// test sizes to make test algorithms stay in index bounds
public static void TestSizes()
{
var nn = new SimpleNeuralNet();
nn.Create(2, 3, 5, 1, 4, 3);
var output = nn.FeedForward(new Vector(1, 2));
nn.Backpropagate(0.1f, output);
}
// test example from https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
public static void Test1()
{
var nn = new SimpleNeuralNet();
nn.Create(2, 2, 2);
nn.W[0][0, 0] = 0.15f;
nn.W[0][0, 1] = 0.20f;
nn.W[0][1, 0] = 0.25f;
nn.W[0][1, 1] = 0.30f;
nn.b[0][0] = 0.35f;
nn.b[0][1] = 0.35f;
nn.W[1][0, 0] = 0.40f;
nn.W[1][0, 1] = 0.45f;
nn.W[1][1, 0] = 0.50f;
nn.W[1][1, 1] = 0.55f;
nn.b[1][0] = 0.60f;
nn.b[1][1] = 0.60f;
nn.f[1] = Vectorize(Logistic);
nn.df[1] = Vectorize(dLogistic);
nn.f[2] = Vectorize(Logistic);
nn.df[2] = Vectorize(dLogistic);
var input = new Vector(0.05f, 0.10f);
// should output ~ 0.75136507, 0.772928465
var output = nn.FeedForward(input);
// desired output
var t1 = new Vector(0.01f, 0.99f);
// error vec
var evec = output - t1;
// should be 0.29837110+
var error = 0.5 * evec.LengthSquared();
var learning = 0.50f;
nn.Backpropagate(learning, evec);
output = nn.FeedForward(input);
// error vec
evec = output - t1;
// should be 0.291027924 - TODO - example did not update bias vecs!
error = 0.5 * evec.LengthSquared();
}