/// <summary>
/// Computes and displays t = t + 1
/// </summary>
public static void Example3()
{
var cns = new ConvNetSharp <float>();
BuilderInstance <float> .Volume = new VolumeBuilder();
// Graph creation
var t = cns.Variable(0.0f, "t", true);
var fun = cns.Assign(t, t + 1.0f);
using (var session = new Session <float>())
{
session.InitializePlaceHolders(fun, new Dictionary <string, Volume <float> > {
{ "t", 1.0f }
});
do
{
session.Run(fun, null);
var x = t.Result.Get(0);
Console.WriteLine(x);
} while (!Console.KeyAvailable);
}
}
public SoftmaxCrossEntropy(ConvNetSharp <T> graph, Op <T> softmax, Op <T> y) : base(graph)
{
AddParent(softmax);
AddParent(y);
this.Result = BuilderInstance <T> .Volume.SameAs(new Shape(1, 1, 1, 1));
}
public AdamOptimizer(ConvNetSharp <T> graph, T learningRate, T beta1, T beta2, T epsilon) : base(graph)
{
this._lr = learningRate;
this._beta1 = beta1;
this._beta2 = beta2;
this._epsilon = epsilon;
this._learningRate = BuilderInstance <T> .Volume.SameAs(new Shape(1));
}
public Assign(ConvNetSharp <T> graph, Op <T> valueOp, Op <T> op) : base(graph)
{
if (!(valueOp is Variable <T>))
{
throw new ArgumentException("Assigned Op should be a Variable", nameof(valueOp));
}
this.AddParent(valueOp);
this.AddParent(op);
}
public void SqrtGradientCheck()
{
var cns = new ConvNetSharp <T>();
var x = cns.PlaceHolder("x");
var fun = cns.Sqrt(x);
var shape = new Shape(2, 2, 3, 4);
var location = this.NewVolume(RandomUtilities.RandomDoubleArray(shape.TotalLength, posisitveOnly: true), shape);
this.GradientCheck(cns, fun, location);
}
public void SigmoidGradientCheck()
{
var cns = new ConvNetSharp <T>();
var x = cns.PlaceHolder("x");
var fun = cns.Sigmoid(x);
var shape = new Shape(2, 2, 3, 4);
var location = this.NewVolume(RandomUtilities.RandomDoubleArray(shape.TotalLength), shape);
this.GradientCheck(cns, fun, location, 1e-3);
}
