C.Function get_mask_and_infer_from_last_dimension(C.Function inputs, C.Function mask)
{
if (mask == null)
{
var inputs_shape = inputs.Output.Shape.Dimensions.ToArray();
var ndims = inputs_shape.Length - 1;
var x = CC.Sqrt(CC.ReduceSum(CC.Square(inputs), new C.Axis(ndims - 1)));
x = CC.Squeeze(x);
System.Diagnostics.Debug.Assert(x.Output.Shape.Dimensions.Count == 1);
x = CC.Argmax(x, new C.Axis(0));
mask = CC.OneHotOp(x, numClass: (uint)inputs_shape[0], outputSparse: false, axis: new C.Axis(0));
}
mask = CC.Reshape(mask, mask.Output.Shape.AppendShape(new int[] { 1 }));
var masked = CC.ElementTimes(inputs, mask);
masked = CC.Flatten(masked);
masked = CC.Squeeze(masked);
return(masked);
}
C.Function create_capsule(string name, int[] input_shape, int[] extra_input_shape, int recognizer_dim, int generator_dim)
{
var input_dim = Util.np_prod(input_shape);
var x = Util.placeholderVariable(input_shape, "x");
var extra_input = Util.placeholderVariable(extra_input_shape, "extra_input");
var x_flat = CC.Flatten(x);
var recognition = Layers.Dense(x_flat, recognizer_dim, computeDevice, "recognition_layer", CC.Sigmoid);
var probability = Layers.Dense(recognition, 1, computeDevice, "probability", CC.Sigmoid);
var learnt_transformation = Layers.Dense(recognition, 2, computeDevice, "xy_prediction");
var learnt_transformation_extended = CC.Plus(learnt_transformation, extra_input, "learnt_transformation_extended");
var generation = Layers.Dense(learnt_transformation_extended, generator_dim, computeDevice, "generator_layer", CC.Sigmoid);
var out_flat = Layers.Dense(generation, input_dim, computeDevice, "output");
out_flat = CC.ElementTimes(out_flat, probability);
var output = CC.Reshape(out_flat, input_shape);
return(output);
}
