Calculates how often predictions match one-hot labels
metric_categorical_accuracy(
y_true,
y_pred,
...,
name = "categorical_accuracy",
dtype = NULL
)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.
Tensor of true targets.
Tensor of predicted targets.
Passed on to the underlying metric. Used for forwards and backwards compatibility.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
You can provide logits of classes as y_pred, since argmax of
logits and probabilities are same.
This metric creates two local variables, total and count that are used to
compute the frequency with which y_pred matches y_true. This frequency is
ultimately returned as categorical accuracy: an idempotent operation that
simply divides total by count.
y_pred and y_true should be passed in as vectors of probabilities, rather
than as labels. If necessary, use tf.one_hot to expand y_true as a vector.
If sample_weight is NULL, weights default to 1.
Use sample_weight of 0 to mask values.
Other metrics:
custom_metric(),
metric_accuracy(),
metric_auc(),
metric_binary_accuracy(),
metric_binary_crossentropy(),
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_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()