(label_smoothing=None, cutoff=0.0)
return base.Fn('WeightedCategoryCrossEntropy', f)
r"""Returns a layer like ``CategoryCrossEntropy``, with weights as third input.

  The layer takes three inputs:

    - A batch of activation vectors. The components in a given vector should
      be pre-softmax activations (mappable to a probability distribution via
      softmax). For performance reasons, the softmax and cross-entropy
      computations are combined inside the layer.

    - A batch of target categories; each target is an integer in
      :math:`\{0, ..., N-1\}`, where :math:`N` is the activation vector
      depth/dimensionality.

    - A batch of weights, which matches or can be broadcast to match the shape
      of the target ndarray. This arg can give uneven weighting to different
      items in the batch (depending, for instance, on the item's target
      category).

  The layer returns the weighted average of these cross-entropy values over all
  items in the batch.

  Args:
    label_smoothing: Creates soft targets if provided. Must be between 0 and 1.
    cutoff: Prevent loss lower than this cutoff (0.0 meaning none by default).
r"""Returns a layer like ``CategoryCrossEntropy``, with weights as third input.
r Returns a layer like CategoryCrossEntropy with weights as third input .
def WeightedCategoryCrossEntropy(label_smoothing=None, cutoff=0.0):
  r"""Returns a layer like ``CategoryCrossEntropy``, with weights as third input.

  The layer takes three inputs:

    - A batch of activation vectors. The components in a given vector should
      be pre-softmax activations (mappable to a probability distribution via
      softmax). For performance reasons, the softmax and cross-entropy
      computations are combined inside the layer.

    - A batch of target categories; each target is an integer in
      :math:`\{0, ..., N-1\}`, where :math:`N` is the activation vector
      depth/dimensionality.

    - A batch of weights, which matches or can be broadcast to match the shape
      of the target ndarray. This arg can give uneven weighting to different
      items in the batch (depending, for instance, on the item's target
      category).

  The layer returns the weighted average of these cross-entropy values over all
  items in the batch.

  Args:
    label_smoothing: Creates soft targets if provided. Must be between 0 and 1.
    cutoff: Prevent loss lower than this cutoff (0.0 meaning none by default).
  """
  def f(model_output, targets, weights):  # pylint: disable=invalid-name
    cross_entropies = _category_cross_entropy(
        model_output, targets, label_smoothing, cutoff)
    return jnp.sum(cross_entropies * weights) / _n_weights_per_core(weights)

  return base.Fn('WeightedCategoryCrossEntropy', f)
def WeightedCategoryCrossEntropy ( label_smoothing = None , cutoff = 0.0 ) : def f ( model_output , targets , weights ) : # pylint: disable=invalid-name cross_entropies = _category_cross_entropy ( model_output , targets , label_smoothing , cutoff ) return jnp . sum ( cross_entropies * weights ) / _n_weights_per_core ( weights ) return base . Fn ( 'WeightedCategoryCrossEntropy' , f )