old_contents: from __future__ import print_function
  import tensorflow as tf
  
  import argparse
  import os
  from six.moves import cPickle
  
  from model import Model
  
  from six import text_type
  
  
  def main():
      parser = argparse.ArgumentParser(
                         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
      parser.add_argument('--save_dir', type=str, default='save',
                          help='model directory to store checkpointed models')
      parser.add_argument('-n', type=int, default=500,
                          help='number of characters to sample')
      parser.add_argument('--prime', type=text_type, default=u' ',
                          help='prime text')
      parser.add_argument('--sample', type=int, default=1,
                          help='0 to use max at each timestep, 1 to sample at '
                               'each timestep, 2 to sample on spaces')
  
      args = parser.parse_args()
      sample(args)
  
  
  def sample(args):
      with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
          saved_args = cPickle.load(f)
      with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f:
          chars, vocab = cPickle.load(f)
      model = Model(saved_args, training=False)
      with tf.Session() as sess:
          tf.global_variables_initializer().run()
          saver = tf.train.Saver(tf.global_variables())
          ckpt = tf.train.get_checkpoint_state(args.save_dir)
          if ckpt and ckpt.model_checkpoint_path:
              saver.restore(sess, ckpt.model_checkpoint_path)
              print(model.sample(sess, chars, vocab, args.n, args.prime,
                                 args.sample).encode('utf-8'))
  
  if __name__ == '__main__':
      main()
  
new_contents: from __future__ import print_function
  import tensorflow as tf
  
  import argparse
  import os
  from six.moves import cPickle
  
  from model import Model
  
  from six import text_type
  
  
  def main():
      parser = argparse.ArgumentParser(
                         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
      parser.add_argument('--save_dir', type=str, default='save',
                          help='model directory to store checkpointed models')
      parser.add_argument('-n', type=int, default=500,
                          help='number of characters to sample')
      parser.add_argument('--prime', type=text_type, default=u'',
                          help='prime text')
      parser.add_argument('--sample', type=int, default=1,
                          help='0 to use max at each timestep, 1 to sample at '
                               'each timestep, 2 to sample on spaces')
  
      args = parser.parse_args()
      sample(args)
  
  
  def sample(args):
      with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f:
          saved_args = cPickle.load(f)
      with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f:
          chars, vocab = cPickle.load(f)
      #Use most frequent char if no prime is given
      if args.prime == '':
          args.prime = chars[0]
      model = Model(saved_args, training=False)
      with tf.Session() as sess:
          tf.global_variables_initializer().run()
          saver = tf.train.Saver(tf.global_variables())
          ckpt = tf.train.get_checkpoint_state(args.save_dir)
          if ckpt and ckpt.model_checkpoint_path:
              saver.restore(sess, ckpt.model_checkpoint_path)
              print(model.sample(sess, chars, vocab, args.n, args.prime,
                                 args.sample).encode('utf-8'))
  
  if __name__ == '__main__':
      main()
  
subject: Use most fequent character as prime instead of ' ' to not fail on models with data not containing a space.