metric_mean_wrapper {keras3} | R Documentation |
Wrap a stateless metric function with the Mean
metric.
Description
You could use this class to quickly build a mean metric from a function. The
function needs to have the signature fn(y_true, y_pred)
and return a
per-sample loss array. metric_mean_wrapper$result()
will return
the average metric value across all samples seen so far.
For example:
mse <- function(y_true, y_pred) { (y_true - y_pred)^2 } mse_metric <- metric_mean_wrapper(fn = mse) mse_metric$update_state(c(0, 1), c(1, 1)) mse_metric$result()
## tf.Tensor(0.5, shape=(), dtype=float32)
Usage
metric_mean_wrapper(..., fn, name = NULL, dtype = NULL)
Arguments
... |
Keyword arguments to pass on to |
fn |
The metric function to wrap, with signature
|
name |
(Optional) string name of the metric instance. |
dtype |
(Optional) data type of the metric result. |
Value
a Metric
instance is returned. The Metric
instance can be passed
directly to compile(metrics = )
, or used as a standalone object. See
?Metric
for example usage.
See Also
Other reduction metrics:
metric_mean()
metric_sum()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
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()