Calculates how often predictions match integer labels


Calculates how often predictions match integer labels


  name = "sparse_categorical_accuracy", 
  dtype = NULL 


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.



acc = k_dot(sample_weight, y_true == k_argmax(y_pred, axis=2))

``You can provide logits of classes asy_pred, since argmax of logits and probabilities are same. This metric creates two local variables,totalandcountthat are used to compute the frequency with whichy_predmatchesy_true. This frequency is ultimately returned assparse categorical accuracy: an idempotent operation that simply dividestotalbycount. Ifsample_weightisNULL, weights default to 1. Usesample_weight` of 0 to mask values.


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_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_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()