()
return base.Fn('MaskedSequenceAccuracy', f)
r"""Returns a layer that computes sequence prediction accuracy with masking.

  This layer type is intended for variable length sequences, especially text,
  represented as a batch of fixed-length sequences via padding for unused
  positions.

  The layer takes three inputs:

    - A batch of sequences of activation vectors. The components in a given
      vector should be mappable to a probability distribution in the following
      loose sense: within a vector, a higher component value corresponds to a
      higher probability, such that argmax within a vector (``axis=-1``) picks
      the index having the highest probablity. In text modeling, the index
      represents a token id from a predetermined token vocabulary (or padding).

    - A batch of target integer sequences, with values in
      :math:`\{0, ..., N-1\}`, where :math:`N` is the activation vector
      depth/dimensionality. In text modeling, these sequences typically
      represent token ids from a predetermined token vocabulary (or padding).

    - A batch of weights/masks, which matches or can be broadcast to match the
      shape of the target ndarray. This arg is used to give weight 0 to padding
      positions, which masks those positions out of the calculation. Only the
      zero/non-zero distinction matters; all non-zero values are treated alike
      as signaling non-masked (i.e., valid/in-use) positions.

  The predicted integer value for each sequence position is the index of the
  highest-valued component of the position's vector. A predicted integer
  sequence is judged correct if it matches the target integer sequence in all
  non-zero-weighted positions. The layer returns the accuracy of predicted
  sequences averaged over the batch.
r"""Returns a layer that computes sequence prediction accuracy with masking.
r Returns a layer that computes sequence prediction accuracy with masking .
def MaskedSequenceAccuracy():
  r"""Returns a layer that computes sequence prediction accuracy with masking.

  This layer type is intended for variable length sequences, especially text,
  represented as a batch of fixed-length sequences via padding for unused
  positions.

  The layer takes three inputs:

    - A batch of sequences of activation vectors. The components in a given
      vector should be mappable to a probability distribution in the following
      loose sense: within a vector, a higher component value corresponds to a
      higher probability, such that argmax within a vector (``axis=-1``) picks
      the index having the highest probablity. In text modeling, the index
      represents a token id from a predetermined token vocabulary (or padding).

    - A batch of target integer sequences, with values in
      :math:`\{0, ..., N-1\}`, where :math:`N` is the activation vector
      depth/dimensionality. In text modeling, these sequences typically
      represent token ids from a predetermined token vocabulary (or padding).

    - A batch of weights/masks, which matches or can be broadcast to match the
      shape of the target ndarray. This arg is used to give weight 0 to padding
      positions, which masks those positions out of the calculation. Only the
      zero/non-zero distinction matters; all non-zero values are treated alike
      as signaling non-masked (i.e., valid/in-use) positions.

  The predicted integer value for each sequence position is the index of the
  highest-valued component of the position's vector. A predicted integer
  sequence is judged correct if it matches the target integer sequence in all
  non-zero-weighted positions. The layer returns the accuracy of predicted
  sequences averaged over the batch.
  """
  def f(model_output, targets, weights):  # pylint: disable=invalid-name
    predictions = jnp.argmax(model_output, axis=-1)
    shapes.assert_same_shape(predictions, targets)
    position_is_padding = jnp.equal(weights, 0)
    position_is_accurate = jnp.logical_or(jnp.equal(predictions, targets),
                                          position_is_padding)
    sequence_is_accurate = jnp.all(position_is_accurate, axis=-1)
    return jnp.average(sequence_is_accurate)

  return base.Fn('MaskedSequenceAccuracy', f)
def MaskedSequenceAccuracy ( ) : def f ( model_output , targets , weights ) : # pylint: disable=invalid-name predictions = jnp . argmax ( model_output , axis = - 1 ) shapes . assert_same_shape ( predictions , targets ) position_is_padding = jnp . equal ( weights , 0 ) position_is_accurate = jnp . logical_or ( jnp . equal ( predictions , targets ) , position_is_padding ) sequence_is_accurate = jnp . all ( position_is_accurate , axis = - 1 ) return jnp . average ( sequence_is_accurate ) return base . Fn ( 'MaskedSequenceAccuracy' , f )