import tensorflow as tf

 import input_data

 import math

 NUM_CLASSES = 10

 IMAGE_SIZE = 28

 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE

 flags = tf.app.flags

 FLAGS = flags.FLAGS

 flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')

 flags.DEFINE_integer('max_steps', 10000, 'Number of steps to run trainer.')

 flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.')

 flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')

 flags.DEFINE_integer('batch_size', 100, 'Batch size.  '

                      'Must divide evenly into the dataset sizes.')

 flags.DEFINE_string('train_dir', 'data', 'Directory to put the training data.')

 flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '

                      'for unit testing.')

 def inference(images, hidden1_units, hidden2_units):

   with tf.name_scope('hidden1'):

     weights = tf.Variable(

         tf.truncated_normal([IMAGE_PIXELS, hidden1_units],

                             stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),

         name='weights')

     biases = tf.Variable(tf.zeros([hidden1_units]),

                          name='biases')

     hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)

   with tf.name_scope('hidden2'):

     weights = tf.Variable(

         tf.truncated_normal([hidden1_units, hidden2_units],

                             stddev=1.0 / math.sqrt(float(hidden1_units))),

         name='weights')

     biases = tf.Variable(tf.zeros([hidden2_units]),

                          name='biases')

     hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)

   with tf.name_scope('softmax_linear'):

     weights = tf.Variable(

         tf.truncated_normal([hidden2_units, NUM_CLASSES],

                             stddev=1.0 / math.sqrt(float(hidden2_units))),

         name='weights')

     biases = tf.Variable(tf.zeros([NUM_CLASSES]),

                          name='biases')

     logits = tf.matmul(hidden2, weights) + biases

   return logits

 def loss(logits, labels):

   labels = tf.to_int64(labels)

   cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(

       logits, labels, name='xentropy')

   loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')

   return loss

 def training(loss, learning_rate):

   tf.scalar_summary(loss.op.name, loss)

   optimizer = tf.train.GradientDescentOptimizer(learning_rate)

   global_step = tf.Variable(0, name='global_step', trainable=False)

   train_op = optimizer.minimize(loss, global_step=global_step)

   return train_op

 def evaluation(logits, labels):

   correct = tf.nn.in_top_k(logits, labels, 1)

   return tf.reduce_sum(tf.cast(correct, tf.int32))

 def placeholder_inputs(batch_size):

   images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,

                                                          IMAGE_PIXELS))

   labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))

   return images_placeholder, labels_placeholder

 def fill_feed_dict(data_set, images_pl, labels_pl):

   images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,

                                                  FLAGS.fake_data)

   feed_dict = {

       images_pl: images_feed,

       labels_pl: labels_feed,

   }

   return feed_dict

 def do_eval(sess,

             eval_correct,

             images_placeholder,

             labels_placeholder,

             data_set):

   true_count = 0

   steps_per_epoch = data_set.num_examples // FLAGS.batch_size

   num_examples = steps_per_epoch * FLAGS.batch_size

   for step in range(steps_per_epoch):

     feed_dict = fill_feed_dict(data_set,

                                images_placeholder,

                                labels_placeholder)

     true_count += sess.run(eval_correct, feed_dict=feed_dict)

   precision = true_count / num_examples

   print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %

         (num_examples, true_count, precision))

 def run_training():

   data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)

   print(FLAGS.train_dir, FLAGS.fake_data)

   with tf.Graph().as_default():

     images_placeholder, labels_placeholder = placeholder_inputs(

         FLAGS.batch_size)

     logits = inference(images_placeholder,

                              FLAGS.hidden1,

                              FLAGS.hidden2)

     loss_minist = loss(logits, labels_placeholder)

     train_op = training(loss_minist, FLAGS.learning_rate)

     eval_correct = evaluation(logits, labels_placeholder)

     summary = tf.merge_all_summaries()

     init = tf.initialize_all_variables()

     sess = tf.Session()

     summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)

     sess.run(init)

     for step in range(FLAGS.max_steps):

       feed_dict = fill_feed_dict(data_sets.train,

                                  images_placeholder,

                                  labels_placeholder)

       _, loss_value = sess.run([train_op, loss_minist],

                                feed_dict=feed_dict)

       if step % 100 == 0:

         print('Step %d: loss = %.2f' % (step, loss_value))

         summary_str = sess.run(summary, feed_dict=feed_dict)

         summary_writer.add_summary(summary_str, step)

         summary_writer.flush()

       if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:

         print('Training Data Eval:')

         do_eval(sess,

                 eval_correct,

                 images_placeholder,

                 labels_placeholder,

                 data_sets.train)

         print('Validation Data Eval:')

         do_eval(sess,

                 eval_correct,

                 images_placeholder,

                 labels_placeholder,

                 data_sets.validation)

         print('Test Data Eval:')

         do_eval(sess,

                 eval_correct,

                 images_placeholder,

                 labels_placeholder,

                 data_sets.test)

 run_training()