本文整理汇总了Python中networks.conditional_generator方法的典型用法代码示例。如果您正苦于以下问题：Python networks.conditional_generator方法的具体用法？Python networks.conditional_generator怎么用？Python networks.conditional_generator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networks的用法示例。
在下文中一共展示了networks.conditional_generator方法的5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: _get_generated_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)
return data 示例2: _get_generated_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 示例3: test_generator_conditional
# 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)) 示例4: main
# 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) 示例5: main
# 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)