text: # Adapted from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_softmax.py
  
  """A very simple MNIST classifier.
  
  See extensive documentation at
  http://tensorflow.org/tutorials/mnist/beginners/index.md
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  import tensorflow as tf
  print("Tensorflow version ", tf.__version__)
  
  # Import data
  mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data', one_hot=True)
  
  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x, W) + b
  
  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])
  
  # The raw formulation of cross-entropy,
  #
  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
  #                                 reduction_indices=[1]))
  #
  # can be numerically unstable.
  #
  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
  # outputs of 'y', and then average across the batch.
  cross_entropy = tf.reduce_mean(
      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
  sess = tf.InteractiveSession()
  tf.global_variables_initializer().run()
  # Train
  for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  
  # Test trained model
  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                      y_: mnist.test.labels}))
  