Here we report the entire source code for the implemented bidirectional RNN:

import tensorflow as tf
from tensorflow.contrib import rnn
import numpy as np 

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_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) 
    lstm_bw_cell = rnn_cell.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 returns output not states  
        outputs = rnn.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) 
New: 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}))