Hi @ptrblck, Thanks for your response! My code is like following:
data = torch.FloatTensor(numClass, numDataPoints, data_dim)
target = torch.zeros(numClass, numDataPoints)
weight = torch.histc(target, bins=10, min = 0, max = 9)
weight = 1/(weight + 1e-3)
sampler = torch.utils.data.sampler.WeightedRandomSampler(weight.type('torch.DoubleTensor'), bs)
data = data.view(-1,data_dim)
target = target.view(-1,1)
dataset_x = data.numpy()
dataset_y = target.numpy().astype(int)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(dataset_x,
                                                    dataset_y,
                                                    test_size=0.33,
                                                    random_state=42,
                                                    stratify = dataset_y)
trainDataset = torch.utils.data.TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train.astype(int)))
validDataset = torch.utils.data.TensorDataset(torch.FloatTensor(X_test), torch.LongTensor(y_test.astype(int)))

trainLoader = torch.utils.data.DataLoader(dataset = trainDataset, batch_size=bs, num_workers=1, sampler = sampler)
testLoader = torch.utils.data.DataLoader(dataset = validDataset, batch_size=bs, shuffle=False, num_workers=1) 

As you have mentioned above, I have written a code:
for data, labels in train_loader:
         # print something

But something went wrong here. It gave me an error like this:
‘StopIteration’
Could you please tell me what is wrong with my code? Actually, I would like to get samples from my data loader 1000 times and then at each step train the network and do backward!