Sum the weights of false positives.

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

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 the metric value variable should be added to. |

`updates_collections` | An optional list of collections that the metric update ops should be added to. |

`name` | An optional variable_scope name. |

Returns | |
---|---|

`value_tensor` | A `Tensor` representing the current value of the metric. |

`update_op` | An operation that accumulates the error from a batch of data. |

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/false_positives