retrain.py

# retrain.py
#
# original file by Google:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
"""
Transfer learning with Inception v3 or Mobilenet models.
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.
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.
To use with TensorBoard:
tensorboard --logdir /path/to/tensorboard_logs
"""
from datetime import datetime
import hashlib
import os
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.contrib.quantize.python import quant_ops
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

# module level variables ##############################################################################################
MIN_NUM_IMAGES_REQUIRED_FOR_TRAINING = 10
MIN_NUM_IMAGES_SUGGESTED_FOR_TRAINING = 100

MIN_NUM_IMAGES_REQUIRED_FOR_TESTING = 3

MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M

# path to folders of labeled images
TRAINING_IMAGES_DIR = os.getcwd() + '/training_images'

TEST_IMAGES_DIR = os.getcwd() + "/test_images/"

# where to save the trained graph
OUTPUT_GRAPH = os.getcwd() + '/' + 'retrained_graph.pb'

# where to save the intermediate graphs
INTERMEDIATE_OUTPUT_GRAPHS_DIR = os.getcwd() + '/intermediate_graph'

# how many steps to store intermediate graph, if "0" then will not store
INTERMEDIATE_STORE_FREQUENCY = 0

# where to save the trained graph's labels
OUTPUT_LABELS = os.getcwd() + '/' + 'retrained_labels.txt'

# where to save summary logs for TensorBoard
TENSORBOARD_DIR = os.getcwd() + '/' + 'tensorboard_logs'

# how many training steps to run before ending
# NOTE: original Google default is 4000, use 4000 (or possibly higher) for production grade results
HOW_MANY_TRAINING_STEPS=1000

# how large a learning rate to use when training
LEARNING_RATE = 0.01

# what percentage of images to use as a test set
TESTING_PERCENTAGE = 10

# what percentage of images to use as a validation set
VALIDATION_PERCENTAGE = 10

# how often to evaluate the training results
EVAL_STEP_INTERVAL = 10

# how many images to train on at a time
TRAIN_BATCH_SIZE = 100

# 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.
TEST_BATCH_SIZE = -1

# 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.
VALIDATION_BATCH_SIZE = 100

# whether to print out a list of all misclassified test images
PRINT_MISCLASSIFIED_TEST_IMAGES = False

# Path to classify_image_graph_def.pb, imagenet_synset_to_human_label_map.txt, and imagenet_2012_challenge_label_map_proto.pbtxt
MODEL_DIR = os.getcwd() + "/" + "model"

# Path to cache bottleneck layer values as files
BOTTLENECK_DIR = os.getcwd() + '/' + 'bottleneck_data'

# the name of the output classification layer in the retrained graph
FINAL_TENSOR_NAME = 'final_result'

# whether to randomly flip half of the training images horizontally
FLIP_LEFT_RIGHT = False

# a percentage determining how much of a margin to randomly crop off the training images
RANDOM_CROP = 0

# a percentage determining how much to randomly scale up the size of the training images by
RANDOM_SCALE = 0

# a percentage determining how much to randomly multiply the training image input pixels up or down by
RANDOM_BRIGHTNESS = 0

# 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 less accurate, but smaller and faster network that's 920 KB
# on disk and takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html for more information on Mobilenet.

# 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:

# example command to run floating-point version of mobilenet:
# ARCHITECTURE = 'mobilenet_1.0_224'

# example command to run quantized version of mobilenet:
# ARCHITECTURE = 'mobilenet_1.0_224_quantized'

# 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.
ARCHITECTURE = 'inception_v3'

#######################################################################################################################
def main():
    print("starting program . . .")
    # make sure the logging output is visible, see https://github.com/tensorflow/tensorflow/issues/3047
    tf.logging.set_verbosity(tf.logging.INFO)

    if not checkIfNecessaryPathsAndFilesExist():
        return
    # end if

    # 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(ARCHITECTURE)
    if not model_info:
        tf.logging.error('Did not recognize architecture flag')
        return -1
    # end if

    # download the model if necessary, then create the model graph
    print("downloading model (if necessary) . . .")
    downloadModelIfNotAlreadyPresent(model_info['data_url'])
    print("creating model graph . . .")
    graph, bottleneck_tensor, resized_image_tensor = (create_model_graph(model_info))

    # Look at the folder structure, and create lists of all the images.
    print("creating image lists . . .")
    image_lists = create_image_lists(TRAINING_IMAGES_DIR, TESTING_PERCENTAGE, VALIDATION_PERCENTAGE)
    class_count = len(image_lists.keys())
    if class_count == 0:
        tf.logging.error('No valid folders of images found at ' + TRAINING_IMAGES_DIR)
        return -1
    # end if
    if class_count == 1:
        tf.logging.error('Only one valid folder of images found at ' + TRAINING_IMAGES_DIR + ' - multiple classes are needed for classification.')
        return -1
    # end if

