model_remediation.min_diff.losses.MinDiffKernel

MinDiffKernel abstract base class.

tile_input Boolean indicating whether to tile inputs before computing the kernel (see below for details).

To be implemented by subclasses:

  • call(): contains the logic for the kernel tensor calculation.

Example subclass Implementation:

class GuassKernel(MinDiffKernel):

  def call(x, y):
    return tf.exp(-tf.reduce_sum(tf.square(x - y), axis=2) / 0.01)

"Tiling" is a way of expanding the rank of the input tensors so that their dimensions work for the operations we need.

If x and y are of rank [N, D] and [M, D] respectively, tiling expands them to be: [N, ?, D] and [?, M, D] where tf broadcasting will ensure that the operations between them work.

Methods

call

View source

Invokes the MinDiffKernel instance.

Arguments
x tf.Tensor of shape [N, M, D].
y tf.Tensor of shape [N, M, D].

This method contains the logic for computing the kernel. It must be implemented by subclasses.

Returns
tf.Tensor of shape [N, M].

__call__

View source

Invokes the kernel instance.

Arguments
x tf.Tensor of shape [N, D] (if tiling input) or [N, M, D] (if not tiling input).
y Optional tf.Tensor of shape [M, D] (if tiling input) or [N, M, D] (if not tiling input).

If y is None, it is set to be the same as x:

if y is None:
  y = x

Inputs are tiled if self.tile_input == True and left as is otherwise.

Returns
tf.Tensor of shape [N, M].