def sweep_core(config, graph_path, res_dir):
# load the data
data = load_data(config)
# create a model
model = build_model(config)
# create trainer
trainer = build_trainer(model, data, config)
# train the model
history = trainer.train()
analyse_model_performance(model,
data,
history,
config,
graph_path=graph_path,
res_dir=res_dir)
# evaluate model
eval_res = trainer.evaluate()
model = trainer.model_train
res_dict = OrderedDict()
for key in history.history.keys():
res_dict[key] = history.history[key][-1]
return res_dict, history.history
def main():
# capture the config path from the run arguments
# then process configuration file
SRC_DIR = os.getcwd()
config = preprocess_meta_data()
# load the data
data = load_data(config)
if not config.quiet:
config.print()
# create a model
model = build_model(config)
# create trainer
trainer = build_trainer(model, data, config)
# train the model
history = trainer.train()
# visualize training performance
graph_path = os.path.join(SRC_DIR, 'graphs')
analyse_model_performance(model, data, graph_path, history)
# evaluate model
trainer.evaluate()
# run on MAFAT test
model = trainer.model_train
test_model(model, SRC_DIR, config)
def main():
# capture the config path from the run arguments
# then process configuration file
SRC_DIR = os.getcwd()
RADAR_DIR = os.path.join(SRC_DIR, os.pardir)
config = preprocess_meta_data()
exp_name = config.exp_name
# for graph_dir and log file
now = datetime.now()
date = now.strftime("%Y_%m_%d_%H_%M_%S")
exp_name_time = '{}_{}'.format(exp_name, date)
# visualize training performance
graph_path = os.path.join(RADAR_DIR, 'graphs', exp_name_time)
LOG_DIR = os.path.join(RADAR_DIR, 'logs')
if os.path.exists(LOG_DIR) is False:
os.makedirs(LOG_DIR)
log_path = '{}/{}.log'.format(LOG_DIR, exp_name_time)
if config.use_mti_improvement is True:
config.__setattr__("model_input_dim", [125, 32, 1])
if bool(re.search('tcn', config.exp_name,
re.IGNORECASE)) and config.use_mti_improvement:
config.__setattr__("model_input_dim", [32, 125, 1])
# load the data
data = load_data(config)
# create a model
model = build_model(config)
# create trainer
trainer = build_trainer(model, data, config)
# train the model
history = trainer.train()
analyse_model_performance(model,
data,
history,
config,
graph_path=graph_path,
res_dir=exp_name_time)
# evaluate model
eval_res = trainer.evaluate()
SUB_DIR = os.path.join(RADAR_DIR, 'submission_files')
if os.path.exists(SUB_DIR) is False:
os.makedirs(SUB_DIR)
sub_path = "{}/submission_{}.csv".format(SUB_DIR, exp_name_time)
test_model(model['train'], sub_path, SRC_DIR, config)
print('#' * 70)
print('log file is located at {}'.format(log_path))
print('graphs are located at {}'.format(graph_path))
print('submission file is at: {}'.format(sub_path))
print('')
def sweep_core(config, graph_path, res_dir, best_preformance_dir,
current_sweep, param_value):
# load the data
data = load_data(config)
# create a model
model = build_model(config)
# create trainer
trainer = build_trainer(model, data, config)
# train the model
history = trainer.train()
if config.learn_background is False:
analyse_model_performance(model,
data,
history,
config,
graph_path=graph_path,
res_dir=res_dir)
result_data = analyse_model_performance(model,
data,
history,
config,
graph_path=graph_path,
res_dir=res_dir)
result_data['Log path'] = res_dir
result_data['Graph path'] = graph_path
result_data[
'Submission path'] = "None - Sweep {} results with value {}".format(
current_sweep, param_value)
result_data['Model name'] = config.model_name
result_data['Exp name'] = config.exp_name
result_data['Snr type'] = config.snr_type
# compare model performance
if os.path.exists(best_preformance_dir) is False:
