def main(_):
console.suppress_logging()
FLAGS.train = TRAIN
FLAGS.overwrite = OVERWRITE
console.start('EXP OB 01')
# Define system
system = define_system()
# Generate data
training_set, validation_set, test_set = generate_data(system)
if len(SYS_LOCK_ORDERS) == 1:
homogeneous_check(system, SYS_LOCK_ORDERS[0], training_set.signls[0],
training_set.responses[0])
# Identification
# .. wiener
wiener = Wiener(degree=WN_DEGREE, memory_depth=WN_MEN_DEPTH)
if WIENER_ON: wiener.identify(training_set, validation_set)
# .. vn
homo_strs = NN_HOMO_STRS
vns = collections.OrderedDict()
for homo_str in homo_strs:
console.show_status('Volterra Net h**o-strength = {:.2f}'.format(homo_str))
vn = init_vn('vn_{:.2f}{}'.format(homo_str, POSTFIX), homo_str=homo_str)
vns[homo_str] = vn
if FLAGS.train:
vn.identify(training_set, validation_set,
batch_size=50, print_cycle=100, epoch=EPOCH)
# Verification
verify(vns, wiener, system, test_set)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = False
FLAGS.overwrite = True
show_false = True
flatten = False
# Start
console.start('MNIST DEMO')
# model = models.vanilla('003_post')
model = models.deep_conv('dc_000')
mnist = load_mnist('../../data/MNIST',
flatten=flatten,
validation_size=5000,
one_hot=True)
# Train or test
if FLAGS.train:
model.train(training_set=mnist[pedia.training],
validation_set=mnist[pedia.validation],
epoch=30,
batch_size=100,
print_cycle=50)
else:
model.evaluate_model(mnist[pedia.test], with_false=show_false)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = False
# Start
console.start("MNIST DCGAN DEMO")
# Get model
model = models.dcgan('dcgan_002')
# model = models.dcgan_h3_rs_nbn()
# Train or test
if FLAGS.train:
mnist = load_mnist('../../data/MNIST', flatten=False, validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training], epoch=10, batch_size=128,
print_cycle=20, snapshot_cycle=200, D_times=1, G_times=1)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
FLAGS.overwrite = False
FLAGS.train = True
play = True
console.suppress_logging()
console.start('TD Gomoku - vanilla')
with tf.Graph().as_default():
model = models.mlp00('mlp00_00')
with tf.Graph().as_default():
opponent = models.mlp00('mlp00_00')
game = Game()
if FLAGS.train:
model.train(game,
episodes=500000,
print_cycle=20,
snapshot_cycle=300,
match_cycle=2000,
rounds=5,
rate_thresh=1.0,
shadow=opponent,
save_cycle=200,
snapshot_function=game.snapshot)
else:
if play:
TkBoard(player=model).show()
else:
model.compete(game, rounds=100, opponent=opponent)
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = True
# Start
console.start('MNIST VANILLA VAE')
# Get model
model = models.vanilla('vanilla_00')
if FLAGS.train:
mnist = load_mnist('../../data/MNIST',
flatten=True,
validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training],
epoch=1000,
batch_size=128,
print_cycle=50,
snapshot_cycle=200)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.overwrite = True
FLAGS.train = True
# Start
console.start()
# Get or define model
model = models.vanilla('vanilla_nov9_02_h2_c', bn=False)
# model = models.dcgan('dcgan_c00')
# model = models.vanilla_h3_rs_nbn('vanilla_nov9_01_h3_nbn_opdef')
# return
# Train or test
if FLAGS.train:
mnist = load_mnist('../../data/MNIST',
flatten=True,
validation_size=0,
one_hot=True)
model.train(training_set=mnist[pedia.training],
epoch=1000,
batch_size=128,
print_cycle=20,
snapshot_cycle=150,
sample_num=25)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def main(_):
console.suppress_logging()
# Setting
FLAGS.train = False
FLAGS.overwrite = True
# FLAGS.shuffle = True
show_false_pred = True
# Start
console.start('CIFAR-10 CONV DEMO')
# Get model
# model = models.deep_conv('dper_do0p5_reg0p2')
model = models.deep_conv('001_pre_bn')
# Train or test
cifar10 = load_cifar10('../../data/CIFAR-10',
flatten=False,
validation_size=5000,
one_hot=True)
if FLAGS.train:
model.train(training_set=cifar10[pedia.training],
validation_set=cifar10[pedia.validation],
epoch=120,
batch_size=64,
print_cycle=100)
else:
model.evaluate_model(cifar10[pedia.test], with_false=show_false_pred)
# End
console.end()
def main(_):
console.suppress_logging()
FLAGS.train = True
FLAGS.overwrite = False
# Start
console.start('CIFAR-10 DCGAN')
# Get model
model = models.dcgan('dcgan_00')
if FLAGS.train:
cifar10 = load_cifar10('../../data/CIFAR-10',
validation_size=0,
one_hot=True)
model.train(training_set=cifar10[pedia.training],
epoch=20000,
batch_size=128,
print_cycle=20,
snapshot_cycle=2000)
else:
samples = model.generate(sample_num=16)
console.show_status('{} samples generated'.format(samples.shape[0]))
imtool.gan_grid_plot(samples, show=True)
# End
console.end()
def launch_model(self, overwrite=False):
if hub.suppress_logging: console.suppress_logging()
# Before launch session, do some cleaning work
if overwrite and hub.overwrite:
paths = []
if hub.summary: paths.append(self.log_dir)
if hub.save_model: paths.append(self.ckpt_dir)
if hub.snapshot: paths.append(self.snapshot_dir)
if hub.export_note: paths.append(self.note_dir)
clear_paths(paths)
if hub.summary: self._check_bash()
# Launch session on self.graph
console.show_status('Launching session ...')
