text: # ==============================================================================
  #   Copyright 2016 Niek Temme.
  
  #   Adapted form the on the MNIST expert tutorial by Google. 
  #
  #   Licensed under the Apache License, Version 2.0 (the "License");
  #   you may not use this file except in compliance with the License.
  #   You may obtain a copy of the License at
  #
  #       http://www.apache.org/licenses/LICENSE-2.0
  #
  #   Unless required by applicable law or agreed to in writing, software
  #   distributed under the License is distributed on an "AS IS" BASIS,
  #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #   See the License for the specific language governing permissions and
  #   limitations under the License.
  # ==============================================================================
  
  # ==============================================================================
  #   Modifications copyright (C) 2018 De Groskovskaja Marco
  # ==============================================================================
  
  """A very simple MNIST classifier.
  Documentation at
  http://niektemme.com/ @@to do
  This script is based on the Tensoflow MNIST expert tutorial
  See extensive documentation for the tutorial at
  https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html
  """
  
  import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  def trainclassifier():
      #import data
      mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
      sess = tf.InteractiveSession()
  
      # Create the model
      x = tf.placeholder(tf.float32, [None, 784])
      y_ = tf.placeholder(tf.float32, [None, 10])
      W = tf.Variable(tf.zeros([784, 10]))
      b = tf.Variable(tf.zeros([10]))
      y = tf.nn.softmax(tf.matmul(x, W) + b)
  
      def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)
  
      def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)
  
  
      def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
      def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')
  
  
      W_conv1 = weight_variable([5, 5, 1, 32])
      b_conv1 = bias_variable([32])
  
      x_image = tf.reshape(x, [-1,28,28,1])
      h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
      h_pool1 = max_pool_2x2(h_conv1)
  
  
      W_conv2 = weight_variable([5, 5, 32, 64])
      b_conv2 = bias_variable([64])
  
      h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
      h_pool2 = max_pool_2x2(h_conv2)
  
      W_fc1 = weight_variable([7 * 7 * 64, 1024])
      b_fc1 = bias_variable([1024])
  
      h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
      h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      keep_prob = tf.placeholder(tf.float32)
      h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      W_fc2 = weight_variable([1024, 10])
      b_fc2 = bias_variable([10])
  
      y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
  
      # Define loss and optimizer
      cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
      train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
      correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
  
      """
      Train the model and save the model to disk as a model.ckpt file
      file is stored in the same directory as this python script is started
      Based on the documentatoin at
      https://www.tensorflow.org/versions/master/how_tos/variables/index.html
      """
      saver = tf.train.Saver()
      sess.run(tf.initialize_all_variables())
  
      for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i%100 == 0:
          train_accuracy = accuracy.eval(feed_dict={
              x:batch[0], y_: batch[1], keep_prob: 1.0})
          print("step %d, training accuracy %g"%(i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
  
      save_path = saver.save(sess, "./model.ckpt")
      print ("Model saved in file: ", save_path)
  
      print("test accuracy %g"%accuracy.eval(feed_dict={
          x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
  
      return None