public void BackPropagateNoBias_2NeuronTest()
{
double expectedWeight = 3;
double actualWeight = 2;
double inputData = 2;
double expectedOutput = inputData * expectedWeight;
double actualOutput = inputData * actualWeight;
var layer = new DenseLayerNoBias(1, 1, new IdentityActivation(), new Distance());
layer.Initialize();
layer.Weights[0, 0] = actualWeight;
var input = new double[] { inputData };
layer.Evaluate(input);
Assert.AreEqual(actualOutput, layer.Output[0]);
var inputError = new NNArray(1);
layer.BackPropagate(input, new double[] { expectedOutput }, 1, inputError);
double expectedErrorInput = (expectedOutput - actualOutput) * actualWeight; // I don't understand should be (expectedOutput - actualOutput) / actualWeight but docmentation show different
Assert.AreEqual(expectedErrorInput, inputError[0]);
}
public void LinearRegressionNeuronNoBiasTest()
{
// y = ax + b
double a = -12, b = 0;
int count = 5;
int epoc;
var layer = new DenseLayerNoBias(1, 1, new IdentityActivation(), new SquaredDistance());
layer.Initialize();
TrainRegression(a, b, count, layer, out epoc);
Assert.IsTrue(epoc < 1000);
Assert.IsTrue(Math.Abs(layer.Weights[0, 0] - a) / a < 0.01); // 1% error margin
}
