tf.metrics.mean_per_class_accuracy

tf.metrics.mean_per_class_accuracy

tf.metrics.mean_per_class_accuracy

mean_per_class_accuracy(
    labels,
    predictions,
    num_classes,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Defined in tensorflow/python/ops/metrics_impl.py.

Calculates the mean of the per-class accuracies.

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 these variables and returns the mean_accuracy.

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 a confusion matrix of dimension = [num_classes, 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.
• updates_collections: An optional list of collections update_op should be added to.
• name: An optional variable_scope name.

Returns:

• mean_accuracy: A Tensor representing the mean per class accuracy.
• update_op: An operation that increments the confusion matrix.

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.