public void BackpropagateRuns()
{
double[,] weights = new double[, ] {
{ 1 }
};
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
Layer layer = new SigmoidLayer(weights);
layer.BackPropagate(outputgradient);
double[] inputGradientCheck = new double[] { 0 };
double[] inputGradientValues = layer.InputGradient.ToArray();
for (int i = 0; i < layer.NumberOfInputs; i++)
{
Assert.AreEqual(inputGradientCheck[i], inputGradientValues[i]);
}
}
public void BackpropagateRunsTwoByThree()
{
double[,] weights = new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
};
Layer layer = new SigmoidLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, 0, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
layer.BackPropagate(outputgradient);
double[] inputGradientCheck = new double[] { 0.31498075621051952, 0.52496792701753248, 0.7349550978245456 };
double[] inputGradientValues = layer.InputGradient.ToArray();
for (int i = 0; i < layer.NumberOfInputs; i++)
{
Assert.AreEqual(inputGradientCheck[i], inputGradientValues[i], string.Format("Failure for input {0}", i));
}
}