<p>Almost. I think that to match the <a href="https://pytorch.org/docs/stable/generated/torch.nn.RNN.html" rel="nofollow noreferrer">common interpretation of an RNN</a> you need to <strong>also</strong> have a new input at each time-step (whereas you used the word &quot;<strong>just</strong>&quot; suggesting otherwise).</p>
<p>What you're describing is some function <span class="math-container">$f_1: \mathbb{R}^n \to  \mathbb{R}^n$</span> whereas the &quot;conventional&quot; RNN is more like <span class="math-container">$f_2: \mathbb{R}^m \times \mathbb{R}^n \to  \mathbb{R}^n$</span>, where <span class="math-container">$\mathbb{R}^m$</span> refers to the <strong>new</strong> information at each time step.</p>
<p>Of course, this is all an exercise in interpreting semantics. So the other answer to your question is: &quot;if you want it to&quot;</p>

CC BY-SA 4.0
2021-06-17T12:11:00.847Z
2021-06-17T12:11:00.847Z
Alexander Soare
<p>Is it possible to consider an RNN as a classical feedforward neural network that just take the precedent output as a part of the input ?</p>

<neural-networks><recurrent-neural-networks>
Can we modelize an RNN by an ANN that takes precedent output as a part of input?
Aramya
# Can we modelize an RNN by an ANN that takes precedent output as a part of input?

(Asked by: username_0 on 2021-06-17)

Is it possible to consider an RNN as a classical feedforward neural network that just take the precedent output as a part of the input ?

**Tags:** neural-networks, recurrent-neural-networks


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## 1 answers

### Answer by username_1 on 2021-06-17. Score: 1

Almost. I think that to match the [common interpretation of an RNN](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html) you need to **also** have a new input at each time-step (whereas you used the word "**just**" suggesting otherwise).

What you're describing is some function $f\_1: \mathbb{R}^n \to \mathbb{R}^n$ whereas the "conventional" RNN is more like $f\_2: \mathbb{R}^m \times \mathbb{R}^n \to \mathbb{R}^n$, where $\mathbb{R}^m$ refers to the **new** information at each time step.

Of course, this is all an exercise in interpreting semantics. So the other answer to your question is: "if you want it to"


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