K predictions.Computes how often integer targets are in the top K predictions.
By default, the arguments expected by update_state() are:
y_true: a tensor of shape (batch_size) representing indices of true
categories.
y_pred: a tensor of shape (batch_size, num_categories) containing the
scores for each sample for all possible categories.
With from_sorted_ids=TRUE, the arguments expected by update_state are:
y_true: a tensor of shape (batch_size) representing indices or IDs of
true categories.
y_pred: a tensor of shape (batch_size, N) containing the indices or
IDs of the top N categories sorted in order from highest score to
lowest score. N must be greater or equal to k.
The from_sorted_ids=TRUE option can be more efficient when the set of
categories is very large and the model has an optimized way to retrieve the
top ones either without scoring or without maintaining the scores for all
the possible categories.
metric_sparse_top_k_categorical_accuracy(
y_true,
y_pred,
k = 5L,
...,
name = "sparse_top_k_categorical_accuracy",
dtype = NULL,
from_sorted_ids = FALSE
)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.
Tensor of true targets.
Tensor of predicted targets.
(Optional) Number of top elements to look at for computing accuracy.
Defaults to 5.
For forward/backward compatability.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
(Optional) When FALSE, the default, the tensor passed
in y_pred contains the unsorted scores of all possible categories.
When TRUE, y_pred contains the indices or IDs for the top
categories.
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$result()
## tf.Tensor(0.3, shape=(), dtype=float32)m <- metric_sparse_top_k_categorical_accuracy(k = 1, from_sorted_ids = TRUE)
m$update_state(array(c(2, 1)), rbind(c(1, 0, 3),
c(1, 2, 3)))
m$result()
## tf.Tensor(0.5, shape=(), dtype=float32)Usage with compile() API:
model %>% compile(optimizer = 'sgd',
loss = 'sparse_categorical_crossentropy',
metrics = list(metric_sparse_top_k_categorical_accuracy()))
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_concordance_correlation()
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_pearson_correlation()
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()