R/metrics.R

# metric_mean_relative_error

## 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.

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()`