deepnovo_model_training.py 文件源码

def sequence_loss(logits,
                  targets,
                  weights,
                  name):
  """TODO(nh2tran): 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 (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, 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 tf.name_scope(name=name,
                     #~ values=logits + targets + weights):
  with ops.op_scope(logits + targets + weights, name):
    cost = math_ops.reduce_sum(sequence_loss_per_sample(logits,
                                                        targets,
                                                        weights))
    batch_size = array_ops.shape(targets[0])[0]
    return cost / math_ops.cast(batch_size, dtypes.float32)