Computes the mean relative error by normalizing with the given values
metric_mean_relative_error(..., normalizer, name = NULL, dtype = NULL)
A (subclassed) Metric
instance that can be passed directly to
compile(metrics = )
, or used as a standalone object. See ?Metric
for
example usage.
Passed on to the underlying metric. Used for forwards and backwards compatibility.
The normalizer values with same shape as predictions.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
This metric creates two local variables, total
and count
that are used to
compute the mean relative error. This is weighted by sample_weight
, and
it is ultimately returned as mean_relative_error
:
an idempotent operation that simply divides total
by count
.
If sample_weight
is NULL
, weights default to 1.
Use sample_weight
of 0 to mask values.
metric = mean(|y_pred - y_true| / normalizer)
For example:
m = metric_mean_relative_error(normalizer=c(1, 3, 2, 3))
m$update_state(c(1, 3, 2, 3), c(2, 4, 6, 8))
# result = mean(c(1, 1, 4, 5) / c(1, 3, 2, 3)) = mean(c(1, 1/3, 2, 5/3))
# = 5/4 = 1.25
m$result()
Other metrics:
custom_metric()
,
metric_accuracy()
,
metric_auc()
,
metric_binary_accuracy()
,
metric_binary_crossentropy()
,
metric_categorical_accuracy()
,
metric_categorical_crossentropy()
,
metric_categorical_hinge()
,
metric_cosine_similarity()
,
metric_false_negatives()
,
metric_false_positives()
,
metric_hinge()
,
metric_kullback_leibler_divergence()
,
metric_logcosh_error()
,
metric_mean_absolute_error()
,
metric_mean_absolute_percentage_error()
,
metric_mean_iou()
,
metric_mean_squared_error()
,
metric_mean_squared_logarithmic_error()
,
metric_mean_tensor()
,
metric_mean_wrapper()
,
metric_mean()
,
metric_poisson()
,
metric_precision_at_recall()
,
metric_precision()
,
metric_recall_at_precision()
,
metric_recall()
,
metric_root_mean_squared_error()
,
metric_sensitivity_at_specificity()
,
metric_sparse_categorical_accuracy()
,
metric_sparse_categorical_crossentropy()
,
metric_sparse_top_k_categorical_accuracy()
,
metric_specificity_at_sensitivity()
,
metric_squared_hinge()
,
metric_sum()
,
metric_top_k_categorical_accuracy()
,
metric_true_negatives()
,
metric_true_positives()