text: Title: Why is tflearn.data_utils.shuffle() not used in all CIFAR-10 Examples? 
  Body: In the **covnet_cifar10.py** and **network_in_network.py** examples the CIFAR-10 data is shuffled after its loaded using the `tflearn.data_utils.shuffle()` function: 
  
  `from tflearn.datasets import cifar10`
  `(X, Y), (X_test, Y_test) = cifar10.load_data()`
  `X, Y = shuffle(X, Y)`
  
  However, in the **residual_network_cifar10.py** and **resnext_cifar10.py** examples this step is not taken after the data is loaded. 
  
  Is there a reason why this shuffle step is not included in these examples? 
  
  Is it just that the data is not required to be shuffled for these models to work? Or, is the shuffling of the data taking place during the `.fit()` training where the shuffle parameter is set to true `shuffle=True`? 
  