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# tf.keras.metrics.SensitivityAtSpecificity

Computes 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.

For additional information about specificity and sensitivity, see the following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity

#### Usage:

````m = tf.keras.metrics.SensitivityAtSpecificity(0.4, num_thresholds=1)`
`_ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])`
`m.result().numpy()`
`0.5`
```
````m.reset_states()`
`_ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],`
`                   sample_weight=[1, 0, 0, 1])`
`m.result().numpy()`
`1.0`
```

Usage with tf.keras API:

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

`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.
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

## Methods

### `reset_states`

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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.

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