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 CanUseBigSigmoidLayer()
{
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);
Layer2 layer = Layer2.CreateLogisticLayer(new NetworkMatrix(weights));
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
double sig0 = logistic(0.0);
for (int i = 0, j = 1000; i < 1000; i++, j++)
{
Assert.AreEqual(logistic((double)i), result[i], "Failed for i = " + i);
Assert.AreEqual(sig0, result[j], "Failed for j = " + j);
}
}
public void SigmoidLayerHasRightRun()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
});
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer2 layer = Layer2.CreateLogisticLayer(weights);
layer.Run(inputvector);
NetworkVector expectedResult = new NetworkVector(new double[] { logistic(4), logistic(3) });
Assert.AreEqual(expectedResult, layer.Output);
}
public void LinearLayerWithBiasesHasRightRun()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
});
NetworkVector biases = new NetworkVector(new double[] { 4, 3 });
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer2 layer = Layer2.CreateLinearLayer(weights, biases);
layer.Run(inputvector);
NetworkVector expectedResult = new NetworkVector(new double[] { 8, 6 });
Assert.AreEqual(expectedResult, layer.Output);
}
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));
}
public void CanUseBigLinearLayer()
{
double[,] matrix = new double[2000, 1000];
double[] input = new double[1000];
for (int i = 0; i < 1000; i++)
{
matrix[i, i] = 1.0;
input[i] = (double)i;
}
NetworkMatrix weights = new NetworkMatrix(matrix);
NetworkVector inputvector = new NetworkVector(input);
Layer2 layer = Layer2.CreateLinearLayer(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);
}
}
