Moving example models from github.com/tensorflow/tensorflow to github.com/tensorflow/models

3 files changed, 0 insertions, 753 deletions

diff --git a/tensorflow/models/image/cifar10/cifar10_input.py b/tensorflow/models/image/cifar10/cifar10_input.py
deleted file mode 100644
index 7bfcb2e054..0000000000
--- a/tensorflow/models/image/cifar10/cifar10_input.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# 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.
-# ==============================================================================
-
-"""Routine for decoding the CIFAR-10 binary file format."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-
-# Process images of this size. Note that this differs from the original CIFAR
-# image size of 32 x 32. If one alters this number, then the entire model
-# architecture will change and any model would need to be retrained.
-IMAGE_SIZE = 24
-
-# Global constants describing the CIFAR-10 data set.
-NUM_CLASSES = 10
-NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
-NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
-
-
-def read_cifar10(filename_queue):
-  """Reads and parses examples from CIFAR10 data files.
-
-  Recommendation: if you want N-way read parallelism, call this function
-  N times.  This will give you N independent Readers reading different
-  files & positions within those files, which will give better mixing of
-  examples.
-
-  Args:
-    filename_queue: A queue of strings with the filenames to read from.
-
-  Returns:
-    An object representing a single example, with the following fields:
-      height: number of rows in the result (32)
-      width: number of columns in the result (32)
-      depth: number of color channels in the result (3)
-      key: a scalar string Tensor describing the filename & record number
-        for this example.
-      label: an int32 Tensor with the label in the range 0..9.
-      uint8image: a [height, width, depth] uint8 Tensor with the image data
-  """
-
-  class CIFAR10Record(object):
-    pass
-  result = CIFAR10Record()
-
-  # Dimensions of the images in the CIFAR-10 dataset.
-  # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
-  # input format.
-  label_bytes = 1  # 2 for CIFAR-100
-  result.height = 32
-  result.width = 32
-  result.depth = 3
-  image_bytes = result.height * result.width * result.depth
-  # Every record consists of a label followed by the image, with a
-  # fixed number of bytes for each.
-  record_bytes = label_bytes + image_bytes
-
-  # Read a record, getting filenames from the filename_queue.  No
-  # header or footer in the CIFAR-10 format, so we leave header_bytes
-  # and footer_bytes at their default of 0.
-  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
-  result.key, value = reader.read(filename_queue)
-
-  # Convert from a string to a vector of uint8 that is record_bytes long.
-  record_bytes = tf.decode_raw(value, tf.uint8)
-
-  # The first bytes represent the label, which we convert from uint8->int32.
-  result.label = tf.cast(
-      tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
-
-  # The remaining bytes after the label represent the image, which we reshape
-  # from [depth * height * width] to [depth, height, width].
-  depth_major = tf.reshape(
-      tf.strided_slice(record_bytes, [label_bytes],
-                       [label_bytes + image_bytes]),
-      [result.depth, result.height, result.width])
-  # Convert from [depth, height, width] to [height, width, depth].
-  result.uint8image = tf.transpose(depth_major, [1, 2, 0])
-
-  return result
-
-
-def _generate_image_and_label_batch(image, label, min_queue_examples,
-                                    batch_size, shuffle):
-  """Construct a queued batch of images and labels.
-
-  Args:
-    image: 3-D Tensor of [height, width, 3] of type.float32.
-    label: 1-D Tensor of type.int32
-    min_queue_examples: int32, minimum number of samples to retain
-      in the queue that provides of batches of examples.
-    batch_size: Number of images per batch.
-    shuffle: boolean indicating whether to use a shuffling queue.
-
-  Returns:
-    images: Images. 4D tensor of [batch_size, height, width, 3] size.
-    labels: Labels. 1D tensor of [batch_size] size.
-  """
-  # Create a queue that shuffles the examples, and then
-  # read 'batch_size' images + labels from the example queue.
-  num_preprocess_threads = 16
-  if shuffle:
-    images, label_batch = tf.train.shuffle_batch(
-        [image, label],
-        batch_size=batch_size,
-        num_threads=num_preprocess_threads,
-        capacity=min_queue_examples + 3 * batch_size,
-        min_after_dequeue=min_queue_examples)
-  else:
-    images, label_batch = tf.train.batch(
-        [image, label],
-        batch_size=batch_size,
-        num_threads=num_preprocess_threads,
-        capacity=min_queue_examples + 3 * batch_size)
-
-  # Display the training images in the visualizer.
-  tf.contrib.deprecated.image_summary('images', images)
-
-  return images, tf.reshape(label_batch, [batch_size])
-
-
-def distorted_inputs(data_dir, batch_size):
-  """Construct distorted input for CIFAR training using the Reader ops.
