text: # 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.
  # ==============================================================================
  r"""Simple transfer learning with Inception v3 or Mobilenet models.
  
  With support for TensorBoard.
  
  This example shows how to take a Inception v3 or Mobilenet model trained on
  ImageNet images, and train a new top layer that can recognize other classes of
  images.
  
  The top layer receives as input a 2048-dimensional vector (1001-dimensional for
  Mobilenet) for each image. We train a softmax layer on top of this
  representation. Assuming the softmax layer contains N labels, this corresponds
  to learning N + 2048*N (or 1001*N)  model parameters corresponding to the
  learned biases and weights.
  
  Here's an example, which assumes you have a folder containing class-named
  subfolders, each full of images for each label. The example folder flower_photos
  should have a structure like this:
  
  ~/flower_photos/daisy/photo1.jpg
  ~/flower_photos/daisy/photo2.jpg
  ...
  ~/flower_photos/rose/anotherphoto77.jpg
  ...
  ~/flower_photos/sunflower/somepicture.jpg
  
  The subfolder names are important, since they define what label is applied to
  each image, but the filenames themselves don't matter. Once your images are
  prepared, you can run the training with a command like this:
  
  ```bash
  bazel build tensorflow/examples/image_retraining:retrain && \
  bazel-bin/tensorflow/examples/image_retraining/retrain \
      --image_dir ~/flower_photos
  ```
  
  Or, if you have a pip installation of tensorflow, `retrain.py` can be run
  without bazel:
  
  ```bash
  python tensorflow/examples/image_retraining/retrain.py \
      --image_dir ~/flower_photos
  ```
  
  You can replace the image_dir argument with any folder containing subfolders of
  images. The label for each image is taken from the name of the subfolder it's
  in.
  
  This produces a new model file that can be loaded and run by any TensorFlow
  program, for example the label_image sample code.
  
  By default this script will use the high accuracy, but comparatively large and
  slow Inception v3 model architecture. It's recommended that you start with this
  to validate that you have gathered good training data, but if you want to deploy
  on resource-limited platforms, you can try the `--architecture` flag with a
  Mobilenet model. For example:
  
  Run floating-point version of mobilenet:
  
  ```bash
  python tensorflow/examples/image_retraining/retrain.py \
      --image_dir ~/flower_photos --architecture mobilenet_1.0_224
  ```
  
  Run mobilenet, instrumented for quantization:
  
  ```bash
  python tensorflow/examples/image_retraining/retrain.py \
      --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quant
  ```
  
  These instrumented models can be converted to fully quantized mobile models via
  TensorFlow Lite.
  
  There are 32 different Mobilenet models to choose from, with a variety of file
  size and latency options. The first number can be '1.0', '0.75', '0.50', or
  '0.25' to control the size, and the second controls the input image size, either
  '224', '192', '160', or '128', with smaller sizes running faster. See
  https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
  for more information on Mobilenet.
  
  To use with TensorBoard:
  
  By default, this script will log summaries to /tmp/retrain_logs directory
  
  Visualize the summaries with this command:
  
  tensorboard --logdir /tmp/retrain_logs
  
  To use with Tensorflow Serving:
  
  ```bash
  tensorflow_model_server --port=9000 --model_name=inception \
      --model_base_path=/tmp/saved_models/
  ```
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  from datetime import datetime
  import hashlib
  import os.path
  import random
  import re
  import sys
  import tarfile
  
  import numpy as np
  from six.moves import urllib
  import tensorflow as tf
  
  from tensorflow.python.framework import graph_util
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.platform import gfile
  from tensorflow.python.util import compat
  
  FLAGS = None
  
  # These are all parameters that are tied to the particular model architecture
  # we're using for Inception v3. These include things like tensor names and their
  # sizes. If you want to adapt this script to work with another model, you will
  # need to update these to reflect the values in the network you're using.
  MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
  
  # The location where variable checkpoints will be stored.
  CHECKPOINT_NAME = '/tmp/_retrain_checkpoint'
  
  
  def create_image_lists(image_dir, testing_percentage, validation_percentage):
    """Builds a list of training images from the file system.
  
    Analyzes the sub folders in the image directory, splits them into stable
    training, testing, and validation sets, and returns a data structure
    describing the lists of images for each label and their paths.
  
    Args:
      image_dir: String path to a folder containing subfolders of images.
      testing_percentage: Integer percentage of the images to reserve for tests.
      validation_percentage: Integer percentage of images reserved for validation.
  
    Returns:
      A dictionary containing an entry for each label subfolder, with images split
      into training, testing, and validation sets within each label.
    """
    if not gfile.Exists(image_dir):
      tf.logging.error("Image directory '" + image_dir + "' not found.")
      return None
    result = {}
    sub_dirs = [x[0] for x in gfile.Walk(image_dir)]
    # The root directory comes first, so skip it.
    is_root_dir = True
    for sub_dir in sub_dirs:
      if is_root_dir:
        is_root_dir = False
        continue
      extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
      file_list = []
      dir_name = os.path.basename(sub_dir)
      if dir_name == image_dir:
        continue
      tf.logging.info("Looking for images in '" + dir_name + "'")
      for extension in extensions:
        file_glob = os.path.join(image_dir, dir_name, '*.' + extension)
        file_list.extend(gfile.Glob(file_glob))
      if not file_list:
        tf.logging.warning('No files found')
        continue
      if len(file_list) < 20:
        tf.logging.warning(
            'WARNING: Folder has less than 20 images, which may cause issues.')
      elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
        tf.logging.warning(
            'WARNING: Folder {} has more than {} images. Some images will '
            'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
      label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
      training_images = []
      testing_images = []
      validation_images = []
      for file_name in file_list:
        base_name = os.path.basename(file_name)
        # We want to ignore anything after '_nohash_' in the file name when
        # deciding which set to put an image in, the data set creator has a way of
        # grouping photos that are close variations of each other. For example
        # this is used in the plant disease data set to group multiple pictures of
        # the same leaf.
        hash_name = re.sub(r'_nohash_.*$', '', file_name)
        # This looks a bit magical, but we need to decide whether this file should
        # go into the training, testing, or validation sets, and we want to keep
        # existing files in the same set even if more files are subsequently
        # added.
        # To do that, we need a stable way of deciding based on just the file name
        # itself, so we do a hash of that and then use that to generate a
        # probability value that we use to assign it.
        hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()
        percentage_hash = ((int(hash_name_hashed, 16) %
                            (MAX_NUM_IMAGES_PER_CLASS + 1)) *
                           (100.0 / MAX_NUM_IMAGES_PER_CLASS))
        if percentage_hash < validation_percentage:
          validation_images.append(base_name)
        elif percentage_hash < (testing_percentage + validation_percentage):
          testing_images.append(base_name)
        else:
          training_images.append(base_name)
      result[label_name] = {
          'dir': dir_name,
          'training': training_images,
          'testing': testing_images,
          'validation': validation_images,
      }
    return result
  
  
  def get_image_path(image_lists, label_name, index, image_dir, category):
    """"Returns a path to an image for a label at the given index.
  
