PTB-release.py 文件源码

def construct_model(config, eval_config, raw_data, opt_method):
  train_data, valid_data, test_data, _ = raw_data

  eval_config.batch_size = 1
  eval_config.num_steps = 1

  initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
  with tf.name_scope("Train"):
    train_input = PTBInput(config=config, data=train_data, name="TrainInput")
    with tf.variable_scope("Model", reuse=None, initializer=initializer):
      m = PTBModel(is_training=True, config=config, input_=train_input, opt_method=opt_method)

  with tf.name_scope("Valid"):
    valid_input = PTBInput(config=config, data=valid_data, name="ValidInput")
    with tf.variable_scope("Model", reuse=True, initializer=initializer):
      mvalid = PTBModel(is_training=False, config=config, input_=valid_input, opt_method=opt_method)

  with tf.name_scope("Test"):
    test_input = PTBInput(config=eval_config, data=test_data, name="TestInput")
    with tf.variable_scope("Model", reuse=True, initializer=initializer):
      mtest = PTBModel(is_training=False, config=eval_config, input_=test_input, opt_method=opt_method)

  return m, mvalid, mtest