from model_graph_cnn import build_graph
elif model_tag == 'csm':
from model_graph_csm import build_graph
elif model_tag == 'rnn':
from model_graph_rnn import build_graph
elif model_tag == 'mlp':
from model_graph_mlp import build_graph
#
# data
dataset = Dataset()
dataset.load_vocab_tokens_and_emb()
#
#
config = ModelSettings()
config.vocab = dataset.vocab
config.model_tag = model_tag
config.model_graph = build_graph
config.is_train = False
config.check_settings()
#
model = ModelWrapper(config)
model.prepare_for_prediction()
#
text_raw = ["这本书不错"]
"""
work_book = xlrd.open_workbook(file_raw)
data_sheet = work_book.sheets()[0]
text_raw = data_sheet.col_values(0)
global_step = total_batch)
#
#
if flag_stop: break # for epoch
#
#
str_info = "training ended after total epoches: %d" % (epoch + 1)
self._log_info(str_info)
self._log_info("")
# print(str_info)
# print()
#
if __name__ == '__main__':
sett = ModelSettings('vocab_placeholder', False)
sett.model_tag = 'cnn'
sett.check_settings()
#print(dir(sett))
#l = [i for i in dir(sett) if inspect.isbuiltin(getattr(sett, i))]
#l = [i for i in dir(sett) if inspect.isfunction(getattr(sett, i))]
#l = [i for i in dir(sett) if not callable(getattr(sett, i))]
print(sett.__dict__.keys())
print()
model = ModelWrapper(sett)
help='restart')
#
return parser.parse_args()
#
if __name__ == '__main__':
"""
"""
args = parse_args()
#
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
#
# settings
settings = ModelSettings()
settings.gpu_available = args.gpu
# data
if args.debug == 1:
train_files = demo_data["train"]
dev_files = demo_data["dev"]
test_files = demo_data["test"]
#
settings.tokens_file = os.path.join(dir_vocab_demo, "vocab_tokens.txt")
settings.base_dir = "../task_mrc_demo"
#
assign_paras_from_dict(settings, debug_paras)
#
else:
train_files = data_all["train"]
#
if model_tag.startswith('cnn'):
from model_graph_cnn import ModelGraph
elif model_tag.startswith('rnn'):
from model_graph_rnn import ModelGraph
elif model_tag.startswith('rnf'):
from model_graph_rnf import ModelGraph
elif model_tag.startswith('msa'):
from model_graph_msa import ModelGraph
elif model_tag.startswith('cap'):
from model_graph_cap import ModelGraph
else:
assert False, "NOT supported model_tag"
#
# settings
settings = ModelSettings()
settings.gpu_available = args.gpu
settings.model_tag = model_tag
#
if run_mode == 'predict':
settings.is_train = False
else:
settings.is_train = True
#
settings.check_settings()
settings.create_or_reset_log_file()
settings.logger.info('running with args : {}'.format(args))
settings.logger.info(settings.trans_info_to_dict())
settings.save_to_json_file("./temp_settings.json")
#
# vocab
