Python model.SummarizationModel() Examples

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")