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tf.keras.metrics.RecallAtPrecision

Computes best recall where precision is >= specified value.

Inherits From: Metric, Layer, Module

For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.

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

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.

precision A scalar value in range [0, 1].
num_thresholds (Optional) Defaults to 200. The number of thresholds to use for matching the given precision.
class_id (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

m = tf.keras.metrics.RecallAtPrecision(0.8)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
m.result().numpy()
0.5
m.reset_state()
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 compile() API:

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

Methods

reset_state

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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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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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Accum