tf.keras.metrics.RecallAtPrecision

Computes best recall where precision is >= specified value.

Inherits From: Metric

tf.keras.metrics.RecallAtPrecision( precision, num_thresholds=200, class_id=None, name=None, dtype=None )

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.

Example:

m = keras.metrics.RecallAtPrecision(0.8) m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9]) m.result() 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() 1.0

Usage with compile() API:

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

dtype

variables

Methods

add_variable

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add_variable( shape, initializer, dtype=None, aggregation='sum', name=None )

add_weight

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add_weight( shape=(), initializer=None, dtype=None, name=None )

from_config

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@classmethod from_config( config )

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset 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()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result( metric_variables )

stateless_update_state

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stateless_update_state( metric_variables, *args, **kwargs )

update_state

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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.

__call__

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__call__( *args, **kwargs )

Call self as a function.