I am trying to implement a custom Dataset for sliding window over my data to be fed into an LSTM.
As you can see, my data is 1913,30490…I wish to grab 14 at a time (batch_size=14)…which should give me an object that is 14,30490
train_data_normalized.shape
(1913, 30490)

My custom Dataset below:
class MyDataset(Dataset):
    def __init__(self, data, window):
        self.data = data
        self.window = window

    def __getitem__(self, index):
        x = self.data[index:index+self.window]
        return x

    def __len__(self):
        return len(self.data) - self.window

my_dataset=MyDataset(train_data_normalized, 1)
my_loader = torch.utils.data.DataLoader(my_dataset, batch_size=14, shuffle = False)
d = next(iter(my_loader))
print(d.shape)
torch.Size([14, 1, 30490])

I am trying to understand why it is producing 14, 1, 30490 instead of 14, 30490
The end result I want to be able to get sequences like 1-14, then 2-15, 3-16, etc so they are of size 14 and advance forward one index at a time.