for attr, value in sorted(args.__dict__.items()):
print("\t{}={}".format(attr.upper(), value))
file.write("\t{}={}\n".format(attr.upper(), value))
file.close()
# model
if args.snapshot is None:
if args.CNN:
print("loading CNN model.....")
model = model_CNN.CNN_Text(args)
elif args.DEEP_CNN:
print("loading DEEP_CNN model......")
model = model_DeepCNN.DEEP_CNN(args)
elif args.LSTM:
print("loading LSTM model......")
model = model_LSTM.LSTM(args)
elif args.GRU:
print("loading GRU model......")
model = model_GRU.GRU(args)
elif args.BiLSTM:
print("loading BiLSTM model......")
model = model_BiLSTM.BiLSTM(args)
elif args.BiLSTM_1:
print("loading BiLSTM_1 model......")
model = model_BiLSTM_1.BiLSTM_1(args)
elif args.CNN_LSTM:
print("loading CNN_LSTM model......")
model = model_CNN_LSTM.CNN_LSTM(args)
elif args.CLSTM:
print("loading CLSTM model......")
model = model_CLSTM.CLSTM(args)
# 处理dev数据
d_data_list_node = dp.datadeal('data/raw.clean.dev', is_traindata=False)
# 处理test数据
test_data_node = dp.datadeal('data/raw.clean.test', is_traindata=False)
if args.out_word_v:
args.word_embed = out_word_vec.add_word_v(data_v)
args.embed_num = len(data_v)
args.class_num = len(lab_v)
print("\nParameters:")
for attr, value in sorted(args.__dict__.items()):
if attr == 'word_embed':
continue
print("\t{}={}".format(attr.upper(), value))
return t_data_list_node, data_v, d_data_list_node, lab_v
if __name__ == "__main__":
train_data_list_node, data_voc, dev_data_list_node, lab_voc = loaddata()
lstm = model_LSTM.LSTM(args)
try:
train.train_lstm(train_data_list_node, data_voc, dev_data_list_node,
lab_voc, lstm, args)
except KeyboardInterrupt:
print('\nstop by human!!!')