# tf.keras.metrics.Mean

Computes the (weighted) mean of the given values.

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

### Used in the notebooks

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])
print('Final result: ', m.result().numpy())  # Final result: 4.0
``````

Usage with tf.keras API:

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

#### Args:

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

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

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

``````update_state(
values, sample_weight=None
)
``````

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.

Update op.