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 CanMakeSigmoidLayer()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 4 }
});
Layer2 layer = Layer2.CreateLogisticLayer(weights);
Assert.IsNotNull(layer);
}
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 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 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));
}