loss_ops.py 文件源码

def cosine_distance(predictions, targets, dim, weight=1.0, scope=None):
  """Adds a cosine-distance loss to the training procedure.

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

  Args:
    predictions: An arbitrary matrix.
    targets: A `Tensor` whose shape matches 'predictions'
    dim: The dimension along which the cosine distance is computed.
    weight: 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.

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

  Raises:
    ValueError: If predictions.shape doesn't match targets.shape, if the ignore
                mask is provided and its shape doesn't match targets.shape or if
                the ignore mask is not boolean valued.
  """
  with ops.name_scope(scope, "cosine_distance_loss",
                      [predictions, targets]) as scope:
    predictions.get_shape().assert_is_compatible_with(targets.get_shape())
    if weight is None:
      raise ValueError("`weight` cannot be None")

    predictions = math_ops.to_float(predictions)
    targets = math_ops.to_float(targets)

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