Python util.mnist_score方法代码示例

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
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) 

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise, one_hot_labels = _get_generator_inputs(
        FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)

  # Generate images.
  with tf.variable_scope('Generator'):  # Same scope as in train job.
    images = networks.conditional_generator((noise, one_hot_labels))

  # Visualize images.
  reshaped_img = tfgan.eval.image_reshaper(
      images, num_cols=FLAGS.num_images_per_class)
  tf.summary.image('generated_images', reshaped_img, max_outputs=1)

  # Calculate evaluation metrics.
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(images, FLAGS.classifier_filename))
  tf.summary.scalar('MNIST_Cross_entropy',
                    util.mnist_cross_entropy(
                        images, one_hot_labels, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = tf.write_file(
      '%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
      tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[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) 

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
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) 

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise, one_hot_labels = _get_generator_inputs(
        FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)

  # Generate images.
  with tf.variable_scope('Generator'):  # Same scope as in train job.
    images = networks.conditional_generator(
        (noise, one_hot_labels), is_training=False)

  # Visualize images.
  reshaped_img = tfgan.eval.image_reshaper(
      images, num_cols=FLAGS.num_images_per_class)
  tf.summary.image('generated_images', reshaped_img, max_outputs=1)

  # Calculate evaluation metrics.
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(images, FLAGS.classifier_filename))
  tf.summary.scalar('MNIST_Cross_entropy',
                    util.mnist_cross_entropy(
                        images, one_hot_labels, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = None
  if FLAGS.write_to_disk:
    image_write_ops = tf.write_file(
        '%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
        tf.image.encode_png(data_provider.float_image_to_uint8(
            reshaped_img[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) 

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  generator_fn = lambda x: networks.infogan_generator(x, len(CAT_SAMPLE_POINTS))
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_img[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) 

# 需要导入模块: import util [as 别名]
# 或者: from util import mnist_score [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  def generator_fn(inputs):
    return networks.infogan_generator(
        inputs, len(CAT_SAMPLE_POINTS), is_training=False)
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  if FLAGS.write_to_disk:
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_img[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)