public void ActivationTest()
{
Activation act = new Activation("relu");
var obj = act.ToPython();
var act1 = Activations.Softmax(np.array <float>(1, 2, 3, 4).reshape(2, 2));
}
public override KerasSymbol[] Invoke(KerasSymbol[] inputs, FuncArgs kwargs = null)
{
List <KerasSymbol> result = new List <KerasSymbol>();
foreach (var input in inputs)
{
result.Add(Activations.Softmax(input, axis));
}
return(result.ToArray());
}
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));
}
public Tensor Forward(Tensor inputs)
{
Tensor weights = BuildWeightTensor();
Tensor biases = BuildBiases();
Tensor transposed = weights.Transpose();
Tensor result = inputs.Dot(transposed).VecSum(biases);
switch (_activation)
{
case ActivationType.ReLU:
Activations.ReLU(result);
break;
case ActivationType.Softmax:
Activations.Softmax(result);
break;
}
return(result);
}