parameters: (decoding, labels, sampling_decoding=None, name=None)
docstring: Returns predictions and loss for sequence of predictions.
  
    Args:
      decoding: List of Tensors with predictions.
      labels: List of Tensors with labels.
      sampling_decoding: Optional, List of Tensor with predictions to be used
        in sampling. E.g. they shouldn't have dependncy on outputs.
        If not provided, decoding is used.
      name: Operation name.
  
    Returns:
      Predictions and losses tensors.
docstring_summary: Returns predictions and loss for sequence of predictions.
docstring_tokens: Returns predictions and loss for sequence of predictions .
function: def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
    """Returns predictions and loss for sequence of predictions.
  
    Args:
      decoding: List of Tensors with predictions.
      labels: List of Tensors with labels.
      sampling_decoding: Optional, List of Tensor with predictions to be used
        in sampling. E.g. they shouldn't have dependncy on outputs.
        If not provided, decoding is used.
      name: Operation name.
  
    Returns:
      Predictions and losses tensors.
    """
    with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
      predictions, xent_list = [], []
      for i, pred in enumerate(decoding):
        xent_list.append(nn.softmax_cross_entropy_with_logits(
            labels=labels[i], logits=pred,
            name="sequence_loss/xent_raw{0}".format(i)))
        if sampling_decoding:
          predictions.append(nn.softmax(sampling_decoding[i]))
        else:
          predictions.append(nn.softmax(pred))
      xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
      loss = math_ops.reduce_sum(xent, name="sequence_loss")
      return array_ops.stack(predictions, axis=1), loss
function_tokens: def sequence_classifier ( decoding , labels , sampling_decoding = None , name = None ) : with ops . name_scope ( name , "sequence_classifier" , [ decoding , labels ] ) : predictions , xent_list = [ ] , [ ] for i , pred in enumerate ( decoding ) : xent_list . append ( nn . softmax_cross_entropy_with_logits ( labels = labels [ i ] , logits = pred , name = "sequence_loss/xent_raw{0}" . format ( i ) ) ) if sampling_decoding : predictions . append ( nn . softmax ( sampling_decoding [ i ] ) ) else : predictions . append ( nn . softmax ( pred ) ) xent = math_ops . add_n ( xent_list , name = "sequence_loss/xent" ) loss = math_ops . reduce_sum ( xent , name = "sequence_loss" ) return array_ops . stack ( predictions , axis = 1 ) , loss