model_remediation.min_diff.losses.MinDiffKernel

MinDiffKernel abstract base class.

model_remediation.min_diff.losses.MinDiffKernel(
    tile_input: bool = True
)

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

@abc.abstractmethod
call(
    x: types.TensorType, y: types.TensorType
)

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].

from_config

View source

@classmethod
from_config(
    config
)

Creates a MinDiffKernel instance fron the config.

Any subclass with additional attributes or a different initialization signature will need to override this method or get_config.

Returns
A new MinDiffKernel instance corresponding to config.

get_config

View source

get_config()

Creates a config dictionary for the MinDiffKernel instance.

Any subclass with additional attributes will need to override this method. When doing so, users will mostly likely want to first call super.

Returns
A config dictionary for the MinDiffKernel isinstance.

__call__

View source

__call__(
    x: types.TensorType, y: Optional[types.TensorType] = None
) -> types.TensorType

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].