metric_ops.py 文件源码

def streaming_mean_relative_error(predictions, labels, normalizer, weights=None,
                                  metrics_collections=None,
                                  updates_collections=None,
                                  name=None):
  """Computes the mean relative error by normalizing with the given values.

  The `streaming_mean_relative_error` function creates two local variables,
  `total` and `count` that are used to compute the mean relative absolute error.
  This average is weighted by `weights`, and it is ultimately returned as
  `mean_relative_error`: 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
  `mean_reative_error`. Internally, a `relative_errors` operation divides the
  absolute value of the differences between `predictions` and `labels` by the
  `normalizer`. Then `update_op` increments `total` with the reduced sum of the
  product of `weights` and `relative_errors`, 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.

  Args:
    predictions: A `Tensor` of arbitrary shape.
    labels: A `Tensor` of the same shape as `predictions`.
    normalizer: A `Tensor` of the same shape as `predictions`.
    weights: An optional `Tensor` whose shape is broadcastable to `predictions`.
    metrics_collections: An optional list of collections that
      `mean_relative_error` 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:
    mean_relative_error: A tensor representing the current mean, the value of
      `total` divided by `count`.
    update_op: An operation that increments the `total` and `count` variables
      appropriately and whose value matches `mean_relative_error`.

  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.
  """
  predictions, labels = metric_ops_util.remove_squeezable_dimensions(
      predictions, labels)
  predictions.get_shape().assert_is_compatible_with(labels.get_shape())

  predictions, normalizer = metric_ops_util.remove_squeezable_dimensions(
      predictions, normalizer)
  predictions.get_shape().assert_is_compatible_with(normalizer.get_shape())
  relative_errors = math_ops.select(
      math_ops.equal(normalizer, 0.0),
      array_ops.zeros_like(labels),
      math_ops.div(math_ops.abs(labels - predictions), normalizer))
  return streaming_mean(relative_errors, weights, metrics_collections,
                        updates_collections, name or 'mean_relative_error')