metric_recall: Computes the recall of the predictions with respect to the...

In keras: R Interface to 'Keras'

Computes the recall of the predictions with respect to the labels

Description

Computes the recall of the predictions with respect to the labels

Usage

metric_recall(
  ...,
  thresholds = NULL,
  top_k = NULL,
  class_id = NULL,
  name = NULL,
  dtype = NULL
)

Arguments

...	Passed on to the underlying metric. Used for forwards and backwards compatibility.
thresholds	(Optional) A float value or a list of float threshold values in ⁠[0, 1]⁠. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate recall with thresholds=0.5.
top_k	(Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating recall.
class_id	(Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval ⁠[0, num_classes)⁠, where num_classes is the last dimension of predictions.
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.

Details

This metric creates two local variables, true_positives and false_negatives, that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides true_positives by the sum of true_positives and false_negatives.

If sample_weight is NULL, weights default to 1. Use sample_weight of 0 to mask values.

If top_k is set, recall will be computed as how often on average a class among the labels of a batch entry is in the top-k predictions.

If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id is in the label, and computing the fraction of them for which class_id is above the threshold and/or in the top-k predictions.

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(), metric_mean_absolute_error(), metric_mean_absolute_percentage_error(), metric_mean_iou(), metric_mean_relative_error(), metric_mean_squared_error(), metric_mean_squared_logarithmic_error(), metric_mean_tensor(), metric_mean_wrapper(), metric_poisson(), metric_precision(), metric_precision_at_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()

keras documentation built on May 29, 2024, 3:20 a.m.