tf.keras.metrics.Mean

Computes the (weighted) mean of the given values.

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 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.

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

Standalone usage:

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

reset_states

View source

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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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

Accumulates statistics for computing the metric.

Args
values Per-example value.
sample_weight Optional weighting of each example. Defaults to 1.

Returns
Update op.