tf.keras.metrics.FBetaScore

Computes F-Beta score.

Inherits From: Metric

tf.keras.metrics.FBetaScore(
    average=None,
    beta=1.0,
    threshold=None,
    name='fbeta_score',
    dtype=None
)

Formula:

b2 = beta ** 2
f_beta_score = (1 + b2) * (precision * recall) / (precision * b2 + recall)

This is the weighted harmonic mean of precision and recall. Its output range is [0, 1]. It works for both multi-class and multi-label classification.

average Type of averaging to be performed across per-class results in the multi-class case. Acceptable values are None, "micro", "macro" and "weighted". Defaults to None. If None, no averaging is performed and result() will return the score for each class. If "micro", compute metrics globally by counting the total true positives, false negatives and false positives. If "macro", compute metrics for each label, and return their unweighted mean. This does not take label imbalance into account. If "weighted", compute metrics for each label, and return their average weighted by support (the number of true instances for each label). This alters "macro" to account for label imbalance. It can result in an score that is not between precision and recall.
beta Determines the weight of given to recall in the harmonic mean between precision and recall (see pseudocode equation above). Defaults to 1.
threshold Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax of y_pred is converted to 1, and the rest to 0.
name Optional. String name of the metric instance.
dtype Optional. Data type of the metric result.

F-Beta Score: float.

Example:

metric = keras.metrics.FBetaScore(beta=2.0, threshold=0.5)
y_true = np.array([[1, 1, 1],
                   [1, 0, 0],
                   [1, 1, 0]], np.int32)
y_pred = np.array([[0.2, 0.6, 0.7],
                   [0.2, 0.6, 0.6],
                   [0.6, 0.8, 0.0]], np.float32)
metric.update_state(y_true, y_pred)
result = metric.result()
result
[0.3846154 , 0.90909094, 0.8333334 ]

dtype

variables

Methods

add_variable

View source

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

add_weight

View source

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

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

Returns the serializable config of the metric.

reset_state

View source

reset_state()

Reset all of the metric state variables.

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

result

View source

result()

Compute the current metric value.

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

stateless_reset_state

View source

stateless_reset_state()

stateless_result

View source

stateless_result(
    metric_variables
)

stateless_update_state

View source

stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulate statistics for the metric.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.