    # determinf if any of the distortion command line flags have been set
    doDistortImages = False
    if (FLIP_LEFT_RIGHT == True or RANDOM_CROP != 0 or RANDOM_SCALE != 0 or RANDOM_BRIGHTNESS != 0):
        doDistortImages = True
    # end if

    print("starting session . . .")
    with tf.Session(graph=graph) as sess:
        # Set up the image decoding sub-graph.
        print("performing jpeg decoding . . .")
        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'])
        print("caching bottlenecks . . .")
        distorted_jpeg_data_tensor = None
        distorted_image_tensor = None
        if doDistortImages:
            # We will be applying distortions, so setup the operations we'll need.
            (distorted_jpeg_data_tensor, distorted_image_tensor) = add_input_distortions(FLIP_LEFT_RIGHT, RANDOM_CROP, RANDOM_SCALE,
                                                                                         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, TRAINING_IMAGES_DIR, BOTTLENECK_DIR, jpeg_data_tensor, decoded_image_tensor,
                              resized_image_tensor, bottleneck_tensor, ARCHITECTURE)
        # end if

        # Add the new layer that we'll be training.
        print("adding final training layer . . .")
        (train_step, cross_entropy, bottleneck_input, ground_truth_input, final_tensor) = add_final_training_ops(len(image_lists.keys()),
                                                                                                                 FINAL_TENSOR_NAME,
                                                                                                                 bottleneck_tensor,
                                                                                                                 model_info['bottleneck_tensor_size'],
                                                                                                                 model_info['quantize_layer'])
        # Create the operations we need to evaluate the accuracy of our new layer.
        print("adding eval ops for final training layer . . .")
        evaluation_step, prediction = add_evaluation_step(final_tensor, ground_truth_input)

        # Merge all the summaries and write them out to the tensorboard_dir
        print("writing TensorBoard info . . .")
        merged = tf.summary.merge_all()
        train_writer = tf.summary.FileWriter(TENSORBOARD_DIR + '/train', sess.graph)
        validation_writer = tf.summary.FileWriter(TENSORBOARD_DIR + '/validation')

        # 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.
        print("performing training . . .")
        for i in range(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 doDistortImages:
                (train_bottlenecks, train_ground_truth) = get_random_distorted_bottlenecks(sess, image_lists, TRAIN_BATCH_SIZE, 'training',
                                                                                           TRAINING_IMAGES_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, TRAIN_BATCH_SIZE, 'training',
                                                                                           BOTTLENECK_DIR, TRAINING_IMAGES_DIR, jpeg_data_tensor,
                                                                                           decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
                                                                                           ARCHITECTURE)
            # end if

            # 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 == HOW_MANY_TRAINING_STEPS)
            if (i % 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))
                validation_bottlenecks, validation_ground_truth, _ = (get_random_cached_bottlenecks(sess, image_lists, VALIDATION_BATCH_SIZE, 'validation',
                                                                                                    BOTTLENECK_DIR, TRAINING_IMAGES_DIR, jpeg_data_tensor,
                                                                                                    decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
                                                                                                    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)))
            # end if

            # Store intermediate results
            intermediate_frequency = INTERMEDIATE_STORE_FREQUENCY

            if (intermediate_frequency > 0 and (i % intermediate_frequency == 0) and i > 0):
                intermediate_file_name = (INTERMEDIATE_OUTPUT_GRAPHS_DIR + 'intermediate_' + str(i) + '.pb')
                tf.logging.info('Save intermediate result to : ' + intermediate_file_name)
                save_graph_to_file(sess, graph, intermediate_file_name)
            # end if
        # end for

        # We've completed all our training, so run a final test evaluation on some new images we haven't used before
        print("running testing . . .")
        test_bottlenecks, test_ground_truth, test_filenames = (get_random_cached_bottlenecks(sess, image_lists, TEST_BATCH_SIZE, 'testing', BOTTLENECK_DIR,
                                                                                             TRAINING_IMAGES_DIR, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
                                                                                             bottleneck_tensor, 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 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]]))
                # end if
            # end for
        # end if

