metric_binary_accuracy {keras3} | R Documentation |
Calculates how often predictions match binary labels.
Description
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.
Usage
metric_binary_accuracy(
y_true,
y_pred,
threshold = 0.5,
...,
name = "binary_accuracy",
dtype = NULL
)
Arguments
y_true |
Tensor of true targets. |
y_pred |
Tensor of predicted targets. |
threshold |
(Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. |
... |
For forward/backward compatability. |
name |
(Optional) string name of the metric instance. |
dtype |
(Optional) data type of the metric result. |
Value
If y_true
and y_pred
are missing, a Metric
instance is returned. The Metric
instance that can be passed directly to
compile(metrics = )
, or used as a standalone object. See ?Metric
for
example usage. If y_true
and y_pred
are provided, then a tensor with
the computed value is returned.
Usage
Standalone usage:
m <- metric_binary_accuracy() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6)) m$result()
## tf.Tensor(0.75, shape=(), dtype=float32)
# 0.75
m$reset_state() m$update_state(rbind(1, 1, 0, 0), rbind(0.98, 1, 0, 0.6), sample_weight = c(1, 0, 0, 1)) m$result()
## tf.Tensor(0.5, shape=(), dtype=float32)
# 0.5
Usage with compile()
API:
model %>% compile(optimizer='sgd', loss='binary_crossentropy', metrics=list(metric_binary_accuracy()))
See Also
Other accuracy metrics:
metric_categorical_accuracy()
metric_sparse_categorical_accuracy()
metric_sparse_top_k_categorical_accuracy()
metric_top_k_categorical_accuracy()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
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()