text: """ Multilayer Perceptron.
  
  A Multilayer Perceptron (Neural Network) implementation example using
  TensorFlow library. This example is using the MNIST database of handwritten
  digits (http://yann.lecun.com/exdb/mnist/).
  
  Links:
      [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).
  
  Author: Aymeric Damien
  Project: https://github.com/aymericdamien/TensorFlow-Examples/
  """
  
  # ------------------------------------------------------------------
  #
  # THIS EXAMPLE HAS BEEN RENAMED 'neural_network.py', FOR SIMPLICITY.
  #
  # ------------------------------------------------------------------
  
  
  from __future__ import print_function
  
  # Import MNIST data
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("../tmp/data/", one_hot=True)
  
  import tensorflow as tf
  
  # Parameters
  learning_rate = 0.001
  training_epochs = 15
  batch_size = 100
  display_step = 1
  
  # Network Parameters
  n_hidden_1 = 256 # 1st layer number of neurons
  n_hidden_2 = 256 # 2nd layer number of neurons
  n_input = 784 # MNIST data input (img shape: 28*28)
  n_classes = 10 # MNIST total classes (0-9 digits)
  
  # tf Graph input
  X = tf.placeholder(tf.float32, [None, n_input])
  Y = tf.placeholder(tf.float32, [None, n_classes])
  
  # Store layers weight & bias
  weights = {
      'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
      'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
      'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
  }
  biases = {
      'b1': tf.Variable(tf.random_normal([n_hidden_1])),
      'b2': tf.Variable(tf.random_normal([n_hidden_2])),
      'out': tf.Variable(tf.random_normal([n_classes]))
  }
  
  
  # Create model
  def multilayer_perceptron(x):
      # Hidden fully connected layer with 256 neurons
      layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
      # Hidden fully connected layer with 256 neurons
      layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
      # Output fully connected layer with a neuron for each class
      out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
      return out_layer
  
  # Construct model
  logits = multilayer_perceptron(X)
  
  # Define loss and optimizer
  loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
      logits=logits, labels=Y))
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
  train_op = optimizer.minimize(loss_op)
  # Initializing the variables
  init = tf.global_variables_initializer()
  
  with tf.Session() as sess:
      sess.run(init)
  
      # Training cycle
      for epoch in range(training_epochs):
          avg_cost = 0.
          total_batch = int(mnist.train.num_examples/batch_size)
          # Loop over all batches
          for i in range(total_batch):
              batch_x, batch_y = mnist.train.next_batch(batch_size)
              # Run optimization op (backprop) and cost op (to get loss value)
              _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x,
                                                              Y: batch_y})
              # Compute average loss
              avg_cost += c / total_batch
          # Display logs per epoch step
          if epoch % display_step == 0:
              print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost))
      print("Optimization Finished!")
  
      # Test model
      pred = tf.nn.softmax(logits)  # Apply softmax to logits
      correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1))
      # Calculate accuracy
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}))
  