public void backwardLearn()
{
var learnRate = GlobalRandom.NextDouble();
int inSize, outSize;
var countExamples = 3;
inSize = outSize = 5;
var input = MatrixD.Build.Random(countExamples, inSize);
var layer = new BiasLayer(inSize);
var nextGradients = MatrixD.Build.Random(countExamples, outSize);
var expectedBiases = layer.Biases.ShallowCopy();
var gradientSums = nextGradients.ColumnSums().AsArray();
for (int i = 0; i < expectedBiases.Length; i++)
{
expectedBiases[i] -= learnRate * gradientSums[i];
}
layer.backwardLearn(input, nextGradients, learnRate);
Assert.IsTrue(expectedBiases.EEquals(layer.Biases));
}
