tf.keras.metrics.SensitivityAtSpecificity

View source on GitHub

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])
print('Final result: ', m.result().numpy())  # Final result: 0.5

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile(
    'sgd',
    loss='mse',
    metrics=[tf.keras.metrics.SensitivityAtSpecificity()])

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.

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

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

result

View source

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

View source

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.