public void SingleLayerTest(int neuronCount, float[] inputs, float[] weightsAndBiases, IActivationFunction activationFunction)
{
var inCnt = inputs.Length;
var outputs = new float[neuronCount];
var memIn = inputs.AsMemory();
var weMemFlat = weightsAndBiases.AsMemory();
var memOut = outputs.AsMemory();
var weMem = HelpersMisc.SliceArray(ref weMemFlat, inCnt + 1, neuronCount);
var layer = new Layer(neuronCount, ref weMem, ref memIn, ref memOut, activationFunction);
layer.CalculateWithoutNeuronParallel();
Assert.Multiple(() =>
{
for (var i = 0; i < neuronCount; i++)
{
var wAndB = weMem[i].ToArray();
//var total = inputs.Select((t, j) => wAndB[j] * t).Sum();
var total = PrivateSum(inputs.Select((t, j) => wAndB[j] * t));
total += wAndB[inputs.Length];
var nOut = activationFunction.Forward(ref total);
Assert.That(outputs[i], Is.EqualTo(nOut) /*.Within(.0001f)*/, $"Output is not as expected on neuron #{i}");
}
});
}
public void SingleLayerSingleNeuron()
{
var inputs = new[] { .5f };
var weightsAndBiases = new[] { .5f, 1f };
var outputs = new[] { 0f };
var actFunc = new Linear();
var memIn = inputs.AsMemory();
var weMemFlat = weightsAndBiases.AsMemory();
var memOut = outputs.AsMemory();
var weMem = HelpersMisc.SliceArray(ref weMemFlat, 2, 1);
var layer = new Layer(1, ref weMem, ref memIn, ref memOut, actFunc);
layer.CalculateWithoutNeuronParallel();
Assert.That(outputs[0], Is.EqualTo(.5f * .5f + 1f));
}