        # write out the trained graph and labels with the weights stored as constants
        print("writing trained graph and labbels with weights")
        save_graph_to_file(sess, graph, OUTPUT_GRAPH)
        with gfile.FastGFile(OUTPUT_LABELS, 'w') as f:
            f.write('\n'.join(image_lists.keys()) + '\n')
        # end with

        print("done !!")
# end function

#######################################################################################################################
def checkIfNecessaryPathsAndFilesExist():
    # if the training directory does not exist, show and error message and bail
    if not os.path.exists(TRAINING_IMAGES_DIR):
        print('')
        print('ERROR: TRAINING_IMAGES_DIR "' + TRAINING_IMAGES_DIR + '" does not seem to exist')
        print('Did you set up the training images?')
        print('')
        return False
    # end if

    # nested class
    class TrainingSubDir:
        # constructor
        def __init__(self):
            self.loc = ""
            self.numImages = 0
        # end constructor
    # end class

    # declare a list of training sub-directories
    trainingSubDirs = []

    # populate the training sub-directories
    for dirName in os.listdir(TRAINING_IMAGES_DIR):
        currentTrainingImagesSubDir = os.path.join(TRAINING_IMAGES_DIR, dirName)
        if os.path.isdir(currentTrainingImagesSubDir):
            trainingSubDir = TrainingSubDir()
            trainingSubDir.loc = currentTrainingImagesSubDir
            trainingSubDirs.append(trainingSubDir)
        # end if
    # end for

    # if no training sub-directories were found, show an error message and return false
    if len(trainingSubDirs) == 0:
        print("ERROR: there don't seem to be any training image sub-directories in " + TRAINING_IMAGES_DIR)
        print("Did you make a separare image sub-directory for each classification type?")
        return False
    # end if

    # populate the number of training images in each training sub-directory
    for trainingSubDir in trainingSubDirs:
        # count how many images are in the current training sub-directory
        for fileName in os.listdir(trainingSubDir.loc):
            if (fileName.endswith(".jpg")) or (fileName.endswith(".png")):
                trainingSubDir.numImages += 1
            # end if
        # end if
    # end for

    # if any training sub-directory has less than the min required number of training images, show an error message and return false
    for trainingSubDir in trainingSubDirs:
        if trainingSubDir.numImages < MIN_NUM_IMAGES_REQUIRED_FOR_TRAINING:
            print("ERROR: there are less than the required " + str(MIN_NUM_IMAGES_REQUIRED_FOR_TRAINING) + " images in " + trainingSubDir.loc)
            print("Did you populate each training sub-directory with images?")
            return False
        # end if
    # end for

    # if any training sub-directory has less than the recommended number of training images, show a warning (but don't return false)
    for trainingSubDir in trainingSubDirs:
        if trainingSubDir.numImages < MIN_NUM_IMAGES_SUGGESTED_FOR_TRAINING:
            print("WARNING: there are less than the suggested " + str(MIN_NUM_IMAGES_SUGGESTED_FOR_TRAINING) + " images in " + trainingSubDir.loc)
            print("More images should be added to this directory for acceptable training results")
            # note we do not return false here b/c this is a warning, not an error
        # end if
    # end for

    # if the test images directory does not exist, show and error message and bail
    if not os.path.exists(TEST_IMAGES_DIR):
        print('')
        print('ERROR: TEST_IMAGES_DIR "' + TEST_IMAGES_DIR + '" does not seem to exist')
        print('Did you break out some test images?')
        print('')
        return False
    # end if

    # count how many images are in the test images directory
    numImagesInTestDir = 0
    for fileName in os.listdir(TEST_IMAGES_DIR):
        if (fileName.endswith(".jpg") or fileName.endswith(".png")):
            numImagesInTestDir += 1
        # end if
    # end for

    # if there are not enough images in the test images directory, show an error and return false
    if numImagesInTestDir < MIN_NUM_IMAGES_REQUIRED_FOR_TESTING:
        print("ERROR: there are not at least " + str(MIN_NUM_IMAGES_REQUIRED_FOR_TESTING) + " images in " + TEST_IMAGES_DIR)
        print("Did you break out some test images?")
        return False
    # end if

    return True
# end function

#######################################################################################################################
def prepare_file_system():
    # Setup the directory we'll write summaries to for TensorBoard
    if tf.gfile.Exists(TENSORBOARD_DIR):
        tf.gfile.DeleteRecursively(TENSORBOARD_DIR)
    # end if
    tf.gfile.MakeDirs(TENSORBOARD_DIR)
    if INTERMEDIATE_STORE_FREQUENCY > 0:
        makeDirIfDoesNotExist(INTERMEDIATE_OUTPUT_GRAPHS_DIR)
    # end if
    return
# end function

