q3_RNNLM.py 文件源码

def generate_text(session, model, config, starting_text='<eos>',
                  stop_length=100, stop_tokens=None, temp=1.0):
  """Generate text from the model.

  Hint: Create a feed-dictionary and use sess.run() to execute the model. Note
        that you will need to use model.initial_state as a key to feed_dict
  Hint: Fetch model.final_state and model.predictions[-1]. (You set
        model.final_state in add_model() and model.predictions is set in
        __init__)
  Hint: Store the outputs of running the model in local variables state and
        y_pred (used in the pre-implemented parts of this function.)

  Args:
    session: tf.Session() object
    model: Object of type RNNLM_Model
    config: A Config() object
    starting_text: Initial text passed to model.                                    # ??text???List of word idxs??generate_text
  Returns:
    output: List of word idxs
  """
  state = model.initial_state.eval()
  # Imagine tokens as a batch size of one, length of len(tokens[0])
  tokens = [model.vocab.encode(word) for word in starting_text.split()]             # ???text???word?????one hot??
  for i in xrange(stop_length):
    ### YOUR CODE HERE
    feed = {model.input_placeholder: [tokens[-1:]],
            model.initial_state: state,
            model.dropout_placeholder: 1}
    state, y_pred = session.run(
        [model.final_state, model.predictions[-1]], feed_dict=feed)                 # ?model??????state, y_pred
    ### END YOUR CODE
    next_word_idx = sample(y_pred[0], temperature=temp)                             # ????????????idx
    tokens.append(next_word_idx)
    if stop_tokens and model.vocab.decode(tokens[-1]) in stop_tokens:
      break
  output = [model.vocab.decode(word_idx) for word_idx in tokens]                    # ?tokens??one hot?????word
  return output