public void backward()
{
var count_examples = 2;
var layerInDims = new int[] { 6 };
var layerInSize = layerInDims.product();
var layer = new NormalizationLayer(layerInDims);
var inputs = MatrixD.Build.DenseOfArray(
new double[, ] {
{ -100, -1, 0, 1, 100, 3 }, { -500, -0.1, 0, 0.1, 500, 3 }
});
var nextGradients = MatrixD.Build.DenseOfArray(
new double[, ] {
{ 0, 0, 0, 1, 100, 3 }, { 0, 0, 0, 0.1, 500, 3 }
});
var expectedGradients = MatrixD.Build.repeat(count_examples, layerInSize, 0);
FuncDD df = layer.DerivateActivationFunc;
for (var i = 0; i < count_examples; i++)
{
for (var j = 0; j < layerInSize; j++)
{
expectedGradients[i, j] = nextGradients[i, j] * df(inputs[i, j]);
}
}
Assert.IsTrue(layer.backward(inputs, nextGradients).EEquals(expectedGradients));
}
public void forward()
{
var inputs = MatrixD.Build.DenseOfArray(
new double[, ] {
{ -100, -1, 0, 1, 100, 3 }, { -500, -0.1, 0, 0.1, 500, 3 }
});
var expectedOutputs = MatrixD.Build.DenseOfArray(
new double[, ] {
{ 0, 0, 0, 1, 100, 3 }, { 0, 0, 0, 0.1, 500, 3 }
});
var layer = new NormalizationLayer(new int[] { 6 });
Assert.IsTrue(layer.forward(inputs).EEquals(expectedOutputs));
}
