def get_layers(self):
layers = get_layers(self.layers)
final_layer = layers[-1]
assert len(np.setdiff1d(layers,
lasagne.layers.get_all_layers(final_layer))) == 0, ("All layers "
"should be used, unused {:s}".format(str(np.setdiff1d(layers,
lasagne.layers.get_all_layers(final_layer)))))
transformed = transform_to_normal_net(final_layer)
return lasagne.layers.get_all_layers(transformed)
def get_layers(self):
layers = get_layers(self.layers)
final_layer = layers[-1]
assert len(
np.setdiff1d(
layers, lasagne.layers.get_all_layers(final_layer))) == 0, (
"All layers "
"should be used, unused {:s}".format(
str(
np.setdiff1d(
layers,
lasagne.layers.get_all_layers(final_layer)))))
transformed = transform_to_normal_net(final_layer)
return lasagne.layers.get_all_layers(transformed)
def main(ui_hostname, ui_port, base_name, params_filename, plot_sensors,
use_ui_server, adapt_model, save_data, n_updates_per_break, batch_size,
learning_rate, n_min_trials, trial_start_offset, break_start_offset,
break_stop_offset,
pred_gap,
incoming_port,load_old_data,use_new_adam_params,
input_time_length,
train_on_breaks,
min_break_samples,
min_trial_samples):
setup_logging()
assert np.little_endian, "Should be in little endian"
train_params = None # for trainer, e.g. adam params
if params_filename is not None:
if params_filename == 'newest':
# sort will already sort temporally with our time string format
all_params_files = sorted(glob(base_name + ".*.model_params.npy"))
assert len(all_params_files) > 0, ("Expect atleast one params file "
"if 'newest' given as argument")
params_filename = all_params_files[-1]
log.info("Loading model params from {:s}".format(params_filename))
params = np.load(params_filename)
train_params_filename = params_filename.replace('model_params.npy',
'trainer_params.npy')
if os.path.isfile(train_params_filename):
if use_new_adam_params:
log.info("Loading trainer params from {:s}".format(train_params_filename))
train_params = np.load(train_params_filename)
else:
log.warn("No train/adam params found, starting optimization params "
"from scratch (model params will be loaded anyways).")
else:
params = np.load(base_name + '.npy')
exp = create_experiment(base_name + '.yaml')
# Possibly change input time length, for exmaple
# if input time length very long during training and should be
# shorter for online
if input_time_length is not None:
log.info("Change input time length to {:d}".format(input_time_length))
set_input_window_length(exp.final_layer, input_time_length)
# probably unnecessary, just for safety
exp.iterator.input_time_length = input_time_length
# Have to set for both exp final layer and actually used model
# as exp final layer might be used for adaptation
# maybe check this all for correctness?
cnt_model = exp.final_layer
set_param_values_backwards_compatible(cnt_model, params)
prediction_model = transform_to_normal_net(cnt_model)
set_param_values_backwards_compatible(prediction_model, params)
data_processor = StandardizeProcessor(factor_new=1e-3)
online_model = OnlineModel(prediction_model)
if adapt_model:
online_trainer = BatchWiseCntTrainer(exp, n_updates_per_break,
batch_size, learning_rate, n_min_trials, trial_start_offset,
break_start_offset=break_start_offset,
break_stop_offset=break_stop_offset,
train_param_values=train_params,
add_breaks=train_on_breaks,
min_break_samples=min_break_samples,
min_trial_samples=min_trial_samples)
else:
log.info("Not adapting model...")
online_trainer = NoTrainer()
coordinator = OnlineCoordinator(data_processor, online_model, online_trainer,
pred_gap=pred_gap)
hostname = ''
server = PredictionServer((hostname, incoming_port), coordinator=coordinator,
ui_hostname=ui_hostname, ui_port=ui_port, plot_sensors=plot_sensors,
use_ui_server=use_ui_server, save_data=save_data,
model_base_name=base_name, adapt_model=adapt_model)
# Compilation takes some time so initialize trainer already
# before waiting in connection in server
online_trainer.initialize()
if adapt_model and load_old_data:
online_trainer.add_data_from_today(data_processor)
log.info("Starting server on port {:d}".format(incoming_port))
server.start()
log.info("Started server")
server.serve_forever()
def main(ui_hostname, ui_port, base_name, params_filename, plot_sensors,
use_ui_server, adapt_model, save_data, n_updates_per_break, batch_size,
learning_rate, n_min_trials, trial_start_offset, break_start_offset,
break_stop_offset,
pred_freq,
incoming_port,load_old_data,use_new_adam_params,
input_time_length):
setup_logging()
assert np.little_endian, "Should be in little endian"
train_params = None # for trainer, e.g. adam params
if params_filename is not None:
if params_filename == 'newest':
# sort will already sort temporally with our time string format
all_params_files = sorted(glob(base_name + ".*.model_params.npy"))
assert len(all_params_files) > 0, ("Expect atleast one params file "
"if 'newest' given as argument")
params_filename = all_params_files[-1]
log.info("Loading model params from {:s}".format(params_filename))
params = np.load(params_filename)
train_params_filename = params_filename.replace('model_params.npy',
'trainer_params.npy')
if os.path.isfile(train_params_filename):
if use_new_adam_params:
log.info("Loading trainer params from {:s}".format(train_params_filename))
train_params = np.load(train_params_filename)
else:
log.warn("No train/adam params found, starting optimization params "
"from scratch (model params will be loaded anyways).")
else:
params = np.load(base_name + '.npy')
exp = create_experiment(base_name + '.yaml')
# Possibly change input time length, for exmaple
# if input time length very long during training and should be
# shorter for online
if input_time_length is not None:
log.info("Change input time length to {:d}".format(input_time_length))
set_input_window_length(exp.final_layer, input_time_length)
# probably unnecessary, just for safety
exp.iterator.input_time_length = input_time_length
# Have to set for both exp final layer and actually used model
# as exp final layer might be used for adaptation
# maybe check this all for correctness?
cnt_model = exp.final_layer
lasagne.layers.set_all_param_values(cnt_model, params)
prediction_model = transform_to_normal_net(cnt_model)
lasagne.layers.set_all_param_values(prediction_model, params)
data_processor = StandardizeProcessor(factor_new=1e-3)
online_model = OnlineModel(prediction_model)
if adapt_model:
online_trainer = BatchWiseCntTrainer(exp, n_updates_per_break,
batch_size, learning_rate, n_min_trials, trial_start_offset,
break_start_offset=break_start_offset,
break_stop_offset=break_stop_offset,
train_param_values=train_params)
else:
log.info("Not adapting model...")
online_trainer = NoTrainer()
coordinator = OnlineCoordinator(data_processor, online_model, online_trainer,
pred_freq=pred_freq)
hostname = ''
server = PredictionServer((hostname, incoming_port), coordinator=coordinator,
ui_hostname=ui_hostname, ui_port=ui_port, plot_sensors=plot_sensors,
use_ui_server=use_ui_server, save_data=save_data,
model_base_name=base_name, adapt_model=adapt_model)
# Compilation takes some time so initialize trainer already
# before waiting in connection in server
online_trainer.initialize()
if adapt_model and load_old_data:
online_trainer.add_data_from_today(data_processor)
log.info("Starting server on port {:d}".format(incoming_port))
server.start()
log.info("Started server")
server.serve_forever()
