text: # Adapted from the TensorFlow Mnist tutorial (see license).
  
  # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # 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.
  # ==============================================================================
  
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  import tensorflow as tf
  sess = tf.InteractiveSession()
  
  # Input images
  x = tf.placeholder(tf.float32, shape=[None, 28*28])
  # Output classes
  y_ = tf.placeholder(tf.float32, shape=[None, 10])
  
  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='VALID')
  
  def max_pool_2x2(x):
    return tf.nn.max_pool(x,
                          ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1],
                          padding='VALID')
  
  x_image = tf.reshape(x, [-1, 28, 28, 1])
  
  LEARNING_RATE = 0.03
  BATCH_SIZE = 10
  CONV1_CHANS = 8
  CONV2_CHANS = 4
  FC_SIZE = 100
  
  # First conv layer
  W_conv1 = weight_variable([5, 5, 1, CONV1_CHANS])
  b_conv1 = bias_variable([CONV1_CHANS])
  
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  
  # Second conv layer
  W_conv2 = weight_variable([5, 5, CONV1_CHANS, CONV2_CHANS])
  b_conv2 = bias_variable([CONV2_CHANS])
  
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  
  # FC layer
  W_fc1 = weight_variable([4 * 4 * CONV2_CHANS, FC_SIZE])
  b_fc1 = bias_variable([FC_SIZE])
  
  h_pool2_flat = tf.reshape(h_pool2, [-1, 4 * 4 * CONV2_CHANS])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
  # Readout
  W_fc2 = weight_variable([FC_SIZE, 10])
  b_fc2 = bias_variable([10])
  
  y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
  
  # Training
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
  train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).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))
  sess.run(tf.global_variables_initializer())
  for i in range(20000):
    batch = mnist.train.next_batch(BATCH_SIZE)
    if i % 100 == 0:
      train_accuracy = accuracy.eval(feed_dict={x:mnist.test.images, y_: mnist.test.labels})
      print("step %d, training accuracy %f"%(i*BATCH_SIZE, train_accuracy))
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
  
  print("test accuracy %g"%accuracy.eval(feed_dict={
      x: mnist.test.images, y_: mnist.test.labels}))
  
  