new_contents: import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  # data process
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  # hyperparameters
  lr = 0.001
  training_iters = 10000
  batch_size = 128
  display_step = 10
  
  n_inputs = 28
  n_steps = 28
  n_hidden_units = 128
  n_classes = 10
  
  # tf graph
  x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
  y = tf.placeholder(tf.float32, [None, n_classes])
  
  # define weights
  weight = {
      'in': tf.Variable(tf.random_normal([n_inputs,n_hidden_units])),
      'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
  }
  biases = {
      'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
      'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
  }
  
  def RNN(X, weight, biases):
      X = tf.reshape(X, [-1, n_inputs])
      X_in = tf.matmul(X, weight['in'])+baises['in']
      X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
      # cell
      lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True)
      _init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32)
      outputs, states = tf.nn.dynamic_run(lstm_cell, X_in, init_state=_init_state, time_major=False)
  
      # output layer
      result = tf.matmul(state[1], weights['out']) + biases['out']
      return result
  
  pred = RNN(x, weight, biases)
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
  train_op = tf.train.AdamOptimizer(lr).minimize(cost)
  
  correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
  
  init = tf.initialize_all_variables()
  
  with tf.Session() as sess:
      sess.run(init)
      step = 0
      while step * batch_size < training_iters:
          batch_xs, batch_ys = mnist.train.next_batch(batch_size)
          batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
          sess.run([train_op], feed_dict={
              x: batch_xs,
              y: batch_ys,
          })
          if step % 20 == 0:
              print(sess.run(accuracy, feed_dict={
                  x: batch_xs,
                  y: batch_ys,
          }))
          step += 1
  
subject: Create RNN.py