def training_loop()

in low_shot.py [0:0]

• 19 lines of code
• 3 McCabe index (conditional complexity)

def training_loop(lowshot_dataset, num_classes, params, batchsize=1000, maxiters=1000):
    featdim = lowshot_dataset.featdim()
    model = nn.Linear(featdim, num_classes)
    model = model.cuda()
    optimizer = torch.optim.SGD(model.parameters(), params.lr, momentum=params.momentum, dampening=params.momentum, weight_decay=params.wd)

    loss_function = nn.CrossEntropyLoss()
    loss_function = loss_function.cuda()
    for i in range(maxiters):
        (x,y) = lowshot_dataset.get_sample(batchsize)
        optimizer.zero_grad()

        x = Variable(x.cuda())
        y = Variable(y.cuda())
        scores = model(x)

        loss = loss_function(scores,y)
        loss.backward()
        optimizer.step()
        if (i%100==0):
            print('{:d}: {:f}'.format(i, loss.data[0]))

    return model