public void LinearLayerHasRightRun()
{
double[,] weights = new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
};
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer layer = new LinearLayer(weights);
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
double[] expectedResult = new double[] { 4, 3 };
Assert.AreEqual(expectedResult[0], result[0]);
Assert.AreEqual(expectedResult[1], result[1]);
}
public void BackPropagateIsCorrect()
{
double[,] weights = new double[, ] {
{ 1, 2 }, { 3, 5 }
};
Layer layer = new LinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 1, -1 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 7, 11 });
layer.BackPropagate(outputgradient);
double[,] weightsCheck = new double[, ] {
{ -6, 9 }, { -8, 16 }
};
LayerState state = layer.State;
for (int i = 0; i < layer.NumberOfInputs; i++)
{
for (int j = 0; j < layer.NumberOfInputs; j++)
{
Assert.AreEqual(weightsCheck[i, j], state.Weights[i, j], string.Format("Failed for (i, j) = ({0}, {1}", i, j));
}
}
double[] biasesCheck = new double[] { -7, -11 };
for (int i = 0; i < layer.NumberOfInputs; i++)
{
Assert.AreEqual(biasesCheck[i], layer.State.Biases[i]);
}
double[] inputGradientCheck = new double[] { 40, 69 };
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));
}
}
public void BackpropagateRunsTwoByThree()
{
double[,] weights = new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
};
Layer layer = new LinearLayer(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[] { 3, 5, 7 };
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));
}
}
public void BackpropagateRunsWithNonzeroLayerInput()
{
double[,] weights = new double[, ] {
{ 1 }
};
Layer layer = new LinearLayer(weights);
NetworkVector layerinput = new NetworkVector(new double[] { 2 });
layer.Run(layerinput);
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
layer.BackPropagate(outputgradient);
double[] inputGradientCheck = new double[] { 1 };
double[] inputGradientValues = layer.InputGradient.ToArray();
for (int i = 0; i < layer.NumberOfInputs; i++)
{
Assert.AreEqual(inputGradientCheck[i], inputGradientValues[i]);
}
}
public void CanUseBigLinearLayer()
{
double[,] weights = new double[2000, 1000];
double[] input = new double[1000];
for (int i = 0; i < 1000; i++)
{
weights[i, i] = 1.0;
input[i] = (double)i;
}
NetworkVector inputvector = new NetworkVector(input);
Layer layer = new LinearLayer(weights);
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
for (int i = 0, j = 1000; i < 1000; i++, j++)
{
Assert.AreEqual((double)i, result[i], "Failed for i = " + i);
Assert.AreEqual(0.0, result[j], "Failed for j = " + j);
}
}