from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
print("- "*35)
print("Start train model : {}".format(model_name))
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(vocab)
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model, method=args.init_method)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter)
from utils_fasttext import build_dataset, build_iterator, get_time_dif
embedding = 'random'
else:
from utils import build_dataset, build_iterator, get_time_dif
x = import_module('models.' + model_name)
config = x.Config(dataset, embedding)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, word)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# train
config.n_vocab = len(vocab)
# to 是为了去除device
model = x.Model(config).to(config.device)
if model_name != 'Transformer':
init_network(model)
print(model.parameters)
train(config, model, train_iter, dev_iter, test_iter)
x = import_module('models.' + model_name) # 根据所选模型名字在models包下 获取相应模块(.py)
config = x.Config(dataset, embedding) # 每一个模块(.py)中都有一个模型定义类 和与该模型相关的配置类(定义该模型的超参数) 初始化配置类的对象
#设置随机种子 确保每次运行的条件(模型参数初始化、数据集的切分或打乱等)是一样的
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True # 保证每次结果一样
start_time = time.time()
print("Loading data...")
vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
train_iter = build_iterator(train_data, config)
dev_iter = build_iterator(dev_data, config)
test_iter = build_iterator(test_data, config)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)
# 构造模型对象
config.n_vocab = len(vocab) # 词典大小可能不确定，在运行时赋值
model = x.Model(config).to(config.device) # 构建模型对象 并to_device
# 记录log，可在TensorbordX中可视化
writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))
if model_name != 'Transformer': # 如果不是Transformer模型 则使用自定义的参数初始化方式
init_network(model) # 也可以采用之前达观杯中的做法 把自定义模型参数的函数 放在模型的定义类中 在__init__中执行
print(model.parameters)
# 训练、验证和测试
train(config, model, train_iter, dev_iter, test_iter,writer)
