tf.compat.v1.losses.compute_weighted

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

tf.compat.v1.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.

TensorFlow

tf.compat / v1 / v1.losses.compute_weighted_loss

Args

Returns

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Eager Compatibility

TensorFlow

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tf.compat / v1 / v1.losses.compute_weighted_loss

Args

Returns

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Eager Compatibility