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TensorFlow 1 version
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View source on GitHub
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Computes best sensitivity where specificity is >= specified value.
Inherits From: Metric, Layer, Module
tf.keras.metrics.SensitivityAtSpecificity(
specificity, num_thresholds=200, 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, 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.
Args | |
|---|---|
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. |
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_states()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
reset_states
reset_states()
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 metric value tensor.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
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. |