-
-  Args:
-    data_dir: Path to the CIFAR-10 data directory.
-    batch_size: Number of images per batch.
-
-  Returns:
-    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
-    labels: Labels. 1D tensor of [batch_size] size.
-  """
-  filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
-               for i in xrange(1, 6)]
-  for f in filenames:
-    if not tf.gfile.Exists(f):
-      raise ValueError('Failed to find file: ' + f)
-
-  # Create a queue that produces the filenames to read.
-  filename_queue = tf.train.string_input_producer(filenames)
-
-  # Read examples from files in the filename queue.
-  read_input = read_cifar10(filename_queue)
-  reshaped_image = tf.cast(read_input.uint8image, tf.float32)
-
-  height = IMAGE_SIZE
-  width = IMAGE_SIZE
-
-  # Image processing for training the network. Note the many random
-  # distortions applied to the image.
-
-  # Randomly crop a [height, width] section of the image.
-  distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
-
-  # Randomly flip the image horizontally.
-  distorted_image = tf.image.random_flip_left_right(distorted_image)
-
-  # Because these operations are not commutative, consider randomizing
-  # the order their operation.
-  distorted_image = tf.image.random_brightness(distorted_image,
-                                               max_delta=63)
-  distorted_image = tf.image.random_contrast(distorted_image,
-                                             lower=0.2, upper=1.8)
-
-  # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_standardization(distorted_image)
-
-  # Set the shapes of tensors.
-  float_image.set_shape([height, width, 3])
-  read_input.label.set_shape([1])
-
-  # Ensure that the random shuffling has good mixing properties.
-  min_fraction_of_examples_in_queue = 0.4
-  min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
-                           min_fraction_of_examples_in_queue)
-  print ('Filling queue with %d CIFAR images before starting to train. '
-         'This will take a few minutes.' % min_queue_examples)
-
-  # Generate a batch of images and labels by building up a queue of examples.
-  return _generate_image_and_label_batch(float_image, read_input.label,
-                                         min_queue_examples, batch_size,
-                                         shuffle=True)
-
-
-def inputs(eval_data, data_dir, batch_size):
-  """Construct input for CIFAR evaluation using the Reader ops.
-
-  Args:
-    eval_data: bool, indicating if one should use the train or eval data set.
-    data_dir: Path to the CIFAR-10 data directory.
-    batch_size: Number of images per batch.
-
-  Returns:
-    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
-    labels: Labels. 1D tensor of [batch_size] size.
-  """
-  if not eval_data:
-    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
-                 for i in xrange(1, 6)]
-    num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
-  else:
-    filenames = [os.path.join(data_dir, 'test_batch.bin')]
-    num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
-
-  for f in filenames:
-    if not tf.gfile.Exists(f):
-      raise ValueError('Failed to find file: ' + f)
-
-  # Create a queue that produces the filenames to read.
-  filename_queue = tf.train.string_input_producer(filenames)
-
-  # Read examples from files in the filename queue.
-  read_input = read_cifar10(filename_queue)
-  reshaped_image = tf.cast(read_input.uint8image, tf.float32)
-
-  height = IMAGE_SIZE
-  width = IMAGE_SIZE
-
-  # Image processing for evaluation.
-  # Crop the central [height, width] of the image.
-  resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
-                                                         width, height)
-
-  # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_standardization(resized_image)
-
-  # Ensure that the random shuffling has good mixing properties.
-  min_fraction_of_examples_in_queue = 0.4
-  min_queue_examples = int(num_examples_per_epoch *
-                           min_fraction_of_examples_in_queue)
-
-  # Generate a batch of images and labels by building up a queue of examples.
-  return _generate_image_and_label_batch(float_image, read_input.label,
-                                         min_queue_examples, batch_size,
-                                         shuffle=False)
diff --git a/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py b/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py
deleted file mode 100644
index c2d1e73f87..0000000000
--- a/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# 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.
-# ==============================================================================
-
-"""A binary to train CIFAR-10 using multiple GPU's with synchronous updates.
-
-Accuracy:
-cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256
-epochs of data) as judged by cifar10_eval.py.
-
-Speed: With batch_size 128.
-
-System        | Step Time (sec/batch)  |     Accuracy
---------------------------------------------------------------------
-1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)
-1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)
-2 Tesla K20m  | 0.13-0.20              | ~84% at 30K steps  (2.5 hours)
-3 Tesla K20m  | 0.13-0.18              | ~84% at 30K steps
-4 Tesla K20m  | ~0.10                  | ~84% at 30K steps
-
-Usage:
-Please see the tutorial and website for how to download the CIFAR-10
-data set, compile the program and train the model.
