tf.keras.metrics.MeanMetricWrapper

Wrap a stateless metric function with the Mean metric.

Inherits From: Mean, Metric

tf.keras.metrics.MeanMetricWrapper(
    fn, name=None, dtype=None, **kwargs
)

You could use this class to quickly build a mean metric from a function. The function needs to have the signature fn(y_true, y_pred) and return a per-sample loss array. MeanMetricWrapper.result() will return the average metric value across all samples seen so far.

For example:

def mse(y_true, y_pred):
    return (y_true - y_pred) ** 2

mse_metric = MeanMetricWrapper(fn=mse)

fn The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
**kwargs Keyword arguments to pass on to fn.

dtype

variables

Methods

add_variable

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add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

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add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset 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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result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulate statistics for the metric.

__call__

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__call__(
    *args, **kwargs
)

Call self as a function.