tf.keras.metrics.Mean

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Computes the (weighted) mean of the given values.

Inherits From: Metric, Layer, Module

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

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.

Standalone usage:

m = tf.keras.metrics.Mean()
m.update_state([1, 3, 5, 7])
m.result().numpy()
4.0
m.reset_state()
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

merge_state

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merge_state(
    metrics
)

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])

m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])

m2.merge_state([m1])
m2.result().numpy()
0.75

reset_state

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reset_state()

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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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

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update_state(
    values, sample_weight=None
)

Accumulates statistics for computing the metric.