tf.contrib.metrics.streaming_sparse_recall_at_k()

tf.contrib.metrics.streaming_sparse_recall_at_k(*args, **kwargs)

Computes recall@k of the predictions with respect to sparse labels. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask).

If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id is in the label, and computing the fraction of them for which class_id is in the top-k predictions. If class_id is not specified, we'll calculate recall as how often on average a class among the labels of a batch entry is in the top-k predictions.

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> + recall_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. Alternatively, if ignore_mask is not None, then mask values where ignore_mask is True.

Args: predictions: Float Tensor with shape [D1, ... DN, num_classes] where N >= 1. 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 labels. Values should be in range [0, num_classes], where num_classes is the last dimension of predictions. 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. ignore_mask: An optional, bool Tensor whose shape is broadcastable to the the first [D1, ... DN] dimensions of predictions and labels. weights: An optional Tensor whose shape is broadcastable to the the first [D1, ... DN] dimensions of predictions and labels. 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 dependant 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 ignore_mask is not None and its shape doesn't match predictions, 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.