#######################################################################################################################
def makeDirIfDoesNotExist(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)
    # end if
# end function

#######################################################################################################################
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
        # end if
        version_string = parts[1]
        if (version_string != '1.0' and version_string != '0.75' and version_string != '0.50' and version_string != '0.25'):
            tf.logging.error(""""The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25', but found '%s' for architecture '%s'""", version_string, architecture)
            return None
        # end if
        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
        # end if
        if len(parts) == 3:
            is_quantized = False
        else:
            if parts[3] != 'quantized':
                tf.logging.error(
                    "Couldn't understand architecture suffix '%s' for '%s'", parts[3], architecture)
                return None
            is_quantized = True
        # end if

        if is_quantized:
            data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
            data_url += version_string + '_' + size_string + '_quantized_frozen.tgz'
            bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
            resized_input_tensor_name = 'Placeholder:0'
            model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string + '_quantized_frozen')
            model_base_name = 'quantized_frozen_graph.pb'
        else:
            data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
            data_url += version_string + '_' + size_string + '_frozen.tgz'
            bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
            resized_input_tensor_name = 'input:0'
            model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
            model_base_name = 'frozen_graph.pb'
        # end if

        bottleneck_tensor_size = 1001
        input_width = int(size_string)
        input_height = int(size_string)
        input_depth = 3
        model_file_name = os.path.join(model_dir_name, model_base_name)
        input_mean = 127.5
        input_std = 127.5
    else:
        tf.logging.error("Couldn't understand architecture name '%s'", architecture)
        raise ValueError('Unknown architecture', architecture)
    # end if

    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, }
# end function

#######################################################################################################################
def downloadModelIfNotAlreadyPresent(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 = MODEL_DIR
    if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
    # end if
    filename = data_url.split('/')[-1]
    filepath = os.path.join(dest_directory, filename)
    if not os.path.exists(filepath):
        # nested function
        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()
        # end def

        filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        tf.logging.info('Successfully downloaded ' + str(filename) + ', statinfo.st_size = ' + str(statinfo.st_size) + ' bytes')
        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')
    # end if
# end function

#######################################################################################################################
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(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'],]))
        # end with
    # end with
    return graph, bottleneck_tensor, resized_input_tensor
# end function

#######################################################################################################################
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 the image directory does not exist, log an error and bail
    if not gfile.Exists(image_dir):
        tf.logging.error("Image directory '" + image_dir + "' not found.")
        return None
    # end if

    # create an empty dictionary to store the results
    result = {}

    # get a list of the sub-directories of the image directory
    sub_dirs = [x[0] for x in gfile.Walk(image_dir)]

    # for each directory in the sub-directories list . . .
    is_root_dir = True
    for sub_dir in sub_dirs:
        # if we're on the 1st (root) directory, mark our boolean for that as false for the next time around and go back to the top of the for loop
        if is_root_dir:
            is_root_dir = False
            continue
        # end if

        dir_name = os.path.basename(sub_dir)
        if dir_name == image_dir:
            continue
        # end if

        # ToDo: This section should be refactored.  The right way to do this would be to get a list of the files that are
        # ToDo: there then append (extend) those, not to get the name except the extension, then append an extension,
        # ToDo: this (current) way is error prone of the original file has an upper case or mixed case extension

        extensions = ['jpg', 'jpeg', 'png']
        file_list = []
        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))
        # end for

        # if the file list is empty at this point, log a warning and bail
        if not file_list:
            tf.logging.warning('No files found')
            continue
        # end if

        # if the length of the file list is less than 20 or more than the max number, log an applicable warning (do not return, however)
        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))
        # end if

        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)
            # end if
        result[label_name] = {'dir': dir_name, 'training': training_images, 'testing': testing_images, 'validation': validation_images,}
    return result
# end function

#######################################################################################################################
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
# end function

#######################################################################################################################
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
    # end if
    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
# end function

#######################################################################################################################
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
    makeDirIfDoesNotExist(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)
            # end for
            how_many_bottlenecks += 1
            if how_many_bottlenecks % 100 == 0:
                tf.logging.info(str(how_many_bottlenecks) + ' bottleneck files created.')
            # end if
# end function

#######################################################################################################################
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)
    makeDirIfDoesNotExist(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)
    # end if

    # read in the contents of the bottleneck file as one big string
    with open(bottleneck_path, 'r') as bottleneck_file:
        bottleneckBigString = bottleneck_file.read()
    # end with

    bottleneckValues = []
    errorOccurred = False
    try:
        # split the bottleneck file contents read in as one big string into individual float values
        bottleneckValues = [float(individualString) for individualString in bottleneckBigString.split(',')]
    except ValueError:
        tf.logging.warning('Invalid float found, recreating bottleneck')
        errorOccurred = True
    # end try

    if errorOccurred:
        # if an error occurred above, create (or re-create) the bottleneck file
        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)

