tf.compat.v1.metrics.precision_at_top

Computes the recall of the predictions with respect to the labels.

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

Used in the notebooks

Used in the tutorials
Exploring the TF-Hub CORD-19 Swivel Embeddings

The recall function creates two local variables, true_positives and false_negatives, that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides true_positives by the sum of true_positives and false_negatives.

For estimation of the metric over a stream of data, the function creates an update_op that updates these variables and returns the recall. update_op weights each prediction by the corresponding value in weights.

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

Args
`labels`	The ground truth values, a `Tensor` whose dimensions must match `predictions`. Will be cast to `bool`.
`predictions`	The predicted values, a `Tensor` of arbitrary dimensions. Will be cast to `bool`.
`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 `recall` 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
`recall`	Scalar float `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_negatives`.
`update_op`	`Operation` that increments `true_positives` and `false_negatives` variables appropriately and whose value matches `recall`.

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.precision_at_top_k

Used in the notebooks

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.precision_at_top_k

Used in the notebooks

Args

Returns

Raises