Calculates how often `predictions`

matches `labels`

.

tf.compat.v1.metrics.accuracy( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )

The `accuracy`

function creates two local variables, `total`

and `count`

that are used to compute the frequency with which `predictions`

matches `labels`

. This frequency is ultimately returned as `accuracy`

: an idempotent operation that simply divides `total`

by `count`

.

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

operation that updates these variables and returns the `accuracy`

. Internally, an `is_correct`

operation computes a `Tensor`

with elements 1.0 where the corresponding elements of `predictions`

and `labels`

match and 0.0 otherwise. Then `update_op`

increments `total`

with the reduced sum of the product of `weights`

and `is_correct`

, and it increments `count`

with the reduced sum of `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 shape matches `predictions` . |

`predictions` | The predicted values, a `Tensor` of any shape. |

`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 `accuracy` 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 | |
---|---|

`accuracy` | A `Tensor` representing the accuracy, the value of `total` divided by `count` . |

`update_op` | An operation that increments the `total` and `count` variables appropriately and whose value matches `accuracy` . |

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/r2.3/api_docs/python/tf/compat/v1/metrics/accuracy