(dataset,
                           train_batch_size,
                           val_batch_size,
                           root='./data',
                           random_seed=42,
                           shuffle=True)
return (trainloader, valloader)
Splits the CIFAR training set into training and validation
    sets (9:1 split) and returns the corresponding data loaders.
Splits the CIFAR training set into training and validation
    sets (9:1 split) and returns the corresponding data loaders.
Splits the CIFAR training set into training and validation sets ( 9 : 1 split ) and returns the corresponding data loaders .
def cifar_train_val_loader(dataset,
                           train_batch_size,
                           val_batch_size,
                           root='./data',
                           random_seed=42,
                           shuffle=True):
    """
    Splits the CIFAR training set into training and validation
    sets (9:1 split) and returns the corresponding data loaders.
    """
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
    ])
    trainset = dataset(root=root,
                       train=True,
                       download=True,
                       transform=transform_train)
    valset = dataset(root=root, train=True, download=True, transform=None)
    num_train = len(trainset)
    indices = list(range(num_train))
    split = 5000

    if shuffle:
        np.random.seed(random_seed)
        np.random.shuffle(indices)

    train_idx, val_idx = indices[split:], indices[:split]
    trainsampler = SubsetRandomSampler(train_idx)
    valsampler = SubsetRandomSampler(val_idx)

    meanstd = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    trainloader = condensa.data.GPUDataLoader(trainset,
                                              batch_size=train_batch_size,
                                              shuffle=False,
                                              num_workers=8,
                                              sampler=trainsampler,
                                              meanstd=meanstd)
    valloader =   condensa.data.GPUDataLoader(valset,
                                              batch_size=val_batch_size,
                                              shuffle=False,
                                              num_workers=8,
                                              sampler=valsampler,
                                              meanstd=meanstd)

    return (trainloader, valloader)
def cifar_train_val_loader ( dataset , train_batch_size , val_batch_size , root = './data' , random_seed = 42 , shuffle = True ) : transform_train = transforms . Compose ( [ transforms . RandomCrop ( 32 , padding = 4 ) , transforms . RandomHorizontalFlip ( ) , ] ) trainset = dataset ( root = root , train = True , download = True , transform = transform_train ) valset = dataset ( root = root , train = True , download = True , transform = None ) num_train = len ( trainset ) indices = list ( range ( num_train ) ) split = 5000 if shuffle : np . random . seed ( random_seed ) np . random . shuffle ( indices ) train_idx , val_idx = indices [ split : ] , indices [ : split ] trainsampler = SubsetRandomSampler ( train_idx ) valsampler = SubsetRandomSampler ( val_idx ) meanstd = ( ( 0.4914 , 0.4822 , 0.4465 ) , ( 0.2023 , 0.1994 , 0.2010 ) ) trainloader = condensa . data . GPUDataLoader ( trainset , batch_size = train_batch_size , shuffle = False , num_workers = 8 , sampler = trainsampler , meanstd = meanstd ) valloader = condensa . data . GPUDataLoader ( valset , batch_size = val_batch_size , shuffle = False , num_workers = 8 , sampler = valsampler , meanstd = meanstd ) return ( trainloader , valloader )