Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-17T12:11:00.847Z
Answers.LastActivityDate: 2021-06-17T12:11:00.847Z
Answers.UserDisplayName: Alexander Soare
Body: <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>
  
Tags: <neural-networks><recurrent-neural-networks>
Title: Can we modelize an RNN by an ANN that takes precedent output as a part of input?
UserDisplayName: Aramya
ThreadText: # 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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