parameters: (logits,
                    targets,
                    weights,
                    average_across_timesteps=True,
                    average_across_batch=True,
                    softmax_loss_function=None,
                    name=None)
docstring: Weighted cross-entropy loss for a sequence of logits, batch-collapsed.
  
    Args:
      logits: List of 2D Tensors of shape [batch_size x num_decoder_symbols].
      targets: List of 1D batch-sized int32 Tensors of the same length as logits.
      weights: List of 1D batch-sized float-Tensors of the same length as logits.
      average_across_timesteps: If set, divide the returned cost by the total
        label weight.
      average_across_batch: If set, divide the returned cost by the batch size.
      softmax_loss_function: Function (labels, logits) -> loss-batch
        to be used instead of the standard softmax (the default if this is None).
        **Note that to avoid confusion, it is required for the function to accept
        named arguments.**
      name: Optional name for this operation, defaults to "sequence_loss".
  
    Returns:
      A scalar float Tensor: The average log-perplexity per symbol (weighted).
  
    Raises:
      ValueError: If len(logits) is different from len(targets) or len(weights).
docstring_summary: Weighted cross-entropy loss for a sequence of logits, batch-collapsed.
docstring_tokens: Weighted cross - entropy loss for a sequence of logits batch - collapsed .
function: def sequence_loss(logits,
                    targets,
                    weights,
                    average_across_timesteps=True,
                    average_across_batch=True,
                    softmax_loss_function=None,
                    name=None):
    """Weighted cross-entropy loss for a sequence of logits, batch-collapsed.
  
    Args:
      logits: List of 2D Tensors of shape [batch_size x num_decoder_symbols].
      targets: List of 1D batch-sized int32 Tensors of the same length as logits.
      weights: List of 1D batch-sized float-Tensors of the same length as logits.
      average_across_timesteps: If set, divide the returned cost by the total
        label weight.
      average_across_batch: If set, divide the returned cost by the batch size.
      softmax_loss_function: Function (labels, logits) -> loss-batch
        to be used instead of the standard softmax (the default if this is None).
        **Note that to avoid confusion, it is required for the function to accept
        named arguments.**
      name: Optional name for this operation, defaults to "sequence_loss".
  
    Returns:
      A scalar float Tensor: The average log-perplexity per symbol (weighted).
  
    Raises:
      ValueError: If len(logits) is different from len(targets) or len(weights).
    """
    with ops.name_scope(name, "sequence_loss", logits + targets + weights):
      cost = math_ops.reduce_sum(
          sequence_loss_by_example(
              logits,
              targets,
              weights,
              average_across_timesteps=average_across_timesteps,
              softmax_loss_function=softmax_loss_function))
      if average_across_batch:
        batch_size = array_ops.shape(targets[0])[0]
        return cost / math_ops.cast(batch_size, cost.dtype)
      else:
        return cost
function_tokens: def sequence_loss ( logits , targets , weights , average_across_timesteps = True , average_across_batch = True , softmax_loss_function = None , name = None ) : with ops . name_scope ( name , "sequence_loss" , logits + targets + weights ) : cost = math_ops . reduce_sum ( sequence_loss_by_example ( logits , targets , weights , average_across_timesteps = average_across_timesteps , softmax_loss_function = softmax_loss_function ) ) if average_across_batch : batch_size = array_ops . shape ( targets [ 0 ] ) [ 0 ] return cost / math_ops . cast ( batch_size , cost . dtype ) else : return cost