tf.compat.v1.metrics.mean_per_class_accuracy

Calculates the mean of the per-class accuracies.

tf.compat.v1.metrics.mean_per_class_accuracy(
    labels,
    predictions,
    num_classes,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Calculates the accuracy for each class, then takes the mean of that.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates the accuracy of each class and returns them.

If weights is None, weights default to 1. Use weights of 0 to mask values.

Args
labels	A Tensor of ground truth labels with shape [batch size] and of type int32 or int64. The tensor will be flattened if its rank > 1.
predictions	A Tensor of prediction results for semantic labels, whose shape is [batch size] and type int32 or int64. The tensor will be flattened if its rank > 1.
num_classes	The possible number of labels the prediction task can have. This value must be provided, since two variables with shape = [num_classes] will be allocated.
weights	Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding labels dimension).
metrics_collections	An optional list of collections that mean_per_class_accuracy' should be added to. </td> </tr><tr> <td>updates_collections<a id="updates_collections"></a> </td> <td> An optional list of collectionsupdate_opshould be added to. </td> </tr><tr> <td>name`	An optional variable_scope name.

Returns
mean_accuracy	A Tensor representing the mean per class accuracy.
update_op	An operation that updates the accuracy tensor.

Raises
ValueError	If predictions and labels have mismatched shapes, or if weights is not None and its shape doesn't match predictions, or if either metrics_collections or updates_collections are not a list or tuple.
RuntimeError	If eager execution is enabled.