def main(): images, labels = dataset.load_test_images() num_scatter = len(images) y_distribution, z = aae.encode_x_yz(images, apply_softmax=False, test=True) y = aae.argmax_onehot_from_unnormalized_distribution(y_distribution) representation = aae.to_numpy(aae.encode_yz_representation(y, z, test=True)) plot.scatter_labeled_z(representation, labels, dir=args.plot_dir)
def main(): # load MNIST images images, labels = dataset.load_test_images() # config config = aae.config num_scatter = len(images) x, _, label_ids = dataset.sample_labeled_data(images, labels, num_scatter, config.ndim_x, config.ndim_y) z = aae.to_numpy(aae.encode_x_z(x, test=True)) visualizer.plot_labeled_z(z, label_ids, dir=args.plot_dir)
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# config
config = aae.config
# settings
num_analogies = 10
pylab.gray()
# generate style vector z
x = dataset.sample_unlabeled_data(images,
num_analogies,
config.ndim_x,
binarize=False)
_, z = aae.encode_x_yz(x, apply_softmax=True)
z = aae.to_numpy(z)
# plot original image on the left
for m in xrange(num_analogies):
pylab.subplot(num_analogies, config.ndim_y + 2, m * 12 + 1)
pylab.imshow(x[m].reshape((28, 28)), interpolation="none")
pylab.axis("off")
all_y = np.identity(config.ndim_y, dtype=np.float32)
for m in xrange(num_analogies):
# copy z as many as the number of classes
fixed_z = np.repeat(z[m].reshape(1, -1), config.ndim_y, axis=0)
gen_x = aae.to_numpy(aae.decode_yz_x(all_y, fixed_z))
# plot images generated from each label
for n in xrange(config.ndim_y):
pylab.subplot(num_analogies, config.ndim_y + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_analogies, config.ndim_y)
pylab.savefig("{}/analogy.png".format(args.plot_dir))
def main():
# load MNIST images
images, labels = dataset.load_test_images()
# config
config = aae.config
num_scatter = len(images)
x, _, labels = dataset.sample_labeled_data(images, labels, num_scatter,
config.ndim_x, config.ndim_y)
y_distribution, z = aae.encode_x_yz(x, apply_softmax=False, test=True)
y = aae.argmax_onehot_from_unnormalized_distribution(y_distribution)
representation = aae.to_numpy(aae.encode_yz_representation(y, z,
test=True))
visualizer.plot_labeled_z(representation, labels, dir=args.plot_dir)
except:
pass
images, labels = dataset.load_test_images()
config = aae.config
num_clusters = config.ndim_y
num_plots_per_cluster = 11
image_width = 28
image_height = 28
ndim_x = image_width * image_height
pylab.gray()
# plot cluster head
head_y = np.identity(config.ndim_y, dtype=np.float32)
zero_z = np.zeros((config.ndim_y, config.ndim_z), dtype=np.float32)
head_x = aae.to_numpy(aae.decode_yz_x(head_y, zero_z, test=True))
head_x = (head_x + 1.0) / 2.0
for n in xrange(num_clusters):
pylab.subplot(num_clusters, num_plots_per_cluster + 2, n * (num_plots_per_cluster + 2) + 1)
pylab.imshow(head_x[n].reshape((image_width, image_height)), interpolation="none")
pylab.axis("off")
# plot elements in cluster
counts = [0 for i in xrange(num_clusters)]
indices = np.arange(len(images))
np.random.shuffle(indices)
batchsize = 500
i = 0
x_batch = np.zeros((batchsize, ndim_x), dtype=np.float32)
for n in xrange(len(images) / batchsize):
def main(): images, labels = dataset.load_test_images() num_scatter = len(images) x, _, label_ids = dataset.sample_labeled_data(images, labels, num_scatter) z = aae.to_numpy(aae.encode_x_z(x, test=True)) plot.scatter_labeled_z(z, label_ids, dir=args.plot_dir)