tf.compat.v1.metrics.recall_at_top

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.

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.

TensorFlow

tf.compat / v1 / v1.metrics.recall_at_top_k

Args

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.metrics.recall_at_top_k

Args

Returns

Raises