tf.contrib.metrics.streaming_sparse_recall_at_k

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

tf.contrib.metrics.streaming_sparse_recall_at_k(
    predictions, labels, k, class_id=None, weights=None, metrics_collections=None,
    updates_collections=None, name=None
)

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

streaming_sparse_recall_at_k creates two local variables, true_positive_at_<k> and false_negative_at_<k>, that are used to compute the recall_at_k frequency. This frequency is ultimately returned as recall_at_<k>: an idempotent operation that simply divides true_positive_at_<k> by total (true_positive_at_<k> + false_negative_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 recall_at_<k>. Internally, a top_k operation computes a Tensor indicating the top k predictions. Set operations applied to top_k and labels calculate the true positives and false negatives weighted by weights. Then update_op increments true_positive_at_<k> and false_negative_at_<k> using these values.

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

Args
predictions	Float Tensor with shape [D1, ... DN, num_classes] where N >= Commonly, N=1 and predictions has shape [batch size, num_classes]. The final dimension contains the logit values for each class. [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 predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions. Values outside this range always count towards false_negative_at_<k>.
k	Integer, k for @k metric.
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
recall	Scalar float64 Tensor with the value of true_positives divided by the sum of true_positives and false_negatives.
update_op	Operation that increments true_positives and false_negatives variables appropriately, and whose value matches recall.

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.