    Args:
      image_lists: Dictionary of training images for each label.
      label_name: Label string we want to get an image for.
      index: Int offset of the image we want. This will be moduloed by the
      available number of images for the label, so it can be arbitrarily large.
      image_dir: Root folder string of the subfolders containing the training
      images.
      category: Name string of set to pull images from - training, testing, or
      validation.
  
    Returns:
      File system path string to an image that meets the requested parameters.
  
    """
    if label_name not in image_lists:
      tf.logging.fatal('Label does not exist %s.', label_name)
    label_lists = image_lists[label_name]
    if category not in label_lists:
      tf.logging.fatal('Category does not exist %s.', category)
    category_list = label_lists[category]
    if not category_list:
      tf.logging.fatal('Label %s has no images in the category %s.',
                       label_name, category)
    mod_index = index % len(category_list)
    base_name = category_list[mod_index]
    sub_dir = label_lists['dir']
    full_path = os.path.join(image_dir, sub_dir, base_name)
    return full_path
  
  
  def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
                          category, architecture):
    """"Returns a path to a bottleneck file for a label at the given index.
  
    Args:
      image_lists: Dictionary of training images for each label.
      label_name: Label string we want to get an image for.
      index: Integer offset of the image we want. This will be moduloed by the
      available number of images for the label, so it can be arbitrarily large.
      bottleneck_dir: Folder string holding cached files of bottleneck values.
      category: Name string of set to pull images from - training, testing, or
      validation.
      architecture: The name of the model architecture.
  
    Returns:
      File system path string to an image that meets the requested parameters.
    """
    return get_image_path(image_lists, label_name, index, bottleneck_dir,
                          category) + '_' + architecture + '.txt'
  
  
  def create_model_graph(model_info):
    """"Creates a graph from saved GraphDef file and returns a Graph object.
  
    Args:
      model_info: Dictionary containing information about the model architecture.
  
    Returns:
      Graph holding the trained Inception network, and various tensors we'll be
      manipulating.
    """
    with tf.Graph().as_default() as graph:
      model_path = os.path.join(FLAGS.model_dir, model_info['model_file_name'])
      print('Model path: ', model_path)
      with gfile.FastGFile(model_path, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        bottleneck_tensor, resized_input_tensor = (tf.import_graph_def(
            graph_def,
            name='',
            return_elements=[
                model_info['bottleneck_tensor_name'],
                model_info['resized_input_tensor_name'],
            ]))
    return graph, bottleneck_tensor, resized_input_tensor
  
  
  def run_bottleneck_on_image(sess, image_data, image_data_tensor,
                              decoded_image_tensor, resized_input_tensor,
                              bottleneck_tensor):
    """Runs inference on an image to extract the 'bottleneck' summary layer.
  
    Args:
      sess: Current active TensorFlow Session.
      image_data: String of raw JPEG data.
      image_data_tensor: Input data layer in the graph.
      decoded_image_tensor: Output of initial image resizing and preprocessing.
      resized_input_tensor: The input node of the recognition graph.
      bottleneck_tensor: Layer before the final softmax.
  
    Returns:
      Numpy array of bottleneck values.
    """
    # First decode the JPEG image, resize it, and rescale the pixel values.
    resized_input_values = sess.run(decoded_image_tensor,
                                    {image_data_tensor: image_data})
    # Then run it through the recognition network.
    bottleneck_values = sess.run(bottleneck_tensor,
                                 {resized_input_tensor: resized_input_values})
    bottleneck_values = np.squeeze(bottleneck_values)
    return bottleneck_values
  
  
  def maybe_download_and_extract(data_url):
    """Download and extract model tar file.
  
    If the pretrained model we're using doesn't already exist, this function
    downloads it from the TensorFlow.org website and unpacks it into a directory.
  
    Args:
      data_url: Web location of the tar file containing the pretrained model.
    """
    dest_directory = FLAGS.model_dir
    if not os.path.exists(dest_directory):
      os.makedirs(dest_directory)
    filename = data_url.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    if not os.path.exists(filepath):
  
      def _progress(count, block_size, total_size):
        sys.stdout.write('\r>> Downloading %s %.1f%%' %
                         (filename,
                          float(count * block_size) / float(total_size) * 100.0))
        sys.stdout.flush()
  
      filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
      print()
      statinfo = os.stat(filepath)
      tf.logging.info('Successfully downloaded %s %d bytes.', filename,
                      statinfo.st_size)
      print('Extracting file from ', filepath)
      tarfile.open(filepath, 'r:gz').extractall(dest_directory)
    else:
      print('Not extracting or downloading files, model already present in disk')
  
  
  def ensure_dir_exists(dir_name):
    """Makes sure the folder exists on disk.
  
    Args:
      dir_name: Path string to the folder we want to create.
    """
    if not os.path.exists(dir_name):
      os.makedirs(dir_name)
  
  
  bottleneck_path_2_bottleneck_values = {}
  
  
  def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                             image_dir, category, sess, jpeg_data_tensor,
                             decoded_image_tensor, resized_input_tensor,
                             bottleneck_tensor):
    """Create a single bottleneck file."""
    tf.logging.info('Creating bottleneck at ' + bottleneck_path)
    image_path = get_image_path(image_lists, label_name, index,
                                image_dir, category)
    if not gfile.Exists(image_path):
      tf.logging.fatal('File does not exist %s', image_path)
    image_data = gfile.FastGFile(image_path, 'rb').read()
    try:
      bottleneck_values = run_bottleneck_on_image(
          sess, image_data, jpeg_data_tensor, decoded_image_tensor,
          resized_input_tensor, bottleneck_tensor)
    except Exception as e:
      raise RuntimeError('Error during processing file %s (%s)' % (image_path,
                                                                   str(e)))
    bottleneck_string = ','.join(str(x) for x in bottleneck_values)
    with open(bottleneck_path, 'w') as bottleneck_file:
      bottleneck_file.write(bottleneck_string)
  
  
  def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
                               category, bottleneck_dir, jpeg_data_tensor,
                               decoded_image_tensor, resized_input_tensor,
                               bottleneck_tensor, architecture):
    """Retrieves or calculates bottleneck values for an image.
  
