text: #!/usr/bin/env python
  
  from __future__ import absolute_import, division, print_function
  
  import matplotlib.pyplot as plt
  import numpy as np
  import tensorflow as tf
  # Import MINST data
  from tensorflow.examples.tutorials.mnist import input_data
  
  mnist = input_data.read_data_sets("./mnist/", one_hot=True)
  
  # Parameters
  learning_rate = 0.01
  training_epochs = 20
  batch_size = 256
  display_step = 1
  examples_to_show = 10
  
  n_hidden_1 = 256  # 1st layer num features
  n_hidden_2 = 128  # 2nd layer num features
  n_input = 784  # MNIST data input (img shape: 28*28)
  
  X = tf.placeholder("float", [None, n_input])
  
  weights = {
      'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
      'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
      'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
      'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
  }
  biases = {
      'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
      'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
      'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
      'decoder_b2': tf.Variable(tf.random_normal([n_input])),
  }
  
  
  # Building the encoder
  def encoder(x):
      layer_1 = tf.nn.sigmoid(tf.add(
          tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
      layer_2 = tf.nn.sigmoid(tf.add(
          tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
      return layer_2
  
  
  # Building the decoder
  def decoder(x):
      layer_1 = tf.nn.sigmoid(tf.add(
          tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
      layer_2 = tf.nn.sigmoid(tf.add(
          tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
      return layer_2
  
  # Construct model
  encoder_op = encoder(X)
  decoder_op = decoder(encoder_op)
  y_pred = decoder_op
  # Targets (Labels) are the input data.
  y_true = X
  
  loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
  train_op = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)
  
  init = tf.initialize_all_variables()
  
  # Launch the graph
  with tf.Session() as sess:
      sess.run(init)
      total_batch = int(mnist.train.num_examples / batch_size)
      # Training cycle
      for epoch in range(training_epochs):
          # Loop over all batches
          for i in range(total_batch):
              batch_xs, batch_ys = mnist.train.next_batch(batch_size)
              _, c = sess.run([train_op, loss], feed_dict={X: batch_xs})
          # Display logs per epoch step
          if epoch % display_step == 0:
              print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c))
  
      print("Optimization Finished!")
  
      # Applying encode and decode over test set
      encode_decode = sess.run(
          y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})
      # Compare original images with their reconstructions
      f, a = plt.subplots(2, 10, figsize=(10, 2))
      for i in range(examples_to_show):
          a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))
          a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))
      f.show()
      plt.draw()
      plt.waitforbuttonpress()
  