-
-http://tensorflow.org/tutorials/deep_cnn/
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from datetime import datetime
-import os.path
-import re
-import time
-
-import numpy as np
-from six.moves import xrange  # pylint: disable=redefined-builtin
-import tensorflow as tf
-from tensorflow.models.image.cifar10 import cifar10
-
-FLAGS = tf.app.flags.FLAGS
-
-tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',
-                           """Directory where to write event logs """
-                           """and checkpoint.""")
-tf.app.flags.DEFINE_integer('max_steps', 1000000,
-                            """Number of batches to run.""")
-tf.app.flags.DEFINE_integer('num_gpus', 1,
-                            """How many GPUs to use.""")
-tf.app.flags.DEFINE_boolean('log_device_placement', False,
-                            """Whether to log device placement.""")
-
-
-def tower_loss(scope):
-  """Calculate the total loss on a single tower running the CIFAR model.
-
-  Args:
-    scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'
-
-  Returns:
-     Tensor of shape [] containing the total loss for a batch of data
-  """
-  # Get images and labels for CIFAR-10.
-  images, labels = cifar10.distorted_inputs()
-
-  # Build inference Graph.
-  logits = cifar10.inference(images)
-
-  # Build the portion of the Graph calculating the losses. Note that we will
-  # assemble the total_loss using a custom function below.
-  _ = cifar10.loss(logits, labels)
-
-  # Assemble all of the losses for the current tower only.
-  losses = tf.get_collection('losses', scope)
-
-  # Calculate the total loss for the current tower.
-  total_loss = tf.add_n(losses, name='total_loss')
-
-  # Attach a scalar summary to all individual losses and the total loss; do the
-  # same for the averaged version of the losses.
-  for l in losses + [total_loss]:
-    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
-    # session. This helps the clarity of presentation on tensorboard.
-    loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)
-    tf.contrib.deprecated.scalar_summary(loss_name, l)
-
-  return total_loss
-
-
-def average_gradients(tower_grads):
-  """Calculate the average gradient for each shared variable across all towers.
-
-  Note that this function provides a synchronization point across all towers.
-
-  Args:
-    tower_grads: List of lists of (gradient, variable) tuples. The outer list
-      is over individual gradients. The inner list is over the gradient
-      calculation for each tower.
-  Returns:
-     List of pairs of (gradient, variable) where the gradient has been averaged
-     across all towers.
-  """
-  average_grads = []
-  for grad_and_vars in zip(*tower_grads):
-    # Note that each grad_and_vars looks like the following:
-    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
-    grads = []
-    for g, _ in grad_and_vars:
-      # Add 0 dimension to the gradients to represent the tower.
-      expanded_g = tf.expand_dims(g, 0)
-
-      # Append on a 'tower' dimension which we will average over below.
-      grads.append(expanded_g)
-
-    # Average over the 'tower' dimension.
-    grad = tf.concat(0, grads)
-    grad = tf.reduce_mean(grad, 0)
-
-    # Keep in mind that the Variables are redundant because they are shared
-    # across towers. So .. we will just return the first tower's pointer to
-    # the Variable.
-    v = grad_and_vars[0][1]
-    grad_and_var = (grad, v)
-    average_grads.append(grad_and_var)
-  return average_grads
-
-
-def train():
-  """Train CIFAR-10 for a number of steps."""
-  with tf.Graph().as_default(), tf.device('/cpu:0'):
-    # Create a variable to count the number of train() calls. This equals the
-    # number of batches processed * FLAGS.num_gpus.
-    global_step = tf.get_variable(
-        'global_step', [],
-        initializer=tf.constant_initializer(0), trainable=False)
-
-    # Calculate the learning rate schedule.
-    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
-                             FLAGS.batch_size)
-    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)
-
-    # Decay the learning rate exponentially based on the number of steps.
-    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,
-                                    global_step,
-                                    decay_steps,
-                                    cifar10.LEARNING_RATE_DECAY_FACTOR,
-                                    staircase=True)
-
-    # Create an optimizer that performs gradient descent.
-    opt = tf.train.GradientDescentOptimizer(lr)
-
-    # Calculate the gradients for each model tower.
-    tower_grads = []
-    for i in xrange(FLAGS.num_gpus):
-      with tf.device('/gpu:%d' % i):
-        with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:
-          # Calculate the loss for one tower of the CIFAR model. This function
-          # constructs the entire CIFAR model but shares the variables across
-          # all towers.
-          loss = tower_loss(scope)
-
-          # Reuse variables for the next tower.