    If a cached version of the bottleneck data exists on-disk, return that,
    otherwise calculate the data and save it to disk for future use.
  
    Args:
      sess: The current active TensorFlow Session.
      image_lists: Dictionary of training images for each label.
      label_name: Label string we want to get an image for.
      index: Integer offset of the image we want. This will be modulo-ed by the
      available number of images for the label, so it can be arbitrarily large.
      image_dir: Root folder string of the subfolders containing the training
      images.
      category: Name string of which set to pull images from - training, testing,
      or validation.
      bottleneck_dir: Folder string holding cached files of bottleneck values.
      jpeg_data_tensor: The tensor to feed loaded jpeg data into.
      decoded_image_tensor: The output of decoding and resizing the image.
      resized_input_tensor: The input node of the recognition graph.
      bottleneck_tensor: The output tensor for the bottleneck values.
      architecture: The name of the model architecture.
  
    Returns:
      Numpy array of values produced by the bottleneck layer for the image.
    """
    label_lists = image_lists[label_name]
    sub_dir = label_lists['dir']
    sub_dir_path = os.path.join(bottleneck_dir, sub_dir)
    ensure_dir_exists(sub_dir_path)
    bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
                                          bottleneck_dir, category, architecture)
    if not os.path.exists(bottleneck_path):
      create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                             image_dir, category, sess, jpeg_data_tensor,
                             decoded_image_tensor, resized_input_tensor,
                             bottleneck_tensor)
    with open(bottleneck_path, 'r') as bottleneck_file:
      bottleneck_string = bottleneck_file.read()
    did_hit_error = False
    try:
      bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
    except ValueError:
      tf.logging.warning('Invalid float found, recreating bottleneck')
      did_hit_error = True
    if did_hit_error:
      create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
                             image_dir, category, sess, jpeg_data_tensor,
                             decoded_image_tensor, resized_input_tensor,
                             bottleneck_tensor)
      with open(bottleneck_path, 'r') as bottleneck_file:
        bottleneck_string = bottleneck_file.read()
      # Allow exceptions to propagate here, since they shouldn't happen after a
      # fresh creation
      bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
    return bottleneck_values
  
  
  def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
                        jpeg_data_tensor, decoded_image_tensor,
                        resized_input_tensor, bottleneck_tensor, architecture):
    """Ensures all the training, testing, and validation bottlenecks are cached.
  
    Because we're likely to read the same image multiple times (if there are no
    distortions applied during training) it can speed things up a lot if we
    calculate the bottleneck layer values once for each image during
    preprocessing, and then just read those cached values repeatedly during
    training. Here we go through all the images we've found, calculate those
    values, and save them off.
  
    Args:
      sess: The current active TensorFlow Session.
      image_lists: Dictionary of training images for each label.
      image_dir: Root folder string of the subfolders containing the training
      images.
      bottleneck_dir: Folder string holding cached files of bottleneck values.
      jpeg_data_tensor: Input tensor for jpeg data from file.
      decoded_image_tensor: The output of decoding and resizing the image.
      resized_input_tensor: The input node of the recognition graph.
      bottleneck_tensor: The penultimate output layer of the graph.
      architecture: The name of the model architecture.
  
    Returns:
      Nothing.
    """
    how_many_bottlenecks = 0
    ensure_dir_exists(bottleneck_dir)
    for label_name, label_lists in image_lists.items():
      for category in ['training', 'testing', 'validation']:
        category_list = label_lists[category]
        for index, unused_base_name in enumerate(category_list):
          get_or_create_bottleneck(
              sess, image_lists, label_name, index, image_dir, category,
              bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
              resized_input_tensor, bottleneck_tensor, architecture)
  
          how_many_bottlenecks += 1
          if how_many_bottlenecks % 100 == 0:
            tf.logging.info(
                str(how_many_bottlenecks) + ' bottleneck files created.')
  
  
  def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
                                    bottleneck_dir, image_dir, jpeg_data_tensor,
                                    decoded_image_tensor, resized_input_tensor,
                                    bottleneck_tensor, architecture):
    """Retrieves bottleneck values for cached images.
  
    If no distortions are being applied, this function can retrieve the cached
    bottleneck values directly from disk for images. It picks a random set of
    images from the specified category.
  
    Args:
      sess: Current TensorFlow Session.
      image_lists: Dictionary of training images for each label.
      how_many: If positive, a random sample of this size will be chosen.
      If negative, all bottlenecks will be retrieved.
      category: Name string of which set to pull from - training, testing, or
      validation.
      bottleneck_dir: Folder string holding cached files of bottleneck values.
      image_dir: Root folder string of the subfolders containing the training
      images.
      jpeg_data_tensor: The layer to feed jpeg image data into.
      decoded_image_tensor: The output of decoding and resizing the image.
      resized_input_tensor: The input node of the recognition graph.
      bottleneck_tensor: The bottleneck output layer of the CNN graph.
      architecture: The name of the model architecture.
  
