metric_kullback_leibler_divergence
Computes Kullback-Leibler divergence
Description
Computes Kullback-Leibler divergence
Usage
metric_kullback_leibler_divergence(
y_true,
y_pred,
...,
name = "kullback_leibler_divergence",
dtype = NULL
) Arguments
| Arguments | Description |
|---|---|
| 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. |
Details
```
metric = y_true * log(y_true / y_pred)
``` See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
Value
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.
See Also
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_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_mean(), metric_poisson(), metric_precision_at_recall(), metric_precision(), metric_recall_at_precision(), metric_recall(), 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()