code: import tensorflow as tf
  import numpy as np
  from tensorflow.contrib import rnn
  
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
  
  learning_rate = 0.001
  training_iters = 100000
  batch_size = 128
  display_step = 10
  
  n_input = 28 
  n_steps = 28 
  n_hidden = 128 
  n_classes = 10 
  
  x = tf.placeholder("float", [None, n_steps, n_input])
  y = tf.placeholder("float", [None, n_classes])
  
  weights = {
      'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
  }
  biases = {
      'out': tf.Variable(tf.random_normal([n_classes]))
  }
  
  def BiRNN(x, weights, biases):
      x = tf.transpose(x, [1, 0, 2])
      x = tf.reshape(x, [-1, n_input])
      x = tf.split(axis=0, num_or_size_splits=n_steps, value=x)
      lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
      lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
      try:
          outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                                dtype=tf.float32)
      except Exception: # Old TensorFlow version only returns outputs not states
          outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                          dtype=tf.float32)
      return tf.matmul(outputs[-1], weights['out']) + biases['out']
  
  pred = BiRNN(x, weights, biases)
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).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.global_variables_initializer()
  
  with tf.Session() as sess:
      sess.run(init)
      step = 1
      while step * batch_size < training_iters:
          batch_x, batch_y = mnist.train.next_batch(batch_size)
          batch_x = batch_x.reshape((batch_size, n_steps, n_input))
          sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
          if step % display_step == 0:
              acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
              loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
              print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                    "{:.6f}".format(loss) + ", Training Accuracy= " + \
                    "{:.5f}".format(acc))
          step += 1
      print("Optimization Finished!")
  
      test_len = 128
      test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
      test_label = mnist.test.labels[:test_len]
      print("Testing Accuracy:", \
  sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
  
apis: tensorflow.placeholder tensorflow.contrib.rnn.BasicLSTMCell tensorflow.random_normal tensorflow.nn.softmax_cross_entropy_with_logits tensorflow.reshape tensorflow.global_variables_initializer tensorflow.contrib.rnn.static_bidirectional_rnn tensorflow.train.AdamOptimizer tensorflow.examples.tutorials.mnist.input_data.read_data_sets tensorflow.matmul tensorflow.cast tensorflow.argmax tensorflow.Session tensorflow.transpose tensorflow.split