本文整理汇总了Python中util.get_eval_noise_categorical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_categorical方法的具体用法？Python util.get_eval_noise_categorical怎么用？Python util.get_eval_noise_categorical使用的例子？那么, 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util的用法示例。
在下文中一共展示了util.get_eval_noise_categorical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_categorical [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) 示例2: main
# 需要导入模块: import util [as 别名]
# 或者: from util import get_eval_noise_categorical [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)