    Returns:
      List of bottleneck arrays, their corresponding ground truths, and the
      relevant filenames.
    """
    class_count = len(image_lists.keys())
    bottlenecks = []
    ground_truths = []
    filenames = []
    if how_many >= 0:
      # Retrieve a random sample of bottlenecks.
      for unused_i in range(how_many):
        label_index = random.randrange(class_count)
        label_name = list(image_lists.keys())[label_index]
        image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
        image_name = get_image_path(image_lists, label_name, image_index,
                                    image_dir, category)
        bottleneck = get_or_create_bottleneck(
            sess, image_lists, label_name, image_index, image_dir, category,
            bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
            resized_input_tensor, bottleneck_tensor, architecture)
        bottlenecks.append(bottleneck)
        ground_truths.append(label_index)
        filenames.append(image_name)
    else:
      # Retrieve all bottlenecks.
      for label_index, label_name in enumerate(image_lists.keys()):
        for image_index, image_name in enumerate(
            image_lists[label_name][category]):
          image_name = get_image_path(image_lists, label_name, image_index,
                                      image_dir, category)
          bottleneck = get_or_create_bottleneck(
              sess, image_lists, label_name, image_index, image_dir, category,
              bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
              resized_input_tensor, bottleneck_tensor, architecture)
          bottlenecks.append(bottleneck)
          ground_truths.append(label_index)
          filenames.append(image_name)
    return bottlenecks, ground_truths, filenames
  
  
  def get_random_distorted_bottlenecks(
      sess, image_lists, how_many, category, image_dir, input_jpeg_tensor,
      distorted_image, resized_input_tensor, bottleneck_tensor):
    """Retrieves bottleneck values for training images, after distortions.
  
    If we're training with distortions like crops, scales, or flips, we have to
    recalculate the full model for every image, and so we can't use cached
    bottleneck values. Instead we find random images for the requested category,
    run them through the distortion graph, and then the full graph to get the
    bottleneck results for each.
  
    Args:
      sess: Current TensorFlow Session.
      image_lists: Dictionary of training images for each label.
      how_many: The integer number of bottleneck values to return.
      category: Name string of which set of images to fetch - training, testing,
      or validation.
      image_dir: Root folder string of the subfolders containing the training
      images.
      input_jpeg_tensor: The input layer we feed the image data to.
      distorted_image: The output node of the distortion graph.
      resized_input_tensor: The input node of the recognition graph.
      bottleneck_tensor: The bottleneck output layer of the CNN graph.
  
    Returns:
      List of bottleneck arrays and their corresponding ground truths.
    """
    class_count = len(image_lists.keys())
    bottlenecks = []
    ground_truths = []
    for unused_i in range(how_many):
      label_index = random.randrange(class_count)
      label_name = list(image_lists.keys())[label_index]
      image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
      image_path = get_image_path(image_lists, label_name, image_index, image_dir,
                                  category)
      if not gfile.Exists(image_path):
        tf.logging.fatal('File does not exist %s', image_path)
      jpeg_data = gfile.FastGFile(image_path, 'rb').read()
      # Note that we materialize the distorted_image_data as a numpy array before
      # sending running inference on the image. This involves 2 memory copies and
      # might be optimized in other implementations.
      distorted_image_data = sess.run(distorted_image,
                                      {input_jpeg_tensor: jpeg_data})
      bottleneck_values = sess.run(bottleneck_tensor,
                                   {resized_input_tensor: distorted_image_data})
      bottleneck_values = np.squeeze(bottleneck_values)
      bottlenecks.append(bottleneck_values)
      ground_truths.append(label_index)
    return bottlenecks, ground_truths
  
  
  def should_distort_images(flip_left_right, random_crop, random_scale,
                            random_brightness):
    """Whether any distortions are enabled, from the input flags.
  
    Args:
      flip_left_right: Boolean whether to randomly mirror images horizontally.
      random_crop: Integer percentage setting the total margin used around the
      crop box.
      random_scale: Integer percentage of how much to vary the scale by.
      random_brightness: Integer range to randomly multiply the pixel values by.
  
    Returns:
      Boolean value indicating whether any distortions should be applied.
    """
    return (flip_left_right or (random_crop != 0) or (random_scale != 0) or
            (random_brightness != 0))
  
  
  def add_input_distortions(flip_left_right, random_crop, random_scale,
                            random_brightness, input_width, input_height,
                            input_depth, input_mean, input_std):
    """Creates the operations to apply the specified distortions.
  
    During training it can help to improve the results if we run the images
    through simple distortions like crops, scales, and flips. These reflect the
    kind of variations we expect in the real world, and so can help train the
    model to cope with natural data more effectively. Here we take the supplied
    parameters and construct a network of operations to apply them to an image.
  
    Cropping
    ~~~~~~~~
  
    Cropping is done by placing a bounding box at a random position in the full
    image. The cropping parameter controls the size of that box relative to the
    input image. If it's zero, then the box is the same size as the input and no
    cropping is performed. If the value is 50%, then the crop box will be half the
    width and height of the input. In a diagram it looks like this:
  
    <       width         >
    +---------------------+
    |                     |
    |   width - crop%     |
    |    <      >         |
    |    +------+         |
    |    |      |         |
    |    |      |         |
    |    |      |         |
    |    +------+         |
    |                     |
    |                     |
    +---------------------+
  
    Scaling
    ~~~~~~~
  
    Scaling is a lot like cropping, except that the bounding box is always
    centered and its size varies randomly within the given range. For example if
    the scale percentage is zero, then the bounding box is the same size as the
    input and no scaling is applied. If it's 50%, then the bounding box will be in
    a random range between half the width and height and full size.
  
    Args:
      flip_left_right: Boolean whether to randomly mirror images horizontally.
      random_crop: Integer percentage setting the total margin used around the
      crop box.
      random_scale: Integer percentage of how much to vary the scale by.
      random_brightness: Integer range to randomly multiply the pixel values by.
      graph.
      input_width: Horizontal size of expected input image to model.
      input_height: Vertical size of expected input image to model.
      input_depth: How many channels the expected input image should have.
      input_mean: Pixel value that should be zero in the image for the graph.
      input_std: How much to divide the pixel values by before recognition.
  
