metric_sparse_top_k_categorical_accuracy: Computes how often integer targets are in the top 'K'...

In rstudio/keras: R Interface to 'Keras'

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 5.
...	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

• https://keras.io/api/metrics/accuracy_metrics#sparsetopkcategoricalaccuracy-class

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()

rstudio/keras documentation built on April 27, 2024, 10:11 p.m.