本文整理汇总了Python中config.config.Config方法的典型用法代码示例。如果您正苦于以下问题：Python config.Config方法的具体用法？Python config.Config怎么用？Python config.Config使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类config.config的用法示例。
在下文中一共展示了config.Config方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: evaluate
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def evaluate(config:Config, model: NNCRF, batch_insts_ids, name:str, insts: List[Instance]):
## evaluation
metrics = np.asarray([0, 0, 0], dtype=int)
batch_id = 0
batch_size = config.batch_size
for batch in batch_insts_ids:
one_batch_insts = insts[batch_id * batch_size:(batch_id + 1) * batch_size]
sorted_batch_insts = sorted(one_batch_insts, key=lambda inst: len(inst.input.words), reverse=True)
batch_max_scores, batch_max_ids = model.decode(batch)
metrics += eval.evaluate_num(sorted_batch_insts, batch_max_ids, batch[-2], batch[1], config.idx2labels)
batch_id += 1
p, total_predict, total_entity = metrics[0], metrics[1], metrics[2]
precision = p * 1.0 / total_predict * 100 if total_predict != 0 else 0
recall = p * 1.0 / total_entity * 100 if total_entity != 0 else 0
fscore = 2.0 * precision * recall / (precision + recall) if precision != 0 or recall != 0 else 0
print("[%s set] Precision: %.2f, Recall: %.2f, F1: %.2f" % (name, precision, recall,fscore), flush=True)
return [precision, recall, fscore] 示例2: get_optimizer
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def get_optimizer(config: Config, model: nn.Module):
params = model.parameters()
if config.optimizer.lower() == "sgd":
print(colored("Using SGD: lr is: {}, L2 regularization is: {}".format(config.learning_rate, config.l2), 'yellow'))
return optim.SGD(params, lr=config.learning_rate, weight_decay=float(config.l2))
elif config.optimizer.lower() == "adam":
print(colored("Using Adam", 'yellow'))
return optim.Adam(params)
else:
print("Illegal optimizer: {}".format(config.optimizer))
exit(1) 示例3: batching_list_instances
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def batching_list_instances(config: Config, insts:List[Instance]):
train_num = len(insts)
batch_size = config.batch_size
total_batch = train_num // batch_size + 1 if train_num % batch_size != 0 else train_num // batch_size
batched_data = []
for batch_id in range(total_batch):
one_batch_insts = insts[batch_id * batch_size:(batch_id + 1) * batch_size]
batched_data.append(simple_batching(config, one_batch_insts))
return batched_data 示例4: test_model
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def test_model(config: Config, test_insts):
dep_model_name = config.dep_model.name
if config.dep_model == DepModelType.dggcn:
dep_model_name += '(' + str(config.num_gcn_layers) + ","+str(config.gcn_dropout)+ ","+str(config.gcn_mlp_layers)+")"
model_name = "model_files/lstm_{}_{}_crf_{}_{}_{}_dep_{}_elmo_{}_{}_gate_{}_epoch_{}_lr_{}_comb_{}.m".format(config.num_lstm_layer, config.hidden_dim,
config.dataset, config.affix,
config.train_num,
dep_model_name,
config.context_emb.name,
config.optimizer.lower(),
config.edge_gate,
config.num_epochs,
config.learning_rate, config.interaction_func)
res_name = "results/lstm_{}_{}_crf_{}_{}_{}_dep_{}_elmo_{}_{}_gate_{}_epoch_{}_lr_{}_comb_{}.results".format(config.num_lstm_layer, config.hidden_dim,
config.dataset, config.affix,
config.train_num,
dep_model_name,
config.context_emb.name,
config.optimizer.lower(),
config.edge_gate,
config.num_epochs,
config.learning_rate, config.interaction_func)
model = NNCRF(config)
model.load_state_dict(torch.load(model_name))
model.eval()
test_batches = batching_list_instances(config, test_insts)
evaluate(config, model, test_batches, "test", test_insts)
write_results(res_name, test_insts) 示例5: get_attr
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def get_attr(attr_key: str):
"""
Helper method for getting values from config override or config template.
"""
if not hasattr(ConfigOverride.Config, attr_key):
return getattr(config_template.Config, attr_key)
return getattr(ConfigOverride.Config, attr_key) 示例6: main
# 需要导入模块: from config import config [as 别名]
# 或者: from config.config import Config [as 别名]
def main():
parser = argparse.ArgumentParser(description="Dependency-Guided LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_conll(conf.train_file, -1, True)
devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
tests = reader.read_conll(conf.test_file, conf.test_num, False)
if conf.context_emb != ContextEmb.none:
print('Loading the {} vectors for all datasets.'.format(conf.context_emb.name))
conf.context_emb_size = reader.load_elmo_vec(conf.train_file.replace(".sd", "").replace(".ud", "").replace(".sud", "").replace(".predsd", "").replace(".predud", "").replace(".stud", "").replace(".ssd", "") + "."+conf.context_emb.name+".vec", trains)
reader.load_elmo_vec(conf.dev_file.replace(".sd", "").replace(".ud", "").replace(".sud", "").replace(".predsd", "").replace(".predud", "").replace(".stud", "").replace(".ssd", "") + "."+conf.context_emb.name+".vec", devs)
reader.load_elmo_vec(conf.test_file.replace(".sd", "").replace(".ud", "").replace(".sud", "").replace(".predsd", "").replace(".predud", "").replace(".stud", "").replace(".ssd", "") + "."+conf.context_emb.name+".vec", tests)
conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains)
conf.build_deplabel_idx(trains + devs + tests)
print("# deplabels: ", len(conf.deplabels))
print("dep label 2idx: ", conf.deplabel2idx)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains + devs + tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
if conf.train_num != -1:
random.shuffle(trains)
trains = trains[:conf.train_num]
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)