metric_mean_relative_error
Computes the mean relative error by normalizing with the given values
Description
Computes the mean relative error by normalizing with the given values
Usage
metric_mean_relative_error(..., normalizer, name = NULL, dtype = NULL) Arguments
| Arguments | Description |
|---|---|
| … | Passed on to the underlying metric. Used for forwards and backwards compatibility. |
| normalizer | The normalizer values with same shape as predictions. |
| name | (Optional) string name of the metric instance. |
| dtype | (Optional) data type of the metric result. |
Details
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()
```
Value
A (subclassed) Metric instance that can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.
See Also
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()