    Returns:
      The jpeg input layer and the distorted result tensor.
    """
  
    jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput')
    decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
    decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
    decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
    margin_scale = 1.0 + (random_crop / 100.0)
    resize_scale = 1.0 + (random_scale / 100.0)
    margin_scale_value = tf.constant(margin_scale)
    resize_scale_value = tf.random_uniform(tensor_shape.scalar(),
                                           minval=1.0,
                                           maxval=resize_scale)
    scale_value = tf.multiply(margin_scale_value, resize_scale_value)
    precrop_width = tf.multiply(scale_value, input_width)
    precrop_height = tf.multiply(scale_value, input_height)
    precrop_shape = tf.stack([precrop_height, precrop_width])
    precrop_shape_as_int = tf.cast(precrop_shape, dtype=tf.int32)
    precropped_image = tf.image.resize_bilinear(decoded_image_4d,
                                                precrop_shape_as_int)
    precropped_image_3d = tf.squeeze(precropped_image, squeeze_dims=[0])
    cropped_image = tf.random_crop(precropped_image_3d,
                                   [input_height, input_width, input_depth])
    if flip_left_right:
      flipped_image = tf.image.random_flip_left_right(cropped_image)
    else:
      flipped_image = cropped_image
    brightness_min = 1.0 - (random_brightness / 100.0)
    brightness_max = 1.0 + (random_brightness / 100.0)
    brightness_value = tf.random_uniform(tensor_shape.scalar(),
                                         minval=brightness_min,
                                         maxval=brightness_max)
    brightened_image = tf.multiply(flipped_image, brightness_value)
    offset_image = tf.subtract(brightened_image, input_mean)
    mul_image = tf.multiply(offset_image, 1.0 / input_std)
    distort_result = tf.expand_dims(mul_image, 0, name='DistortResult')
    return jpeg_data, distort_result
  
  
  def variable_summaries(var):
    """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
    with tf.name_scope('summaries'):
      mean = tf.reduce_mean(var)
      tf.summary.scalar('mean', mean)
      with tf.name_scope('stddev'):
        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
      tf.summary.scalar('stddev', stddev)
      tf.summary.scalar('max', tf.reduce_max(var))
      tf.summary.scalar('min', tf.reduce_min(var))
      tf.summary.histogram('histogram', var)
  
  
  def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
                            bottleneck_tensor_size, quantize_layer, is_training):
    """Adds a new softmax and fully-connected layer for training and eval.
  
    We need to retrain the top layer to identify our new classes, so this function
    adds the right operations to the graph, along with some variables to hold the
    weights, and then sets up all the gradients for the backward pass.
  
    The set up for the softmax and fully-connected layers is based on:
    https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html
  
    Args:
      class_count: Integer of how many categories of things we're trying to
          recognize.
      final_tensor_name: Name string for the new final node that produces results.
      bottleneck_tensor: The output of the main CNN graph.
      bottleneck_tensor_size: How many entries in the bottleneck vector.
      quantize_layer: Boolean, specifying whether the newly added layer should be
          instrumented for quantized.
      is_training: Boolean, specifying whether the newly add layer is for training
          or eval.
  
    Returns:
      The tensors for the training and cross entropy results, and tensors for the
      bottleneck input and ground truth input.
    """
    with tf.name_scope('input'):
      bottleneck_input = tf.placeholder_with_default(
          bottleneck_tensor,
          shape=[None, bottleneck_tensor_size],
          name='BottleneckInputPlaceholder')
  
      ground_truth_input = tf.placeholder(
          tf.int64, [None], name='GroundTruthInput')
  
    # Organizing the following ops so they are easier to see in TensorBoard.
    layer_name = 'final_retrain_ops'
    with tf.name_scope(layer_name):
      with tf.name_scope('weights'):
        initial_value = tf.truncated_normal(
            [bottleneck_tensor_size, class_count], stddev=0.001)
        layer_weights = tf.Variable(initial_value, name='final_weights')
        variable_summaries(layer_weights)
  
      with tf.name_scope('biases'):
        layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
        variable_summaries(layer_biases)
  
      with tf.name_scope('Wx_plus_b'):
        logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
        tf.summary.histogram('pre_activations', logits)
  
    final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
  
    # The tf.contrib.quantize functions rewrite the graph in place for
    # quantization. The imported model graph has already been rewritten, so upon
    # calling these rewrites, only the newly added final layer will be
    # transformed.
    if quantize_layer:
      if is_training:
        tf.contrib.quantize.create_training_graph()
      else:
        tf.contrib.quantize.create_eval_graph()
  
    tf.summary.histogram('activations', final_tensor)
  
    # If this is an eval graph, we don't need to add loss ops or an optimizer.
    if not is_training:
      return None, None, bottleneck_input, ground_truth_input, final_tensor
  
    with tf.name_scope('cross_entropy'):
      cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
          labels=ground_truth_input, logits=logits)
  
    tf.summary.scalar('cross_entropy', cross_entropy_mean)
  
    with tf.name_scope('train'):
      optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
      train_step = optimizer.minimize(cross_entropy_mean)
  
    return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
            final_tensor)
  
  
  def add_evaluation_step(result_tensor, ground_truth_tensor):
    """Inserts the operations we need to evaluate the accuracy of our results.
  
    Args:
      result_tensor: The new final node that produces results.
      ground_truth_tensor: The node we feed ground truth data
      into.
  
    Returns:
      Tuple of (evaluation step, prediction).
    """
    with tf.name_scope('accuracy'):
      with tf.name_scope('correct_prediction'):
        prediction = tf.argmax(result_tensor, 1)
        correct_prediction = tf.equal(prediction, ground_truth_tensor)
      with tf.name_scope('accuracy'):
        evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar('accuracy', evaluation_step)
    return evaluation_step, prediction
  
  
  def run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
                     decoded_image_tensor, resized_image_tensor,
                     bottleneck_tensor):
    """Runs a final evaluation on an eval graph using the test data set.
  
    Args:
      sess: Session for the train graph.
      model_info: Model info dictionary from create_model_info()
      class_count: Number of classes
      image_lists: Dictionary of training images for each label.
      jpeg_data_tensor: The layer to feed jpeg image data into.
      decoded_image_tensor: The output of decoding and resizing the image.
      resized_image_tensor: The input node of the recognition graph.
      bottleneck_tensor: The bottleneck output layer of the CNN graph.
    """
    (sess, bottleneck_input, ground_truth_input, evaluation_step,
     prediction) = build_eval_session(model_info, class_count)
  
    test_bottlenecks, test_ground_truth, test_filenames = (
        get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size,
                                      'testing', FLAGS.bottleneck_dir,
                                      FLAGS.image_dir, jpeg_data_tensor,
                                      decoded_image_tensor, resized_image_tensor,
                                      bottleneck_tensor, FLAGS.architecture))
    test_accuracy, predictions = sess.run(
        [evaluation_step, prediction],
        feed_dict={
            bottleneck_input: test_bottlenecks,
            ground_truth_input: test_ground_truth
        })
    tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
                    (test_accuracy * 100, len(test_bottlenecks)))
  
    if FLAGS.print_misclassified_test_images:
      tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
      for i, test_filename in enumerate(test_filenames):
        if predictions[i] != test_ground_truth[i]:
          tf.logging.info('%70s  %s' % (test_filename,
                                        list(image_lists.keys())[predictions[i]]))
  
  
  def build_eval_session(model_info, class_count):
    """Builds an restored eval session without train operations for exporting.
  
