tf.metrics.precision_at_top_k

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Computes precision@k of the predictions with respect to sparse labels.

tf.metrics.precision_at_top_k(
    labels, predictions_idx, k=None, class_id=None, weights=None,
    metrics_collections=None, updates_collections=None, name=None
)

Differs from sparse_precision_at_k in that predictions must be in the form of top k class indices, whereas sparse_precision_at_k expects logits. Refer to sparse_precision_at_k for more details.

labels int64 Tensor or SparseTensor with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and labels has shape [batch_size, num_labels]. [D1, ... DN] must match predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions. Values outside this range are ignored.
predictions_idx Integer Tensor with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the top k predicted class indices. [D1, ... DN] must match labels.
k Integer, k for @k metric. Only used for the default op name.
class_id Integer class ID for which we want binary metrics. This should be in range [0, num_classes], where num_classes is the last dimension of predictions. If class_id is outside this range, the method returns NAN.
weights Tensor whose rank is either 0, or n-1, where n is the rank of labels. If the latter, it 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 values should be added to.
updates_collections An optional list of collections that updates should be added to.
name Name of new update operation, and namespace for other dependent ops.

precision Scalar float64 Tensor with the value of true_positives divided by the sum of true_positives and false_positives.
update_op Operation that increments true_positives and false_positives variables appropriately, and whose value matches precision.

ValueError 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.