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Compute the (weighted) sum of the given values.

Source: R/metrics.R
metric_sum.Rd

For example, if values is [1, 3, 5, 7] then their sum is 16. If sample_weight was specified as [1, 1, 0, 0] then the sum would be 4.

This metric creates one variable, total. This is ultimately returned as the sum value.

Usage

metric_sum(..., name = "sum", dtype = NULL)

Arguments

...

For forward/backward compatability.

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.

Examples

m <- metric_sum()
m$update_state(c(1, 3, 5, 7))
m$result()

## tf.Tensor(16.0, shape=(), dtype=float32)


m <- metric_sum()
m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0))
m$result()

## tf.Tensor(4.0, shape=(), dtype=float32)

See also

Other reduction metrics:
metric_mean()
metric_mean_wrapper()

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_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()