from aim import Session
sess = Session()
sess.set_params({
'foo': 'bar',
})
for i in range(10):
sess.track(i, name='val')
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 30 == 0:
print('Epoch [{}/{}], Step [{}/{}], '
'Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1,
total_step, loss.item()))
# aim - Track model loss function
aim_sess.track(loss.item(),
name='loss',
epoch=epoch,
subset='train')
correct = 0
total = 0
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
acc = 100 * correct / total
# aim - Track metrics
aim_sess.track(acc, name='accuracy', epoch=epoch, subset='train')
# TODO: Do actual validation
if i % 300 == 0:
aim_sess.track(loss.item(),
from aim import Session
sess1 = Session(experiment='line')
sess2 = Session(experiment='linex2')
sess1.set_params({
'k': '1',
})
sess2.set_params({
'k': '2',
})
for i in range(10):
sess1.track(i, name='val')
sess2.track(i*2, name='val')