    Args:
      model_info: Model info dictionary from create_model_info()
      class_count: Number of classes
  
    Returns:
      Eval session containing the restored eval graph.
      The bottleneck input, ground truth, eval step, and prediction tensors.
    """
    # If quantized, we need to create the correct eval graph for exporting.
    eval_graph, bottleneck_tensor, _ = create_model_graph(model_info)
  
    eval_sess = tf.Session(graph=eval_graph)
    with eval_graph.as_default():
      # Add the new layer for exporting.
      (_, _, bottleneck_input,
       ground_truth_input, final_tensor) = add_final_retrain_ops(
           class_count, FLAGS.final_tensor_name, bottleneck_tensor,
           model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
           False)
  
      # Now we need to restore the values from the training graph to the eval
      # graph.
      tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME)
  
      evaluation_step, prediction = add_evaluation_step(final_tensor,
                                                        ground_truth_input)
  
    return (eval_sess, bottleneck_input, ground_truth_input, evaluation_step,
            prediction)
  
  
  def save_graph_to_file(graph, graph_file_name, model_info, class_count):
    """Saves an graph to file, creating a valid quantized one if necessary."""
    sess, _, _, _, _ = build_eval_session(model_info, class_count)
    graph = sess.graph
  
    output_graph_def = graph_util.convert_variables_to_constants(
        sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
  
    with gfile.FastGFile(graph_file_name, 'wb') as f:
      f.write(output_graph_def.SerializeToString())
  
  
  def prepare_file_system():
    # Setup the directory we'll write summaries to for TensorBoard
    if tf.gfile.Exists(FLAGS.summaries_dir):
      tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
    tf.gfile.MakeDirs(FLAGS.summaries_dir)
    if FLAGS.intermediate_store_frequency > 0:
      ensure_dir_exists(FLAGS.intermediate_output_graphs_dir)
    return
  
  
  def create_model_info(architecture):
    """Given the name of a model architecture, returns information about it.
  
    There are different base image recognition pretrained models that can be
    retrained using transfer learning, and this function translates from the name
    of a model to the attributes that are needed to download and train with it.
  
    Args:
      architecture: Name of a model architecture.
  
    Returns:
      Dictionary of information about the model, or None if the name isn't
      recognized
  
    Raises:
      ValueError: If architecture name is unknown.
    """
    architecture = architecture.lower()
    is_quantized = False
    if architecture == 'inception_v3':
      # pylint: disable=line-too-long
      data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
      # pylint: enable=line-too-long
      bottleneck_tensor_name = 'pool_3/_reshape:0'
      bottleneck_tensor_size = 2048
      input_width = 299
      input_height = 299
      input_depth = 3
      resized_input_tensor_name = 'Mul:0'
      model_file_name = 'classify_image_graph_def.pb'
      input_mean = 128
      input_std = 128
    elif architecture.startswith('mobilenet_'):
      parts = architecture.split('_')
      if len(parts) != 3 and len(parts) != 4:
        tf.logging.error("Couldn't understand architecture name '%s'",
                         architecture)
        return None
      version_string = parts[1]
      if (version_string != '1.0' and version_string != '0.75' and
          version_string != '0.5' and version_string != '0.25'):
        tf.logging.error(
            """"The Mobilenet version should be '1.0', '0.75', '0.5', or '0.25',
    but found '%s' for architecture '%s'""", version_string, architecture)
        return None
      size_string = parts[2]
      if (size_string != '224' and size_string != '192' and
          size_string != '160' and size_string != '128'):
        tf.logging.error(
            """The Mobilenet input size should be '224', '192', '160', or '128',
   but found '%s' for architecture '%s'""",
            size_string, architecture)
        return None
      if len(parts) == 3:
        is_quantized = False
      else:
        if parts[3] != 'quant':
          tf.logging.error(
              "Couldn't understand architecture suffix '%s' for '%s'", parts[3],
              architecture)
          return None
        is_quantized = True
  
      data_url = 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/'
      model_name = 'mobilenet_v1_' + version_string + '_' + size_string
      if is_quantized:
        model_name += '_quant'
      data_url += model_name + '.tgz'
      bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
      resized_input_tensor_name = 'input:0'
      model_file_name = model_name + '_frozen.pb'
  
      bottleneck_tensor_size = 1001
      input_width = int(size_string)
      input_height = int(size_string)
      input_depth = 3
      input_mean = 127.5
      input_std = 127.5
    else:
      tf.logging.error("Couldn't understand architecture name '%s'", architecture)
      raise ValueError('Unknown architecture', architecture)
  
    return {
        'data_url': data_url,
        'bottleneck_tensor_name': bottleneck_tensor_name,
        'bottleneck_tensor_size': bottleneck_tensor_size,
        'input_width': input_width,
        'input_height': input_height,
        'input_depth': input_depth,
        'resized_input_tensor_name': resized_input_tensor_name,
        'model_file_name': model_file_name,
        'input_mean': input_mean,
        'input_std': input_std,
        'quantize_layer': is_quantized,
    }
  
  
  def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
                        input_std):
    """Adds operations that perform JPEG decoding and resizing to the graph..
  
    Args:
      input_width: Desired width of the image fed into the recognizer graph.
      input_height: Desired width of the image fed into the recognizer graph.
      input_depth: Desired channels of the image fed into the recognizer graph.
      input_mean: Pixel value that should be zero in the image for the graph.
      input_std: How much to divide the pixel values by before recognition.
  
    Returns:
      Tensors for the node to feed JPEG data into, and the output of the
        preprocessing steps.
    """
    jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
    decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
    decoded_image_as_float = tf.cast(decoded_image, dtype=tf.float32)
    decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0)
    resize_shape = tf.stack([input_height, input_width])
    resize_shape_as_int = tf.cast(resize_shape, dtype=tf.int32)
    resized_image = tf.image.resize_bilinear(decoded_image_4d,
                                             resize_shape_as_int)
    offset_image = tf.subtract(resized_image, input_mean)
    mul_image = tf.multiply(offset_image, 1.0 / input_std)
    return jpeg_data, mul_image
  
  
  def export_model(model_info, class_count, saved_model_dir):
    """Exports model for serving.
  
