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- # 这个模型需要在pytorch=0.4下运行，weight_drop不支持1.0
-
- # 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
- import os
- os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
- os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
-
- from fastNLP.io.data_loader import IMDBLoader
- from fastNLP.embeddings import StaticEmbedding
- from model.awd_lstm import AWDLSTMSentiment
-
- from fastNLP import CrossEntropyLoss, AccuracyMetric
- from fastNLP import Trainer
- from torch.optim import Adam
-
-
- class Config():
- train_epoch= 10
- lr=0.001
-
- num_classes=2
- hidden_dim=256
- num_layers=1
- nfc=128
- wdrop=0.5
-
- task_name = "IMDB"
- datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
- save_model_path="./result_IMDB_test/"
-
- opt=Config()
-
-
- # load data
- dataloader=IMDBLoader()
- datainfo=dataloader.process(opt.datapath)
-
- # print(datainfo.datasets["train"])
- # print(datainfo)
-
-
- # define model
- vocab=datainfo.vocabs['words']
- embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
- model=AWDLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc, wdrop=opt.wdrop)
-
-
- # define loss_function and metrics
- loss=CrossEntropyLoss()
- metrics=AccuracyMetric()
- optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
-
-
- def train(datainfo, model, optimizer, loss, metrics, opt):
- trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
- metrics=metrics, dev_data=datainfo.datasets['test'], device=0, check_code_level=-1,
- n_epochs=opt.train_epoch, save_path=opt.save_model_path)
- trainer.train()
-
-
- if __name__ == "__main__":
- train(datainfo, model, optimizer, loss, metrics, opt)
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