static void Main(string[] args)
{
var rd = new Random();
var values = new[] { .1f, .002f };
var watch = System.Diagnostics.Stopwatch.StartNew();
ProcessingDevice.Device = Device.CPU;
watch.Stop();
Console.WriteLine($"Device Time: {watch.ElapsedMilliseconds}ms");
var hiddens = new LayerBuilder(2, 4, values[0])
.Supervised()
.WithLeakRelu()
.Hidden()
.WithSGD()
.WithMomentum(values[1])
.FullSynapse()
.Build();
var hiddens2 = new LayerBuilder(2, 2, values[0])
.Supervised()
.WithLeakRelu()
.Hidden()
.WithSGD()
.WithMomentum(values[1])
.FullSynapse()
.Build();
var outputs = new LayerBuilder(2, 2, values[0])
.Supervised()
.WithSigmoid()
.Output()
.WithSGD()
.WithMomentum(values[1])
.FullSynapse()
.Build();
var loss = new SquareLossFunction();
watch = System.Diagnostics.Stopwatch.StartNew();
watch.Stop();
Console.WriteLine($"Sinapse Time: {watch.ElapsedMilliseconds}ms");
var trainingValues = new[]
{
new[] { 0f, 0f, 0f, 0f },
new[] { 1f, 0f, 0f, 0f },
new[] { 0f, 1f, 0f, 0f },
new[] { 0f, 0f, 1f, 0f },
new[] { 0f, 0f, 0f, 1f },
new[] { 1f, 0f, 0f, 0f },
new[] { 1f, 1f, 0f, 0f },
new[] { 0f, 1f, 1f, 0f },
new[] { 0f, 0f, 1f, 1f },
new[] { 0f, 0f, 0f, 1f },
new[] { 0f, 1f, 0f, 0f },
new[] { 1f, 0f, 1f, 0f },
new[] { 0f, 1f, 0f, 1f },
new[] { 1f, 0f, 1f, 0f },
new[] { 0f, 1f, 0f, 1f },
new[] { 0f, 0f, 1f, 0f },
new[] { 1f, 0f, 0f, 1f },
new[] { 1f, 1f, 0f, 0f },
new[] { 0f, 1f, 1f, 0f },
new[] { 0f, 0f, 1f, 1f },
new[] { 1f, 1f, 1f, 0f },
new[] { 1f, 1f, 1f, 1f }
};
var desiredValues = new[]
{
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 1f, 0f },
new[] { 0f, 1f },
new[] { 1f, 0f },
new[] { 0f, 1f }
};
int cont = 0;
int sizeTrain = 10;
var e = double.MaxValue;
while (true)
{
watch = System.Diagnostics.Stopwatch.StartNew();
e = 0;
for (int i = 0; i < sizeTrain; i++)
{
var index = rd.Next(0, trainingValues.Length);
var inputs = trainingValues[index];
var desireds = desiredValues[index];
//watch = System.Diagnostics.Stopwatch.StartNew();
// Feed Forward
var _h = hiddens.Output(inputs);
var _h2 = hiddens2.Output(_h);
var _o = outputs.Output(_h2);
//watch.Stop();
//Console.WriteLine($"\nForward Time: { watch.ElapsedMilliseconds}ms");
//Thread.Sleep(100);
//watch = System.Diagnostics.Stopwatch.StartNew();
// Backward
var _oe = ((ISupervisedLearning)outputs).Learn(_h2, desireds);
var _he2 = ((ISupervisedLearning)hiddens2).Learn(_h, _oe);
((ISupervisedLearning)hiddens).Learn(inputs, _he2);
//watch.Stop();
//Console.WriteLine($"\nBackward Time: { watch.ElapsedMilliseconds}ms");
// Error
var e0 = Math.Abs(_o[0] - desireds[0]);
var e1 = Math.Abs(_o[1] - desireds[1]);
var error = Math.Sqrt(Math.Abs(e0 * e0 + e1 * e0));
e += error / 2.0;
}
e /= sizeTrain;
cont++;
watch.Stop();
var time = watch.ElapsedMilliseconds;
Console.WriteLine($"Interactions: {cont}\nError: {e}");
//Console.WriteLine($"Interactions: {cont}\nError: {e}\nTime: {time / (double)sizeTrain}ms");
Console.Title =
$"TSPS (Training Sample per Second): {Math.Ceiling(1000d / ((double) time / (double)sizeTrain))}";
}
}