|TensorFlow 1 version||View source on GitHub|
Computes best sensitivity where specificity is >= specified value.
See Migration guide for more details.
tf.keras.metrics.SensitivityAtSpecificity( specificity, num_thresholds=200, class_id=None, name=None, dtype=None )
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,
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.
None, weights default to 1.
sample_weight of 0 to mask values.
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
For additional information about specificity and sensitivity, see the following.
A scalar value in range
||(Optional) Defaults to 200. The number of thresholds to use for matching the given specificity.|
(Optional) Integer class ID for which we want binary metrics.
This must be in the half-open interval
||(Optional) string name of the metric instance.|
||(Optional) data type of the metric result.|
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.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
sample_weight=[1, 1, 2, 2, 1])
model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.SensitivityAtSpecificity()])
merge_state( metrics )
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([, ], [, ])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([, ], [, ])
||an iterable of metrics. The metrics must have compatible state.|
||If the provided iterable does not contain metrics matching the metric's required specifications.|
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
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( y_true, y_pred, sample_weight=None )
Accumulates confusion matrix statistics.
||The ground truth values.|
||The predicted values.|
Optional weighting of each example. Defaults to 1. Can be a