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# tf.keras.metrics.BinaryAccuracy

Calculates how often predictions match binary labels.

Inherits From: MeanMetricWrapper, Mean, Metric, Layer, Module

### Used in the notebooks

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.

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_state()
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

### merge_state

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Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75

Args
metrics an iterable of metrics. The metrics must have compatible state.

Raises
ValueError If the provided iterable does not contain metrics matching the metric's required specifications.

### reset_state

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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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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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Accumulates metric statistics.

For sparse categorical metrics, the shapes of y_true and y_pred are different.

Args
y_true Ground truth label values. shape = [batch_size, d0, .. dN-1] or shape = [batch_size, d0, .. dN-1, 1].
y_pred </