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

tf.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.

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. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/metrics/root_mean_squared_error