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.
  # ==============================================================================
  
  """Train and Eval the MNIST network.
  
  This version is like fully_connected_feed.py but uses data converted
  to a TFRecords file containing tf.train.Example protocol buffers.
  See tensorflow/g3doc/how_tos/reading_data.md#reading-from-files
  for context.
  
  YOU MUST run convert_to_records before running this (but you only need to
  run it once).
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os.path
  import time
  
  import numpy
  import tensorflow as tf
  
  from tensorflow.examples.tutorials.mnist import mnist
  
  
  # Basic model parameters as external flags.
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
  flags.DEFINE_integer('num_epochs', 2, 'Number of epochs 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.')
  flags.DEFINE_string('train_dir', '/tmp/data',
                      'Directory with the training data.')
  
  # Constants used for dealing with the files, matches convert_to_records.
  TRAIN_FILE = 'train.tfrecords'
  VALIDATION_FILE = 'validation.tfrecords'
  
  
  def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
            'label': tf.FixedLenFeature([], tf.int64),
        })
  
    # Convert from a scalar string tensor (whose single string has
    # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
    # [mnist.IMAGE_PIXELS].
    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([mnist.IMAGE_PIXELS])
  
    # OPTIONAL: Could reshape into a 28x28 image and apply distortions
    # here.  Since we are not applying any distortions in this
    # example, and the next step expects the image to be flattened
    # into a vector, we don't bother.
  
    # Convert from [0, 255] -> [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
  
    # Convert label from a scalar uint8 tensor to an int32 scalar.
    label = tf.cast(features['label'], tf.int32)
  
    return image, label
  
  
  def inputs(train, batch_size, num_epochs):
    """Reads input data num_epochs times.
  
    Args:
      train: Selects between the training (True) and validation (False) data.
      batch_size: Number of examples per returned batch.
      num_epochs: Number of times to read the input data, or 0/None to
         train forever.
  
    Returns:
      A tuple (images, labels), where:
      * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
        in the range [-0.5, 0.5].
      * labels is an int32 tensor with shape [batch_size] with the true label,
        a number in the range [0, mnist.NUM_CLASSES).
      Note that an tf.train.QueueRunner is added to the graph, which
      must be run using e.g. tf.train.start_queue_runners().
    """
    if not num_epochs: num_epochs = None
    filename = os.path.join(FLAGS.train_dir,
                            TRAIN_FILE if train else VALIDATION_FILE)
  
    with tf.name_scope('input'):
      filename_queue = tf.train.string_input_producer(
          [filename], num_epochs=num_epochs)
  
      # Even when reading in multiple threads, share the filename
      # queue.
      image, label = read_and_decode(filename_queue)
  
      # Shuffle the examples and collect them into batch_size batches.
      # (Internally uses a RandomShuffleQueue.)
      # We run this in two threads to avoid being a bottleneck.
      images, sparse_labels = tf.train.shuffle_batch(
          [image, label], batch_size=batch_size, num_threads=2,
          capacity=1000 + 3 * batch_size,
          # Ensures a minimum amount of shuffling of examples.
          min_after_dequeue=1000)
  
      return images, sparse_labels
  
  
  def run_training():
    """Train MNIST for a number of steps."""
  
    # Tell TensorFlow that the model will be built into the default Graph.
    with tf.Graph().as_default():
      # Input images and labels.
      images, labels = inputs(train=True, batch_size=FLAGS.batch_size,
                              num_epochs=FLAGS.num_epochs)
  
      # Build a Graph that computes predictions from the inference model.
      logits = mnist.inference(images,
                               FLAGS.hidden1,
                               FLAGS.hidden2)
  
      # Add to the Graph the loss calculation.
      loss = mnist.loss(logits, labels)
  
      # Add to the Graph operations that train the model.
      train_op = mnist.training(loss, FLAGS.learning_rate)
  
      # The op for initializing the variables.
      init_op = tf.initialize_all_variables()
  
      # Create a session for running operations in the Graph.
      sess = tf.Session()
  
      # Initialize the variables (the trained variables and the
      # epoch counter).
      sess.run(init_op)
  
      # Start input enqueue threads.
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(sess=sess, coord=coord)
  
      try:
        step = 0
        while not coord.should_stop():
          start_time = time.time()
  
          # Run one step of the model.  The return values are
          # the activations from the `train_op` (which is
          # discarded) and the `loss` op.  To inspect the values
          # of your ops or variables, you may include them in
          # the list passed to sess.run() and the value tensors
          # will be returned in the tuple from the call.
          _, loss_value = sess.run([train_op, loss])
  
          duration = time.time() - start_time
  
          # Print an overview fairly often.
          if step % 100 == 0:
            print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
                                                       duration))
          step += 1
      except tf.errors.OutOfRangeError:
        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
      finally:
        # When done, ask the threads to stop.
        coord.request_stop()
  
      # Wait for threads to finish.
      coord.join(threads)
      sess.close()
  
  
  def main(_):
    run_training()
  
  
  if __name__ == '__main__':
    tf.app.run()
  