text: import tensorflow as tf
  
  import argparse
  import time
  import os
  import pickle
  
  from utils import DataLoader
  from model import Model
  
  
  def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--rnn_size', type=int, default=256,
                       help='size of RNN hidden state')
    parser.add_argument('--num_layers', type=int, default=2,
                       help='number of layers in the RNN')
    parser.add_argument('--model', type=str, default='lstm',
                       help='rnn, gru, or lstm')
    parser.add_argument('--batch_size', type=int, default=50,
                       help='minibatch size')
    parser.add_argument('--seq_length', type=int, default=300,
                       help='RNN sequence length')
    parser.add_argument('--num_epochs', type=int, default=30,
                       help='number of epochs')
    parser.add_argument('--save_every', type=int, default=500,
                       help='save frequency')
    parser.add_argument('--model_dir', type=str, default='checkpoints',
                       help='directory to save model to')
    parser.add_argument('--grad_clip', type=float, default=10.,
                       help='clip gradients at this value')
    parser.add_argument('--learning_rate', type=float, default=0.005,
                       help='learning rate')
    parser.add_argument('--decay_rate', type=float, default=0.95,
                       help='decay rate for rmsprop')
    parser.add_argument('--num_mixture', type=int, default=20,
                       help='number of gaussian mixtures')
    parser.add_argument('--data_scale', type=float, default=20,
                       help='factor to scale raw data down by')
    parser.add_argument('--keep_prob', type=float, default=0.8,
                       help='dropout keep probability')
    args = parser.parse_args()
    train(args)
  
  def train(args):
      data_loader = DataLoader(args.batch_size, args.seq_length, args.data_scale)
  
      if args.model_dir != '' and not os.path.exists(args.model_dir):
        os.makedirs(args.model_dir)
  
      with open(os.path.join(args.model_dir, 'config.pkl'), 'wb') as f:
          pickle.dump(args, f)
  
      model = Model(args)
  
      with tf.Session() as sess:
          summary_writer = tf.summary.FileWriter(os.path.join(args.model_dir, 'log'), sess.graph) 
          
          tf.global_variables_initializer().run()
          saver = tf.train.Saver(tf.global_variables())
          for e in range(args.num_epochs):
              sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
              data_loader.reset_batch_pointer()
              v_x, v_y = data_loader.validation_data()
              valid_feed = {model.input_data: v_x, model.target_data: v_y, model.state_in: model.state_in.eval()}
              state = model.state_in.eval()
              for b in range(data_loader.num_batches):
                  i = e * data_loader.num_batches + b
                  start = time.time()
                  x, y = data_loader.next_batch()
                  feed = {model.input_data: x, model.target_data: y, model.state_in: state}
                  train_loss_summary, train_loss, state, _ = sess.run([model.train_loss_summary, model.cost, model.state_out, model.train_op], feed) 
                  summary_writer.add_summary(train_loss_summary, i)                 
                  
                  valid_loss_summary, valid_loss, = sess.run([model.valid_loss_summary, model.cost], valid_feed)
                  summary_writer.add_summary(valid_loss_summary, i)                 
  
                  end = time.time()
                  print(
                      "{}/{} (epoch {}), train_loss = {:.3f}, valid_loss = {:.3f}, time/batch = {:.3f}"  \
                      .format(
                          i,
                          args.num_epochs * data_loader.num_batches,
                          e, 
                          train_loss, valid_loss, end - start))
                  if (e * data_loader.num_batches + b) % args.save_every == 0 and ((e * data_loader.num_batches + b) > 0):
                      checkpoint_path = os.path.join(args.model_dir, 'model.ckpt')
                      saver.save(sess, checkpoint_path, global_step = e * data_loader.num_batches + b)
                      print("model saved to {}".format(checkpoint_path))
  
  if __name__ == '__main__':
    main()
  
  
  