tf.contrib.metrics.streaming_sparse_precision_at_top_k

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

tf.contrib.metrics.streaming_sparse_precision_at_top_k(
    top_k_predictions, labels, class_id=None, weights=None,
    metrics_collections=None, updates_collections=None, name=None
)

If class_id is not specified, we calculate precision as the ratio of true positives (i.e., correct predictions, items in top_k_predictions that are found in the corresponding row in labels) to positives (all top_k_predictions). If class_id is specified, we calculate precision by considering only the rows in the batch for which class_id is in the top k highest predictions, and computing the fraction of them for which class_id is in the corresponding row in labels.

We expect precision to decrease as k increases.

streaming_sparse_precision_at_top_k creates two local variables, true_positive_at_k and false_positive_at_k, that are used to compute the precision@k frequency. This frequency is ultimately returned as precision_at_k: an idempotent operation that simply divides true_positive_at_k by total (true_positive_at_k + false_positive_at_k).

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the precision_at_k. Internally, set operations applied to top_k_predictions and labels calculate the true positives and false positives weighted by weights. Then update_op increments true_positive_at_k and false_positive_at_k using these values.

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

Args
top_k_predictions	Integer Tensor with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and top_k_predictions has shape [batch size, k]. The final dimension contains the indices of top-k labels. [D1, ... DN] must match labels.
labels	int64 Tensor or SparseTensor with shape [D1, ... DN, num_labels], where 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 top_k_predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions. Values outside this range are ignored.
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.

Returns
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.

Raises
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.
ValueError	If top_k_predictions has rank < 2.