public void BackPropagationIsCorrect()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 5 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 7, 11 });
NetworkMatrix weightsGradientCheck = new NetworkMatrix(new double[, ] {
{ 7, -7 }, { 11, -11 }
});
Assert.AreEqual(weightsGradientCheck, layer.WeightsGradient(outputgradient));
NetworkVector biasesGradientCheck = new NetworkVector(new double[] { 7, 11 });
Assert.AreEqual(biasesGradientCheck, layer.BiasesGradient(outputgradient));
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 40, 69 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void InputGradientRuns()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
Layer2 layer = Layer2.CreateLogisticLayer(weights);
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 0 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void InputGradientRunsTwoByThree()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, 0, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 3, 5, 7 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void BackpropagateRunsWithNonzeroLayerInput()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
Layer2 layer = Layer2.CreateLinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 2 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 1 });
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
public void InputGradientRunsTwoByThree()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
Layer2 layer = Layer2.CreateLogisticLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, 0, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1, 1 });
NetworkVector inputGradientCheck = new NetworkVector(
new double[] { 0.31498075621051952, 0.52496792701753248, 0.7349550978245456 }
);
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
}
