def train(self, opt_path: str = "./config/default.json"):
opt = load_opt(opt_path)
results = {}
results["SVM"] = _train_svm(opt)
results["AE"] = _train_ae(opt)
results["MLP"] = _train_mlp(opt)
pprint.pprint(results)
def finetuning(self, opt_path: str = "./config/default.json"):
opt = load_opt(opt_path)
results = {}
for model_name, Trainer in TRAINER_MAP.items():
if model_name == "AE":
hps = combine_hps(True)
else:
hps = combine_hps(False)
for hp in hps:
for k, v in hp.items():
opt[k] = v
trainer = Trainer(opt)
result = trainer.train()
if model_name not in results.keys():
results[model_name] = result
elif results[model_name]["avg_accuracy"] < result["avg_accuracy"]:
results[model_name] = result
results["SVM"] = _train_svm(opt)
pprint.pprint(results)
def visualize(self, opt_path: str = "./config/default.json" , data_path: str = "./syncdiff/data/data.json"):
opt = load_opt(opt_path)
visualize_raw_data(data_path, opt["saved_folder"])
def train_mlp(self, opt_path: str = "./config/default.json"):
opt = load_opt(opt_path)
result = _train_mlp(opt)
pprint.pprint(result)