本文整理汇总了Python中networks.unconditional_generator方法的典型用法代码示例。如果您正苦于以下问题：Python networks.unconditional_generator方法的具体用法？Python networks.unconditional_generator怎么用？Python networks.unconditional_generator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类networks的用法示例。
在下文中一共展示了networks.unconditional_generator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 networks [as 别名]
# 或者: from networks import unconditional_generator [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) 示例2: main
# 需要导入模块: import networks [as 别名]
# 或者: from networks import unconditional_generator [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)