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# change dropout layer to MCDropout
model = patch_module(model)
if use_cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=hyperparams["lr"], momentum=0.9)
# Wraps the model into a usable API.
model = ModelWrapper(model, criterion)
logs = {}
logs['epoch'] = 0
# for prediction we use a smaller batchsize
# since it is slower
active_loop = ActiveLearningLoop(active_set,
model.predict_on_dataset,
heuristic,
hyperparams.get('n_data_to_label', 1),
batch_size=10,
iterations=hyperparams['iterations'],
use_cuda=use_cuda)
for epoch in tqdm(range(args.epoch)):
model.train_on_dataset(active_set, optimizer, hyperparams["batch_size"], 1, use_cuda)
# Validation!
model.test_on_dataset(test_set, hyperparams["batch_size"], use_cuda)
metrics = model.metrics
if epoch % hyperparams['learning_epoch'] == 0:
should_continue = active_loop.step()