text: # Copyright 2015 Google Inc. 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.
  # ==============================================================================
  
  """A very simple MNIST classifier, modified to display data in TensorBoard.
  
  See extensive documentation for the original model at
  http://tensorflow.org/tutorials/mnist/beginners/index.md
  
  See documentation on the TensorBoard specific pieces at
  http://tensorflow.org/how_tos/summaries_and_tensorboard/index.md
  
  If you modify this file, please update the excerpt in
  how_tos/summaries_and_tensorboard/index.md.
  
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  flags.DEFINE_boolean('fake_data', False, 'If true, uses fake data '
                       'for unit testing.')
  flags.DEFINE_integer('max_steps', 1000, 'Number of steps to run trainer.')
  flags.DEFINE_float('learning_rate', 0.5, 'Initial learning rate.')
  flags.DEFINE_string('data_dir', '/tmp/data', 'Directory for storing data')
  flags.DEFINE_string('summaries_dir', '/tmp/mnist_logs', 'Summaries directory')
  
  
  def main(_):
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True,
                                      fake_data=FLAGS.fake_data)
  
    sess = tf.InteractiveSession()
  
    # Create the model
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    W = tf.Variable(tf.zeros([784, 10]), name='weights')
    b = tf.Variable(tf.zeros([10]), name='bias')
  
    # Use a name scope to organize nodes in the graph visualizer
    with tf.name_scope('Wx_b'):
      y = tf.nn.softmax(tf.matmul(x, W) + b)
  
    # Add summary ops to collect data
    tf.histogram_summary('weights', W)
    tf.histogram_summary('biases', b)
    tf.histogram_summary('y', y)
  
    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
    # More name scopes will clean up the graph representation
    with tf.name_scope('xent'):
      cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
      tf.scalar_summary('cross entropy', cross_entropy)
    with tf.name_scope('train'):
      train_step = tf.train.GradientDescentOptimizer(
          FLAGS.learning_rate).minimize(cross_entropy)
  
    with tf.name_scope('test'):
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
      tf.scalar_summary('accuracy', accuracy)
  
    # Merge all the summaries and write them out to /tmp/mnist_logs (by default)
    merged = tf.merge_all_summaries()
    writer = tf.train.SummaryWriter(FLAGS.summaries_dir, sess.graph)
    tf.initialize_all_variables().run()
  
    # Train the model, and feed in test data and record summaries every 10 steps
  
    for i in range(FLAGS.max_steps):
      if i % 10 == 0:  # Record summary data and the accuracy
        if FLAGS.fake_data:
          batch_xs, batch_ys = mnist.train.next_batch(
              100, fake_data=FLAGS.fake_data)
          feed = {x: batch_xs, y_: batch_ys}
        else:
          feed = {x: mnist.test.images, y_: mnist.test.labels}
        result = sess.run([merged, accuracy], feed_dict=feed)
        summary_str = result[0]
        acc = result[1]
        writer.add_summary(summary_str, i)
        print('Accuracy at step %s: %s' % (i, acc))
      else:
        batch_xs, batch_ys = mnist.train.next_batch(
            100, fake_data=FLAGS.fake_data)
        feed = {x: batch_xs, y_: batch_ys}
        sess.run(train_step, feed_dict=feed)
  
  if __name__ == '__main__':
    tf.app.run()
  