Answers.Body: <p>Not quite sure about RNN &amp; LSTM (and it always depends on the task), but for CNN the answer is clearly <strong>no</strong>; CNN routinely include FC layers. Quoting from the highly popular (and recommended) Stanford course <a href="https://cs231n.github.io/convolutional-networks/#architectures" rel="nofollow noreferrer">CS231n: Convolutional Neural Networks for Visual Recognition</a>:</p>
  <blockquote>
  <h2>ConvNet Architectures</h2>
  <p>We have seen that Convolutional Networks are commonly made up of only
  three layer types: CONV, POOL (we assume Max pool unless stated
  otherwise) and FC (short for fully-connected).</p>
  </blockquote>
  <p>You can easily verify that this is the case for practically all popular CNN models for computer vision in any relevant exposition (e.g. <a href="https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d" rel="nofollow noreferrer">Illustrated: 10 CNN Architectures</a>).</p>
  <p>In fact, the all-convolutional NN (i.e. without FC layers) is considered a special case: see <a href="https://arxiv.org/abs/1412.6806" rel="nofollow noreferrer">Striving for Simplicity: The All Convolutional Net</a>.</p>
  
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Answers.CreationDate: 2021-04-08T20:44:34.220Z
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Body: <p>In the paper <a href="https://www.researchgate.net/publication/325333417_Wrist-worn_blood_pressure_tracking_in_healthy_free-living_individuals_using_neural_networks" rel="nofollow noreferrer">Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks</a>, the authors talk about a combination of feed-forward and recurrent layers, as if FC layers cannot be part of the RNN.</p>
  <p>So, must all Convolutional Neural Networks and Recurrent Neural Networks <strong>not</strong> have a fully connected layer in order to be considered CNNs and RNNs, respectively? If yes, should we consider CNNs and RNNs with an FC layer &quot;hybrid models&quot;?</p>
  
Tags: <convolutional-neural-networks><recurrent-neural-networks><long-short-term-memory><terminology><dense-layers>
Title: Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?
UserDisplayName: witdev
ThreadText: # Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?
  
  (Asked by: username_0 on 2021-04-07)
  
  In the paper [Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks](https://www.researchgate.net/publication/325333417_Wrist-worn_blood_pressure_tracking_in_healthy_free-living_individuals_using_neural_networks), the authors talk about a combination of feed-forward and recurrent layers, as if FC layers cannot be part of the RNN.
  
  So, must all Convolutional Neural Networks and Recurrent Neural Networks **not** have a fully connected layer in order to be considered CNNs and RNNs, respectively? If yes, should we consider CNNs and RNNs with an FC layer "hybrid models"?
  
  **Tags:** convolutional-neural-networks, recurrent-neural-networks, long-short-term-memory, terminology, dense-layers
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-04-08. Score: 2
  
  Not quite sure about RNN & LSTM (and it always depends on the task), but for CNN the answer is clearly **no**; CNN routinely include FC layers. Quoting from the highly popular (and recommended) Stanford course [CS231n: Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/convolutional-networks/#architectures):
  
  > ## ConvNet Architectures
  >
  > We have seen that Convolutional Networks are commonly made up of only
  > three layer types: CONV, POOL (we assume Max pool unless stated
  > otherwise) and FC (short for fully-connected).
  
  You can easily verify that this is the case for practically all popular CNN models for computer vision in any relevant exposition (e.g. [Illustrated: 10 CNN Architectures](https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d)).
  
  In fact, the all-convolutional NN (i.e. without FC layers) is considered a special case: see [Striving for Simplicity: The All Convolutional Net](https://arxiv.org/abs/1412.6806).
  
  
  ---
  
  