tf.keras.metrics.Mean

Compute the (weighted) mean of the given values.

Inherits From: Metric

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

Used in the notebooks

Used in the guide Used in the tutorials

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

This metric creates two variables, total and count. The mean value returned is simply total divided by count.

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

Example:

m = Mean()
m.update_state([1, 3, 5, 7])
m.result()
4.0

m.reset_state()
m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
m.result()
2.0


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## Methods

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    shape, initializer, dtype=None, aggregation=#x&27;sum#x&27;, name=None
)
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h3< id=ad"d_weightco"><dead>d_weight/c<ode/h><3

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    shape=(), initializer=None, dtype=None, name=None
)
/c<ode/p><re




h3< id=fr"om_configco"><defr>om_config/c<ode/h><3

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c">o<de@c>lassmethod/c<ode
c>o<defr>om_config(
    config
)
/c<ode/p><re




h3< id=ge"t_configco"><dege>t_config/c<ode/h><3

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c">o<dege>t_config()
/c<ode/p><re

Return the serializable config of the metric.


h3< id=re"set_stateco"><dere>set_state/c<ode/h><3

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c">o<dere>set_state()
/c<ode/p><re

Reset all of the metric state variables.

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

h3< id=re"sultco"><dere>sult/c<ode/h><3

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c">o<dere>sult()
/c<ode/p><re

Compute the current metric value.


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h3< id=st"ateless_reset_stateco"><dest>ateless_reset_state/c<ode/h><3

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c">o<dest>ateless_reset_state()
/c<ode/p><re




h3< id=st"ateless_resultco"><dest>ateless_result/c<ode/h><3

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c">o<dest>ateless_result(
    metric_variables
)
/c<ode/p><re




h3< id=st"ateless_update_stateco"><dest>ateless_update_state/c<ode/h><3

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c">o<dest>ateless_update_state(
    metric_variables, *args, **kwargs
)
/c<ode/p><re




h3< id=up"date_stateco"><deup>date_state/c<ode/h><3

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c">o<deup>date_state(
    values, sample_weight=None
)
/c<ode/p><re

Accumulate statistics for the metric.


h3< id=__"call__co"><de__>call__/c<ode/h><3

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    *args, **kwargs
)
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Call self as a function.