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.
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
.Weighted loss float Tensor
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
ValueError
: If predictions
shape doesn't match labels
shape, or axis
, labels
, predictions
or weights
is None
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/losses/cosine_distance