tf.contrib.metrics.streaming_recall_at_k()

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

Computes the recall@k of the predictions with respect to dense 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).

The streaming_recall_at_k function creates two local variables, total and count, that are used to compute the recall@k frequency. This frequency is ultimately returned as recall_at_<k>: an idempotent operation that simply divides total by count.

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, an in_top_k operation computes a Tensor with shape [batch_size] whose elements indicate whether or not the corresponding label is in the top k predictions. Then update_op increments total with the reduced sum of weights where in_top_k is True, and it increments count with the reduced sum of weights.

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: A floating point tensor of dimension [batch_size, num_classes] labels: A tensor of dimension [batch_size] whose type is in int32, int64. k: The number of top elements to look at for computing recall. ignore_mask: An optional, bool Tensor whose shape matches predictions. weights: An optional Tensor whose shape is broadcastable to predictions. metrics_collections: An optional list of collections that recall_at_k should be added to. updates_collections: An optional list of collections update_op should be added to. name: An optional variable_scope name.

Returns: recall_at_k: A tensor representing the recall@k, the fraction of labels which fall into the top k predictions. update_op: An operation that increments the total and count variables appropriately and whose value matches recall_at_k.

Raises: ValueError: If predictions and labels have mismatched shapes, or 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.