public void DerivativeAndNandNoRegularizationTest()
{
var net = NetBuilder.CreateAndNandNet();
var func = net.CostFunction;
var inputs = DenseMatrix.OfArray(new double[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
{
var outputIndices = new int[] { 1, 1, 1, 0 };
func.SetInputsOutputs(inputs, outputIndices, 0);
int dims = func.DimensionsCount;
var rand = new Random(0);
FunctionDerivativeTester.PerformDerivativeTest(func,
Enumerable.Range(0, 100).Select(x =>
Enumerable.Range(0, dims).Select(y => rand.NextDouble() * 10 - 5).ToArray()
).ToArray()
);
}
{
var outputIndices = new int[] { 0, 0, 0, 1 };
func.SetInputsOutputs(inputs, outputIndices, 0);
int dims = func.DimensionsCount;
var rand = new Random(0);
FunctionDerivativeTester.PerformDerivativeTest(func,
Enumerable.Range(0, 100).Select(x =>
Enumerable.Range(0, dims).Select(y => rand.NextDouble() * 10 - 5).ToArray()
).ToArray()
);
}
}
public void DerivativeDeepNetTest()
{
var net = new Net(new NetLayout(5, 10, 5, 7, 2), new SigomidActivationFunc(), new NetCostFunction());
var inputs = DenseMatrix.OfArray(new double[, ] {
{ 0, 0, 1, 0, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 1, 0, 1 },
{ 0, 1, 0, 1, 0 },
});
var outputIndices = new int[] { 0, 1, 0, 0, 1 };
net.CostFunction.SetInputsOutputs(inputs, outputIndices, 0);
int dims = net.CostFunction.DimensionsCount;
var rand = new Random(0);
FunctionDerivativeTester.PerformDerivativeTest(net.CostFunction,
Enumerable.Range(0, 20).Select(x =>
Enumerable.Range(0, dims).Select(y => rand.NextDouble() * 10 - 5).ToArray()
).ToArray()
);
}
public void SigomidDerivativeTest()
{
FunctionDerivativeTester.PerformDerivativeTest(new SigomidActivationFunc(),
Enumerable.Range(-100, 200).Select(x => new double[] { x / 10.0 }).ToArray());
}
