public void ComputeTwiceGradientShouldYieldTheSameResult()
{
const int inputWidth = 10;
const int inputHeight = 10;
const int inputDepth = 2;
const int filterWidth = 3;
const int filterHeight = 3;
const int filterCount = 2;
// Create layer
var layer = new ConvLayer <double>(filterWidth, filterHeight, filterCount)
{
Stride = 2, BiasPref = 0.1
};
layer.Init(inputWidth, inputHeight, inputDepth);
// Forward pass
var input = BuilderInstance <double> .Volume.Random(new Shape(inputWidth, inputHeight, inputDepth));
var output = layer.DoForward(input, true);
// Set output gradients to 1
var outputGradient = BuilderInstance <double> .Volume.From(new double[output.Shape.TotalLength].Populate(1.0), output.Shape);
// Backward pass to retrieve gradients
layer.Backward(outputGradient);
var step1 = ((Volume <double>)layer.InputActivationGradients.Clone()).ToArray();
layer.Backward(outputGradient);
var step2 = ((Volume <double>)layer.InputActivationGradients.Clone()).ToArray();
Assert.IsTrue(step1.SequenceEqual(step2));
}
public void ForwardBackwardTest()
{
var input = new TensorOld(new double[]
{
4, 6, 1, 4,
8, 4, 5, 1,
5, 3, 5, 7,
1, 7, 2, 8,
}, 1, 1, 4, 4);
var conv = new ConvLayer(4, 2, 1, 1);
conv.SetFilters(new TensorOld(new double[]
{
1, 1, 0, 0,
0, 0, 1, 1,
1, 0, 1, 0,
0, 1, 0, 1,
}, 4, 1, 2, 2));
var expected = new TensorOld(new double[]
{
0, 0, 0, 0, 0,
4, 10, 7, 5, 4,
8, 12, 9, 6, 1,
5, 8, 8, 12, 7,
1, 8, 9, 10, 8,
4, 10, 7, 5, 4,
8, 12, 9, 6, 1,
5, 8, 8, 12, 7,
1, 8, 9, 10, 8,
0, 0, 0, 0, 0,
0, 4, 6, 1, 4,
0, 12, 10, 6, 5,
0, 13, 7, 10, 8,
0, 6, 10, 7, 15,
0, 1, 7, 2, 8,
4, 6, 1, 4, 0,
12, 10, 6, 5, 0,
13, 7, 10, 8, 0,
6, 10, 7, 15, 0,
1, 7, 2, 8, 0,
}, 1, 4, 5, 5);
conv.PrepareTrain(input);
var acutal = conv.Forward(input);
Assert.Equal(acutal, expected);
var error = expected / 10;
var back = conv.Backward(error);
}
