Python util.mnist_frechet_distance() Examples

The following are 5 code examples of util.mnist_frechet_distance(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module util , or try the search function .
Example #1
Source File: eval.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]))
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #2
Source File: eval.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
          is_training=False)
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #3
Source File: eval.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]))
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #4
Source File: eval.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
          is_training=False)
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #5
Source File: eval.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
          is_training=False)
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)