public void LogisticInputGradient()
{
Layer layer = new Layer(
new WeightsMatrix(
Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 1 }
})
),
new BiasesVector(1),
NeuralFunction.__Logistic,
NeuralFunction.__LogisticDerivative
);
DataVector zeroVector = new DataVector(1);
VectorBatch result = layer.Run(zeroVector);
DataVector oneVector = new DataVector(
Vector <double> .Build.DenseOfArray(new double[] { 1 })
);
VectorBatch inputGradient = layer.BackPropagate(oneVector);
DataVector inputGradientCheck = new DataVector(
Vector <double> .Build.DenseOfArray(
new double[] { NeuralFunction.__LogisticDerivative(0, NeuralFunction.__Logistic(0)) })
);
Assert.AreEqual(inputGradientCheck, inputGradient);
}
public void LogisticCorrectRun()
{
Layer layer = new Layer(
new WeightsMatrix(
Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 2, 3, 5 }
})
),
new BiasesVector(1),
NeuralFunction.__Logistic,
NeuralFunction.__LogisticDerivative
);
VectorBatch input = new VectorBatch(
Matrix <double> .Build.DenseOfArray(new double[, ] {
{ 7, 11, 13 }
})
);
VectorBatch outputcheck = new VectorBatch(
Matrix <double> .Build.DenseOfArray(new double[, ] {
{ NeuralFunction.__Logistic(112) }
})
);
VectorBatch result = layer.Run(input);
Assert.AreEqual(outputcheck, result);
}
public void CanAddFixed()
{
NeuralFunction nf = new NeuralFunction(1);
NetComponentChain layerlist = new NetComponentChain();
layerlist.AddFixed(nf);
List <NetComponent> allComponents = new List <NetComponent>(layerlist.ForwardEnumeration);
List <NetComponent> trainableComponents = new List <NetComponent>(layerlist.ForwardTrainableComponentsEnumeration);
Assert.AreEqual(1, layerlist.NumberOfComponents);
Assert.IsTrue(allComponents.Contains(nf));
Assert.IsFalse(trainableComponents.Contains(nf));
}
public virtual void setFunctions(NeuralFunction neuralFunction, WeigthingFunction weigthingFunction)
{
this.neuralFunction = neuralFunction;
this.weigthingFunction = weigthingFunction;
}