many2one_seq2seq.py 文件源码

def model_with_buckets(encoder_inputs, decoder_inputs, targets, weights, seq_len, 
                       buckets, seq2seq, softmax_loss_function=None,
                       per_example_loss=False, name=None):
  if len(encoder_inputs) < buckets[-1][0]:
    raise ValueError("Length of encoder_inputs (%d) must be at least that of la"
                     "st bucket (%d)." % (len(encoder_inputs), buckets[-1][0]))
  if len(targets) < buckets[-1][1]:
    raise ValueError("Length of targets (%d) must be at least that of last"
                     "bucket (%d)." % (len(targets), buckets[-1][1]))
  if len(weights) < buckets[-1][1]:
    raise ValueError("Length of weights (%d) must be at least that of last"
                     "bucket (%d)." % (len(weights), buckets[-1][1]))

  all_inputs = encoder_inputs + decoder_inputs + targets + weights
  losses = []
  outputs = []
  with tf.name_scope(name, "model_with_buckets", all_inputs):
    for j, bucket in enumerate(buckets):
      with variable_scope.variable_scope(variable_scope.get_variable_scope(),
                                         reuse=True if j > 0 else None):
        bucket_outputs, _ = seq2seq(encoder_inputs[:bucket[0]],
                                    decoder_inputs[:bucket[1]],
                                    seq_len)

        outputs.append(bucket_outputs)
        if per_example_loss:
          losses.append(sequence_loss_by_example(
              outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
              softmax_loss_function=softmax_loss_function))
        else:
          losses.append(sequence_loss(
              outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
              softmax_loss_function=softmax_loss_function))

  return outputs, losses