : -1);
// for derivation purposes activation functions expect already processed input
var error = new Tensor(output.Shape);
func.Compute(targetOutput, output, error);
var derivative = new Tensor(output.Shape);
func.Derivative(targetOutput, output, derivative);
var error1 = new Tensor(output.Shape);
func.Compute(targetOutput, output.Sub(LOSS_DERIVATIVE_EPSILON), error1);
var error2 = new Tensor(output.Shape);
func.Compute(targetOutput, output.Add(LOSS_DERIVATIVE_EPSILON), error2);
var result = new Tensor(output.Shape);
error2.Sub(error1, result);
var approxDerivative = result.Div(2 * LOSS_DERIVATIVE_EPSILON);
return(approxDerivative.Equals(derivative, tolerance));
}
public void Optimize(Optimizers.OptimizerBase optimizer, LossFunc loss)
{
Optimizer = optimizer;
Model.Optimize();
LossFuncs = new LossFunc[Model.GetOutputLayersCount()];
for (int i = 0; i < LossFuncs.Length; ++i)
{
LossFuncs[i] = loss;
}
}
public void Optimize(Optimizers.OptimizerBase optimizer, Dictionary <string, LossFunc> lossDict)
{
Optimizer = optimizer;
Model.Optimize();
#if VALIDATION_ENABLED
if (lossDict.Count != Model.GetOutputLayersCount())
{
throw new Exception($"Mismatched number of loss functions ({lossDict.Count}) and output layers ({Model.GetOutputLayersCount()})!");
}
#endif
LossFuncs = new LossFunc[Model.GetOutputLayersCount()];
int i = 0;
foreach (var outLayer in Model.GetOutputLayers())
{
LossFuncs[i++] = lossDict[outLayer.Name];
}
}
public static bool VerifyLossFunc(LossFunc func, Tensor targetOutput, Func <float, float, float> testFunc, bool onlyPositiveOutput = false, int batchSize = 1)
{
Tensor.SetOpMode(Tensor.OpMode.CPU);
var output = new Tensor(new Shape(3, 3, 3, batchSize));
output.FillWithRand(10, onlyPositiveOutput ? 0 : -1);
var error = new Tensor(output.Shape);
func.Compute(targetOutput, output, error);
for (int i = 0; i < output.Shape.Length; ++i)
{
if (Math.Abs(error.GetFlat(i) - testFunc(targetOutput.GetFlat(i), output.GetFlat(i))) > 1e-4)
{
return(false);
}
}
return(true);
}