Calculates how often predictions match one-hot labels
metric_categorical_accuracy(
y_true,
y_pred,
...,
name = "categorical_accuracy",
dtype = NULL
)
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.
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.
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()