tf.keras.metrics.SpecificityAtSensitivity

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Computes best specificity where sensitivity is >= specified value.

Inherits From: Metric, Layer, Module

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Main aliases

tf.metrics.SpecificityAtSensitivity

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.SpecificityAtSensitivity

tf.keras.metrics.SpecificityAtSensitivity(
    sensitivity, num_thresholds=200, name=None, dtype=None
)

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.

Args
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

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reset_states()

Resets all of the metric state variables.

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

result

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result()

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

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update_state(
    y_true, y_pred, sample_weight=None
)

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.