metric_kullback_leibler_divergence | R Documentation |
Computes Kullback-Leibler divergence
metric_kullback_leibler_divergence(
y_true,
y_pred,
...,
name = "kullback_leibler_divergence",
dtype = NULL
)
y_true |
Tensor of true targets. |
y_pred |
Tensor of predicted targets. |
... |
Passed on to the underlying metric. Used for forwards and backwards compatibility. |
name |
(Optional) string name of the metric instance. |
dtype |
(Optional) data type of the metric result. |
metric = y_true * log(y_true / y_pred)
See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
If y_true
and y_pred
are missing, a (subclassed) Metric
instance is returned. The Metric
object can be passed directly to
compile(metrics = )
or used as a standalone object. See ?Metric
for
example usage.
Alternatively, if called with y_true
and y_pred
arguments, then the
computed case-wise values for the mini-batch are returned directly.
Other metrics:
custom_metric()
,
metric_accuracy()
,
metric_auc()
,
metric_binary_accuracy()
,
metric_binary_crossentropy()
,
metric_categorical_accuracy()
,
metric_categorical_crossentropy()
,
metric_categorical_hinge()
,
metric_cosine_similarity()
,
metric_false_negatives()
,
metric_false_positives()
,
metric_hinge()
,
metric_logcosh_error()
,
metric_mean()
,
metric_mean_absolute_error()
,
metric_mean_absolute_percentage_error()
,
metric_mean_iou()
,
metric_mean_relative_error()
,
metric_mean_squared_error()
,
metric_mean_squared_logarithmic_error()
,
metric_mean_tensor()
,
metric_mean_wrapper()
,
metric_poisson()
,
metric_precision()
,
metric_precision_at_recall()
,
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()
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