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Python util.get_eval_noise_continuous_dim2方法代码示例

本文整理汇总了Python中util.get_eval_noise_continuous_dim2方法的典型用法代码示例。如果您正苦于以下问题:Python util.get_eval_noise_continuous_dim2方法的具体用法?Python util.get_eval_noise_continuous_dim2怎么用?Python util.get_eval_noise_continuous_dim2使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在util的用法示例。


在下文中一共展示了util.get_eval_noise_continuous_dim2方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: import util [as 别名]
# 或者: from util import get_eval_noise_continuous_dim2 [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) 
开发者ID:rky0930,项目名称:yolo_v2,代码行数:56,代码来源:infogan_eval.py

示例2: main

# 需要导入模块: import util [as 别名]
# 或者: from util import get_eval_noise_continuous_dim2 [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) 
开发者ID:itsamitgoel,项目名称:Gun-Detector,代码行数:59,代码来源:infogan_eval.py


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