77a6ec73c53c5cc62a2ae451694537144afa5644,mnist/main.py,,test,#Any#,74

Before Change

        print("Testing model: {}/{}".format(i, test_data.size(0)), end="\r")
        batch_data.data[:] = test_data[i:i+TEST_BATCH_SIZE]
        batch_targets.data[:] = test_labels[i:i+TEST_BATCH_SIZE]
        test_loss += criterion(model(batch_data), batch_targets)

    test_loss = test_loss.data[0]
    test_loss /= (test_data.size(0) / TEST_BATCH_SIZE) // criterion averages over batch size

After Change

    test_loss = 0
    batch_data = Variable(torch.cuda.FloatTensor(TEST_BATCH_SIZE, 1, 28, 28), volatile=True)
    batch_targets = Variable(torch.cuda.FloatTensor(TEST_BATCH_SIZE), volatile=True)
    correct = 0
    for i in range(0, test_data.size(0), TEST_BATCH_SIZE):
        print("Testing model: {}/{}".format(i, test_data.size(0)), end="\r")
        batch_data.data[:] = test_data[i:i+TEST_BATCH_SIZE]
        batch_targets.data[:] = test_labels[i:i+TEST_BATCH_SIZE]
        output = model(batch_data)
        test_loss += criterion(output, batch_targets)
        pred = output.data.max(1)[1]
        correct += pred.long().eq(batch_targets.data.long()).sum()

    test_loss = test_loss.data[0]
    test_loss /= (test_data.size(0) / TEST_BATCH_SIZE) // criterion averages over batch size
    print("TEST SET RESULTS:" + " " * 20)

Instances