tf.keras.metrics.SensitivityAtSpecificity

TensorFlow 1 version

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Computes the sensitivity at a given specificity.

tf.keras.metrics.SensitivityAtSpecificity(
    specificity, 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 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()])

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.