# tf.compat.v1.metrics.root_mean_squared_error

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/metrics_impl.py#L2918-L2986) |

Computes the root mean squared error between the labels and predictions.

    tf.compat.v1.metrics.root_mean_squared_error(
        labels,
        predictions,
        weights=None,
        metrics_collections=None,
        updates_collections=None,
        name=None
    )

The `root_mean_squared_error` function creates two local variables,
`total` and `count` that are used to compute the root mean squared error.
This average is weighted by `weights`, and it is ultimately returned as
`root_mean_squared_error`: an idempotent operation that takes the square root
of the division of `total` by `count`.

For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`root_mean_squared_error`. Internally, a `squared_error` operation computes
the element-wise square of the difference between `predictions` and `labels`.
Then `update_op` increments `total` with the reduced sum of the product of
`weights` and `squared_error`, and it increments `count` with the reduced sum
of `weights`.

If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

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| ## Args ||
|---|---|
| `labels` | A `Tensor` of the same shape as `predictions`. |
| `predictions` | A `Tensor` of arbitrary shape. |
| `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 `labels` dimension). |
| `metrics_collections` | An optional list of collections that `root_mean_squared_error` should be added to. |
| `updates_collections` | An optional list of collections that `update_op` should be added to. |
| `name` | An optional variable_scope name. |

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| ## Returns ||
|---|---|
| `root_mean_squared_error` | A `Tensor` representing the current mean, the value of `total` divided by `count`. |
| `update_op` | An operation that increments the `total` and `count` variables appropriately and whose value matches `root_mean_squared_error`. |

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| ## Raises ||
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| `ValueError` | If `predictions` and `labels` have mismatched shapes, or if `weights` is not `None` and its shape doesn't match `predictions`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. |
| `RuntimeError` | If eager execution is enabled. |

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