def improve_solution(self, results, model):
self.processor.load_state_dict(model.copy())
inputs = TorchUtils.get_tensor_from_numpy(results['inputs'])
targets = TorchUtils.get_tensor_from_numpy(results['targets'])
TorchUtils.init_seed(results['seed'], deterministic=True)
new_results = self.task.run_task(inputs, targets, results['mask'])
plt.figure()
plt.plot(results['best_output'])
plt.plot(new_results['best_output'])
plt.show()
def search_solution(self, gap):
self.init_dirs(gap)
self.task.configs['ring_data']['gap'] = gap
inputs, targets, mask = self.data_loader.generate_new_data(
self.configs['algorithm_configs']['processor'], gap=gap)
self.reset(inputs.shape[0])
for run in range(self.configs['runs']):
print(f'########### RUN {run} ################')
seed = TorchUtils.init_seed(None, deterministic=True)
results = self.task.run_task(inputs, targets, mask)
results['seed'] = seed
self.update_search_stats(results, run)
if self.best_run == None or results[
'best_performance'] < self.best_run['best_performance']:
self.update_best_run(results, run)
self.task.plot_results(results)
self.close_search()
def train_surrogate_model(configs, main_folder='training_data'):
if 'seed' in configs:
seed = configs['seed']
else:
seed = None
seed = TorchUtils.init_seed(seed, deterministic=True)
configs['seed'] = seed
# Get training and validation data
INPUTS, TARGETS, INPUTS_VAL, TARGETS_VAL, INFO = get_training_data(configs)
# Train the model
model_generator = get_algorithm(configs, is_main=True)
data = model_generator.optimize(INPUTS, TARGETS, validation_data=(INPUTS_VAL, TARGETS_VAL), data_info=INFO)
results_dir = os.path.join(model_generator.base_dir, main_folder)
create_directory(results_dir)
train_targets = INFO['processor']['amplification'] * TorchUtils.get_numpy_from_tensor(TARGETS[data.results['target_indices']][:len(INPUTS_VAL)])
train_output = INFO['processor']['amplification'] * data.results['best_output_training']
plot_all(train_targets, train_output, results_dir, name='TRAINING')
val_targets = INFO['processor']['amplification'] * TorchUtils.get_numpy_from_tensor(TARGETS_VAL)
val_output = INFO['processor']['amplification'] * data.results['best_output']
plot_all(val_targets, val_output, results_dir, name='VALIDATION')
training_profile = data.results['performance_history'] * (INFO['processor']['amplification']**2)
plt.figure()
plt.plot(training_profile)
plt.title(f'Training profile')
plt.legend(['training', 'validation'])
plt.savefig(os.path.join(results_dir, 'training_profile'))
model_generator.path_to_model = os.path.join(model_generator.base_dir, 'reproducibility', 'model.pt')
return model_generator