tf.losses.cosine_distance

tf.losses.cosine_distance(
    labels,
    predictions,
    axis=None,
    weights=1.0,
    scope=None,
    loss_collection=tf.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
    dim=None
)

Defined in tensorflow/python/ops/losses/losses_impl.py.

Adds a cosine-distance loss to the training procedure. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: dim is deprecated, use axis instead

Note that the function assumes that predictions and labels are already unit-normalized.

Args:

  • labels: Tensor whose shape matches 'predictions'
  • predictions: An arbitrary matrix.
  • axis: The dimension along which the cosine distance is computed.
  • weights: Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
  • scope: The scope for the operations performed in computing the loss.
  • loss_collection: collection to which this loss will be added.
  • reduction: Type of reduction to apply to loss.
  • dim: The old (deprecated) name for axis.

Returns:

Weighted loss float Tensor. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.

Raises:

  • ValueError: If predictions shape doesn't match labels shape, or axis, labels, predictions or weights is None.

@compatbility(eager) The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model. @end_compatibility