def main(args):
if args.debug:
debug()
if args.cuda:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
else:
config = tf.ConfigProto(device_count={'GPU': 0})
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
data_class = SingleTurnDialog.load_class(args.dataset)
wordvec_class = WordVector.load_class(args.wvclass)
if wordvec_class == None:
wordvec_class = Glove
if args.cache:
data = try_cache(data_class, (args.datapath,), args.cache_dir)
vocab = data.vocab_list
embed = try_cache(lambda wv, ez, vl: wordvec_class(wv).load(ez, vl),
(args.wvpath, args.embedding_size, vocab),
args.cache_dir, wordvec_class.__name__)
else:
data = data_class(args.datapath)
wv = wordvec_class(args.wvpath)
vocab = data.vocab_list
embed = wv.load(args.embedding_size, vocab)
embed = np.array(embed, dtype = np.float32)
with tf.Session(config=config) as sess:
model = create_model(sess, data, args, embed)
if args.mode == "train":
model.train_process(sess, data, args)
else:
model.test_process(sess, data, args)
def main(args):
logging.basicConfig(\
filename=0,\
level=logging.DEBUG,\
format='%(asctime)s %(filename)s[line:%(lineno)d] %(message)s',\
datefmt='%H:%M:%S')
if args.debug:
debug()
logging.info(json.dumps(args, indent=2))
cuda_init(0, args.cuda)
volatile = Storage()
data_class = SingleTurnDialog.load_class(args.dataset)
wordvec_class = WordVector.load_class(args.wvclass)
if wordvec_class is None:
wordvec_class = Glove
if args.cache:
dm = try_cache(data_class, (args.datapath, ), args.cache_dir)
volatile.wordvec = try_cache(\
lambda wv, ez, vl: wordvec_class(wv).load(ez, vl), \
(args.wvpath, args.embedding_size, dm.vocab_list),
args.cache_dir, wordvec_class.__name__)
else:
dm = data_class(args.datapath)
wv = wordvec_class(args.wvpath)
volatile.wordvec = wv.load(args.embedding_size, dm.vocab_list)
volatile.dm = dm
param = Storage()
param.args = args
param.volatile = volatile
model = Seq2seq(param)
if args.mode == "train":
model.train_process()
elif args.mode == "test":
model.test_process()
else:
raise ValueError("Unknown mode")
def main(args):
if args.debug:
debug()
if args.cuda:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
else:
config = tf.ConfigProto(device_count={'GPU': 0})
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
data_class = LanguageGeneration.load_class(args.dataset)
wordvec_class = WordVector.load_class(args.wvclass)
if wordvec_class == None:
wordvec_class = Glove
if args.cache:
data = try_cache(data_class, (args.datapath, ), args.cache_dir)
vocab = data.vocab_list
embed = try_cache(lambda wv, ez, vl: wordvec_class(wv).load(ez, vl),
(args.wvpath, args.embedding_size, vocab),
args.cache_dir, wordvec_class.__name__)
else:
data = data_class(args.datapath)
wv = wordvec_class(args.wvpath)
vocab = data.vocab_list
embed = wv.load(args.embedding_size, vocab)
embed = np.array(embed, dtype=np.float32)
with tf.Session(config=config) as sess:
generator, discriminator, rollout_gen = create_model(
sess, data, args, embed)
if args.mode == "train":
if args.pre_train:
#Start pretraining
print('Start pre-training generator...')
generator.pre_train_process(sess, data)
print('Start pre-training discriminator...')
discriminator.train_process(generator, data, sess,
args.dis_pre_epoch_num)
#Start testing
generator.test_process(sess, data)
#Start adversarial training
for batch in range(args.total_adv_batch):
print("Adversarial training %d" % batch)
print('Start adversarial training generator...')
generator.adv_train_process(sess, data, rollout_gen,
discriminator)
testout = generator.pre_evaluate(sess, data, args.batch_size,
"test")
if (batch % args.test_per_epoch == 0
or batch == args.total_adv_batch - 1) and batch != 0:
print('total_batch: ', batch, 'test_loss: ', testout[0])
generator.test_process(sess, data)
print('Start adversarial training discriminator...')
discriminator.train_process(generator, data, sess,
args.dis_adv_epoch_num)
else:
print("Start testing...")
generator.test_process(sess, data)