def do_train_dirac_batch_same_size(verbose=True):
def dirac(cur_batch, nb_of_batch):
if cur_batch == 0:
return 1
else:
return 0
bay_net = GaussianClassifier(rho, number_of_classes=10)
bay_net.to(device)
loss_bbb = BBBLoss(bay_net, criterion, dirac)
optimizer = optim.Adam(bay_net.parameters())
observables = AccuracyAndUncertainty()
train_bayesian_modular(
bay_net,
optimizer,
loss_bbb,
observables,
number_of_epochs=nb_of_epochs,
trainloader=trainloader,
device=device,
verbose=verbose,
)
return eval_bayesian(bay_net,
evalloader,
nb_of_tests,
device=device,
verbose=verbose)
def test_training_no_validation():
randomloader = RandomLoader()
device = "cpu"
bay_net = GaussianClassifier(-3, (0, 0), (1, 1), number_of_classes=10)
bay_net.to(device)
criterion = nn.CrossEntropyLoss()
loss = BaseLoss(criterion)
observables = AccuracyAndUncertainty()
optimizer = optim.Adam(bay_net.parameters())
train_bayesian_modular(bay_net,
optimizer,
loss,
observables,
1,
trainloader=randomloader,
device=device,
number_of_tests=2,
verbose=False)
def do_train_ce(verbose=True):
bay_net = GaussianClassifier(rho, number_of_classes=10)
bay_net.to(device)
criterion = nn.CrossEntropyLoss()
loss_bbb = BaseLoss(criterion)
optimizer = optim.Adam(bay_net.parameters())
observables = AccuracyAndUncertainty()
train_bayesian_modular(
bay_net,
optimizer,
loss_bbb,
observables,
number_of_epochs=nb_of_epochs,
trainloader=trainloader,
device=device,
verbose=verbose,
)
return eval_bayesian(bay_net,
evalloader,
nb_of_tests,
device=device,
verbose=verbose)
def step_function(batch_idx, number_of_batches):
return 2**(number_of_batches - batch_idx) / (2**number_of_batches - 1)
loss = BBBLoss(bay_net, criterion, step_function)
else:
loss = BaseLoss(criterion)
optimizer = optim.Adam(bay_net.parameters())
observables = AccuracyAndUncertainty()
train_bayesian_modular(
bay_net,
optimizer,
loss,
observables,
number_of_tests=number_of_tests,
number_of_epochs=epoch,
trainloader=trainloader,
# valloader=valloader,
# output_dir_tensorboard='./output',
device=device,
verbose=True,
)
true_train_labels, all_outputs_train = eval_bayesian(
bay_net,
trainloader,
return_accuracy=False,
number_of_tests=number_of_tests,
device=device,
)