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)