Function cosine_distance

tensorflow/python/ops/losses/losses_impl.py:261–312

Adds a cosine-distance loss to the training procedure. 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

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

@tf_export(v1=["losses.cosine_distance"])
@deprecated_args(None, "dim is deprecated, use axis instead", "dim")
def cosine_distance(
    labels, predictions, axis=None, weights=1.0, scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
    dim=None):
  """Adds a cosine-distance loss to the training procedure.

  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`.

  @compatibility(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
  """
  axis = deprecated_argument_lookup("axis", axis, "dim", dim)
  if axis is None:
    raise ValueError("You must specify 'axis'.")
  if labels is None:
    raise ValueError("labels must not be None.")
  if predictions is None:
    raise ValueError("predictions must not be None.")
  with ops.name_scope(scope, "cosine_distance_loss",
                      (predictions, labels, weights)) as scope:
    predictions = math_ops.cast(predictions, dtype=dtypes.float32)
    labels = math_ops.cast(labels, dtype=dtypes.float32)
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())

    radial_diffs = math_ops.multiply(predictions, labels)
    losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True)
    return compute_weighted_loss(
        losses, weights, scope, loss_collection, reduction=reduction)


@tf_export(v1=["losses.hinge_loss"])