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tf.keras.metrics.SpecificityAtSensitivity

Computes best specificity where sensitivity is >= specified value.

`Sensitivity` measures the proportion of actual positives that are correctly identified as such (tp / (tp + fn)). `Specificity` measures the proportion of actual negatives that are correctly identified as such (tn / (tn + fp)).

This metric creates four local variables, `true_positives`, `true_negatives`, `false_positives` and `false_negatives` that are used to compute the specificity at the given sensitivity. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.

If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values.

For additional information about specificity and sensitivity, see the following.

`sensitivity` A scalar value in range `[0, 1]`.
`num_thresholds` (Optional) Defaults to 200. The number of thresholds to use for matching the given sensitivity.
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

Standalone usage:

````m = tf.keras.metrics.SpecificityAtSensitivity(0.5)`
`m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])`
`m.result().numpy()`
`0.66666667`
```
````m.reset_states()`
`m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],`
`               sample_weight=[1, 1, 2, 2, 2])`
`m.result().numpy()`
`0.5`
```

Usage with `compile()` API:

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

Methods

`reset_states`

View source

Resets all of the metric state variables.

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

`result`

View source

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`

View source

Accumulates confusion matrix statistics.

Args
`y_true` The ground truth values.
`y_pred` The predicted values.
`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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]