callbacks["tflogger"] = TensorboardLogger() return callbacks log_dir = "logs" workers = 4 batch_size = 10 epoch_size_train = 200 epoch_size_val = 200 num_epochs = 3 dataManager = DataManager(data_path="./reduced", start_end_func=train_start_end_func) dataManager.load_all() dataManager.init_norm_params() dataManager.init_splits() dataManagerVal = DataManager(data_path="./reduced", start_end_func=val_start_end_func) dataManagerVal.load_all() dataManagerVal.init_norm_params() dataManagerVal.init_splits() dataset_train = Currency_Dataset(dataManager, epoch_size_train) # Trick because deepcopy does not work with generators # doesn`t work... need 2 datamanagers (load data twice... idea, # load once, then pass) - manual deepcopy dataset_val = Currency_Dataset(dataManagerVal, epoch_size_val) dataloader_train = DataLoader(dataset=dataset_train, batch_size=batch_size, shuffle=True, num_workers=workers) dataloader_val = DataLoader(dataset=dataset_val,
def normparams_splits_test(): dataManager = DataManager() dataManager.load_all() dataManager.init_norm_params() dataManager.init_splits() return dataManager.norm_params, dataManager.bins