metric_true_positives {keras3} | R Documentation |
Calculates the number of true positives.
Description
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.
Usage
metric_true_positives(..., thresholds = NULL, name = NULL, dtype = NULL)
Arguments
... |
For forward/backward compatability. |
thresholds |
(Optional) Defaults to |
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()
## tf.Tensor(2.0, shape=(), dtype=float32)
m$reset_state() m$update_state(c(0, 1, 1, 1), c(1, 0, 1, 1), sample_weight = c(0, 0, 1, 0)) m$result()
## tf.Tensor(1.0, shape=(), dtype=float32)
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()