# save model in this time
shutil.copy("./models/model_BiLSTM_1.py", "./snapshot/" + mulu)
if args.cuda is True:
print("using cuda......")
model = model.cuda()
print(model)
# train
print("\n cpu_count \n", mu.cpu_count())
torch.set_num_threads(args.num_threads)
if os.path.exists("./Test_Result.txt"):
os.remove("./Test_Result.txt")
if args.CNN is True:
print("CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter, model,
args)
elif args.BiLSTM_1 is True:
print("BiLSTM_1 training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter, model,
args)
print("Model_count", model_count)
resultlist = []
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt")
for line in file.readlines():
if line[:10] == "Evaluation":
resultlist.append(float(line[34:41]))
result = sorted(resultlist)
file.close()
file = open("./Test_Result.txt", "a")
def main():
# if args.use_cuda is True:
# use deterministic algorithm for cnn
# torch.backends.cudnn.deterministic = True
args.kernel_sizes = [int(k) for k in args.kernel_sizes.split(',')]
# save file
mulu = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
args.mulu = mulu
args.save_dir = os.path.join(args.save_dir, mulu)
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
# build vocab and iterator
text_field = data.Field(lower=True)
label_field = data.Field(sequential=False)
if args.SST_1 is True:
print("loading sst-1 dataset......")
train_iter, dev_iter, test_iter = load_SST_1(
text_field,
label_field,
train_path=args.train_path,
dev_path=args.dev_path,
test_path=args.test_path,
device=args.gpu_device,
repeat=False,
shuffle=args.epochs_shuffle,
sort=False)
if args.SST_2 is True:
print("loading sst-2 dataset......")
train_iter, dev_iter, test_iter = load_SST_2(
text_field,
label_field,
train_path=args.train_path,
dev_path=args.dev_path,
test_path=args.test_path,
device=args.gpu_device,
repeat=False,
shuffle=args.epochs_shuffle,
sort=False)
if args.TREC is True:
print("loading TREC dataset......")
train_iter, test_iter = load_TREC(text_field,
label_field,
train_path=args.train_path,
test_path=args.test_path,
device=args.gpu_device,
repeat=False,
shuffle=args.epochs_shuffle,
sort=False)
args.embed_num = len(text_field.vocab)
args.class_num = len(label_field.vocab) - 1
args.PaddingID = text_field.vocab.stoi[text_field.pad_token]
print("embed_num : {}, class_num : {}".format(args.embed_num,
args.class_num))
print("PaddingID {}".format(args.PaddingID))
# pretrained word embedding
if args.word_Embedding:
pretrain_embed = load_pretrained_emb_zeros(
path=args.word_Embedding_Path,
text_field_words_dict=text_field.vocab.itos,
pad=text_field.pad_token)
calculate_oov(path=args.word_Embedding_Path,
text_field_words_dict=text_field.vocab.itos,
pad=text_field.pad_token)
args.pretrained_weight = pretrain_embed
# print params
show_params()
# load model and start train
if args.CNN is True:
print("loading CNN model.....")
# model = model_CNN.CNN_Text(args)
model = model_SumPooling.SumPooling(args)
# for param in model.parameters():
# param.requires_grad = False
shutil.copy("./models/model_CNN.py", args.save_dir)
print(model)
if args.use_cuda is True:
print("using cuda......")
model = model.cuda()
print("CNN training start......")
if os.path.exists("./Test_Result.txt"):
os.remove("./Test_Result.txt")
if args.SST_1 is True or args.SST_2 is True:
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, args)
if args.TREC is True:
model_count = train_CV.train(train_iter, test_iter, model, args)
# calculate the best result
cal_result()
def start_train(model, train_iter, dev_iter, test_iter):
"""
:function：start train
:param model:
:param train_iter:
:param dev_iter:
:param test_iter:
:return:
"""
if config.predict is not None:
label = train_ALL_CNN.predict(config.predict, model, config.text_field,
config.label_field)
print('\n[Text] {}[Label] {}\n'.format(config.predict, label))
elif config.test:
try:
print(test_iter)
train_ALL_CNN.test_eval(test_iter, model, config)
except Exception as e:
print("\nSorry. The test dataset doesn't exist.\n")
else:
print("\n cpu_count \n", mu.cpu_count())
torch.set_num_threads(config.num_threads)
if os.path.exists("./Test_Result.txt"):
os.remove("./Test_Result.txt")
if config.CNN:
print("CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, config)
elif config.DEEP_CNN:
print("DEEP_CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, config)
elif config.LSTM:
print("LSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.GRU:
print("GRU training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.BiLSTM:
print("BiLSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.BiLSTM_1:
print("BiLSTM_1 training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CNN_LSTM:
print("CNN_LSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CLSTM:
print("CLSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CBiLSTM:
print("CBiLSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CGRU:
print("CGRU training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CNN_BiLSTM:
print("CNN_BiLSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.BiGRU:
print("BiGRU training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CNN_BiGRU:
print("CNN_BiGRU training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
elif config.CNN_MUI:
print("CNN_MUI training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, config)
elif config.DEEP_CNN_MUI:
print("DEEP_CNN_MUI training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, config)
elif config.HighWay_CNN is True:
print("HighWay_CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter,
model, config)
elif config.HighWay_BiLSTM_1 is True:
print("HighWay_BiLSTM_1 training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter,
model, config)
print("Model_count", model_count)
resultlist = []
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt")
for line in file.readlines():
if line[:10] == "Evaluation":
resultlist.append(float(line[34:41]))
result = sorted(resultlist)
file.close()
file = open("./Test_Result.txt", "a")
file.write("\nThe Best Result is : " +
str(result[len(result) - 1]))
file.write("\n")
file.close()
shutil.copy("./Test_Result.txt",
"./snapshot/" + config.mulu + "/Test_Result.txt")
label = train_ALL_CNN.predict(args.predict, model, text_field, label_field)
print('\n[Text] {}[Label] {}\n'.format(args.predict, label))
elif args.test:
try:
print(test_iter)
train_ALL_CNN.test_eval(test_iter, model, args)
except Exception as e:
print("\nSorry. The test dataset doesn't exist.\n")
else:
print("\n cpu_count \n", mu.cpu_count())
torch.set_num_threads(args.num_threads)
if os.path.exists("./Test_Result.txt"):
os.remove("./Test_Result.txt")
if args.CNN:
print("CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter, model, args)
elif args.DEEP_CNN:
print("DEEP_CNN training start......")
model_count = train_ALL_CNN.train(train_iter, dev_iter, test_iter, model, args)
elif args.LSTM:
print("LSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter, model, args)
elif args.GRU:
print("GRU training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter, model, args)
elif args.BiLSTM:
print("BiLSTM training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter, model, args)
elif args.BiLSTM_1:
print("BiLSTM_1 training start......")
model_count = train_ALL_LSTM.train(train_iter, dev_iter, test_iter, model, args)