<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>

CC BY-SA 4.0
2021-07-09T06:16:02.247Z
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
Recessive
CC BY-SA 4.0
2021-07-09T20:54:27.837Z
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)?
nbro
<neural-networks><deep-learning><ai-design><conditional-random-field>
Conditional input deep neural network
Mehmet Cengiz
# 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)?  

---

## 0 answers

