# tf.compat.v1.losses.cosine_distance

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/losses/losses_impl.py#L270-L324) |

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=ops.GraphKeys.LOSSES,
        reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
        dim=None
    )

> [!WARNING]
> **Deprecated:** SOME ARGUMENTS ARE DEPRECATED: `(dim)`. They will be removed in a future version. Instructions for updating: dim is deprecated, use axis instead

Note that the function assumes that `predictions` and `labels` are already
unit-normalized.

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| ## 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`. |

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| ## Returns ||
|---|---|
| Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same shape as `labels`; otherwise, it is scalar. ||

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| ## Raises ||
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| `ValueError` | If `predictions` shape doesn't match `labels` shape, or `axis`, `labels`, `predictions` or `weights` is `None`. |

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## eager compatibility

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The `loss_collection` argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a [`tf.keras.Model`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/Model).

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