I met the situation that the testing data accuracy is higher than the training data, I have checked the dataset and I am sure that the training data is the training data while the testing is testing… What is this indicate to and how to understand it?
I used the following code to calculate the accuracy in certain epochs,

        cnn = cnn.eval()
        train_correct = 0
        for _, (images, labels) in enumerate(train2_loader):
            images = Variable(images).cuda()
            labels = Variable(labels).long().cuda()
            #test
            outputs = cnn(images)
            train_loss = criterion(outputs, labels)
            train_pred = outputs.data.max(1, keepdim=True)[1] # get the index of the max log-probability
            train_correct += train_pred.eq(labels.data.view_as(train_pred)).long().sum().item()

        test_correct = 0
        for _, (images, labels) in enumerate(test_loader):
            images = Variable(images).cuda()
            labels = Variable(labels).long().cuda()
            #test
            outputs = cnn(images)
            test_loss = criterion(outputs, labels)
            test_pred = outputs.data.max(1, keepdim=True)[1] # get the index of the max log-probability
            test_correct += test_pred.eq(labels.data.view_as(test_pred)).long().sum().item()
        
        cnn = cnn.train()


while here the

train2_loader

is the subset of my train data and the test_loader is my test data.
For some example, one of the output is:

CNN Epoch [10/10], Iter [100/1563] Loss: 0.3904, Batch correct: 0.8516, Train Correct: 0.7488, Train Loss: 0.2358, Test Correct: 0.7503, Test Loss: 0.1708, At time: 4.08CNN
Epoch [10/10], Iter [200/1563] Loss: 0.4498, Batch correct: 0.8125, Train Correct: 0.7690, Train Loss: 0.2809, Test Correct: 0.7677, Test Loss: 0.2149, At time: 4.11CNN
Epoch [10/10], Iter [300/1563] Loss: 0.4276, Batch correct: 0.8125, Train Correct: 0.7468, Train Loss: 0.2255, Test Correct: 0.7510, Test Loss: 0.1658, At time: 4.14CNN
Epoch [10/10], Iter [400/1563] Loss: 0.4731, Batch correct: 0.7852, Train Correct: 0.7264, Train Loss: 0.2473, Test Correct: 0.7294, Test Loss: 0.2133, At time: 4.17CNN
Epoch [10/10], Iter [500/1563] Loss: 0.4603, Batch correct: 0.7891, Train Correct: 0.7352, Train Loss: 0.2176, Test Correct: 0.7389, Test Loss: 0.1578, At time: 4.20CNN
Epoch [10/10], Iter [600/1563] Loss: 0.4361, Batch correct: 0.8047, Train Correct: 0.7471, Train Loss: 0.2297, Test Correct: 0.7493, Test Loss: 0.1737, At time: 4.23CNN
Epoch [10/10], Iter [700/1563] Loss: 0.4625, Batch correct: 0.8125, Train Correct: 0.7709, Train Loss: 0.2767, Test Correct: 0.7704, Test Loss: 0.2082, At time: 4.26CNN
Epoch [10/10], Iter [800/1563] Loss: 0.4046, Batch correct: 0.8359, Train Correct: 0.7140, Train Loss: 0.1969, Test Correct: 0.7171, Test Loss: 0.1459, At time: 4.29CNN
Epoch [10/10], Iter [900/1563] Loss: 0.4715, Batch correct: 0.7812, Train Correct: 0.7494, Train Loss: 0.2575, Test Correct: 0.7533, Test Loss: 0.2058, At time: 4.32CNN
Epoch [10/10], Iter [1000/1563] Loss: 0.3714, Batch correct: 0.8516, Train Correct: 0.7497, Train Loss: 0.2552, Test Correct: 0.7497, Test Loss: 0.1978, At time: 4.35CNN
Epoch [10/10], Iter [1100/1563] Loss: 0.4417, Batch correct: 0.8125, Train Correct: 0.7710, Train Loss: 0.2845, Test Correct: 0.7725, Test Loss: 0.2007, At time: 4.38CNN
Epoch [10/10], Iter [1200/1563] Loss: 0.4808, Batch correct: 0.7891, Train Correct: 0.7563, Train Loss: 0.2644, Test Correct: 0.7565, Test Loss: 0.2081, At time: 4.41
CNN Epoch [10/10], Iter [1300/1563] Loss: 0.4471, Batch correct: 0.8125, Train Correct: 0.7532, Train Loss: 0.2323, Test Correct: 0.7512, Test Loss: 0.1790, At time: 4.44
CNN Epoch [10/10], Iter [1400/1563] Loss: 0.4374, Batch correct: 0.8281, Train Correct: 0.7438, Train Loss: 0.2278, Test Correct: 0.7448, Test Loss: 0.1688, At time: 4.46
CNN Epoch [10/10], Iter [1500/1563] Loss: 0.4210, Batch correct: 0.8125, Train Correct: 0.7587, Train Loss: 0.2712, Test Correct: 0.7592, Test Loss: 0.2311, At time: 4.49

Some of the “Train correct” are higher and some are lower than the “Test correct”, and the “Test Loss” is smaller than “Train Loss”.  Does this mean there is potential to increase the neural network complexity?