tf.metrics.mean_squared_error(
labels,
predictions,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes the mean squared error between the labels and predictions.
The mean_squared_error
function creates two local variables,
total
and count
that are used to compute the mean squared error.
This average is weighted by weights
, and it is ultimately returned as
mean_squared_error
: 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_squared_error
. Internally, a squared_error
operation computes the
element-wise square of the difference between predictions
and labels
. Then
update_op
increments total
with the reduced sum of the product of
weights
and squared_error
, 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:
labels
: ATensor
of the same shape aspredictions
.predictions
: ATensor
of arbitrary shape.weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglabels
dimension).metrics_collections
: An optional list of collections thatmean_squared_error
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_squared_error
: ATensor
representing the current mean, the value oftotal
divided bycount
.update_op
: An operation that increments thetotal
andcount
variables appropriately and whose value matchesmean_squared_error
.
Raises:
ValueError
: Ifpredictions
andlabels
have mismatched shapes, or ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.