tf.losses.cosine_distance

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

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tf.compat.v1.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
)

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.

Eager Compatibility

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