Computes [email protected] of the predictions with respect to sparse labels.

tf.metrics.precision_at_k( labels, predictions, k, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None )

If `class_id`

is specified, we calculate precision by considering only the entries in the batch for which `class_id`

is in the top-k highest `predictions`

, and computing the fraction of them for which `class_id`

is indeed a correct label. If `class_id`

is not specified, we'll calculate precision as how often on average a class among the top-k classes with the highest predicted values of a batch entry is correct and can be found in the label for that entry.

`precision_at_k`

creates two local variables, `true_positive_at_<k>`

and `false_positive_at_<k>`

, that are used to compute the [email protected] frequency. This frequency is ultimately returned as `precision_at_<k>`

: an idempotent operation that simply divides `true_positive_at_<k>`

by total (`true_positive_at_<k>`

+ `false_positive_at_<k>`

).

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_at_<k>`

. Internally, a `top_k`

operation computes a `Tensor`

indicating the top `k`

`predictions`

. Set operations applied to `top_k`

and `labels`

calculate the true positives and false positives weighted by `weights`

. Then `update_op`

increments `true_positive_at_<k>`

and `false_positive_at_<k>`

using these values.

If `weights`

is `None`

, weights default to 1. Use weights of 0 to mask values.

Args | |
---|---|

`labels` | `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and `labels` has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions` . Values should be in range [0, num_classes), where num_classes is the last dimension of `predictions` . Values outside this range are ignored. |

`predictions` | Float `Tensor` with shape [D1, ... DN, num_classes] where N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes]. The final dimension contains the logit values for each class. [D1, ... DN] must match `labels` . |

`k` | Integer, k for @k metric. |

`class_id` | Integer class ID for which we want binary metrics. This should be in range [0, num_classes], where num_classes is the last dimension of `predictions` . If `class_id` is outside this range, the method returns NAN. |

`weights` | `Tensor` whose rank is either 0, or n-1, where n is the rank of `labels` . If the latter, it 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 values should be added to. |

`updates_collections` | An optional list of collections that updates should be added to. |

`name` | Name of new update operation, and namespace for other dependent ops. |

Returns | |
---|---|

`precision` | Scalar `float64` `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives` . |

`update_op` | `Operation` that increments `true_positives` and `false_positives` variables appropriately, and whose value matches `precision` . |

Raises | |
---|---|

`ValueError` | 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_k