metric_poisson: Computes the Poisson metric between 'y_true' and 'y_pred'

In keras: R Interface to 'Keras'

Computes the Poisson metric between y_true and y_pred

Description

metric = y_pred - y_true * log(y_pred)

Usage

metric_poisson(y_true, y_pred, ..., name = "poisson", dtype = NULL)

Arguments

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.

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_kullback_leibler_divergence(), 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_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()

keras documentation built on May 29, 2024, 3:20 a.m.