loss_ops.py 文件源码

def cosine_distance(
    predictions, labels=None, dim=None, weights=_WEIGHT_SENTINEL, scope=None,
    targets=None, weight=_WEIGHT_SENTINEL):
  """Adds a cosine-distance loss to the training procedure.

  Note that the function assumes that `predictions` and `labels` are already
  unit-normalized.

  Args:
    predictions: An arbitrary matrix.
    labels: A `Tensor` whose shape matches 'predictions'
    dim: The dimension along which the cosine distance is computed.
    weights: Coefficients for the loss a scalar, a tensor of shape
      [batch_size] or a tensor whose shape matches `predictions`.
    scope: The scope for the operations performed in computing the loss.
    targets: Deprecated alias for `labels`.
    weight: Deprecated alias for `weights`.

  Returns:
    A scalar `Tensor` representing the loss value.

  Raises:
    ValueError: If `predictions` shape doesn't match `labels` shape, or
      `weights` is `None`.
  """
  labels = _labels(labels, targets)
  weights = _weights(weights, weight)
  if dim is None:
    raise ValueError("`dim` cannot be None.")
  with ops.name_scope(scope, "cosine_distance_loss",
                      [predictions, labels, weights]) as scope:
    predictions.get_shape().assert_is_compatible_with(labels.get_shape())

    predictions = math_ops.to_float(predictions)
    labels = math_ops.to_float(labels)

    radial_diffs = math_ops.mul(predictions, labels)
    losses = 1 - math_ops.reduce_sum(radial_diffs, reduction_indices=[dim,])
    return compute_weighted_loss(losses, weights)