def sample_once(it):
rows = 10
columns = 10
feed = {random_z: np.random.normal(size=[rows * columns, z_dim])}
list_of_generators = image_sets # used for sampling images
list_of_names = ['it%d-c%d.jpg' %(it,i) for i in range(len(image_sets))]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess, list_of_generators=list_of_generators, feed_dict=feed,
list_of_names=list_of_names, save_dir=save_dir)
def sample_once(it):
rows = 10
columns = 10
feed = {random_z: np.random.normal(size=[rows * columns, z_dim])}
list_of_generators = [images_from_g] # used for sampling images
list_of_names = ['it%d-g.jpg' % it]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess, list_of_generators=list_of_generators, feed_dict=feed,
list_of_names=list_of_names, save_dir=save_dir)
def sample_once(it):
rows = 10
columns = 10
feed = {sample_from_gmm: sample_gmm(size=rows*columns),
sample_from_c1: sample_from_gaussian(mus[0], cov, 12),
sample_from_c2: sample_from_gaussian(mus[1], cov, 12),
sample_from_c3: sample_from_gaussian(mus[2], cov, 12)}
list_of_generators = [images_form_de, images_form_c1, images_form_c2, images_form_c3] # used for sampling images
list_of_names = ['it%d-de.jpg' % it, 'it%d-c1.jpg' % it, 'it%d-c2.jpg' % it,'it%d-c3.jpg' % it,]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess, list_of_generators=list_of_generators, feed_dict=feed,
list_of_names=list_of_names, save_dir=save_dir)
def sample_once(it):
rows = 10
columns = 10
feed = {z: sample_from_gmm(batch_size, k),
z1: sample_from_gaussian(mean=mus[0],cov=cov, size=batch_size),
z2: sample_from_gaussian(mean=mus[1],cov=cov, size=batch_size),
z3: sample_from_gaussian(mean=mus[2],cov=cov, size=batch_size),
z4: sample_from_gaussian(mean=mus[3],cov=cov, size=batch_size)}
list_of_generators = [images_form_g, images_form_c1, images_form_c2, images_form_c3,
images_form_c4] # used for sampling images
list_of_names = ['it%d-g.jpg' % it, 'it%d-c1.jpg' % it, 'it%d-c2.jpg' % it,
'it%d-c3.jpg' % it, 'it%d-c4.jpg' % it]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess, list_of_generators=list_of_generators, feed_dict=feed,
list_of_names=list_of_names, save_dir=save_dir)
def sample_once(it):
rows = 10
columns = 10
list_of_generators = image_sets # used for sampling images
list_of_names = []
for i in range(len(image_sets)):
list_of_names.append('it%d-g%d.jpg' % (it, i))
# list_of_names = ['g1-it%d.jpg' % it, 'g2-it%d.jpg' % it]
feeds = {z: np.random.normal(size=[rows * columns, z_dim])}
my_utils.sample_and_save(sess,
list_of_generators,
list_of_names,
feeds,
dir + "/sample_imgs",
rows,
columns,
normalize=True)
print('save sample images')
def sample_once(it):
rows = 10
columns = 10
feed = {
random_z: np.random.normal(size=[rows * columns, z_dim]),
random_z_2: np.random.normal(size=[rows * columns, z_dim])
}
list_of_generators = [
images_form_de, images_form_c1, images_form_c2, images_form_c3
] # used for sampling images
list_of_names = [
'it%d-de.jpg' % it,
'it%d-c1.jpg' % it,
'it%d-c2.jpg' % it,
'it%d-c3.jpg' % it,
]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess,
list_of_generators=list_of_generators,
feed_dict=feed,
list_of_names=list_of_names,
save_dir=save_dir)
def sample_once(it):
rows = 10
columns = 10
feed = create_feed(
size=rows * columns,
additional_items={z: sample_from_gmm(size=rows * columns, k=k)})
list_of_generators = [
images_form_g, images_form_c1, images_form_c2, images_form_c3,
images_form_c4
] # used for sampling images
list_of_names = [
'it%d-g.jpg' % it,
'it%d-c1.jpg' % it,
'it%d-c2.jpg' % it,
'it%d-c3.jpg' % it,
'it%d-c4.jpg' % it
]
save_dir = dir + "/sample_imgs"
my_utils.sample_and_save(sess=sess,
list_of_generators=list_of_generators,
feed_dict=feed,
list_of_names=list_of_names,
save_dir=save_dir)