/TensorFlow 2.4

# tf.keras.metrics.Mean

Computes the (weighted) mean of the given values.

Inherits From: `Metric`, `Layer`, `Module`

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.

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

#### Standalone 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 `compile()` API:

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

## Methods

### `reset_states`

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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 statistics for computing the metric.

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