Hello everybody,

I’m pretty new to Pytorch. I have two questions, hope you can help me.

Imagine the following sequence:

```
input = [[1,2,3,4,5,6,7,8,9,10]]
```

Let’s say I want to learn this sequence using 2 look backs. So I would have:

```
input = [[1,2], [2,3], [3,4]....] and so on.
```

So my first question is, how do I have pass the input to a LSTM? I know I could shape the input like `(seq_len, batch_size, features`

) so here it would be `(2, 9, 1)`

, right?

Does the following has the same meaning? So i loop over the batches and pass them to the LSTM but with `batch_size=1`

. After all batches I do the `backward()`

?

```
...
for i in range(input.size(0)):
x = [input[i].view(2,1,1)
seq.forward(x)
....
....
loss.backward()
```

Is there a difference of setting the `batch_size`

parameter or just looping over the batches?

- Let’s say I want to predict a value out of 4 look backs. And I have 200 samples of these sequences. How do I have to build the input vector now? Does the number of training samples acutally stands for the batch_size?

Thank you!