C.Function create_transforming_autoencoder(
int num_capsules,
int[] input_shape,
int[] extra_input_shape,
int recognizer_dim,
int generator_dim)
{
var capsules_outputs = new List <C.Function>();
for (int i = 0; i < num_capsules; i++)
{
var capsule = create_capsule($"capsule_{i}", input_shape, extra_input_shape, recognizer_dim, generator_dim);
var capsule_output = Model.invoke_model(capsule, new C.Variable[] { imageVariable, transformationVariable });
capsules_outputs.Add(capsule_output);
}
var inference = capsules_outputs[0];
for (int i = 1; i < capsules_outputs.Count; i++)
{
inference = CC.Plus(inference, capsules_outputs[i]);
}
inference = CC.Sigmoid(inference, "autoencoder_inference");
return(inference);
}
public Tensor binary_crossentropy(Tensor output, Tensor target, bool from_logits = false)
{
log(new { output, target, from_logits });
var _output = new Variable(In(output).function);
var _target = new Variable(In(target).function);
if (from_logits)
{
_output = C.Sigmoid(_output);
}
// scale preds so that the class probas of each sample sum to 1
var eps = InConstant(epsilon());
var omeps = InConstant(1.0);
// avoid numerical instability with _EPSILON clipping
_output = C.Clip(_output, eps, omeps);
var a = new Variable(C.Negate(C.ElementTimes(_target, C.Log(_output))));
var b = new Variable(C.Negate(
C.Minus(C.ElementTimes(InConstant(1.0), _target),
C.Minus(C.Log(InConstant(1.0)), _output))));
_output = a + b;
return(Out(_output));
}
public static Func <Variable, Function> Sigmoid(string name = null)
{
return(x => C.Sigmoid(x, name));
}
public Tensor sigmoid(Tensor x)
{
return(Out(C.Sigmoid(In(x))));
}
