# tf.losses.cosine_distance(labels, predictions, dim=None, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES)

### 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.

#### Args:

• 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.

#### Returns:

A scalar Tensor representing the loss value.

#### Raises:

• ValueError: If predictions shape doesn't match labels shape, or weights is None.