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);
Layer layer = new SigmoidLayer(weights);
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
double sig0 = sigmoid(0.0);
for (int i = 0, j = 1000; i < 1000; i++, j++)
{
Assert.AreEqual(sigmoid((double)i), result[i], "Failed for i = " + i);
Assert.AreEqual(sig0, result[j], "Failed for j = " + j);
}
}
public void SigmoidLayerHasRightRun()
{
double[,] weights = new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
};
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
Layer layer = new SigmoidLayer(weights);
layer.Run(inputvector);
double[] result = layer.Output.ToArray();
double[] expectedResult = new double[] { sigmoid(4), sigmoid(3) };
Assert.AreEqual(expectedResult[0], result[0]);
Assert.AreEqual(expectedResult[1], result[1]);
}
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));
}
}
