当前位置: 首页>>代码示例>>Python>>正文


Python model.SummarizationModel方法代码示例

本文整理汇总了Python中model.SummarizationModel方法的典型用法代码示例。如果您正苦于以下问题:Python model.SummarizationModel方法的具体用法?Python model.SummarizationModel怎么用?Python model.SummarizationModel使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在model的用法示例。


在下文中一共展示了model.SummarizationModel方法的1个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: import model [as 别名]
# 或者: from model import SummarizationModel [as 别名]
def main(unused_argv):
  if len(unused_argv) != 1: # prints a message if you've entered flags incorrectly
    raise Exception("Problem with flags: %s" % unused_argv)

  tf.logging.set_verbosity(tf.logging.INFO) # choose what level of logging you want
  tf.logging.info('Starting seq2seq_attention in %s mode...', (FLAGS.mode))

  # Change log_root to FLAGS.log_root/FLAGS.exp_name and create the dir if necessary
  FLAGS.log_root = os.path.join(FLAGS.log_root, FLAGS.exp_name)
  if not os.path.exists(FLAGS.log_root):
    if FLAGS.mode=="train":
      os.makedirs(FLAGS.log_root)
    else:
      raise Exception("Logdir %s doesn't exist. Run in train mode to create it." % (FLAGS.log_root))

  vocab = Vocab(FLAGS.vocab_path, FLAGS.vocab_size) # create a vocabulary

  # If in decode mode, set batch_size = beam_size
  # Reason: in decode mode, we decode one example at a time.
  # On each step, we have beam_size-many hypotheses in the beam, so we need to make a batch of these hypotheses.
  if FLAGS.mode == 'decode':
    FLAGS.batch_size = FLAGS.beam_size

  # If single_pass=True, check we're in decode mode
  if FLAGS.single_pass and FLAGS.mode!='decode':
    raise Exception("The single_pass flag should only be True in decode mode")

  # Make a namedtuple hps, containing the values of the hyperparameters that the model needs
  hparam_list = ['mode', 'lr', 'adagrad_init_acc', 'rand_unif_init_mag', 'trunc_norm_init_std', 'max_grad_norm', 'hidden_dim', 'emb_dim', 'batch_size', 'max_dec_steps', 'max_enc_steps', 'coverage', 'cov_loss_wt', 'pointer_gen']
  hps_dict = {}
  for key,val in FLAGS.__flags.iteritems(): # for each flag
    if key in hparam_list: # if it's in the list
      hps_dict[key] = val # add it to the dict
  hps = namedtuple("HParams", hps_dict.keys())(**hps_dict)

  # Create a batcher object that will create minibatches of data
  batcher = Batcher(FLAGS.data_path, vocab, hps, single_pass=FLAGS.single_pass)

  tf.set_random_seed(111) # a seed value for randomness

  if hps.mode == 'train':
    print "creating model..."
    model = SummarizationModel(hps, vocab)
    setup_training(model, batcher)
  elif hps.mode == 'eval':
    model = SummarizationModel(hps, vocab)
    run_eval(model, batcher, vocab)
  elif hps.mode == 'decode':
    decode_model_hps = hps  # This will be the hyperparameters for the decoder model
    decode_model_hps = hps._replace(max_dec_steps=1) # The model is configured with max_dec_steps=1 because we only ever run one step of the decoder at a time (to do beam search). Note that the batcher is initialized with max_dec_steps equal to e.g. 100 because the batches need to contain the full summaries
    model = SummarizationModel(decode_model_hps, vocab)
    decoder = BeamSearchDecoder(model, batcher, vocab)
    decoder.decode() # decode indefinitely (unless single_pass=True, in which case deocde the dataset exactly once)
  else:
    raise ValueError("The 'mode' flag must be one of train/eval/decode") 
开发者ID:abisee,项目名称:pointer-generator,代码行数:57,代码来源:run_summarization.py


注:本文中的model.SummarizationModel方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。