tf.losses.cosine_distance(labels, predictions, dim=None, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)
Adds a cosine-distance loss to the training procedure.
Note that the function assumes that predictions
and labels
are already unit-normalized.
labels
: Tensor
whose shape matches 'predictions'predictions
: An arbitrary matrix.dim
: 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.A scalar Tensor
representing the loss value.
ValueError
: If predictions
shape doesn't match labels
shape, or weights
is None
.Defined in tensorflow/python/ops/losses/losses_impl.py
.
© 2017 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