def test_vae(self):
dae = DAE()
vae = VAE(dae)
vars = vae.get_vars()
# Check size of optimizing vars
self.assertEqual(len(vars), 14 + 12)
def main(argv):
data_manager = DataManager()
data_manager.prepare()
dae = DAE()
vae = VAE(dae, beta=flags.vae_beta)
scan = SCAN(dae, vae, beta=flags.scan_beta, lambd=flags.scan_lambda)
scan_recomb = SCANRecombinator(dae, vae, scan)
dae_saver = CheckPointSaver(flags.checkpoint_dir, "dae", dae.get_vars())
vae_saver = CheckPointSaver(flags.checkpoint_dir, "vae", vae.get_vars())
scan_saver = CheckPointSaver(flags.checkpoint_dir, "scan", scan.get_vars())
scan_recomb_saver = CheckPointSaver(flags.checkpoint_dir, "scan_recomb",
scan_recomb.get_vars())
sess = tf.Session()
# Initialze variables
init = tf.global_variables_initializer()
sess.run(init)
# For Tensorboard log
summary_writer = tf.summary.FileWriter(flags.log_file, sess.graph)
# Load from checkpoint
dae_saver.load(sess)
vae_saver.load(sess)
scan_saver.load(sess)
scan_recomb_saver.load(sess)
# Train
if flags.train_dae:
train_dae(sess, dae, data_manager, dae_saver, summary_writer)
if flags.train_vae:
train_vae(sess, vae, data_manager, vae_saver, summary_writer)
disentangle_check(sess, vae, data_manager)
if flags.train_scan:
train_scan(sess, scan, data_manager, scan_saver, summary_writer)
sym2img_check(sess, scan, data_manager)
img2sym_check(sess, scan, data_manager)
if flags.train_scan_recomb:
train_scan_recomb(sess, scan_recomb, data_manager, scan_recomb_saver,
summary_writer)
recombination_check(sess, scan_recomb, data_manager)
sess.close()
def main(argv):
data_manager = DataManager()
data_manager.prepare()
dae = DAE()
vae = VAE(dae)
scan = SCAN(dae, vae)
dae_saver = CheckPointSaver(CHECKPOINT_DIR, "dae", dae.get_vars())
vae_saver = CheckPointSaver(CHECKPOINT_DIR, "vae", vae.get_vars())
scan_saver = CheckPointSaver(CHECKPOINT_DIR, "scan", scan.get_vars())
sess = tf.Session()
# Initialze variables
init = tf.global_variables_initializer()
sess.run(init)
# For Tensorboard log
summary_writer = tf.summary.FileWriter(LOG_FILE, sess.graph)
# Load from checkpoint
dae_saver.load(sess)
vae_saver.load(sess)
scan_saver.load(sess)
# Train
train_dae(sess, dae, data_manager, dae_saver, summary_writer)
train_vae(sess, vae, data_manager, vae_saver, summary_writer)
disentangle_check(sess, vae, data_manager)
train_scan(sess, scan, data_manager, scan_saver, summary_writer)
sym2img_check(sess, scan, data_manager)
img2sym_check(sess, scan, data_manager)
sess.close()
