content: # View more python learning tutorial on my Youtube and Youku channel!!!
  
  # My tutorial website: https://morvanzhou.github.io/tutorials/
  
  from __future__ import division, print_function, absolute_import
  
  import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  
  # Import MNIST data
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)
  
  
  # Visualize decoder setting
  # Parameters
  learning_rate = 0.01
  training_epochs = 5
  batch_size = 256
  display_step = 1
  examples_to_show = 10
  
  # Network Parameters
  n_input = 784  # MNIST data input (img shape: 28*28)
  
  # tf Graph input (only pictures)
  X = tf.placeholder("float", [None, n_input])
  
  # hidden layer settings
  n_hidden_1 = 256 # 1st layer num features
  n_hidden_2 = 128 # 2nd layer num features
  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):
      # Encoder Hidden layer with sigmoid activation #1
      layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),
                                     biases['encoder_b1']))
      # Decoder Hidden layer with sigmoid activation #2
      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):
      # Encoder Hidden layer with sigmoid activation #1
      layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),
                                     biases['decoder_b1']))
      # Decoder Hidden layer with sigmoid activation #2
      layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
                                     biases['decoder_b2']))
      return layer_2
  
  
  """
  
  # Visualize encoder setting
  # Parameters
  learning_rate = 0.01    # 0.01 this learning rate will be better! Tested
  training_epochs = 10
  batch_size = 256
  display_step = 1
  
  # Network Parameters
  n_input = 784  # MNIST data input (img shape: 28*28)
  
  # tf Graph input (only pictures)
  X = tf.placeholder("float", [None, n_input])
  
  # hidden layer settings
  n_hidden_1 = 128
  n_hidden_2 = 64
  n_hidden_3 = 10
  n_hidden_4 = 2
  
  weights = {
      'encoder_h1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1],)),
      'encoder_h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2],)),
      'encoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3],)),
      'encoder_h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4],)),
  
      'decoder_h1': tf.Variable(tf.truncated_normal([n_hidden_4, n_hidden_3],)),
      'decoder_h2': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_2],)),
      'decoder_h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1],)),
      'decoder_h4': tf.Variable(tf.truncated_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])),
      'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
      'encoder_b4': tf.Variable(tf.random_normal([n_hidden_4])),
  
      'decoder_b1': tf.Variable(tf.random_normal([n_hidden_3])),
      'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
      'decoder_b3': tf.Variable(tf.random_normal([n_hidden_1])),
      'decoder_b4': tf.Variable(tf.random_normal([n_input])),
  }
  
  
  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']))
      layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']),
                                     biases['encoder_b3']))
      layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),
                                      biases['encoder_b4'])
      return layer_4
  
  
  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']))
      layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']),
                                  biases['decoder_b3']))
      layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['decoder_h4']),
                                  biases['decoder_b4']))
      return layer_4
  """
  
  # Construct model
  encoder_op = encoder(X)
  decoder_op = decoder(encoder_op)
  
  # Prediction
  y_pred = decoder_op
  # Targets (Labels) are the input data.
  y_true = X
  
  # Define loss and optimizer, minimize the squared error
  cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
  optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
  
  
  # Launch the graph
  with tf.Session() as sess:
      # tf.initialize_all_variables() no long valid from
      # 2017-03-02 if using tensorflow >= 0.12
      sess.run(tf.initialize_all_variables())
      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)  # max(x) = 1, min(x) = 0
              # Run optimization op (backprop) and cost op (to get loss value)
              _, c = sess.run([optimizer, cost], 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))) #Raw image
          a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) # encode -> decode image
      plt.show()
  
      # encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images})
      # plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels)
      # plt.colorbar()
      # plt.show()
  