metric_sparse_categorical_accuracy
Calculates how often predictions match integer labels
Description
Calculates how often predictions match integer labels
Usage
metric_sparse_categorical_accuracy(
y_true,
y_pred,
..., name = "sparse_categorical_accuracy",
dtype = NULL
)
Arguments
Arguments | Description |
---|---|
y_true | Tensor of true targets. |
y_pred | Tensor of predicted targets. |
… | Passed on to the underlying metric. Used for forwards and backwards compatibility. |
name | (Optional) string name of the metric instance. |
dtype | (Optional) data type of the metric result. |
Details
```
acc = k_dot(sample_weight, y_true == k_argmax(y_pred, axis=2))
``You can provide logits of classes as
y_pred, since argmax of logits and probabilities are same. This metric creates two local variables,
totaland
countthat are used to compute the frequency with which
y_predmatches
y_true. This frequency is ultimately returned as
sparse categorical accuracy: an idempotent operation that simply divides
totalby
count. If
sample_weightis
NULL, weights default to 1. Use
sample_weight` of 0 to mask values.
Value
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.
See Also
Other metrics: custom_metric()
, metric_accuracy()
, metric_auc()
, metric_binary_accuracy()
, 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_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()