If sample_weight
is given, calculates the sum of the weights of
true positives. This metric creates one local variable, true_positives
that is used to keep track of the number of true positives.
If sample_weight
is NULL
, weights default to 1.
Use sample_weight
of 0 to mask values.
Arguments
- ...
For forward/backward compatability.
- thresholds
(Optional) Defaults to
0.5
. A float value, or a Python 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 isTRUE
, below isFALSE
). If used with a loss function that setsfrom_logits=TRUE
(i.e. no sigmoid applied to predictions),thresholds
should be set to 0. One metric value is generated for each threshold value.- 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.
Usage
Standalone usage:
m <- metric_true_positives()
m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1))
m$result()
See also
Other confusion metrics: metric_auc()
metric_false_negatives()
metric_false_positives()
metric_precision()
metric_precision_at_recall()
metric_recall()
metric_recall_at_precision()
metric_sensitivity_at_specificity()
metric_specificity_at_sensitivity()
metric_true_negatives()
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_mean_wrapper()
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()