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# tf.keras.metrics.SensitivityAtSpecificity

Computes best sensitivity where specificity is >= specified value.

Inherits From: `Metric`, `Layer`, `Module`

the sensitivity at a given specificity.

`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 sensitivity at the given specificity. The threshold for the given specificity value is computed and used to evaluate the corresponding sensitivity.

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

If `class_id` is specified, we calculate precision by considering only the entries in the batch for which `class_id` is above the threshold predictions, and computing the fraction of them for which `class_id` is indeed a correct label.

`specificity` A scalar value in range `[0, 1]`.
`num_thresholds` (Optional) Defaults to 200. The number of thresholds to use for matching the given specificity.
`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.

#### Standalone usage:

````m = tf.keras.metrics.SensitivityAtSpecificity(0.5)`
`m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])`
`m.result().numpy()`
`0.5`
```
````m.reset_state()`
`m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],`
`               sample_weight=[1, 1, 2, 2, 1])`
`m.result().numpy()`
`0.333333`
```

Usage with `compile()` API:

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

## Methods

### `merge_state`

View source

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

````m1 = tf.keras.metrics.Accuracy()`
`_ = m1.update_state([[1], [2]], [[0], [2]])`
```
````m2 = tf.keras.metrics.Accuracy()`
`_ = m2.update_state([[3], [4]], [[3], [4]])`
```
````m2.merge_state([m1])`
`m2.result().numpy()`
`0.75`
```

Args
`metrics` an iterable of metrics. The metrics must have compatible state.

Raises
`ValueError` If the provided iterable does not contain metrics matching the metric's required specifications.

### `reset_state`

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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.