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# tf.keras.metrics.Mean

Computes the (weighted) mean of the given values.

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.

#### Usage:

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

Usage with tf.keras API:

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

`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

## Methods

### `reset_states`

View source

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

### `result`

View source

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`

View source

Accumulates statistics for computing the reduction metric.

For example, if `values` is [1, 3, 5, 7] and reduction=SUM_OVER_BATCH_SIZE, then the value of `result()` is 4. If the `sample_weight` is specified as [1, 1, 0, 0] then value of `result()` would be 2.

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

Returns
Update op.

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