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

Inherits From: `Metric`

tf.keras.metrics.Precision( thresholds=None, top_k=None, class_id=None, name=None, dtype=None )

The metric creates two local variables, `true_positives`

and `false_positives`

that are used to compute the precision. This value is ultimately returned as `precision`

, an idempotent operation that simply divides `true_positives`

by the sum of `true_positives`

and `false_positives`

.

If `sample_weight`

is `None`

, weights default to 1. Use `sample_weight`

of 0 to mask values.

If `top_k`

is set, 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.

If `class_id`

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

is above the threshold and/or in the top-k highest predictions, and computing the fraction of them for which `class_id`

is indeed a correct label.

Args | |
---|---|

`thresholds` | (Optional) A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true` , below is `false` ). One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate precision with `thresholds=0.5` . |

`top_k` | (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating precision. |

`class_id` | (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval `[0, num_classes)` , where `num_classes` is the last dimension of predictions. |

`name` | (Optional) string name of the metric instance. |

`dtype` | (Optional) data type of the metric result. |

m = tf.keras.metrics.Precision() m.update_state([0, 1, 1, 1], [1, 0, 1, 1]) m.result().numpy() 0.6666667

m.reset_states() m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0]) m.result().numpy() 1.0

# With top_k=2, it will calculate precision over y_true[:2] and y_pred[:2] m = tf.keras.metrics.Precision(top_k=2) m.update_state([0, 0, 1, 1], [1, 1, 1, 1]) m.result().numpy() 0.0

# With top_k=4, it will calculate precision over y_true[:4] and y_pred[:4] m = tf.keras.metrics.Precision(top_k=4) m.update_state([0, 0, 1, 1], [1, 1, 1, 1]) m.result().numpy() 0.5

Usage with `compile()`

API:

model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Precision()])

`reset_states`

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

`update_state`

update_state( y_true, y_pred, sample_weight=None )

Accumulates true positive and false positive statistics.

Args | |
---|---|

`y_true` | The ground truth values, with the same dimensions as `y_pred` . Will be cast to `bool` . |

`y_pred` | The predicted values. Each element must be in the range `[0, 1]` . |

`sample_weight` | Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true` , and must be broadcastable to `y_true` . |

Returns | |
---|---|

Update op. |

© 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/r2.3/api_docs/python/tf/keras/metrics/Precision