    Args:
      model_info: The modelinfo for the current model.
      class_count: The number of classes.
      saved_model_dir: Directory in which to save exported model and variables.
    """
    # The SavedModel should hold the eval graph.
    sess, _, _, _, _ = build_eval_session(model_info, class_count)
    graph = sess.graph
    with graph.as_default():
      input_tensor = model_info['resized_input_tensor_name']
      in_image = sess.graph.get_tensor_by_name(input_tensor)
      inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
  
      out_classes = sess.graph.get_tensor_by_name('final_result:0')
      outputs = {
          'prediction': tf.saved_model.utils.build_tensor_info(out_classes)
      }
  
      signature = tf.saved_model.signature_def_utils.build_signature_def(
          inputs=inputs,
          outputs=outputs,
          method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
  
      legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
  
      # Save out the SavedModel.
      builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
      builder.add_meta_graph_and_variables(
          sess, [tf.saved_model.tag_constants.SERVING],
          signature_def_map={
              tf.saved_model.signature_constants.
              DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                  signature
          },
          legacy_init_op=legacy_init_op)
      builder.save()
  
  
  def main(_):
    # Needed to make sure the logging output is visible.
    # See https://github.com/tensorflow/tensorflow/issues/3047
    tf.logging.set_verbosity(tf.logging.INFO)
  
    # Prepare necessary directories that can be used during training
    prepare_file_system()
  
    # Gather information about the model architecture we'll be using.
    model_info = create_model_info(FLAGS.architecture)
    if not model_info:
      tf.logging.error('Did not recognize architecture flag')
      return -1
  
    # Look at the folder structure, and create lists of all the images.
    image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
                                     FLAGS.validation_percentage)
    class_count = len(image_lists.keys())
    if class_count == 0:
      tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir)
      return -1
    if class_count == 1:
      tf.logging.error('Only one valid folder of images found at ' +
                       FLAGS.image_dir +
                       ' - multiple classes are needed for classification.')
      return -1
  
    # See if the command-line flags mean we're applying any distortions.
    do_distort_images = should_distort_images(
        FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
        FLAGS.random_brightness)
  
    # Set up the pre-trained graph.
    maybe_download_and_extract(model_info['data_url'])
    graph, bottleneck_tensor, resized_image_tensor = (
        create_model_graph(model_info))
  
    # Add the new layer that we'll be training.
    with graph.as_default():
      (train_step, cross_entropy, bottleneck_input,
       ground_truth_input, final_tensor) = add_final_retrain_ops(
           class_count, FLAGS.final_tensor_name, bottleneck_tensor,
           model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
           True)
  
    with tf.Session(graph=graph) as sess:
      # Set up the image decoding sub-graph.
      jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(
          model_info['input_width'], model_info['input_height'],
          model_info['input_depth'], model_info['input_mean'],
          model_info['input_std'])
  
      if do_distort_images:
        # We will be applying distortions, so setup the operations we'll need.
        (distorted_jpeg_data_tensor,
         distorted_image_tensor) = add_input_distortions(
             FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
             FLAGS.random_brightness, model_info['input_width'],
             model_info['input_height'], model_info['input_depth'],
             model_info['input_mean'], model_info['input_std'])
      else:
        # We'll make sure we've calculated the 'bottleneck' image summaries and
        # cached them on disk.
        cache_bottlenecks(sess, image_lists, FLAGS.image_dir,
                          FLAGS.bottleneck_dir, jpeg_data_tensor,
                          decoded_image_tensor, resized_image_tensor,
                          bottleneck_tensor, FLAGS.architecture)
  
      # Create the operations we need to evaluate the accuracy of our new layer.
      evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input)
  
      # Merge all the summaries and write them out to the summaries_dir
      merged = tf.summary.merge_all()
      train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
                                           sess.graph)
  
      validation_writer = tf.summary.FileWriter(
          FLAGS.summaries_dir + '/validation')
  
      # Create a train saver that is used to restore values into an eval graph
      # when exporting models.
      train_saver = tf.train.Saver()
  
      # Set up all our weights to their initial default values.
      init = tf.global_variables_initializer()
      sess.run(init)
  
      # Run the training for as many cycles as requested on the command line.
      for i in range(FLAGS.how_many_training_steps):
        # Get a batch of input bottleneck values, either calculated fresh every
        # time with distortions applied, or from the cache stored on disk.
        if do_distort_images:
          (train_bottlenecks,
           train_ground_truth) = get_random_distorted_bottlenecks(
               sess, image_lists, FLAGS.train_batch_size, 'training',
               FLAGS.image_dir, distorted_jpeg_data_tensor,
               distorted_image_tensor, resized_image_tensor, bottleneck_tensor)
        else:
          (train_bottlenecks,
           train_ground_truth, _) = get_random_cached_bottlenecks(
               sess, image_lists, FLAGS.train_batch_size, 'training',
               FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
               decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
               FLAGS.architecture)
        # Feed the bottlenecks and ground truth into the graph, and run a training
        # step. Capture training summaries for TensorBoard with the `merged` op.
        train_summary, _ = sess.run(
            [merged, train_step],
            feed_dict={bottleneck_input: train_bottlenecks,
                       ground_truth_input: train_ground_truth})
        train_writer.add_summary(train_summary, i)
  
        # Every so often, print out how well the graph is training.
        is_last_step = (i + 1 == FLAGS.how_many_training_steps)
        if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
          train_accuracy, cross_entropy_value = sess.run(
              [evaluation_step, cross_entropy],
              feed_dict={bottleneck_input: train_bottlenecks,
                         ground_truth_input: train_ground_truth})
          tf.logging.info('%s: Step %d: Train accuracy = %.1f%%' %
                          (datetime.now(), i, train_accuracy * 100))
          tf.logging.info('%s: Step %d: Cross entropy = %f' %
                          (datetime.now(), i, cross_entropy_value))
          # TODO(suharshs): Make this use an eval graph, to avoid quantization
          # moving averages being updated by the validation set, though in
          # practice this makes a negligable difference.
          validation_bottlenecks, validation_ground_truth, _ = (
              get_random_cached_bottlenecks(
                  sess, image_lists, FLAGS.validation_batch_size, 'validation',
                  FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
                  decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
                  FLAGS.architecture))
          # Run a validation step and capture training summaries for TensorBoard
          # with the `merged` op.
          validation_summary, validation_accuracy = sess.run(
              [merged, evaluation_step],
              feed_dict={bottleneck_input: validation_bottlenecks,
                         ground_truth_input: validation_ground_truth})
          validation_writer.add_summary(validation_summary, i)
          tf.logging.info('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
                          (datetime.now(), i, validation_accuracy * 100,
                           len(validation_bottlenecks)))
  
