metric_false_negatives {keras3}

Calculates the number of false negatives.

Description

If sample_weight is given, calculates the sum of the weights of false negatives. This metric creates one local variable, accumulator that is used to keep track of the number of false negatives.

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

Usage

metric_false_negatives(..., thresholds = NULL, name = NULL, dtype = NULL)

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 is TRUE, below is FALSE). If used with a loss function that sets from_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_false_negatives()
m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0))
m$result()

## tf.Tensor(2.0, shape=(), dtype=float32)

m$reset_state()
m$update_state(c(0, 1, 1, 1), c(0, 1, 0, 0), sample_weight=c(0, 0, 1, 0))
m$result()

## tf.Tensor(1.0, shape=(), dtype=float32)

# 1.0

See Also

• https://keras.io/api/metrics/classification_metrics#falsenegatives-class

Other confusion metrics:
metric_auc()
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()
metric_true_positives()

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_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()
metric_true_positives()