private void btnPredecir_Click(object sender, EventArgs e)
{
var sepalLength = ParsearControlInput(txtSepalLength);
if (float.IsNaN(sepalLength))
{
return;
}
var sepalWidth = ParsearControlInput(txtSepalWidth);
if (float.IsNaN(sepalWidth))
{
return;
}
var petalLength = ParsearControlInput(txtPetalLength);
if (float.IsNaN(petalLength))
{
return;
}
var petalWidth = ParsearControlInput(txtPetalWidth);
if (float.IsNaN(petalWidth))
{
return;
}
var valoresSalida = _neuralNetwork.Forward(new[] { sepalLength, sepalWidth, petalLength, petalWidth });
MostrarValorSalida(new (Control Control, float Valor)[]
public float[] Predict(float[] input, float epsilon) //epsilon = percentage of explorarion)
{
if (_random.NextDouble() > epsilon)
{
return(_network.Forward(input));
}
var result = new float[_outputs];
for (var i = 0; i < _outputs; i++)
{
result[i] = (float)_random.NextDouble();
}
return(result);
}
private static void ForwardTest([NotNull] INeuralNetwork n1, [NotNull] INeuralNetwork n2)
{
float[,] x = new float[257, n1.InputInfo.Size];
for (int i = 0; i < 257; i++)
{
for (int j = 0; j < n1.InputInfo.Size; j++)
{
x[i, j] = ThreadSafeRandom.NextFloat();
}
}
float[,]
y1 = n1.Forward(x),
y2 = n2.Forward(x);
Assert.IsTrue(y1.ContentEquals(y2));
}
// Tries to process a random batch through the network (this method should just not crash)
private static void ValidateGraph([NotNull] INeuralNetwork network)
{
float[,]
x = new float[200, network.InputInfo.Size],
y = new float[200, network.OutputInfo.Size];
for (int i = 0; i < 200; i++)
{
for (int j = 0; j < x.GetLength(1); j++)
{
x[i, j] = ThreadSafeRandom.NextFloat();
}
y[i, ThreadSafeRandom.NextInt(max: y.GetLength(1))] = 1;
}
_ = network.Forward(x);
SamplesBatch batch = new SamplesBatch(x, y);
ComputationGraphNetwork graph = network.To <INeuralNetwork, ComputationGraphNetwork>();
graph.Backpropagate(batch, 0.5f, WeightsUpdaters.AdaDelta(TrainingAlgorithms.AdaDelta(), graph));
_ = network.ExtractDeepFeatures(x);
}