        # Store intermediate results
        intermediate_frequency = FLAGS.intermediate_store_frequency
  
        if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
            and i > 0):
          # If we want to do an intermediate save, save a checkpoint of the train
          # graph, to restore into the eval graph.
          train_saver.save(sess, CHECKPOINT_NAME)
          intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
                                    'intermediate_' + str(i) + '.pb')
          tf.logging.info('Save intermediate result to : ' +
                          intermediate_file_name)
          save_graph_to_file(graph, intermediate_file_name, model_info,
                             class_count)
  
      # After training is complete, force one last save of the train checkpoint.
      train_saver.save(sess, CHECKPOINT_NAME)
  
      # We've completed all our training, so run a final test evaluation on
      # some new images we haven't used before.
      run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
                     decoded_image_tensor, resized_image_tensor,
                     bottleneck_tensor)
  
      # Write out the trained graph and labels with the weights stored as
      # constants.
      save_graph_to_file(graph, FLAGS.output_graph, model_info, class_count)
      with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
        f.write('\n'.join(image_lists.keys()) + '\n')
  
      export_model(model_info, class_count, FLAGS.saved_model_dir)
  
  
  if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--image_dir',
        type=str,
        default='',
        help='Path to folders of labeled images.'
    )
    parser.add_argument(
        '--output_graph',
        type=str,
        default='/tmp/output_graph.pb',
        help='Where to save the trained graph.'
    )
    parser.add_argument(
        '--intermediate_output_graphs_dir',
        type=str,
        default='/tmp/intermediate_graph/',
        help='Where to save the intermediate graphs.'
    )
    parser.add_argument(
        '--intermediate_store_frequency',
        type=int,
        default=0,
        help="""\
           How many steps to store intermediate graph. If "0" then will not
           store.\
        """
    )
    parser.add_argument(
        '--output_labels',
        type=str,
        default='/tmp/output_labels.txt',
        help='Where to save the trained graph\'s labels.'
    )
    parser.add_argument(
        '--summaries_dir',
        type=str,
        default='/tmp/retrain_logs',
        help='Where to save summary logs for TensorBoard.'
    )
    parser.add_argument(
        '--how_many_training_steps',
        type=int,
        default=4000,
        help='How many training steps to run before ending.'
    )
    parser.add_argument(
        '--learning_rate',
        type=float,
        default=0.01,
        help='How large a learning rate to use when training.'
    )
    parser.add_argument(
        '--testing_percentage',
        type=int,
        default=10,
        help='What percentage of images to use as a test set.'
    )
    parser.add_argument(
        '--validation_percentage',
        type=int,
        default=10,
        help='What percentage of images to use as a validation set.'
    )
    parser.add_argument(
        '--eval_step_interval',
        type=int,
        default=10,
        help='How often to evaluate the training results.'
    )
    parser.add_argument(
        '--train_batch_size',
        type=int,
        default=100,
        help='How many images to train on at a time.'
    )
    parser.add_argument(
        '--test_batch_size',
        type=int,
        default=-1,
        help="""\
        How many images to test on. This test set is only used once, to evaluate
        the final accuracy of the model after training completes.
        A value of -1 causes the entire test set to be used, which leads to more
        stable results across runs.\
        """
    )
    parser.add_argument(
        '--validation_batch_size',
        type=int,
        default=100,
        help="""\
        How many images to use in an evaluation batch. This validation set is
        used much more often than the test set, and is an early indicator of how
        accurate the model is during training.
        A value of -1 causes the entire validation set to be used, which leads to
        more stable results across training iterations, but may be slower on large
        training sets.\
        """
    )
    parser.add_argument(
        '--print_misclassified_test_images',
        default=False,
        help="""\
        Whether to print out a list of all misclassified test images.\
        """,
        action='store_true'
    )
    parser.add_argument(
        '--model_dir',
        type=str,
        default='/tmp/imagenet',
        help="""\
        Path to classify_image_graph_def.pb,
        imagenet_synset_to_human_label_map.txt, and
        imagenet_2012_challenge_label_map_proto.pbtxt.\
        """
    )
    parser.add_argument(
        '--bottleneck_dir',
        type=str,
        default='/tmp/bottleneck',
        help='Path to cache bottleneck layer values as files.'
    )
    parser.add_argument(
        '--final_tensor_name',
        type=str,
        default='final_result',
        help="""\
        The name of the output classification layer in the retrained graph.\
        """
    )
    parser.add_argument(
        '--flip_left_right',
        default=False,
        help="""\
        Whether to randomly flip half of the training images horizontally.\
        """,
        action='store_true'
    )
    parser.add_argument(
        '--random_crop',
        type=int,
        default=0,
        help="""\
        A percentage determining how much of a margin to randomly crop off the
        training images.\
        """
    )
    parser.add_argument(
        '--random_scale',
        type=int,
        default=0,
        help="""\
        A percentage determining how much to randomly scale up the size of the
        training images by.\
        """
    )
    parser.add_argument(
        '--random_brightness',
        type=int,
        default=0,
        help="""\
        A percentage determining how much to randomly multiply the training image
        input pixels up or down by.\
        """
    )
    parser.add_argument(
        '--architecture',
        type=str,
        default='inception_v3',
        help="""\
        Which model architecture to use. 'inception_v3' is the most accurate, but
        also the slowest. For faster or smaller models, chose a MobileNet with the
        form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example,
        'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224
        pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much
        smaller and less accurate model, taking 128x128 images, and instrumented
        for eventual quantization via TensorFlow Lite.
        See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
        for more information on Mobilenet.\
        """)
    parser.add_argument(
        '--saved_model_dir',
        type=str,
        default='/tmp/saved_models/1/',
        help='Where to save the exported graph.')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
  