public void CanMakeLayer()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 4 }
});
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights);
Assert.IsNotNull(layer);
}
public void CanMakeLayerWithBiases()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 4 }
});
NetworkVector biases = new NetworkVector(new double[] { 5, 7 });
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights, biases);
Assert.IsNotNull(layer);
}
public void NeuralFunctionNotNullRequiresDerivativeNotNull()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2, 3 }, { 3, 4, 5 }
});
NetworkVector biases = new NetworkVector(new double[] { 1, 2 });
try
{
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights, biases, x => 1.0, null);
Assert.Fail("Attempt to create Layer with non-null _neuralFunction and null _neuralFunctioinDerivative should throw and Argument exception, but did not.");
}
catch (ArgumentException)
{ }
}
public void CannotMakeLayerWithMismatchedBiases()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 2 }, { 3, 4 }
});
NetworkVector biases = new NetworkVector(new double[] { 5, 7, 11 });
try
{
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights, biases);
Assert.Fail("ArgumentException expected but not thrown");
}
catch (ArgumentException)
{ }
}
public void InputGradientRequiresCorrectInputSize()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector badInput = new NetworkVector(new double[] { 1, 2, 3 });
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights);
try
{
layer.InputGradient(badInput);
Assert.Fail("Backpropogate should throw an ArgumentException if input dimension is not equal to NumberOfNeuron, but did not.");
}
catch (ArgumentException) { }
}
public void LinearLayerHasRightRun()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1, 0, 1 }, { 1, 1, 0 }
});
NetworkVector biases = new NetworkVector(new double[] { 0, 0 });
NetworkVector inputvector = new NetworkVector(new double[] { 1, 2, 3 });
NeuralNet.NetComponent.Layer2 layer = NeuralNet.NetComponent.Layer2.CreateLinearLayer(weights, biases);
layer.Run(inputvector);
NetworkVector result = layer.Output;
NetworkVector expectedResult = new NetworkVector(new double[] { 4, 3 });
Assert.AreEqual(expectedResult, result);
}
public void BackpropagateRuns()
{
NetworkMatrix weights = new NetworkMatrix(new double[, ] {
{ 1 }
});
NetworkVector outputgradient = new NetworkVector(new double[] { 1 });
NeuralNet.NetComponent.Layer2 layer = new NeuralNet.NetComponent.Layer2(weights);
NetworkVector inputGradientCheck = new NetworkVector(new double[] { 1 });
NetworkVector biasesGradientCheck = new NetworkVector(new double[] { 1 });
NetworkMatrix weightsGradientCheck = new NetworkMatrix(new double[, ] {
{ 0 }
});
Assert.AreEqual(inputGradientCheck, layer.InputGradient(outputgradient));
Assert.AreEqual(biasesGradientCheck, layer.BiasesGradient(outputgradient));
Assert.AreEqual(weightsGradientCheck, layer.WeightsGradient(outputgradient));
}
