t0 = time()
utils.adjust_learning_rate(optimizer, metrics.lr_linear(epoch, 0, epochs, lr_start))
net.train()
training_loss = 0
accs = []
steps = 0
for i, (inputs, labels) in enumerate(trainloader, 0):
steps += 1
inputs, labels = Variable(inputs.cuda(async=True)), Variable(labels.cuda(async=True))
optimizer.zero_grad()
outputs = Variable(torch.zeros(inputs.shape[0], net.num_classes, flags.train_ens).cuda())
for j in range(flags.train_ens):
outputs[:, :, j] = F.log_softmax(net(inputs), dim=1)
log_outputs = utils.logmeanexp(outputs, dim=2)
loss = criterion(log_outputs, labels)
loss.backward()
optimizer.step()
accs.append(metrics.logit2acc(log_outputs.data, labels))
training_loss += loss.cpu().data.numpy()[0]
logger.add(epoch, tr_loss=training_loss/steps, tr_acc=np.mean(accs))
# Ens 100 test
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=100)
logger.add(epoch, te_nll_ens100=nll, te_acc_ens100=acc)
t0 = time()
utils.adjust_learning_rate(optimizer, metrics.lr_linear(epoch, 0, epochs, lr_start))
net.train()
training_loss = 0
accs = []
steps = 0
for i, (inputs, labels) in enumerate(trainloader, 0):
steps += 1
inputs, labels = Variable(inputs.cuda(async=True)), Variable(labels.cuda(async=True))
optimizer.zero_grad()
outputs = Variable(torch.zeros(inputs.shape[0], net.num_classes, flags.train_ens).cuda())
for j in range(flags.train_ens):
outputs[:, :, j] = F.log_softmax(net(inputs), dim=1)
log_outputs = utils.logmeanexp(outputs, dim=2)
loss = criterion(log_outputs, labels)
loss.backward()
optimizer.step()
accs.append(metrics.logit2acc(log_outputs.data, labels))
training_loss += loss.cpu().data.numpy()[0]
logger.add(epoch, tr_loss=training_loss/steps, tr_acc=np.mean(accs))
# Ens 100 test
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=100)
logger.add(epoch, te_nll_ens100=nll, te_acc_ens100=acc)