config = tf.ConfigProto()
if not hub.allow_growth:
value = hub.gpu_memory_fraction
config.gpu_options.per_process_gpu_memory_fraction = value
self._session = tf.Session(graph=self._graph, config=config)
console.show_status('Session launched')
# Prepare some tools
self._saver = tf.train.Saver(var_list=self._model.variable_to_save)
if hub.summary or hub.hp_tuning:
self._summary_writer = tf.summary.FileWriter(self.log_dir)
# Initialize all variables
self._session.run(tf.global_variables_initializer())
# Try to load exist model
load_flag, self._model.counter = self.load()
if not load_flag:
assert self._model.counter == 0
# Add graph
if hub.summary: self._summary_writer.add_graph(self._session.graph)
# Write model description to file
if hub.snapshot:
description_path = os.path.join(self.snapshot_dir,
'description.txt')
write_file(description_path, self._model.description)
# Show status
console.show_status('New model initiated')
self._model.launched = True
self.take_notes('Model launched')
return load_flag
def launch_model(self, overwrite=False):
if hub.suppress_logging: console.suppress_logging()
# Before launch session, do some cleaning work
if overwrite and hub.overwrite:
paths = []
if hub.summary: paths.append(self.log_dir)
if hub.save_model: paths.append(self.ckpt_dir)
if hub.snapshot: paths.append(self.snapshot_dir)
if hub.export_note: paths.append(self.note_dir)
clear_paths(paths)
if hub.summary: self._check_bash()
# Launch session on self.graph
console.show_status('Launching session ...')
config = tf.ConfigProto()
if hub.visible_gpu_id is not None:
gpu_id = hub.visible_gpu_id
if isinstance(gpu_id, int): gpu_id = '{}'.format(gpu_id)
elif not isinstance(gpu_id, str): raise TypeError(
'!! Visible GPU id provided must be an integer or a string')
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id
if not hub.allow_growth:
value = hub.gpu_memory_fraction
config.gpu_options.per_process_gpu_memory_fraction = value
self._session = tf.Session(graph=self._graph, config=config)
console.show_status('Session launched')
# Prepare some tools
self.reset_saver()
if hub.summary or hub.hp_tuning:
self._summary_writer = tf.summary.FileWriter(self.log_dir)
# Initialize all variables
self._session.run(tf.global_variables_initializer())
# Set init_val for pruner if necessary
# .. if existed model is loaded, variables will be overwritten
if hub.prune_on: context.pruner.set_init_val_lottery18()
# Try to load exist model
load_flag, self._model.counter, self._model.rounds = self.load()
# Sanity check
if hub.prune_on and hub.pruning_iterations > 0:
if not load_flag: raise AssertionError(
'!! Model {} should be initialized'.format(self._model.mark))
if not load_flag:
assert self._model.counter == 0
# Add graph
if hub.summary: self._summary_writer.add_graph(self._session.graph)
# Write model description to file
if hub.snapshot:
description_path = os.path.join(self.snapshot_dir, 'description.txt')
write_file(description_path, self._model.description)
# Show status
console.show_status('New model initiated')
elif hub.branch_suffix not in [None, '']:
hub.mark += hub.branch_suffix
self._model.mark = hub.mark
console.show_status('Checkpoint switched to branch `{}`'.format(hub.mark))
self._model.launched = True
self.take_notes('Model launched')
# Handle structure detail here
self._model.handle_structure_detail()
return load_flag
# 3. trainer setup
# ---------------------------------------------------------------------------
th.epoch = 1000
th.batch_size = 64
th.validation_per_round = 5
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
th.patience = 5
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.train = True
th.save_model = True
th.overwrite = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = suffix
th.mark = prefix + '{}({}){}'.format(model_name, th.num_layers, tail)
th.gather_summ_name = prefix + summ_name + tail + '.sum'
core.activate(True)
if __name__ == '__main__':
console.suppress_logging()
tf.app.run()
