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# tf.keras.metrics.Mean

## Class Mean

Computes the (weighted) mean of the given values.

### Aliases:

• Class tf.compat.v1.keras.metrics.Mean
• Class tf.compat.v2.keras.metrics.Mean
• Class tf.compat.v2.metrics.Mean

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

## __init__

View source

__init__(
name='mean',
dtype=None
)

Creates a Mean instance.

#### 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.

View source

result()

### 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.