<p>Yes, it can be those multiple inputs, you would have to design the network like that (to have 3 input layers and then pool them afterwards).</p>
<p>But, an easier solution would be to flatten this array and put it through 1D CNN and finally put it through a softmaxx.</p>

CC BY-SA 4.0
2021-03-17T07:52:45.980Z
in order to have 3 input layers, i have to set the input_shape as (,3), yes?
TheGame
CC BY-SA 4.0
2021-03-17T11:51:21.200Z
No you need to define 3 Input layers.
Abhishek Verma
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2021-03-17T12:17:36.303Z
pls, edit your answer with an example for 3 input layers
TheGame
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2021-03-18T18:43:20.547Z
It is better to stack them vertically and then apply 1D CNN. I don't recommend the multiple input format; it is not prevalent and haven't seen it anywhere. But, it is possible.
Abhishek Verma
CC BY-SA 4.0
2021-03-18T18:48:20.057Z
but they are different data, they dont represent the same measure
TheGame
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2021-03-18T19:10:08.767Z
You mean the modality (image, text, etc.) is different or the scale is different?
Abhishek Verma
CC BY-SA 4.0
2021-03-18T19:23:40.570Z
Scale you can normalize. And in case of modality, just pass the data first through a feature extractor. VGG or ResNet features for Image and BERT or Language model features for text. If there is some other modality, do tell. Then, stack them vertically and do the training.
Abhishek Verma
CC BY-SA 4.0
2021-03-18T20:04:52.393Z
the data are numerical and specifically displacement, time and distance
TheGame
CC BY-SA 4.0
2021-03-19T13:30:34.947Z
Oh, you have timeeeeeeeee. Hey, you are posing this wrong, it should be a sequence model. Think LSTMs, RNNs and Transformers.
Abhishek Verma
CC BY-SA 4.0
2021-03-19T15:37:39.727Z
I was thinking GRU but i dont know how to set the input and outpur size. The output i want to be an image
TheGame
CC BY-SA 4.0
2021-03-19T15:50:46.560Z
Think Image Transformer. But, that would be a bit arduous for anybody.
Abhishek Verma
CC BY-SA 4.0
2021-03-17T04:25:34.710Z
2021-03-17T04:25:34.710Z
Abhishek Verma
<p>So I am new to NN and I'm trying to go deep and apply it to my subject. I would like to ask: the input of the NN can be 2 or more values for example-&gt; the measurement of a value, distance, and time? An example of input data would be [ [1,2,3, ....],[11,22,33, .....],[5] ] whose output is a value 1 for example or cat or an generated model.</p>

CC BY-SA 4.0
2021-03-16T11:26:04.753Z
Hello and welcome to this community :) To clarify what your question is, Can you please put it in the title? "About the Input in a NN" is very vague and not a question.
nbro
<neural-networks><tensorflow>
Can we use Multiple data as Input in a NN for a single Output?
TheGame
# Can we use Multiple data as Input in a NN for a single Output?

(Asked by: username_0 on 2021-03-16)

So I am new to NN and I'm trying to go deep and apply it to my subject. I would like to ask: the input of the NN can be 2 or more values for example-> the measurement of a value, distance, and time? An example of input data would be [ [1,2,3, ....],[11,22,33, .....],[5] ] whose output is a value 1 for example or cat or an generated model.

**Tags:** neural-networks, tensorflow

> **Comments:**
> **username_1**: Hello and welcome to this community :) To clarify what your question is, Can you please put it in the title? "About the Input in a NN" is very vague and not a question.  

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## 1 answers

### Answer by username_2 on 2021-03-17. Score: 0 | Accepted answer

Yes, it can be those multiple inputs, you would have to design the network like that (to have 3 input layers and then pool them afterwards).

But, an easier solution would be to flatten this array and put it through 1D CNN and finally put it through a softmaxx.

> **Comments:**
> **username_0**: in order to have 3 input layers, i have to set the input_shape as (,3), yes?  
> **username_2**: No you need to define 3 Input layers.  
> **username_0**: pls, edit your answer with an example for 3 input layers  
> **username_2**: It is better to stack them vertically and then apply 1D CNN. I don't recommend the multiple input format; it is not prevalent and haven't seen it anywhere. But, it is possible.  
> **username_0**: but they are different data, they dont represent the same measure  
> **username_2**: You mean the modality (image, text, etc.) is different or the scale is different?  
> **username_2**: Scale you can normalize. And in case of modality, just pass the data first through a feature extractor. VGG or ResNet features for Image and BERT or Language model features for text. If there is some other modality, do tell. Then, stack them vertically and do the training.  
> **username_0**: the data are numerical and specifically displacement, time and distance  
> **username_2**: Oh, you have timeeeeeeeee. Hey, you are posing this wrong, it should be a sequence model. Think LSTMs, RNNs and Transformers.  
> **username_0**: I was thinking GRU but i dont know how to set the input and outpur size. The output i want to be an image  
> **username_2**: Think Image Transformer. But, that would be a bit arduous for anybody.  

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