-          tf.get_variable_scope().reuse_variables()
-
-          # Retain the summaries from the final tower.
-          summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
-
-          # Calculate the gradients for the batch of data on this CIFAR tower.
-          grads = opt.compute_gradients(loss)
-
-          # Keep track of the gradients across all towers.
-          tower_grads.append(grads)
-
-    # We must calculate the mean of each gradient. Note that this is the
-    # synchronization point across all towers.
-    grads = average_gradients(tower_grads)
-
-    # Add a summary to track the learning rate.
-    summaries.append(tf.contrib.deprecated.scalar_summary('learning_rate', lr))
-
-    # Add histograms for gradients.
-    for grad, var in grads:
-      if grad is not None:
-        summaries.append(
-            tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients',
-                                                    grad))
-
-    # Apply the gradients to adjust the shared variables.
-    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
-
-    # Add histograms for trainable variables.
-    for var in tf.trainable_variables():
-      summaries.append(
-          tf.contrib.deprecated.histogram_summary(var.op.name, var))
-
-    # Track the moving averages of all trainable variables.
-    variable_averages = tf.train.ExponentialMovingAverage(
-        cifar10.MOVING_AVERAGE_DECAY, global_step)
-    variables_averages_op = variable_averages.apply(tf.trainable_variables())
-
-    # Group all updates to into a single train op.
-    train_op = tf.group(apply_gradient_op, variables_averages_op)
-
-    # Create a saver.
-    saver = tf.train.Saver(tf.global_variables())
-
-    # Build the summary operation from the last tower summaries.
-    summary_op = tf.contrib.deprecated.merge_summary(summaries)
-
-    # Build an initialization operation to run below.
-    init = tf.global_variables_initializer()
-
-    # Start running operations on the Graph. allow_soft_placement must be set to
-    # True to build towers on GPU, as some of the ops do not have GPU
-    # implementations.
-    sess = tf.Session(config=tf.ConfigProto(
-        allow_soft_placement=True,
-        log_device_placement=FLAGS.log_device_placement))
-    sess.run(init)
-
-    # Start the queue runners.
-    tf.train.start_queue_runners(sess=sess)
-
-    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
-
-    for step in xrange(FLAGS.max_steps):
-      start_time = time.time()
-      _, loss_value = sess.run([train_op, loss])
-      duration = time.time() - start_time
-
-      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
-
-      if step % 10 == 0:
-        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
-        examples_per_sec = num_examples_per_step / duration
-        sec_per_batch = duration / FLAGS.num_gpus
-
-        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
-                      'sec/batch)')
-        print (format_str % (datetime.now(), step, loss_value,
-                             examples_per_sec, sec_per_batch))
-
-      if step % 100 == 0:
-        summary_str = sess.run(summary_op)
-        summary_writer.add_summary(summary_str, step)
-
-      # Save the model checkpoint periodically.
-      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
-        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
-        saver.save(sess, checkpoint_path, global_step=step)
-
-
-def main(argv=None):  # pylint: disable=unused-argument
-  cifar10.maybe_download_and_extract()
-  if tf.gfile.Exists(FLAGS.train_dir):
-    tf.gfile.DeleteRecursively(FLAGS.train_dir)
-  tf.gfile.MakeDirs(FLAGS.train_dir)
-  train()
-
-
-if __name__ == '__main__':
-  tf.app.run()
diff --git a/tensorflow/models/image/imagenet/classify_image.py b/tensorflow/models/image/imagenet/classify_image.py
deleted file mode 100644
index c2850f58ea..0000000000
--- a/tensorflow/models/image/imagenet/classify_image.py
+++ /dev/null
@@ -1,227 +0,0 @@
-# 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.
-# ==============================================================================
-
-"""Simple image classification with Inception.
-
-Run image classification with Inception trained on ImageNet 2012 Challenge data
-set.
-
-This program creates a graph from a saved GraphDef protocol buffer,
-and runs inference on an input JPEG image. It outputs human readable
-strings of the top 5 predictions along with their probabilities.
-
-Change the --image_file argument to any jpg image to compute a
-classification of that image.
-
-Please see the tutorial and website for a detailed description of how
-to use this script to perform image recognition.
-
-https://tensorflow.org/tutorials/image_recognition/
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import os.path
-import re
-import sys
-import tarfile
-
-import numpy as np
-from six.moves import urllib
-import tensorflow as tf
-
-FLAGS = None
-
-# pylint: disable=line-too-long
-DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
-# pylint: enable=line-too-long
-
-
-class NodeLookup(object):
-  """Converts integer node ID's to human readable labels."""
