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

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

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. |

© 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/recall