tf.keras.metrics.BinaryAccuracy  |  TensorFlow v2.16.1 (original) (raw)

tf.keras.metrics.BinaryAccuracy

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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

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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
)

Accumulate statistics for the metric.

__call__

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__call__(
    *args, **kwargs
)

Call self as a function.