# tf.keras.metrics.Mean

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L605-L649) |

Computes the (weighted) mean of the given values.

Inherits From: [`Metric`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/Metric), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

#### View aliases


**Main aliases**

[`tf.metrics.Mean`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/Mean)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.metrics.Mean\`

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    tf.keras.metrics.Mean(
        name='mean', dtype=None
    )

For example, if values is \[1, 3, 5, 7\] then the mean is 4.
If the weights were specified as \[1, 1, 0, 0\] then the mean would be 2.

This metric creates two variables, `total` and `count` that are used to
compute the average of `values`. This average is ultimately returned as
`mean` which is 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.

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

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#### Standalone usage:

    m = tf.keras.metrics.Mean()
    m.update_state([1, 3, 5, 7])
    m.result().numpy()
    4.0
    m.reset_state()
    m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
    m.result().numpy()
    2.0

Usage with `compile()` API:

    model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs))
    model.compile(optimizer='sgd', loss='mse')

## Methods

### `merge_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L288-L326)

    merge_state(
        metrics
    )

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

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| Args ||
|---|---|
| `metrics` | an iterable of metrics. The metrics must have compatible state. |

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

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### `reset_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L249-L265)

    reset_state()

Resets 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/v2.13.1/keras/metrics/base_metric.py#L551-L563)

    result()

Computes and returns the scalar metric value tensor or a dict of scalars.

Result computation is an idempotent operation that simply calculates the
metric value using the state variables.

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

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### `update_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/base_metric.py#L469-L549)

    update_state(
        values, sample_weight=None
    )

Accumulates statistics for computing the metric.

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| Args ||
|---|---|
| `values` | Per-example value. |
| `sample_weight` | Optional weighting of each example. Defaults to `1`. |

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| Returns ||
|---|---|
| Update op. ||

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