new_contents: import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
  
  def softmax():
      # Model variables
      x = tf.placeholder(tf.float32, shape=[None, 784])
      y_ = tf.placeholder(tf.float32, shape=[None, 10])
      W = tf.Variable(tf.zeros([784, 10]))
      b = tf.Variable(tf.zeros([10]))
      y = tf.nn.softmax(tf.matmul(x, W) + b)
  
      # Setup cost function and Gradient Descent Optimizer
      cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
      train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
      # Setup input variables and session
      sess = tf.InteractiveSession()
      sess.run(tf.initialize_all_variables())
  
      for i in xrange(1000):
          if (i + 1) % 100 == 0:
              print "training step {}".format(i + 1)
          batch_xs, batch_ys = mnist.train.next_batch(50)
          train_step.run(feed_dict={x: batch_xs, y_: batch_ys})
  
      # Evaluate model
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      print "Test accuracy: {}".format(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
  
  
  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')
  
  
  def convolutional_network():
      # Model variables
      x = tf.placeholder(tf.float32, shape=[None, 784])
      y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
      # Layer 1
      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)
  
      # Layer 2
      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)
  
      # Layer 3
      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)
  
      # Dropout
      keep_prob = tf.placeholder(tf.float32)
      h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      # Softmax readout layer
      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)
  
      # Evaluate model
      cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
      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))
  
      # Setup input variables and session
      sess = tf.InteractiveSession()
      sess.run(tf.initialize_all_variables())
  
      for i in xrange(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})
  
      print("Test accuracy: %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
  
  
  def main():
      print "\nRunning softmax model..."
      softmax()
  
      print "\nRunning convolutional neural network..."
      convolutional_network()
  
  if __name__ == '__main__':
      main()
  
subject: add script that compares results of simple softmax and convolutional NN in TensorFlow