tf.compat.v1.metrics.mean_relative_error | TensorFlow v2.16.1 (original) (raw)
tf.compat.v1.metrics.mean_relative_error
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Computes the mean relative error by normalizing with the given values.
tf.compat.v1.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 asmean_relative_error
: an idempotent operation that simply divides total
bycount
.
For estimation of the metric over a stream of data, the function creates anupdate_op
operation that updates these variables and returns themean_reative_error
. Internally, a relative_errors
operation divides the absolute value of the differences between predictions
and labels
by thenormalizer
. 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 aslabels, 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 thatmean_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 oftotal 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 ifweights 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. |