tf.metrics.mean
tf.metrics.mean
tf.metrics.mean
mean( values, weights=None, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes the (weighted) mean of the given values.
The mean
function creates two local 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
.
For estimation of the metric over a stream of data, the function creates an update_op
operation that updates these variables and returns the mean
. update_op
increments total
with the reduced sum of the product of values
and weights
, and it increments count
with the reduced sum of weights
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args:
-
values
: ATensor
of arbitrary dimensions. -
weights
: OptionalTensor
whose rank is either 0, or the same rank asvalues
, and must be broadcastable tovalues
(i.e., all dimensions must be either1
, or the same as the correspondingvalues
dimension). -
metrics_collections
: An optional list of collections thatmean
should be added to. -
updates_collections
: An optional list of collections thatupdate_op
should be added to. -
name
: An optional variable_scope name.
Returns:
-
mean
: ATensor
representing the current mean, the value oftotal
divided bycount
. -
update_op
: An operation that increments thetotal
andcount
variables appropriately and whose value matchesmean_value
.
Raises:
-
ValueError
: Ifweights
is notNone
and its shape doesn't matchvalues
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/metrics/mean