public void SimpleNet_OneIter_Dropout()
{
// arrange
var drate = 0.5D;
var dseed = 1;
var net = Mocks.SimpleLinearNetworkWithDropout(drate, dseed);
var sample = new ClassifiedSample <double[][, ]>();
var point = new double[1][, ] {
new[, ] {
{ 1.0D }
}
};
sample[point] = new Class("a", 0);
var alg = new BackpropAlgorithm(net);
alg.LearningRate = 2.0D;
alg.LossFunction = Loss.Euclidean;
alg.Build();
// act
alg.RunIteration(point, new double[] { 1.0D });
// assert
Assert.AreEqual(12, alg.Values[0][0][0, 0]);
Assert.AreEqual(33, alg.Values[1][0][0, 0]);
Assert.AreEqual(66, alg.Values[2][0][0, 0]);
Assert.AreEqual(-128, alg.Values[3][0][0, 0]);
Assert.AreEqual(3, net[0].ActivationFunction.DerivativeFromValue(alg.Values[0][0][0, 0]));
Assert.AreEqual(3, net[1].ActivationFunction.DerivativeFromValue(alg.Values[1][0][0, 0]));
Assert.AreEqual(2, net[3].ActivationFunction.DerivativeFromValue(alg.Values[3][0][0, 0]));
Assert.AreEqual(-129 * 2, alg.Errors[3][0][0, 0]);
Assert.AreEqual(-258 * (-1), alg.Errors[2][0][0, 0]);
Assert.AreEqual(258 * 3 / drate, alg.Errors[1][0][0, 0]);
Assert.AreEqual(1548 * 3, alg.Errors[0][0][0, 0]);
Assert.AreEqual(-258 * 66, alg.Gradient[3][0]);
Assert.AreEqual(-258, alg.Gradient[3][1]);
Assert.AreEqual(0, alg.Gradient[2].Length);
Assert.AreEqual(0, alg.Gradient[2].Length);
Assert.AreEqual(1548 * 12, alg.Gradient[1][0]);
Assert.AreEqual(1548, alg.Gradient[1][1]);
Assert.AreEqual(4644 * 1, alg.Gradient[0][0]);
Assert.AreEqual(4644, alg.Gradient[0][1]);
// act
alg.FlushGradient();
// assert
Assert.AreEqual(2 + 2 * 258, net[3].Weights[1]);
Assert.AreEqual(-1 + 2 * 258 * 66, net[3].Weights[0]);
Assert.AreEqual(-1 + 2 * (-1548), net[1].Weights[1]);
Assert.AreEqual(1 + 2 * (-1548 * 12), net[1].Weights[0]);
Assert.AreEqual(1 + 2 * (-4644), net[0].Weights[1]);
Assert.AreEqual(3 + 2 * (-4644 * 1), net[0].Weights[0]);
}
public void SimpleNet_Euclidean_OneIter()
{
// arrange
var net = Mocks.SimpleLinearNetwork();
var sample = new ClassifiedSample <double[][, ]>();
var point = new double[1][, ] {
new[, ] {
{ 1.0D }
}
};
var cls = new Class("a", 0);
sample[point] = cls;
var alg = new BackpropAlgorithm(sample, net);
alg.LearningRate = 2.0D;
alg.LossFunction = Loss.Euclidean;
alg.Build();
// act
alg.RunIteration(point, cls);
// assert
Assert.AreEqual(12, alg.Values[0][0][0, 0]);
Assert.AreEqual(33, alg.Values[1][0][0, 0]);
Assert.AreEqual(-62, alg.Values[2][0][0, 0]);
Assert.AreEqual(3, net[0].ActivationFunction.DerivativeFromValue(alg.Values[0][0][0, 0]));
Assert.AreEqual(3, net[1].ActivationFunction.DerivativeFromValue(alg.Values[1][0][0, 0]));
Assert.AreEqual(2, net[2].ActivationFunction.DerivativeFromValue(alg.Values[2][0][0, 0]));
Assert.AreEqual(-126, alg.Errors[2][0][0, 0]);
Assert.AreEqual(378, alg.Errors[1][0][0, 0]);
Assert.AreEqual(1134, alg.Errors[0][0][0, 0]);
Assert.AreEqual(-126 * 33, alg.Gradient[2][0]);
AssertNetGradient(alg, point, 2, 0);
Assert.AreEqual(-126, alg.Gradient[2][1]);
AssertNetGradient(alg, point, 2, 1);
Assert.AreEqual(378 * 12, alg.Gradient[1][0]);
AssertNetGradient(alg, point, 1, 0);
Assert.AreEqual(378, alg.Gradient[1][1]);
AssertNetGradient(alg, point, 1, 1);
Assert.AreEqual(1134 * 1, alg.Gradient[0][0]);
AssertNetGradient(alg, point, 0, 0);
Assert.AreEqual(1134, alg.Gradient[0][1]);
AssertNetGradient(alg, point, 0, 1);
alg.FlushGradient();
Assert.AreEqual(-1 + 2 * 126 * 33, net[2].Weights[0]);
Assert.AreEqual(2 + 2 * 126, net[2].Weights[1]);
Assert.AreEqual(1 + 2 * (-378 * 12), net[1].Weights[0]);
Assert.AreEqual(-1 + 2 * (-378), net[1].Weights[1]);
Assert.AreEqual(3 + 2 * (-1134 * 1), net[0].Weights[0]);
Assert.AreEqual(1 + 2 * (-1134), net[0].Weights[1]);
}