C.Function get_length_and_remove_last_dimension(C.Function x, string name)
{
var number_dimensions = x.Output.Shape.Dimensions.Count;
x = CC.Square(x);
var sum_entries = CC.ReduceSum(x, new C.Axis(number_dimensions - 1));
var epsilon = C.Constant.Scalar(C.DataType.Float, 1e-7, computeDevice);
x = CC.Sqrt(CC.Plus(sum_entries, epsilon));
x = CC.Squeeze(x);
return(x);
}
C.Function squash(C.Function vectors, string name, int axis)
{
var squared_values = CC.Square(vectors);
var s_squared_sum = CC.ReduceSum(squared_values, new C.AxisVector(new C.Axis[] { new C.Axis(axis) }), keepDims: true);
var epsilon = C.Constant.Scalar(C.DataType.Float, 1e-7, computeDevice);
var one = C.Constant.Scalar(C.DataType.Float, 1.0, computeDevice);
var normalize_factor = CC.Plus(CC.Sqrt(s_squared_sum), epsilon);
var one_plus_s_squared_sum = CC.Plus(s_squared_sum, one);
var scale = CC.ElementDivide(s_squared_sum, one_plus_s_squared_sum);
scale = CC.ElementDivide(scale, normalize_factor);
var result = CC.ElementTimes(scale, vectors, name);
return(result);
}
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);
}
public Tensor sqrt(Tensor x)
{
return(Out(C.Sqrt(In(x))));
}
public Tensor sqrt(Tensor x)
{
log(new { x });
return(Out(C.Sqrt(In(x))));
}
