def main(args):
# create dataloader
data_loader = DataLoaderFactory()
train_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
test_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
dev_data_loader = data_loader.get_input_for_ccf(file_path,
batch_size,
max_seq_length,
shuffle,
drop_last)
# 设置task model
task_model = TaskModel(num_labels=len(args.label_to_id), dropout_prob=args.dropout_prob,
bret_pretrainded_path=args.bert_pretrain_path)
# 重新加载模型参数
# print("从test_model.bin 加载参数")
# task_model.load_state_dict(torch.load(os.path.join(os.path.dirname(curr_path), "model_save", "model_89.15.bin"),
# "cuda" if torch.cuda.is_available() else None))
# 设置优化器
optimizer = get_optim(task_model.parameters(), args)
# print config
print("args", args)
# 开始模型训练
cls_app = ModelRunner(task_type="cls", is_bert=False, label_to_id=args.label_to_id)
cls_app.train(total_step=args.total_step, eval_per_step=args.eval_per_step, task_model=task_model,
model_save_path=args.model_save_path, optimizer=optimizer,
train_data_loader=train_data_loader, dev_data_loader=dev_data_loader,
eval_label_list=list(args.label_to_id.values()), compare_param="f1",
eval_file_save_name=args.eval_file_save_name)
cls_app.predict_for_ccf(dataiter=dev_data_loader, model=task_model,
save_file_path="test_predict_out.txt",
model_path=args.model_save_path, load_from_onnx=False)
print("模型运行完成")
def main(argv):
data_loader = BaseLoader.get_loader_from_flags(FLAGS.data_set)
train_set, valid_set, test_set = data_loader.load_dataset(FLAGS.num_steps, FLAGS.shuffle_train)
model = DualStageRNN(encoder_dim=FLAGS.encoder_dim,
decoder_dim=FLAGS.decoder_dim,
num_steps=FLAGS.num_steps,
num_series=data_loader.num_series,
use_cur_exg=FLAGS.use_cur_exg)
save_path = 'data/data_nasdaq'
model_runner = ModelRunner(model, data_loader.label_scaler, FLAGS,save_path)
model_runner.train(train_set, valid_set, test_set, FLAGS.max_epoch)
# model_runner.restore('logs/ModelWrapper/lr-0.001_encoder-32_decoder-32/20190922-125838/saved_model')
model_runner.evaluate(test_set, plot=FLAGS.plot_prediction)
return
def main():
parser = argparse.ArgumentParser(description="-----[IMDB-classifier]-----")
parser.add_argument("--sample",
default=False,
action='store_true',
help="flag whether use sample dataset")
parser.add_argument(
"--mode",
default="train",
help="train: train (with test) a model / test: test saved models")
parser.add_argument("--model",
default="simple-gru",
help="available models: simple-gru, ...")
parser.add_argument("--epoch",
default=10,
type=int,
help="number of max epoch")
parser.add_argument("--learning_rate",
default=0.001,
type=float,
help="learning rate")
parser.add_argument("--batch_size",
default=32,
type=int,
help="batch size")
options = parser.parse_args()
params = {
'sample': options.sample,
'model': options.model,
'mode': options.mode,
'batch_size': options.batch_size,
'epoch': options.epoch,
'learning_rate': options.learning_rate
}
modelRunner = ModelRunner(params)
if options.mode == 'train':
print("=" * 20 + "TRAINING STARTED" + "=" * 20)
modelRunner.train()
elif options.mode == 'test':
print("=" * 20 + "TESTING STARTED" + "=" * 20)
modelRunner.load_model()
modelRunner.test()