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Computes best specificity where sensitivity is >= specified value.
Inherits From: Metric
, Layer
, Module
tf.keras.metrics.SpecificityAtSensitivity(
sensitivity, num_thresholds=200, class_id=None, 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.
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.
For additional information about specificity and sensitivity, see the following.
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_state()
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
merge_state
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([[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
reset_state()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Computes and returns the scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
Returns | |
---|---|
A scalar tensor, or a dictionary of scalar tensors. |
update_state
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. |