train_stg6.py File Reference

Namespaces
namespace	train_stg6

Functions
	train_stg6.train (dataloader, model, loss_fn, optimizer, device)
	If batch size equal to 1 it's a Stochastic Gradiente Descent (SGD), otherwise it's a mini-batch gradient descent.
	train_stg6.test (dataloader, model, loss_fn, device, loss_name)
	train_stg6.train_test (train_dataloader, test_dataloader, model, loss_fn, optimizer, device, epochs, lr_sch)
	train_stg6.main ()

Variables
	train_stg6.parser = argparse.ArgumentParser(description=constants.script_description)
	train_stg6.type
	train_stg6.args = parser.parse_args()
	train_stg6.beta = args.b
	train_stg6.learning_rate = args.lr
	train_stg6.loss_threshold = float("-inf")
int	train_stg6.QP = 32
	train_stg6.batch_size = args.batch
	train_stg6.iterations = args.i
float	train_stg6.decay = 0.01
	train_stg6.decay_controler = args.dcontr
	train_stg6.device = args.dev
	train_stg6.num_workers = args.workers
	train_stg6.n_mod = args.nmod
	train_stg6.l_path_train = args.labelsTrain
	train_stg6.l_path_test = args.labelsTest
	train_stg6.writer = SummaryWriter("runs/MSECNN_"+n_mod)
str	train_stg6.files_mod_name_stats = "_multi_batch_iter_{ite}_batch_{batch}_QP_{QP}_beta_{be}_lr_{lr}_{n_mod}".format(ite=iterations, batch=batch_size, QP=QP, be=beta, lr=learning_rate, n_mod=n_mod)
int	train_stg6.cnt_train = 0
int	train_stg6.cnt_test_train = 0
int	train_stg6.cnt_test_test = 0