Computes precision values for different `thresholds`

on `predictions`

.

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

The `precision_at_thresholds`

function creates four local variables, `true_positives`

, `true_negatives`

, `false_positives`

and `false_negatives`

for various values of thresholds. `precision[i]`

is defined as the total weight of values in `predictions`

above `thresholds[i]`

whose corresponding entry in `labels`

is `True`

, divided by the total weight of values in `predictions`

above `thresholds[i]`

(`true_positives[i] / (true_positives[i] + false_positives[i])`

).

For estimation of the metric over a stream of data, the function creates an `update_op`

operation that updates these variables and returns the `precision`

.

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` | A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]` . |

`thresholds` | A python list or tuple of float thresholds in `[0, 1]` . |

`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 `auc` 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 | |
---|---|

`precision` | A float `Tensor` of shape `[len(thresholds)]` . |

`update_op` | An operation that increments the `true_positives` , `true_negatives` , `false_positives` and `false_negatives` variables that are used in the computation of `precision` . |

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/precision_at_thresholds