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Computes the mean absolute error between the labels and predictions.
tf.compat.v1.metrics.mean_absolute_error(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_absolute_error
function creates two local variables,
total
and count
that are used to compute the mean absolute error. This
average is weighted by weights
, and it is ultimately returned as
mean_absolute_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_absolute_error
. Internally, an absolute_errors
operation computes the
absolute value of the differences between predictions
and labels
. Then
update_op
increments total
with the reduced sum of the product of
weights
and absolute_errors
, 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.
labels
: A Tensor
of the same shape as predictions
.predictions
: A Tensor
of arbitrary shape.weights
: Optional Tensor
whose rank is either 0, or the same rank as
labels
, and must be broadcastable to labels
(i.e., all dimensions must
be either 1
, or the same as the corresponding labels
dimension).metrics_collections
: An optional list of collections that
mean_absolute_error
should be added to.updates_collections
: An optional list of collections that update_op
should
be added to.name
: An optional variable_scope name.mean_absolute_error
: A Tensor
representing the current mean, the value of
total
divided by count
.update_op
: An operation that increments the total
and count
variables
appropriately and whose value matches mean_absolute_error
.ValueError
: If predictions
and labels
have mismatched shapes, or if
weights
is not None
and its shape doesn't match predictions
, or if
either metrics_collections
or updates_collections
are not a list or
tuple.RuntimeError
: If eager execution is enabled.