deepnovo_model_training.py 文件源码

def sequence_loss_per_sample(logits,
                             targets,
                             weights):
  """TODO(nh2tran): docstring.
  Weighted cross-entropy loss for a sequence of logits (per example).

  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.
    softmax_loss_function: Function (inputs-batch, labels-batch) -> loss-batch
      to be used instead of the standard softmax (the default if this is None).
    name: Optional name for this operation, default: "sequence_loss_by_example".

  Returns:
    1D batch-sized float Tensor: The log-perplexity for each sequence.

  Raises:
    ValueError: If len(logits) is different from len(targets) or len(weights).
  """

  #~ with tf.name_scope(name="sequence_loss_by_example",
                     #~ values=logits + targets + weights):
  with ops.op_scope(logits + targets + weights,
                    None,
                    "sequence_loss_by_example"):

    log_perp_list = []
    for logit, target, weight in zip(logits, targets, weights):
      target = array_ops.reshape(math_ops.to_int64(target), [-1])
      crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(logits=logit,
                                                                 labels=target)
      log_perp_list.append(crossent * weight)
    log_perps = math_ops.add_n(log_perp_list)

    # average_across_timesteps:
    total_size = math_ops.add_n(weights)
    total_size += 1e-12  # Just to avoid division by 0 for all-0 weights.
    log_perps /= total_size

  return log_perps