text: 
  # This code is based on the following example:
  # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_deep.py
  
  """A deep MNIST classifier using convolutional layers.
  See extensive documentation at
  https://www.tensorflow.org/get_started/mnist/pros
  """
  # Disable linter warnings to maintain consistency with tutorial.
  # pylint: disable=invalid-name
  # pylint: disable=g-bad-import-order
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  import os
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  import tensorflow as tf
  
  import numpy
  
  from tensorflow.python.framework import graph_io
  from tensorflow.python.framework import graph_util
  
  FLAGS = None
  
  
  def deepnn(x):
      """deepnn builds the graph for a deep net for classifying digits.
      Args:
      x: an input tensor with the dimensions (N_examples, 784), where 784 is the
      number of pixels in a standard MNIST image.
      Returns:
      A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
      equal to the logits of classifying the digit into one of 10 classes (the
      digits 0-9).
      """
      # Reshape to use within a convolutional neural net.
      # Last dimension is for "features" - there is only one here, since images are
      # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
      with tf.name_scope('reshape'):
          x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # First convolutional layer - maps one grayscale image to 16 feature maps.
      with tf.name_scope('conv1'):
          W_conv1 = weight_variable([5, 5, 1, 16])
          b_conv1 = bias_variable([16])
          h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
      # Pooling layer - downsamples by 2X.
      with tf.name_scope('pool1'):
          h_pool1 = avg_pool_2x2(h_conv1)
  
      # Second convolutional layer -- maps 16 feature maps to 16.
      with tf.name_scope('conv2'):
          W_conv2 = weight_variable([5, 5, 16, 16])
          b_conv2 = bias_variable([16])
          h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
      # Second pooling layer.
      with tf.name_scope('pool2'):
          h_pool2 = avg_pool_2x2(h_conv2)
  
      # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
      # is down to 256 feature maps -- maps this to 1024 features.
      with tf.name_scope('fc1'):
          W_fc1 = weight_variable([256, 100])
          b_fc1 = bias_variable([100])
  
          h_pool2_flat = tf.reshape(h_pool2, [-1, 256])
          h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Map the 256 features to 10 classes, one for each digit
      with tf.name_scope('fc2'):
          W_fc2 = weight_variable([100, 10])
          b_fc2 = bias_variable([10])
  
          y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
      return y_conv
  
  
  def conv2d(x, W):
      """conv2d returns a 2d convolution layer with full stride."""
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')
  
  
  def avg_pool_2x2(x):
      """avg_pool_2x2 downsamples a feature map by 2X."""
      return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
  
  
  def weight_variable(shape):
      """weight_variable generates a weight variable of a given shape."""
      initial = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(initial)
  
  
  def bias_variable(shape):
      """bias_variable generates a bias variable of a given shape."""
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)
  
  
  def main(_):
      # Import data
      mnist = input_data.read_data_sets(FLAGS.data_dir)
  
      # Create the model
      x = tf.placeholder(tf.float32, [None, 784])
  
      # Define loss and optimizer
      y_ = tf.placeholder(tf.int64, [None])
  
      # Build the graph for the deep net
      y_conv = deepnn(x)
  
      with tf.name_scope('loss'):
          cross_entropy = tf.losses.sparse_softmax_cross_entropy(
              labels=y_, logits=y_conv)
          cross_entropy = tf.reduce_mean(cross_entropy)
  
      with tf.name_scope('adam_optimizer'):
          train_step = tf.train.AdamOptimizer(1e-2).minimize(cross_entropy)
  
      with tf.name_scope('accuracy'):
          correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
          correct_prediction = tf.cast(correct_prediction, tf.float32)
          accuracy = tf.reduce_mean(correct_prediction)
  
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          for i in range(2000):
              batch = mnist.train.next_batch(50)
              if i % 100 == 0:
                  train_accuracy = accuracy.eval(feed_dict={
                      x: batch[0], y_: batch[1]})
                  print('step %d, training accuracy %g' % (i, train_accuracy))
              train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  
          # compute in batches to avoid OOM on GPUs
          accuracy_l = []
          for _ in range(20):
              batch = mnist.test.next_batch(500, shuffle=False)
  
              accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], y_: batch[1]}))
  
          print('test accuracy %g' % numpy.mean(accuracy_l))
  
          # Export model to protobuf file
          current_dir = os.getcwd()
          pb_filename = '/test_data/mnist_model.pb'
          export_to_pb(sess, y_conv, current_dir + pb_filename)
  
          # Export sample mnist data
          np_filename = '/test_data/mnist_input.npy'
          numpy.save(current_dir + np_filename, mnist.test.images[0])
  
  
  def export_to_pb(sess, x, filename):
      pred_names = ['output']
      tf.identity(x, name=pred_names[0])
  
      graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), pred_names)
  
      graph = graph_util.remove_training_nodes(graph)
      path = graph_io.write_graph(graph, ".", filename, as_text=False)
      print('saved the frozen graph (ready for inference) at: ', filename)
  
      return path
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--data_dir', type=str,
                          default='/tmp/tensorflow/mnist/input_data',
                          help='Directory for storing input data')
      FLAGS, unparsed = parser.parse_known_args()
      tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  