fully_connected_reader.py

# Copyright 2018 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.
# ==============================================================================
"""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:
https://www.tensorflow.org/programmers_guide/reading_data#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 argparse
import os.path
import sys
import time

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import mnist

# Basic model parameters as external flags.
FLAGS = None

# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'


def decode(serialized_example):
  """Parses an image and label from the given `serialized_example`."""
  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))

  # Convert label from a scalar uint8 tensor to an int32 scalar.
  label = tf.cast(features['label'], tf.int32)

  return image, label


def augment(image, label):
  """Placeholder for data augmentation."""
  # 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.
  return image, label


def normalize(image, label):
  """Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
  image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
  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).

    This function creates a one_shot_iterator, meaning that it will only iterate
    over the dataset once. On the other hand there is no special initialization
    required.
  """
  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'):
    # TFRecordDataset opens a binary file and reads one record at a time.
    # `filename` could also be a list of filenames, which will be read in order.
    dataset = tf.data.TFRecordDataset(filename)

    # The map transformation takes a function and applies it to every element
    # of the dataset.
    dataset = dataset.map(decode)
    dataset = dataset.map(augment)
    dataset = dataset.map(normalize)

    # The shuffle transformation uses a finite-sized buffer to shuffle elements
    # in memory. The parameter is the number of elements in the buffer. For
    # completely uniform shuffling, set the parameter to be the same as the
    # number of elements in the dataset.
    dataset = dataset.shuffle(1000 + 3 * batch_size)

    dataset = dataset.repeat(num_epochs)
    dataset = dataset.batch(batch_size)

    iterator = dataset.make_one_shot_iterator()
  return iterator.get_next()


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.
    image_batch, label_batch = 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(image_batch, FLAGS.hidden1, FLAGS.hidden2)

    # Add to the Graph the loss calculation.
    loss = mnist.loss(logits, label_batch)

    # 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.group(tf.global_variables_initializer(),
                       tf.local_variables_initializer())

    # Create a session for running operations in the Graph.
    with tf.Session() as sess:
      # Initialize the variables (the trained variables and the
      # epoch counter).
      sess.run(init_op)
      try:
        step = 0
        while True:  # Train until OutOfRangeError
          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))


def main(_):
  run_training()


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--learning_rate',
      type=float,
      default=0.01,
      help='Initial learning rate.')
  parser.add_argument(
      '--num_epochs',
      type=int,
      default=2,
      help='Number of epochs to run trainer.')
  parser.add_argument(
      '--hidden1',
      type=int,
      default=128,
      help='Number of units in hidden layer 1.')
  parser.add_argument(
      '--hidden2',
      type=int,
      default=32,
      help='Number of units in hidden layer 2.')
  parser.add_argument('--batch_size', type=int, default=100, help='Batch size.')
  parser.add_argument(
      '--train_dir',
      type=str,
      default='/tmp/data',
      help='Directory with the training data.')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)