metric_sparse_top_k_categorical_accuracy {keras3} | R Documentation |
Computes how often integer targets are in the top K
predictions.
Description
Computes how often integer targets are in the top K
predictions.
Usage
metric_sparse_top_k_categorical_accuracy(
y_true,
y_pred,
k = 5L,
...,
name = "sparse_top_k_categorical_accuracy",
dtype = NULL
)
Arguments
y_true |
Tensor of true targets. |
y_pred |
Tensor of predicted targets. |
k |
(Optional) Number of top elements to look at for computing accuracy.
Defaults to |
... |
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_sparse_top_k_categorical_accuracy(k = 1L) m$update_state( rbind(2, 1), op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32") ) m$result()
## tf.Tensor(0.5, shape=(), dtype=float32)
m$reset_state() m$update_state( rbind(2, 1), op_array(rbind(c(0.1, 0.9, 0.8), c(0.05, 0.95, 0)), dtype = "float32"), sample_weight = c(0.7, 0.3) ) m$result()
## tf.Tensor(0.3, shape=(), dtype=float32)
Usage with compile()
API:
model %>% compile(optimizer = 'sgd', loss = 'sparse_categorical_crossentropy', metrics = list(metric_sparse_top_k_categorical_accuracy()))
See Also
Other accuracy metrics:
metric_binary_accuracy()
metric_categorical_accuracy()
metric_sparse_categorical_accuracy()
metric_top_k_categorical_accuracy()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
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_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()