private KerasSymbol _Call(KerasSymbol x)
{
switch (activation)
{
case "elu":
return(Activations.Elu(x));
case "exp":
return(Activations.Exponential(x));
case "hard_sigmoid":
return(Activations.HardSigmoid(x));
case "linear":
return(Activations.Linear(x));
case "relu":
return(Activations.Relu(x));
case "selu":
return(Activations.Selu(x));
case "sigmoid":
return(Activations.Sigmoid(x));
case "softmax":
return(Activations.Softmax(x));
case "softplus":
return(Activations.Softplus(x));
case "softsign":
return(Activations.Softsign(x));
case "tanh":
return(Activations.Tanh(x));
default:
break;
}
return(Activations.Linear(x));
}
static void Main(string[] args)
{
var X = Matrix <double> .Build.Random(5, 100);
var Y = Matrix <double> .Build.Random(1, 100);
var test = Matrix <double> .Build.Random(5, 1);
var model = new DeepCat();
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(5, Activations.Relu(), weightInitializer: Initializations.RandomNormal()));
model.Add(new Dense(1, Activations.Sigmoid()));
model.Compile(X.RowCount, LossFunctions.CrossEntropy(), Optimizers.GradientDescent(0.002));
model.Fit(X, Y, 100);
model.Predict(test);
var x = 1;
}
