Body: <p>I am still new to CNNs, but I would like to check my understanding between when to use convolutional layers versus fully connected layers.</p>
  <p>From what I have read, we can use convolutional layers with filters, rather than fully connected layers, with images, text, and audio. However, with regular data, for example, the iris dataset, a convolutional layer would not perform well because of the structure. As in the columns can be swapped, yet the record or sample itself does not change. For example we can swap the order of the Petal Length column with Petal Width and the record does not change. Where as in an image or audio, changing the column items would result in a different image or audio file.</p>
  <p>These convolutional layers are <em>&quot;better&quot;</em> for images and audio because not all the features need to connect to the next layer. For example, we do not need the background of a car image to know it is a car, thus we do not need all the connections and we save computational costs.</p>
  <p>Is this the right way to think about when to use convolutional layers versus fully connected layers?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-29T22:37:40.023Z
Comments.Text: I am not sure I understand your logic. First, you say that convolutional layers would not perform well on the Iris dataset, then you say "As in the columns can be manipulated, yet the record or sample itself does not change", but I don't understand what you mean by this sentence. Can you reformulate that sentence and clarify how it is related to the previous sentence? Then you say "These convolutional layers are "better" because not all the features need to connect to the next layer", but before that you say that convolutional layers would not perform well on the Iris dataset.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-29T22:37:41.747Z
Comments.Text: Please, edit your post to clarify it.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-30T15:27:42.207Z
Comments.Text: Apologies on the confusion. I have updated it to clarify as requested.
Comments.UserDisplayName: Jack Armstrong
Tags: <convolutional-neural-networks><dense-layers><convolutional-layers>
Title: When to use convolutional layers as opposed to fully connected layers?
UserDisplayName: Jack Armstrong
ThreadText: # When to use convolutional layers as opposed to fully connected layers?
  
  (Asked by: username_0 on 2020-06-29)
  
  I am still new to CNNs, but I would like to check my understanding between when to use convolutional layers versus fully connected layers.
  
  From what I have read, we can use convolutional layers with filters, rather than fully connected layers, with images, text, and audio. However, with regular data, for example, the iris dataset, a convolutional layer would not perform well because of the structure. As in the columns can be swapped, yet the record or sample itself does not change. For example we can swap the order of the Petal Length column with Petal Width and the record does not change. Where as in an image or audio, changing the column items would result in a different image or audio file.
  
  These convolutional layers are *"better"* for images and audio because not all the features need to connect to the next layer. For example, we do not need the background of a car image to know it is a car, thus we do not need all the connections and we save computational costs.
  
  Is this the right way to think about when to use convolutional layers versus fully connected layers?
  
  **Tags:** convolutional-neural-networks, dense-layers, convolutional-layers
  > **Comments:**
  > **username_1**: I am not sure I understand your logic. First, you say that convolutional layers would not perform well on the Iris dataset, then you say "As in the columns can be manipulated, yet the record or sample itself does not change", but I don't understand what you mean by this sentence. Can you reformulate that sentence and clarify how it is related to the previous sentence? Then you say "These convolutional layers are "better" because not all the features need to connect to the next layer", but before that you say that convolutional layers would not perform well on the Iris dataset.  
  > **username_1**: Please, edit your post to clarify it.  
  > **username_0**: Apologies on the confusion. I have updated it to clarify as requested.  
  
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  ## 0 answers
  
  