# tf.metrics.root_mean_squared_error

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

### Aliases:

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

Defined in `python/ops/metrics_impl.py`.

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.

#### 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.

#### 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`.

#### Raises:

• `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.