tf.keras.metrics.BinaryAccuracy

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Calculates how often predictions match binary labels.

Inherits From: Mean, Metric, Layer, Module

View aliases

Main aliases

tf.metrics.BinaryAccuracy

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.BinaryAccuracy

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

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.

Standalone usage:

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

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

Usage with compile() API:

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

Methods

reset_states

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reset_states()

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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result()

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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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
y_true	Ground truth values. shape = [batch_size, d0, .. dN].
y_pred	The predicted values. shape = [batch_size, d0, .. dN].
sample_weight	Optional sample_weight acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.