tf.keras.metrics.PrecisionAtRecall

Computes best precision where recall is >= specified value.

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the precision at the given recall. The threshold for the given recall value is computed and used to evaluate the corresponding precision.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

recall A scalar value in range [0, 1].
num_thresholds (Optional) Defaults to 200. The number of thresholds to use for matching the given recall.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

m = tf.keras.metrics.PrecisionAtRecall(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=[2, 2, 2, 1, 1])
m.result().numpy()
0.33333333

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.PrecisionAtRecall(recall=0.8)])

Methods

reset_states

View source

Resets all of the metric state variables