os.makedirs(best_preformance_dir)
compare_to_best_model_performance(result_data, model, best_preformance_dir,
config)
# evaluate model
eval_res = trainer.evaluate()
model = trainer.model_train
# predictions for later use in sweep visualizations
pred_dict = get_predictions_dict_per_model(model, data)
res_dict = OrderedDict()
for key in history.history.keys():
res_dict[key] = history.history[key][-1]
return res_dict, history.history, pred_dict
def main():
# capture the config path from the run arguments
# then process configuration file
config = preprocess_meta_data()
# load the data
data = load_data(config)
if not config.quiet:
config.print()
# create a model
model = build_model(config)
# create trainer and pass all the previous components to it
trainer = build_trainer(model, data, config)
# train the model
trainer.train()
def main():
# capture the config path from the run arguments
# then process configuration file
SRC_DIR = os.getcwd()
RADAR_DIR = os.path.join(SRC_DIR, os.pardir)
config = preprocess_meta_data(SRC_DIR)
exp_name = config.exp_name
# for graph_dir and log file
now = datetime.now()
date = now.strftime("%Y_%m_%d_%H_%M_%S")
exp_name_time = '{}_{}'.format(exp_name, date)
# visualize training performance
graph_path = os.path.join(RADAR_DIR, 'graphs', exp_name_time)
if os.path.exists(graph_path) is False:
os.makedirs(graph_path)
LOG_DIR = os.path.join(RADAR_DIR, 'logs')
if os.path.exists(LOG_DIR) is False:
os.makedirs(LOG_DIR)
log_path = '{}/{}.log'.format(LOG_DIR, exp_name_time)
'''
Configure multiprocess
'''
strategy = tf.distribute.MirroredStrategy()
if strategy.num_replicas_in_sync != 1:
config.__setattr__("batch_size", config.batch_size * strategy.num_replicas_in_sync)
config = adjust_input_size(config)
# assert configurations
assert not(config.learn_background and (config.with_rect_augmentation or config.with_preprocess_rect_augmentation))
# assert not(config.background_implicit_inference)
# load the data
data = load_data(config)
with strategy.scope():
# create a model
model = build_model(config)
# create trainer
trainer = build_trainer(model, data, config)
# train the model
history = trainer.train()
# evaluate model
eval_res = trainer.evaluate()
SUB_DIR = os.path.join(RADAR_DIR,'submission_files')
BEST_RESULT_DIR = os.path.join(RADAR_DIR, 'best_preformance_history')
if os.path.exists(SUB_DIR) is False:
os.makedirs(SUB_DIR)
sub_path = "{}/submission_{}.csv".format(SUB_DIR,exp_name_time)
test_model(model['train'], sub_path, SRC_DIR, config,BEST_RESULT_DIR)
if config.learn_background is False:
result_data = analyse_model_performance(model, data, history, config, graph_path=graph_path, res_dir=exp_name_time)
result_data['Log path'] = log_path
result_data['Graph path'] = graph_path
result_data['Submission path'] = sub_path
result_data['Model name'] = config.model_name
result_data['Exp name'] = config.exp_name
result_data['Snr type'] = config.snr_type
# compare model performance
if os.path.exists(BEST_RESULT_DIR) is False:
os.makedirs(BEST_RESULT_DIR)
compare_to_best_model_performance(result_data, model, BEST_RESULT_DIR, config)
PREVIOUS_MODELS_DIR = os.path.join(RADAR_DIR, 'previous_models_files')
if config.save_model is True:
if os.path.exists(PREVIOUS_MODELS_DIR) is False:
os.makedirs(PREVIOUS_MODELS_DIR)
os.chdir(PREVIOUS_MODELS_DIR)
save_model(name='{}_{}_{}'.format(config.model_name,config.exp_name,exp_name_time), model=model['train'])
#if config.save_history_buffer is True:
print('#' * 70)
print('log file is located at {}'.format(log_path))
print('graphs are located at {}'.format(graph_path))
print('submission file is at: {}'.format(sub_path))
print('')
