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Computes the mean relative error by normalizing with the given values.
tf.metrics.mean_relative_error(
labels, predictions, normalizer, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_relative_error
function creates two local variables,
total
and count
that are used to compute the mean relative absolute error.
This average is weighted by weights
, and it is ultimately returned as
mean_relative_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_reative_error
. Internally, a relative_errors
operation divides the
absolute value of the differences between predictions
and labels
by the
normalizer
. Then update_op
increments total
with the reduced sum of the
product of weights
and relative_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.
Args | |
---|---|
labels
|
A Tensor of the same shape as predictions .
|
predictions
|
A Tensor of arbitrary shape.
|
normalizer
|
A Tensor of the same shape as predictions .
|
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_relative_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. |
Returns | |
---|---|
mean_relative_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_relative_error .
|
Raises | |
---|---|
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. |