For example, if values is c(1, 3, 5, 7) then the mean is 4.
If sample_weight was specified as c(1, 1, 0, 0) then the mean would be 2.
This metric creates two variables, total and count.
The mean value returned is simply total divided by count.
metric_mean(..., name = "mean", dtype = NULL)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.
For forward/backward compatability.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
m <- metric_mean()
m$update_state(c(1, 3, 5, 7))
m$result()
## tf.Tensor(4.0, shape=(), dtype=float32)# calling a metric directly is equivalent to calling
# m$update_state(); m$result()
m <- metric_mean()
m(c(1, 3, 5, 7))
## tf.Tensor(4.0, shape=(), dtype=float32)m$reset_state()
m$update_state(c(1, 3, 5, 7), sample_weight = c(1, 1, 0, 0))
m$result()
## tf.Tensor(2.0, shape=(), dtype=float32)Other reduction metrics:
metric_mean_wrapper()
metric_sum()
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_concordance_correlation()
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_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_pearson_correlation()
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()