tf.contrib.metrics.streaming_precision()

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

Computes the precision of the predictions with respect to the 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_precision function creates two local variables, true_positives and false_positives, that are used to compute the precision. This value is ultimately returned as precision, an idempotent operation that simply divides true_positives by the sum of true_positives and false_positives.

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. update_op weights each prediction by the corresponding value in 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: The predicted values, a bool Tensor of arbitrary shape. labels: The ground truth values, a bool Tensor whose dimensions must match predictions. 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 precision should be added to. updates_collections: An optional list of collections that update_op should be added to. name: An optional variable_scope name.

Returns: precision: Scalar float 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 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.