def main():
if FLAGS.exp == 'dir64':
opts = configs.config_dir64
else:
assert False, 'Unknown experiment configuration'
if FLAGS.zdim is not None:
opts['zdim'] = FLAGS.zdim
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
wae = WAE(opts)
wae.restore_checkpoint(FLAGS.checkpoint)
batch_noise = wae.sample_pz(10)
sample_gen = wae.sess.run(wae.decoded,
feed_dict={
wae.sample_noise: batch_noise,
wae.is_training: False
})
img = np.hstack(sample_gen)
img = (img + 1.0) / 2
plt.imshow(img)
plt.savefig('img.png')
def main():
if FLAGS.exp == 'dir64':
opts = configs.config_dir64
else:
assert False, 'Unknown experiment configuration'
if FLAGS.zdim is not None:
opts['zdim'] = FLAGS.zdim
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
data = DataHandler(opts)
wae = WAE(opts)
wae.restore_checkpoint(FLAGS.checkpoint)
batch_img = data.data[0:2]
enc_vec = wae.sess.run(wae.encoded,
feed_dict={
wae.sample_points: batch_img,
wae.is_training: False
})
vdiff = enc_vec[1] - enc_vec[0]
vdiff = vdiff / 10
gen_vec = np.zeros((10, vdiff.shape[0]), dtype=np.float32)
for i in range(10):
gen_vec[i, :] = enc_vec[0] + vdiff * i
sample_gen = wae.sess.run(wae.decoded,
feed_dict={
wae.sample_noise: gen_vec,
wae.is_training: False
})
img = np.hstack(sample_gen)
img = (img + 1.0) / 2
plt.imshow(img)
plt.savefig('analogy.png')