# tf.keras.metrics.BinaryAccuracy

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## Class `BinaryAccuracy`

Calculates how often predictions matches labels.

### Aliases:

• Class `tf.compat.v1.keras.metrics.BinaryAccuracy`
• Class `tf.compat.v2.keras.metrics.BinaryAccuracy`
• Class `tf.compat.v2.metrics.BinaryAccuracy`
• Class `tf.metrics.BinaryAccuracy`

For example, if `y_true` is [1, 1, 0, 0] and `y_pred` is [0.98, 1, 0, 0.6] then the binary accuracy is 3/4 or .75. If the weights were specified as [1, 0, 0, 1] then the binary accuracy would be 1/2 or .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.

#### Usage:

``````m = tf.keras.metrics.BinaryAccuracy()
m.update_state([1, 1, 0, 0], [0.98, 1, 0, 0.6])
print('Final result: ', m.result().numpy())  # Final result: 0.75
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()])
``````

## `__init__`

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``````__init__(
name='binary_accuracy',
dtype=None,
threshold=0.5
)
``````

Creates a `BinaryAccuracy` instance.

#### 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.

## 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.

### `update_state`

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``````update_state(
y_true,
y_pred,
sample_weight=None
)
``````

Accumulates metric statistics.

`y_true` and `y_pred`免费成长做爱直播有哪些 should have the same shape.

#### Args:

• `y_true`: The ground truth values.
• `y_pred`: The predicted values.
• `sample_weight`: Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Update op.