def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
'''
Record an observation of the objective function
parameter_id : int
parameters : dict of parameters
value: final metrics of the trial, including reward
'''
logger.debug('acquiring lock for param {}'.format(parameter_id))
self.thread_lock.acquire()
logger.debug('lock for current acquired')
reward = extract_scalar_reward(value)
if self.optimize_mode is OptimizeMode.Minimize:
reward = -reward
logger.debug('receive trial result is:\n')
logger.debug(str(parameters))
logger.debug(str(reward))
indiv = Individual(indiv_id=int(
os.path.split(parameters['save_dir'])[1]),
graph_cfg=graph_loads(parameters['graph']),
result=reward)
self.population.append(indiv)
logger.debug('releasing lock')
self.thread_lock.release()
self.events[indiv.indiv_id].set()
cfg = GAGConfig()
cfg.batch_size = args.batch_size
cfg.learning_rate = float(args.learning_rate)
cfg.dropout = args.dropout_rate
cfg.rnn_units = args.rnn_units
cfg.labelsmoothing = args.labelsmoothing
cfg.num_heads = args.num_heads
timer = Timer()
qp_pairs, dev_qp_pairs = load_data()
logger.debug('Init finish.')
original_params = nni.get_next_parameter()
'''
with open('data.json') as f:
original_params = json.load(f)
'''
try:
graph = graph.graph_loads(original_params)
except Exception:
logger.debug('Can\'t load graph.')
train_loss, best_acc = train_with_graph(graph, qp_pairs, dev_qp_pairs)
logger.debug('Send best acc: %s', str(best_acc))
nni.report_final_result(best_acc)
logger.debug('Send final result done')
except:
logger.exception('Catch exception in trial.py.')
raise
cfg.learning_rate = float(args.learning_rate)
cfg.dropout = args.dropout_rate
cfg.rnn_units = args.rnn_units
cfg.labelsmoothing = args.labelsmoothing
cfg.num_heads = args.num_heads
timer = Timer()
qp_pairs, dev_qp_pairs = load_data()
logger.debug('Init finish.')
original_params = nni.get_next_parameter()
'''
with open('data.json') as f:
original_params = json.load(f)
'''
p_graph = graph.graph_loads(original_params['graph'])
save_path = original_params['save_dir']
os.makedirs(save_path)
restore_path = original_params['restore_dir']
restore_shared = [
hash_id + '/' for hash_id in original_params['shared_id']
] if original_params['shared_id'] is not None else [] + [
'word_embed', 'char_embed', 'char_encoding/'
]
train_loss, best_acc = train_with_graph(p_graph, qp_pairs,
dev_qp_pairs)
logger.debug('Send best acc: %s', str(best_acc))
nni.report_final_result(best_acc)
logger.debug('Send final result done')
except: