sdca_ops.py 文件源码

def regularized_loss(self, examples):
    """Add operations to compute the loss with regularization loss included.

    Args:
      examples: Examples to compute loss on.

    Returns:
      An Operation that computes mean (regularized) loss for given set of
      examples.
    Raises:
      ValueError: if examples are not well defined.
    """
    self._assertSpecified(['example_labels', 'example_weights',
                           'sparse_features', 'dense_features'], examples)
    self._assertList(['sparse_features', 'dense_features'], examples)
    with name_scope('sdca/regularized_loss'):
      weights = convert_to_tensor(examples['example_weights'])
      return ((
          self._l1_loss() +
          # Note that here we are using the raw regularization
          # (as specified by the user) and *not*
          # self._symmetric_l2_regularization().
          self._l2_loss(self._options['symmetric_l2_regularization'])) /
              math_ops.reduce_sum(math_ops.cast(weights, dtypes.float64)) +
              self.unregularized_loss(examples))