metric_ops.py 文件源码

def _streaming_true_positives(predictions, labels, weights=None,
                              metrics_collections=None,
                              updates_collections=None,
                              name=None):
  """Sum the weights of true_positives.

  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
      dimensions.
    labels: The ground truth values, a `bool` `Tensor` whose dimensions must
      match `predictions`.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that the metric
      value variable should be added to.
    updates_collections: An optional list of collections that the metric update
      ops should be added to.
    name: An optional variable_scope name.

  Returns:
    value_tensor: A tensor representing the current value of the metric.
    update_op: An operation that accumulates the error from a batch of data.

  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.
  """
  with variable_scope.variable_scope(
      name, 'true_positives', [predictions, labels]):

    predictions.get_shape().assert_is_compatible_with(labels.get_shape())
    is_true_positive = math_ops.logical_and(math_ops.equal(labels, 1),
                                            math_ops.equal(predictions, 1))
    return _count_condition(is_true_positive, weights, metrics_collections,
                            updates_collections)