public void EvaluateTest()
{
var layer = new DenseLayer(6, 3, new IdentityActivation(), new Distance());
layer.Initialize();
var input = NNArray.Random(6);
layer.Evaluate(input);
}
public void MatrixCalc()
{
// W*I+B test calculattin Input*Weights + Biases
int nbInput = 6;
int nbOutput = 3;
NNArray I = NNArray.Random(nbInput);
NNMatrix W = NNMatrix.Random(nbInput, nbOutput);
NNArray B = NNArray.Random(nbOutput);
var tmp = W * I;
var O = tmp + B;
Assert.AreEqual(O.Length, nbOutput);
}
public void BackPropagateTest()
{
int nbInput = 6;
var input = NNArray.Random(nbInput);
var output = new double[] { 0, 1 };
IActivation activation;
activation = new IdentityActivation();
TrainNetwork(activation, input, output);
activation = new Sigmoid();
TrainNetwork(activation, input, output);
activation = new Tanh();
TrainNetwork(activation, input, output);
activation = new Relu();
TrainNetwork(activation, input, output);
}
public void MatrixPerformance()
{
// W*I+B test calculattin Input*Weights + Biases
int nbInput = 300;
int nbOutput = 200;
NNArray I = NNArray.Random(nbInput);
NNMatrix W = NNMatrix.Random(nbInput, nbOutput);
NNArray B = NNArray.Random(nbOutput);
int count = 200;
DateTime start;
DateTime end;
double duration;
// Not optimized
start = DateTime.Now;
for (int i = 0; i < count; i++)
{
NNArray res = W * I + B;
}
end = DateTime.Now;
duration = (end - start).TotalMilliseconds / 1000;
Console.WriteLine("Duration Non Optimized: "+duration);
// Optimized
NNArray O = NNArray.Random(nbOutput);
start = DateTime.Now;
for (int i = 0; i < count; i++)
{
W.Multiply(I,O);
O.Add(B,O);
}
end = DateTime.Now;
duration = (end - start).TotalMilliseconds / 1000;
Console.WriteLine("Duration Optimized: " + duration);
}