data.HP_rethink_iter = args.rethink_iter
else:
data = Data()
data.HP_gpu = gpu
data.HP_batch_size = args.batch_size
data.HP_num_layer = args.num_layer
data.HP_iteration = args.num_iter
data.use_bigram = args.use_biword
data.gaz_dropout = 0.5
data.norm_gaz_emb = False
data.HP_fix_gaz_emb = False
data.label_comment = args.labelcomment
data.result_file = args.resultfile
data.HP_lr = args.lr
data.HP_hidden_dim = args.hidden_dim
data.HP_use_posi = args.use_posi
data.HP_rethink_iter = args.rethink_iter
data_initialization(data, gaz_file, train_file, dev_file,
test_file)
data.generate_instance_with_gaz(train_file, 'train')
data.generate_instance_with_gaz(dev_file, 'dev')
data.generate_instance_with_gaz(test_file, 'test')
data.build_word_pretrain_emb(char_emb)
data.build_biword_pretrain_emb(bichar_emb)
data.build_gaz_pretrain_emb(gaz_file)
print('Dumping data')
with open(save_data_name, 'wb') as f:
pickle.dump(data, f)
set_seed(seed_num)
print("Gaz file:", gaz_file)
if status == 'train':
print("Model saved to:", save_model_dir)
# 立即把stdout缓存内容输出
sys.stdout.flush()
if status == 'train':
data = Data()
data.model_name = model_name
data.HP_gpu = gpu
data.use_bichar = conf_dict['use_bichar']
data.HP_batch_size = conf_dict['HP_batch_size'] # 1
data.HP_iteration = conf_dict['HP_iteration'] # 100
data.HP_lr = conf_dict['HP_lr'] # 0.015
data.HP_lr_decay = conf_dict['HP_lr_decay'] # 0.5
data.HP_hidden_dim = conf_dict['HP_hidden_dim']
data.MAX_SENTENCE_LENGTH = conf_dict['MAX_SENTENCE_LENGTH']
data.HP_lstm_layer = conf_dict['HP_lstm_layer']
data_initialization(data, gaz_file, train_file, dev_file, test_file)
if data.model_name in ['CNN_model', 'LSTM_model']:
data.generate_instance_with_gaz_2(train_file, 'train')
data.generate_instance_with_gaz_2(dev_file, 'dev')
data.generate_instance_with_gaz_2(test_file, 'test')
elif data.model_name in ['WC-LSTM_model']:
data.generate_instance_with_gaz_3(train_file, 'train')
data.generate_instance_with_gaz_3(dev_file, 'dev')
data.generate_instance_with_gaz_3(test_file, 'test')
else:
print("model_name is not set!")
sys.exit(1)
data.norm_gaz_emb = False #词向量是否归一化
data.HP_fix_gaz_emb = True #词向量表大小是否固定
data.HP_bilstm = True
data.random_seed = seed_num
# 整体参数设定位置
data.HP_lr = 0.01
data.HP_lr_decay = 0.01
data.HP_iteration = 150
data.HP_batch_size = 20
data.gaz_dropout = 0.4
data.weight_decay = 0.00000005
data.use_clip = False #是否控制梯度
data.HP_clip = 30 #最大梯度
# LSTM参数
data.HP_hidden_dim = 300
data.HP_dropout = 0.7
data_initialization(data, train_file, dev_file, test_file)
data.build_word_pretrain_emb(word_emb_file)
print('finish loading')
data.generate_instance(train_file, 'train')
print("train_file done")
data.generate_instance(dev_file, 'dev')
print("dev_file done")
data.generate_instance(test_file, 'test')
print("test_file done")
print('random seed: ' + str(seed_num))
# 模型的声明
model = BiLSTM_CRF(data)
print("打印模型可优化的参数名称")
for name, param in model.named_parameters():
