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

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.

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.