        # read in the contents of the newly created bottleneck file
        with open(bottleneck_path, 'r') as bottleneck_file:
            bottleneckBigString = bottleneck_file.read()
        # end with

        # split the bottleneck file contents read in as one big string into individual float values again
        bottleneckValues = [float(individualString) for individualString in bottleneckBigString.split(',')]
    # end if
    return bottleneckValues
# end function

#######################################################################################################################
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'
# end function

#######################################################################################################################
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)
    # end if
    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)))
    # end try

    bottleneck_string = ','.join(str(x) for x in bottleneck_values)
    with open(bottleneck_path, 'w') as bottleneck_file:
        bottleneck_file.write(bottleneck_string)
    # end with
# end function

#######################################################################################################################
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
# end function

#######################################################################################################################
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)
    # end if
    label_lists = image_lists[label_name]
    if category not in label_lists:
        tf.logging.fatal('Category does not exist %s.', category)
    # end if
    category_list = label_lists[category]
    if not category_list:
        tf.logging.fatal('Label %s has no images in the category %s.', label_name, category)
    # end if
    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
# end function

#######################################################################################################################
def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor, bottleneck_tensor_size, quantize_layer):
    """
    Adds a new softmax and fully-connected layer for training.
    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 quantized.
    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')
    # end with

    # Organizing the following ops as `final_training_ops` so they're easier to see in TensorBoard
    layer_name = 'final_training_ops'
    with tf.name_scope(layer_name):
        quantized_layer_weights = None
        quantized_layer_biases = None
        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')
            if quantize_layer:
                quantized_layer_weights = quant_ops.MovingAvgQuantize(layer_weights, is_training=True)
                attachTensorBoardSummaries(quantized_layer_weights)
            # end if

            # this comment is necessary to suppress an unnecessary PyCharm warning
            # noinspection PyTypeChecker
            attachTensorBoardSummaries(layer_weights)
        # end with
        with tf.name_scope('biases'):
            layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
            if quantize_layer:
                quantized_layer_biases = quant_ops.MovingAvgQuantize(layer_biases, is_training=True)
                attachTensorBoardSummaries(quantized_layer_biases)
            # end if

            # this comment is necessary to suppress an unnecessary PyCharm warning
            # noinspection PyTypeChecker
            attachTensorBoardSummaries(layer_biases)
        # end with
        with tf.name_scope('Wx_plus_b'):
            if quantize_layer:
                logits = tf.matmul(bottleneck_input, quantized_layer_weights) + quantized_layer_biases
                logits = quant_ops.MovingAvgQuantize(logits, init_min=-32.0, init_max=32.0, is_training=True, num_bits=8,
                                                     narrow_range=False, ema_decay=0.5)
                tf.summary.histogram('pre_activations', logits)
            else:
                logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
                tf.summary.histogram('pre_activations', logits)
            # end if
        # end with
    # end with
    final_tensor = tf.nn.softmax(logits, name=final_tensor_name)

    tf.summary.histogram('activations', final_tensor)

    with tf.name_scope('cross_entropy'):
        cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(labels=ground_truth_input, logits=logits)
    # end with

    tf.summary.scalar('cross_entropy', cross_entropy_mean)

    with tf.name_scope('train'):
        optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE)
        train_step = optimizer.minimize(cross_entropy_mean)
    # end with

    return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input, final_tensor)
# end function

#######################################################################################################################
def attachTensorBoardSummaries(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)))
        # end with
        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)
    # end with
# end function

#######################################################################################################################
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)
        # end with
        with tf.name_scope('accuracy'):
            evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        # end with
    tf.summary.scalar('accuracy', evaluation_step)
    return evaluation_step, prediction
# end function

#######################################################################################################################
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)
        # end if
        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)
    # end for
    return bottlenecks, ground_truths
# end function

#######################################################################################################################
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)
        # end for
    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
# end function

#######################################################################################################################
def save_graph_to_file(sess, graph, graph_file_name):
    output_graph_def = graph_util.convert_variables_to_constants(sess, graph.as_graph_def(), [FINAL_TENSOR_NAME])
    with gfile.FastGFile(graph_file_name, 'wb') as f:
        f.write(output_graph_def.SerializeToString())
    # end with
    return
# end function

#######################################################################################################################
if __name__ == '__main__':
    main()