Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-03-17T07:52:45.980Z
Answers.Text: in order to have 3 input layers, i have to set the input_shape as (,3), yes?
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Answers.CreationDate: 2021-03-17T11:51:21.200Z
Answers.Text: No you need to define 3 Input layers.
Answers.UserDisplayName: Abhishek Verma
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Answers.CreationDate: 2021-03-17T12:17:36.303Z
Answers.Text: pls, edit your answer with an example for 3 input layers
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Answers.CreationDate: 2021-03-18T18:43:20.547Z
Answers.Text: 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.
Answers.UserDisplayName: Abhishek Verma
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Answers.Text: but they are different data, they dont represent the same measure
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Answers.CreationDate: 2021-03-18T19:10:08.767Z
Answers.Text: You mean the modality (image, text, etc.) is different or the scale is different?
Answers.UserDisplayName: Abhishek Verma
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Answers.Text: 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.
Answers.UserDisplayName: Abhishek Verma
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Answers.Text: the data are numerical and specifically displacement, time and distance
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Answers.CreationDate: 2021-03-19T13:30:34.947Z
Answers.Text: Oh, you have timeeeeeeeee. Hey, you are posing this wrong, it should be a sequence model. Think LSTMs, RNNs and Transformers.
Answers.UserDisplayName: Abhishek Verma
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Answers.CreationDate: 2021-03-19T15:37:39.727Z
Answers.Text: I was thinking GRU but i dont know how to set the input and outpur size. The output i want to be an image
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Answers.Text: Think Image Transformer. But, that would be a bit arduous for anybody.
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Answers.UserDisplayName: Abhishek Verma
Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-16T11:26:04.753Z
Comments.Text: 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.
Comments.UserDisplayName: nbro
Tags: <neural-networks><tensorflow>
Title: Can we use Multiple data as Input in a NN for a single Output?
UserDisplayName: TheGame
ThreadText: # 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.  
  
  ---
  
  ## 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.  
  
  ---
  
  