def new_net():
net = netter.CustomResNet18()
net = net.to("cuda:0")
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)
return nf.NetTrainer(net=net, criterion=criterion, optimizer=optimizer)
def new_network():
# net = netter.ResNet18()
net = netter.CustomResNet18()
net = net.to("cuda:0")
criterion = nn.CrossEntropyLoss() # probabilmente la dovremo cambiare
optimizer = optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=1e-4)
return nf.NetTrainer(net=net, criterion=criterion, optimizer=optimizer)
have_to_save_it = False
thename = "starting_net"
if have_to_save_it:
save_the_net(thename=thename)
net, b, c, d = load_the_net(thename=thename)
net = net.to("cuda:0")
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=1e-4)
net_trainer = nf.NetTrainer(net=net, criterion=criterion, optimizer=optimizer)
dataset = CifarLoader(transform=None,
first_time_multiplier=first_time_multiplier,
name=None,
joking=True).restore(b,
c,
d,
transform=traintrans_01,
name=None)
el_for_active = [x for x in dataset.already_selected_indices]
# active_indices = net_trainer.bestofn(dataset, [x for x in dataset.train_indices if x not in el_for_active], tslp)
net_trainer.distance_and_varratio(
dataset, [x for x in dataset.train_indices if x not in el_for_active],
tslp, el_for_active)
# net_trainer.greedy_k_centers(dataset, [x for x in dataset.train_indices if x not in el_for_active], tslp, dataset.select_for_train(el_for_active))