http://d2l.ai/chapter_recurrent-neural-networks/sequence.html

Greetings.

Thanks for making this book available.

In the toy example 8.1.2, when creating the dataset, I was wondering if it was normal for both the train set and the test set to be the same. Namely:

```
train_iter = d2l.load_array((features[:n_train], labels[:n_train]),
batch_size, is_train=True)
test_iter = d2l.load_array((features[:n_train], labels[:n_train]),
batch_size, is_train=False)
```

For test_iter, I would have expected something like:

```
test_iter = d2l.load_array((features[n_train:], labels[n_train:]),
batch_size, is_train=False)
```

Thanks for your time.

I see. I did not get that far down yet haha.

## 8.1.2. A Toy Example

`features = d2l.zeros((T-tau, tau))`

**AttributeError** : module ‘d2l.torch’ has no attribute ‘zeros’

Then I search http://preview.d2l.ai/d2l-en/PR-1077/search.html?q=d2l.zeros&check_keywords=yes&area=default

No source code:

http://preview.d2l.ai/d2l-en/PR-1077/chapter_appendix-tools-for-deep-learning/d2l.html?highlight=d2l%20zeros#d2l.torch.zeros

I can use ``features = d2l.torch.zeros((T-tau, tau))` to replace now, and try to code next time!

An hour to debug!

```
for i in range(tau):
features[:, i] = x[i: i + T - tau - max_steps + 1].T
```

What’s the purpose of `.T`

at the end of the line above? It seems making no difference

I can’t agree more. Transposing a 1-rank tensor returns exactly itself.

Also this code

```
for i in range(n_train + tau, T):
multistep_preds[i] = d2l.reshape(net(
multistep_preds[i - tau: i].reshape(1, -1)), 1)
```

can be simply written as

```
for i in range(n_train + tau, T):
multistep_preds[i] = net(multistep_preds[i - tau: i])
```

I agree. Fixing: https://github.com/d2l-ai/d2l-en/pull/1542

Next time you can PR first: http://preview.d2l.ai/d2l-en/master/chapter_appendix-tools-for-deep-learning/contributing.html

I couldn’t help but notice the similarities between the latent autoregressive model and hidden Markov models. The difference being that in the case of latent autoregressive model the hidden sequence h_t might change over time t and in the case of Hidden markov models the hidden sequence h_t remains the same for all t. Am I correct in assuming this?

Hi everybody,

I have a question about math. In particular what does the sum on x_t in eqution 8.1.4 mean?

Is that the sum over all the possible state x_t? But that does not make a lot of sense to me, because if I have observed x_(t+1) there is just one possible x_t.

Could someone help me in understanding that?

Thanks a lot!