R/metrics.R

metric_hinge

Computes the hinge metric between y_true and y_pred

Description

y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1.

Usage

 
metric_hinge(y_true, y_pred, ..., name = "hinge", 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


loss = tf$reduce_mean(tf$maximum(1 - y_true * y_pred, 0L), axis=-1L) 

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_kullback_leibler_divergence(), 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()