I have a dataset of 3-D(time_step*inputsize*total_num) matrix which is a .mat file. I want to use DataLoader to get a input dataset for LSTM which batch_size is 5. My code is as following:

file_path = "…/database/frameLength100/notOverlap/a.mat"

mat_data = s.loadmat(file_path)

tensor_data = torch.from_numpy(mat_data[‘a’]) #Tensor

class CustomDataset(Dataset):

```
def __init__(self, tensor_data):
self.tensor_data = tensor_data
def __getitem__(self, index):
data = self.tensor_data[index]
label = 1;
return data, label
def __len__(self):
return len(self.tensor_data)
```

custom_dataset = CustomDataset(tensor_data=tensor_data)

train_loader = DataLoader(dataset=custom_dataset, batch_size=5, shuffle=True)

I think the code is wrong but I have no idea how to correct it. What makes me confused is how how can I make DataLoader know which dimension is ‘total_num’ so that I get the dataset which batch size is 5.