def train(model_name, restore=True):
import_lib()
global config, logger
config = Config.config
dataset = Dataset.Dataset()
dataset.prepare_dataset()
logger = utils.get_logger(model_name)
model = PHVM.PHVM(len(dataset.vocab.id2featCate),
len(dataset.vocab.id2featVal),
len(dataset.vocab.id2word),
len(dataset.vocab.id2category),
key_wordvec=None,
val_wordvec=None,
tgt_wordvec=dataset.vocab.id2vec,
type_vocab_size=len(dataset.vocab.id2type))
init = {'epoch': 0, 'worse_step': 0}
if restore:
init['epoch'], init['worse_step'], model = model_utils.restore_model(
model,
config.checkpoint_dir + "/" + model_name + config.tmp_model_dir,
config.checkpoint_dir + "/" + model_name + config.best_model_dir)
config.check_ckpt(model_name)
summary = tf.summary.FileWriter(config.summary_dir, model.graph)
_train(model_name, model, dataset, summary, init)
logger.info("finish training {}".format(model_name))
def main():
args = get_args()
if args.train:
train(args.model_name, args.restore)
else:
import_lib()
dataset = Dataset.Dataset()
model = PHVM.PHVM(len(dataset.vocab.id2featCate),
len(dataset.vocab.id2featVal),
len(dataset.vocab.id2word),
len(dataset.vocab.id2category),
key_wordvec=None,
val_wordvec=None,
tgt_wordvec=dataset.vocab.id2vec,
type_vocab_size=len(dataset.vocab.id2type))
best_checkpoint_dir = config.checkpoint_dir + "/" + args.model_name + config.best_model_dir
tmp_checkpoint_dir = config.checkpoint_dir + "/" + args.model_name + config.tmp_model_dir
model_utils.restore_model(model, best_checkpoint_dir,
tmp_checkpoint_dir)
dataset.prepare_dataset()
texts = infer(model, dataset, dataset.test)
dump(texts, config.result_dir + "/{}.json".format(args.model_name))
utils.print_out("finish file test")
def gen_sen(make_test, hide_strategy, STRATEGY, gen_model=None):#输入手动输入的二元组 / 生成句子模型名称，自命名区分
"""
:param make_test:
:param gen_model:
:return:
"""
import_lib()
from Models.model_utils import to_testfile, load_data
dataset = Dataset.EPWDataset()
model = PHVM.PHVM(len(dataset.vocab.id2featCate), len(dataset.vocab.id2featVal), len(dataset.vocab.id2word),
len(dataset.vocab.id2category), hide_strategy=hide_strategy, STRATEGY=STRATEGY,
key_wordvec=None, val_wordvec=None, tgt_wordvec=dataset.vocab.id2vec,
type_vocab_size=len(dataset.vocab.id2type))
config = Config.config
# to_testfile(load_data(config.data_dir + '/topics/' + make_test)) # 制造训练集
to_testfile(make_test)
best_checkpoint_dir = config.checkpoint_dir + "/PHVM" + config.best_model_dir
tmp_checkpoint_dir = config.checkpoint_dir + "/PHVM" + config.tmp_model_dir
model_utils.restore_model(model, best_checkpoint_dir, tmp_checkpoint_dir) # 当最优模型is not None，选择最优模型进行求解
dataset.prepare_dataset()
_bitfile = load_data(config.bit_file)
_bitfile.extend(_bitfile)
_bitfile.extend(_bitfile)
_bitfile.extend(_bitfile)
_bitfile.extend(_bitfile)
bitfile = []
if config.bit_per_word == 1 or hide_strategy == 'AC':
bitfile = _bitfile
elif hide_strategy == 'RS':
for bit in range(int(len(_bitfile) / config.bit_per_word)):
lower = bit * config.bit_per_word
upper = lower + config.bit_per_word
bitfile.append(bits2int(_bitfile[lower: upper]))
texts, texts_id, bit_len = infer(model, dataset, dataset.test, bitfile) # 输出生成文本
# dump(texts, config.result_dir + "/{}.json".format(gen_model))
return texts_id, bit_len