Python networks.conditional_generator方法代码示例

# 需要导入模块: import networks [as 别名]
# 或者: from networks import conditional_generator [as 别名]
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs)

  return data 

# 需要导入模块: import networks [as 别名]
# 或者: from networks import conditional_generator [as 别名]
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs, is_training=False)

  return data 

# 需要导入模块: import networks [as 别名]
# 或者: from networks import conditional_generator [as 别名]
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1)) 

# 需要导入模块: import networks [as 别名]
# 或者: from networks import conditional_generator [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 networks [as 别名]
# 或者: from networks import conditional_generator [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)