031392ff2cbb5703558d17a676a456521f754190,mnist/main.py,,train,#Any#,67

Before Change

    batch_targets_t = torch.LongTensor(BATCH_SIZE)
    if cuda:
        batch_data_t = batch_data_t.cuda()
        batch_targets_t = batch_targets_t.cuda()
    batch_data = Variable(batch_data_t, requires_grad=False)
    batch_targets = Variable(batch_targets_t, requires_grad=False)
    for i in range(0, training_data.size(0), BATCH_SIZE):
        optimizer.zero_grad()
        batch_data.data[:] = training_data[i:i+BATCH_SIZE]
        batch_targets.data[:] = training_labels[i:i+BATCH_SIZE]

After Change

        loss = loss.data[0]
        optimizer.step()
        print("Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.4f}"
              .format(epoch, end, opt.trainSize, float(end)/opt.trainSize*100, loss))

def test(epoch):
    // create buffers for mini-batch

Instances