Calculates how often predictions match integer labels

metric_sparse_categorical_accuracy(
  y_true,
  y_pred,
  ...,
  name = "sparse_categorical_accuracy",
  dtype = NULL
)

Arguments

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.

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.

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, total and count that are used to compute the frequency with which y_pred matches y_true. This frequency is ultimately returned as sparse categorical 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.

See also