tf.keras.metrics.BinaryAccuracy

Calculates how often predictions match binary labels.

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.BinaryAccuracy(
    name='binary_accuracy', dtype=None, threshold=0.5
)

Used in the notebooks

Used in the tutorials
Transfer learning and fine-tuning Classification on imbalanced data TensorFlow Constrained Optimization Example Using CelebA Dataset TFX Keras Component Tutorial

This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true. This frequency is ultimately returned as binary accuracy: an idempotent operation that simply divides total by count.

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

Args
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.
threshold	(Optional) Float representing the threshold for deciding whether prediction values are 1 or 0.

Example:

m = keras.metrics.BinaryAccuracy()
m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
m.result()
0.75

m.reset_state()
m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
               sample_weight=[1, 0, 0, 1])
m.result()
0.5

Usage with compile() API:

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

Attributes
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

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get_config()

Return 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

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stateless_reset_state()

stateless_result

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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.