Python load_labeled_images Examples

Example #1

File: visualize.py Project: musyoku/variational-autoencoder

import util
from args import args
from model import conf, vae
from vae_m1 import GaussianM1VAE
from chainer import functions as F
from PIL import Image

try:
	os.mkdir(args.vis_dir)
except:
	pass

dist = "bernoulli"
if isinstance(vae, GaussianM1VAE):
	dist = "gaussian"
dataset, labels = util.load_labeled_images(args.test_image_dir, dist=dist)

num_images = 5000
x, y_labeled, label_ids = util.sample_x_and_label_variables(num_images, conf.ndim_x, 10, dataset, labels, gpu_enabled=False)
if conf.gpu_enabled:
	x.to_gpu()
z = vae.encoder(x, test=True)
_x = vae.decoder(z, True, True)
if conf.gpu_enabled:
	z.to_cpu()
	_x.to_cpu()
util.visualize_x(_x.data, dir=args.vis_dir)
print "visualizing x"
util.visualize_z(z.data, dir=args.vis_dir)
print "visualizing z"
util.visualize_labeled_z(z.data, label_ids.data, dir=args.vis_dir)

Example #2

import sampler

max_epoch = 1000
num_trains_per_epoch = 1000
batchsize = 100
n_steps_to_optimize_dis = 1

# Create labeled/unlabeled split in training set
n_types_of_label = conf.ndim_y
n_labeled_data = args.n_labeled_data
n_validation_data = 10000

# Export result to csv
csv_epoch = []

dataset, labels = util.load_labeled_images(args.train_image_dir)
labeled_dataset, labels, unlabeled_dataset, validation_dataset, validation_labels = util.create_semisupervised(
    dataset, labels, n_validation_data, n_labeled_data, n_types_of_label)


def sample_labeled_data():
    x, y_onehot, y_id = util.sample_x_and_label_variables(
        batchsize,
        conf.ndim_x,
        conf.ndim_y,
        labeled_dataset,
        labels,
        gpu_enabled=conf.gpu_enabled)
    noise = sampler.gaussian(batchsize,
                             conf.ndim_x,
                             mean=0,

Example #3

File: visualize.py Project: musyoku/variational-autoencoder

sys.path.append(os.path.split(os.getcwd())[0])
import util
from args import args
from model import conf1, vae1, conf2, vae2
from vae_m1 import GaussianM1VAE

try:
    os.mkdir(args.vis_dir)
except:
    pass

dist = "bernoulli"
if isinstance(vae1, GaussianM1VAE):
    dist = "gaussian"
dataset, labels = util.load_labeled_images(args.test_image_dir, dist=dist)

num_plot = 10000
x = util.sample_x_variable(num_plot,
                           conf1.ndim_x,
                           dataset,
                           gpu_enabled=conf1.gpu_enabled)
z1 = vae1.encoder(x, test=True)
y = vae2.sample_x_y(z1, test=True)
z2 = vae2.encode_xy_z(z1, y, test=True)

_z1 = vae2.decode_zy_x(z2, y, test=True, apply_f=True)
_x = vae1.decoder(_z1, test=True)
if conf1.gpu_enabled:
    z2.to_cpu()
    _x.to_cpu()