public void BackPropagateIsCorrect()
{
int numberOfBanks = 3;
Layer layer = new Layer(new WeightsMatrix(new double[, ] {
{ 1 }
}));
LayerBank bank = new LayerBank(layer, numberOfBanks);
NetworkVector inputVector = new NetworkVector(new double[] { 1, 2, 3 });
NetworkVector bpVector = new NetworkVector(new double[] { 5, 7, 11 });
bank.Run(inputVector);
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 5, 7, 11 });
Assert.AreEqual(inputGradientCheck, bank.InputGradient(bpVector));
bank.BackPropagate(bpVector, inputVector);
bank.Update(new GradientDescent());
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ -51 }
});
NetworkVector biasCheck = new NetworkVector(new double[] { -23 });
Assert.AreEqual(biasCheck, bank.Biases);
Assert.AreEqual(weightsCheck, bank.Weights);
}
public void InputGradient()
{
int numberOfBanks = 3;
Layer layer = new Layer(new WeightsMatrix(new double[, ] {
{ 1 }
}));
LayerBank bank = new LayerBank(layer, numberOfBanks);
NetworkVector zeroVector = new NetworkVector(numberOfBanks);
Assert.AreEqual(zeroVector, bank.InputGradient(zeroVector));
}
public void CorrectInputGradientOneInput()
{
int numberOfBanks = 3;
Layer layer = new Layer(new WeightsMatrix(new double[, ] {
{ 1 }
}));
LayerBank bank = new LayerBank(layer, numberOfBanks);
NetworkVector input = new NetworkVector(new double[] { 2, 3, 5 });
NetworkVector outputGradient = new NetworkVector(new double[] { 2, 3, 5 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 2, 3, 5 });
Assert.AreEqual(inputGradientCheck, bank.InputGradient(outputGradient));
}