model_deploy.py 文件源码

def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
                    **kwargs):
    """Compute losses and gradients for a single clone.

      Args:
        optimizer: A tf.Optimizer  object.
        clone: A Clone namedtuple.
        num_clones: The number of clones being deployed.
        regularization_losses: Possibly empty list of regularization_losses
          to add to the clone losses.
        **kwargs: Dict of kwarg to pass to compute_gradients().

      Returns:
        A tuple (clone_loss, clone_grads_and_vars).
          - clone_loss: A tensor for the total loss for the clone.  Can be None.
          - clone_grads_and_vars: List of (gradient, variable) for the clone.
            Can be empty.
      """
    sum_loss = _gather_clone_loss(clone, num_clones, regularization_losses)
    clone_grad = None
    if sum_loss is not None:
        with tf.device(clone.device):
            clone_grad = optimizer.compute_gradients(sum_loss, **kwargs)
    return sum_loss, clone_grad