tf.contrib.metrics.streaming_precision

tf.contrib.metrics.streaming_precision(
    predictions,
    labels,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py.

Computes the precision of the predictions with respect to the labels. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.metrics.precision. Note that the order of the labels and predictions arguments has been switched.

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.

Args:

  • predictions: The predicted values, a bool Tensor of arbitrary shape.
  • labels: The ground truth values, a bool Tensor whose dimensions must match predictions.
  • weights: Tensor whose rank is either 0, or the same rank as labels, and 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 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 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.