`R/metrics.R`

`metric_categorical_accuracy.Rd`

Calculates how often predictions match one-hot labels

metric_categorical_accuracy( y_true, y_pred, ..., name = "categorical_accuracy", dtype = NULL )

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. |

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.

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 `categorical accuracy`

: an idempotent operation that
simply divides `total`

by `count`

.

`y_pred`

and `y_true`

should be passed in as vectors of probabilities, rather
than as labels. If necessary, use `tf.one_hot`

to expand `y_true`

as a vector.

If `sample_weight`

is `NULL`

, weights default to 1.
Use `sample_weight`

of 0 to mask values.

Other metrics:
`custom_metric()`

,
`metric_accuracy()`

,
`metric_auc()`

,
`metric_binary_accuracy()`

,
`metric_binary_crossentropy()`

,
`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_accuracy()`

,
`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()`