|TensorFlow 1 version||View source on GitHub|
Computes the (weighted) mean of the given values.
See Migration guide for more details.
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 the weights were specified as [1, 1, 0, 0] then the mean would be 2.
This metric creates two variables,
count that are used to
compute the average of
values. This average is ultimately returned as
which is an idempotent operation that simply divides
None, weights default to 1.
sample_weight of 0 to mask values.
||(Optional) string name of the metric instance.|
||(Optional) data type of the metric result.|
m = tf.keras.metrics.Mean()
m.update_state([1, 3, 5, 7])
m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs)) model.compile(optimizer='sgd', loss='mse')
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([, ], [, ])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([, ], [, ])
||an iterable of metrics. The metrics must have compatible state.|
||If the provided iterable does not contain metrics matching the metric's required specifications.|
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
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( values, sample_weight=None )
Accumulates statistics for computing the me