-
-  def __init__(self,
-               label_lookup_path=None,
-               uid_lookup_path=None):
-    if not label_lookup_path:
-      label_lookup_path = os.path.join(
-          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
-    if not uid_lookup_path:
-      uid_lookup_path = os.path.join(
-          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
-    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
-
-  def load(self, label_lookup_path, uid_lookup_path):
-    """Loads a human readable English name for each softmax node.
-
-    Args:
-      label_lookup_path: string UID to integer node ID.
-      uid_lookup_path: string UID to human-readable string.
-
-    Returns:
-      dict from integer node ID to human-readable string.
-    """
-    if not tf.gfile.Exists(uid_lookup_path):
-      tf.logging.fatal('File does not exist %s', uid_lookup_path)
-    if not tf.gfile.Exists(label_lookup_path):
-      tf.logging.fatal('File does not exist %s', label_lookup_path)
-
-    # Loads mapping from string UID to human-readable string
-    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
-    uid_to_human = {}
-    p = re.compile(r'[n\d]*[ \S,]*')
-    for line in proto_as_ascii_lines:
-      parsed_items = p.findall(line)
-      uid = parsed_items[0]
-      human_string = parsed_items[2]
-      uid_to_human[uid] = human_string
-
-    # Loads mapping from string UID to integer node ID.
-    node_id_to_uid = {}
-    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
-    for line in proto_as_ascii:
-      if line.startswith('  target_class:'):
-        target_class = int(line.split(': ')[1])
-      if line.startswith('  target_class_string:'):
-        target_class_string = line.split(': ')[1]
-        node_id_to_uid[target_class] = target_class_string[1:-2]
-
-    # Loads the final mapping of integer node ID to human-readable string
-    node_id_to_name = {}
-    for key, val in node_id_to_uid.items():
-      if val not in uid_to_human:
-        tf.logging.fatal('Failed to locate: %s', val)
-      name = uid_to_human[val]
-      node_id_to_name[key] = name
-
-    return node_id_to_name
-
-  def id_to_string(self, node_id):
-    if node_id not in self.node_lookup:
-      return ''
-    return self.node_lookup[node_id]
-
-
-def create_graph():
-  """Creates a graph from saved GraphDef file and returns a saver."""
-  # Creates graph from saved graph_def.pb.
-  with tf.gfile.FastGFile(os.path.join(
-      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
-    graph_def = tf.GraphDef()
-    graph_def.ParseFromString(f.read())
-    _ = tf.import_graph_def(graph_def, name='')
-
-
-def run_inference_on_image(image):
-  """Runs inference on an image.
-
-  Args:
-    image: Image file name.
-
-  Returns:
-    Nothing
-  """
-  if not tf.gfile.Exists(image):
-    tf.logging.fatal('File does not exist %s', image)
-  image_data = tf.gfile.FastGFile(image, 'rb').read()
-
-  # Creates graph from saved GraphDef.
-  create_graph()
-
-  with tf.Session() as sess:
-    # Some useful tensors:
-    # 'softmax:0': A tensor containing the normalized prediction across
-    #   1000 labels.
-    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
-    #   float description of the image.
-    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
-    #   encoding of the image.
-    # Runs the softmax tensor by feeding the image_data as input to the graph.
-    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
-    predictions = sess.run(softmax_tensor,
-                           {'DecodeJpeg/contents:0': image_data})
-    predictions = np.squeeze(predictions)
-
-    # Creates node ID --> English string lookup.
-    node_lookup = NodeLookup()
-
-    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
-    for node_id in top_k:
-      human_string = node_lookup.id_to_string(node_id)
-      score = predictions[node_id]
-      print('%s (score = %.5f)' % (human_string, score))
-
-
-def maybe_download_and_extract():
-  """Download and extract model tar file."""
-  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)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-  tarfile.open(filepath, 'r:gz').extractall(dest_directory)
-
-
-def main(_):
-  maybe_download_and_extract()
-  image = (FLAGS.image_file if FLAGS.image_file else
-           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
-  run_inference_on_image(image)
-
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  # classify_image_graph_def.pb:
-  #   Binary representation of the GraphDef protocol buffer.
-  # imagenet_synset_to_human_label_map.txt:
-  #   Map from synset ID to a human readable string.
-  # imagenet_2012_challenge_label_map_proto.pbtxt:
-  #   Text representation of a protocol buffer mapping a label to synset ID.
-  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(
-      '--image_file',
-      type=str,
-      default='',
-      help='Absolute path to image file.'
-  )
-  parser.add_argument(
-      '--num_top_predictions',
-      type=int,
-      default=5,
-      help='Display this many predictions.'
-  )
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)