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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.