Body: <p>I need to input data conditionally to my deep network. In order to explain cases, I'd like to give an example. Assume that I have a 50-attribute dataset. For some attributes, a specific part of hidden layers is responsible, and for others, a different part is responsible. Also, for some cases, the same parts of the hidden layers might intersect. I think I can decide which attributes must go which hidden neurons in the input layer by using some kind of if-else block. However, I could not figure out how.</p>
  <blockquote>
  <p><strong>My current idea</strong></p>
  </blockquote>
  <p>I can enter an identity element for some attributes. For example, I have att1, att2, att3, etc. I have ins1, ins2, etc.
  For ins1 -&gt; att1 = 0.5, att2 = 0.2, att3 = None
  For ins2 -&gt; att1 = 0.1, att2 = None, att3 = None</p>
  <p>But, if I do this approach, the number of attributes for an instance becomes bigger unnecessarily.</p>
  <blockquote>
  <p><strong>End of my current idea</strong></p>
  </blockquote>
  <p>Are there any opinions on this? Should I rearrange my excel file or is there any way to use if-else conditions?
  Regards,</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-09T06:16:02.247Z
Comments.Text: This is very confusing, so to clarify: You have (or want?) a neural network that has that capacity to accept all 50 attributes, BUT is able to only accept a subset of them at a time? So you can input attributes [2,5,7] and nothing else? Or am I misunderstanding
Comments.UserDisplayName: Recessive
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-09T20:54:27.837Z
Comments.Text: Hello. Welcome to AI SE. Could you please put your **specific** question in the title (to give an idea of what your question is to people that scroll down the list of questions)?
Comments.UserDisplayName: nbro
Tags: <neural-networks><deep-learning><ai-design><conditional-random-field>
Title: Conditional input deep neural network
UserDisplayName: Mehmet Cengiz
ThreadText: # Conditional input deep neural network
  
  (Asked by: username_0 on 2021-07-08)
  
  I need to input data conditionally to my deep network. In order to explain cases, I'd like to give an example. Assume that I have a 50-attribute dataset. For some attributes, a specific part of hidden layers is responsible, and for others, a different part is responsible. Also, for some cases, the same parts of the hidden layers might intersect. I think I can decide which attributes must go which hidden neurons in the input layer by using some kind of if-else block. However, I could not figure out how.
  
  > **My current idea**
  
  I can enter an identity element for some attributes. For example, I have att1, att2, att3, etc. I have ins1, ins2, etc.
  For ins1 -> att1 = 0.5, att2 = 0.2, att3 = None
  For ins2 -> att1 = 0.1, att2 = None, att3 = None
  
  But, if I do this approach, the number of attributes for an instance becomes bigger unnecessarily.
  
  > **End of my current idea**
  
  Are there any opinions on this? Should I rearrange my excel file or is there any way to use if-else conditions?
  Regards,
  
  **Tags:** neural-networks, deep-learning, ai-design, conditional-random-field
  
  > **Comments:**
  > **username_1**: This is very confusing, so to clarify: You have (or want?) a neural network that has that capacity to accept all 50 attributes, BUT is able to only accept a subset of them at a time? So you can input attributes [2,5,7] and nothing else? Or am I misunderstanding  
  > **username_2**: Hello. Welcome to AI SE. Could you please put your **specific** question in the title (to give an idea of what your question is to people that scroll down the list of questions)?  
  
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  ## 0 answers
  
  