<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/accuracy_metrics.py#L15-L59) |

Calculates how often predictions equal labels.

Inherits From: [`MeanMetricWrapper`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanMetricWrapper), [`Mean`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Mean), [`Metric`](https://www.tensorflow.org/api_docs/python/tf/keras/Metric)

    tf.keras.metrics.Accuracy(
        name='accuracy', dtype=None
    )

### Used in the notebooks

| Used in the guide | Used in the tutorials |
|---|---|
| - [Migrate metrics and optimizers](https://www.tensorflow.org/guide/migrate/metrics_optimizers) | - [Parameter server training with ParameterServerStrategy](https://www.tensorflow.org/tutorials/distribute/parameter_server_training) - [Custom training: walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) - [CropNet: Cassava Disease Detection](https://www.tensorflow.org/hub/tutorials/cropnet_cassava) |

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.

<br />

<br />

<br />

| ## Args ||
|---|---|
| `name` | (Optional) string name of the metric instance. |
| `dtype` | (Optional) data type of the metric result. |

<br />

#### Examples:

    m = keras.metrics.Accuracy()
    m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
    m.result()
    0.75

    m.reset_state()
    m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
                   sample_weight=[1, 1, 0, 0])
    m.result()
    0.5

Usage with `compile()` API:

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

<br />

<br />

<br />

| ## Attributes ||
|---|---|
| `dtype` | <br /> <br /> |
| `variables` | <br /> <br /> |

<br />

## Methods

### `add_variable`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L192-L209)

    add_variable(
        shape, initializer, dtype=None, aggregation='sum', name=None
    )

### `add_weight`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L211-L215)

    add_weight(
        shape=(), initializer=None, dtype=None, name=None
    )

### `from_config`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/reduction_metrics.py#L215-L219)

    @classmethod
    from_config(
        config
    )

### `get_config`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/accuracy_metrics.py#L58-L59)

    get_config()

Return the serializable config of the metric.

### `reset_state`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/reduction_metrics.py#L151-L153)

    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](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/reduction_metrics.py#L155-L158)

    result()

Compute the current metric value.

<br />

<br />

<br />

| Returns ||
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. ||

<br />

### `stateless_reset_state`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L170-L183)

    stateless_reset_state()

### `stateless_result`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L154-L168)

    stateless_result(
        metric_variables
    )

### `stateless_update_state`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L121-L144)

    stateless_update_state(
        metric_variables, *args, **kwargs
    )

### `update_state`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/reduction_metrics.py#L201-L207)

    update_state(
        y_true, y_pred, sample_weight=None
    )

Accumulate statistics for the metric.

### `__call__`

[View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/metrics/metric.py#L224-L227)

    __call__(
        *args, **kwargs
    )

Call self as a function.