Calculates how often predictions match binary labels
metric_binary_accuracy(
y_true,
y_pred,
threshold = 0.5,
...,
name = "binary_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.
(Optional) Float representing the threshold for deciding whether prediction values are 1 or 0.
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.
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 binary accuracy
: an idempotent operation that simply
divides total
by count
.
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_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_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()