def train_generator(
model_save_suffix=model_save_suffixes["[no_prefix]train_generator"]):
assert flags.pretrain_model_dir, "pretrain_model_dir is required"
save_model_path = osp.join(flags.save_model_dir, model_save_suffix)
pretrained_model_pathes = {
"EMBEDDING":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["[no_prefix]pre_train_cl_model"]),
"FG_S":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["[no_prefix]train_lm_model"]),
"SEQ_G_LSTM_1":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["train_lm_model"]),
"SEQ_G_LSTM_2":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["train_ae_model"]),
"RNN_TO_EMBEDDING":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["train_lm_model"]),
}
if flags["adv_type"] == "adv":
generator_model = AdversarialDDGModel(
init_modules=AdversarialDDGModel.stepA_modules)
elif flags["adv_type"] == "vir_adv":
generator_model = VirtualAdversarialDDGModel(
init_modules=VirtualAdversarialDDGModel.stepA_modules)
else:
raise Exception("Unknow adv_type: %s" % flags["adv_type"])
generator_model.build(stepA=True, restorer_tag_notifier=["EMBEDDING"])
generator_model.fit(save_model_path=save_model_path,
pretrain_model_pathes=pretrained_model_pathes)
def eval_cl_model():
eval_from_vals = ["pretrain_cl", "final_cl"]
assert flags.eval_from in eval_from_vals, "eval_from must be one of %s" % eval_from_vals
if flags.eval_from == "final_cl":
model_save_suffix = model_save_suffixes["train_cl_model"]
else:
model_save_suffix = model_save_suffixes["pre_train_cl_model"]
save_model_path = osp.join(flags.save_model_dir, model_save_suffix)
generator_model = AdversarialDDGModel(
init_modules=AdversarialDDGModel.eval_cl_modules)
generator_model.build(eval_cl=True)
generator_model.eval(save_model_path=save_model_path)
def eval_generator(eval_batch_size=flags["eval_batch_size"],
eval_topic_count=flags["eval_topic_count"],
eval_seq_length=flags["eval_seq_length"]):
eval_from_vals = ["generator", "topic_generator"]
assert flags.eval_from in eval_from_vals, "eval_from must be one of %s" % eval_from_vals
if flags.eval_from == "generator":
model_save_suffix = model_save_suffixes["train_generator"]
else:
model_save_suffix = model_save_suffixes["train_topic_generator"]
save_model_path = osp.join(flags.save_model_dir, model_save_suffix)
generator_model = AdversarialDDGModel(
init_modules=AdversarialDDGModel.eval_graph_modules)
generator_model.build(eval_seq=True,
batch_size=eval_batch_size,
topic_count=eval_topic_count,
seq_length=eval_seq_length)
generator_model.eval(save_model_path=save_model_path)
def pre_train_cl_model(
model_save_suffix=model_save_suffixes["pre_train_cl_model"]):
assert flags.pretrain_model_dir, "pretrain_model_dir is required"
save_model_path = osp.join(flags.save_model_dir, model_save_suffix)
pretrained_model_pathes = {
"EMBEDDING":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["[no_prefix]train_lm_model"]),
"T_S":
osp.join(flags.pretrain_model_dir,
model_save_suffixes["[no_prefix]train_lm_model"])
}
if flags["adv_type"] == "adv":
adv_cl_model = AdversarialDDGModel(
init_modules=AdversarialDDGModel.stepB_modules)
elif flags["adv_type"] == "vir_adv":
adv_cl_model = VirtualAdversarialDDGModel(
init_modules=VirtualAdversarialDDGModel.stepB_modules)
else:
raise Exception("Unknow adv_type: %s" % flags["adv_type"])
adv_cl_model.build(stepB=True, restorer_tag_notifier=[])
adv_cl_model.fit(save_model_path=save_model_path,
pretrain_model_pathes=pretrained_model_pathes)
import sys
sys.path.insert(0, ".")
from adversarial_net.AdversarialDDGModel import AdversarialDDGModel
from adversarial_net.preprocessing import WordCounter
from adversarial_net import osp
from adversarial_net import arguments as flags
if __name__ == "__main__":
vocab_freqs = WordCounter().load(
osp.join(flags["inputs"]["datapath"],
"imdb_word_freqs.pickle")).most_common_freqs(
flags["lm_sequence"]["vocab_size"])
flags.add_variable(name="vocab_freqs", value=vocab_freqs)
adv_model = AdversarialDDGModel(
init_modules=AdversarialDDGModel.stepA_modules)
# adv_model.build(eval_seq=True, batch_size=2, topic_count=2, seq_length=200)
adv_model.build(stepA=True)
# adv_model.eval(None)
adv_model.fit()