Body: <p>I have developed a small encoding algorithm that accepts a time series of <code>n = 750</code> samples and <code>m = 1</code> feature from a scientific instrument, and encodes/transforms it into a new ordered sequence with an <code>n</code> that can vary between <code>1</code> and <code>12</code>, and <code>m = 3</code>.</p>
  <p>I want to build a neural network that can read the sequence of elements (likely via 1D convolution and subsequent dense layers?), and make a prediction as to which element is the 'element of interest', which corresponds to a particular position in the original time series.</p>
  <p>I am not sure if I am going about structure this the right way, so I wanted to ask if this looks roughly correct. The network should ideally read this input tensor, consider each element's features in the context of adjacent elements' features and then associate a fractional probability that a particular element (say, the green one, in the image) is the one I care about.</p>
  <p><a href="https://i.sstatic.net/NMKmn.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/NMKmn.png" alt="enter image description here" /></a></p>
  <p>Step 1 takes a time series, finds point of interests, and associated features with those points. This step may find anywhere between 1 and 12 points of interest.</p>
  <p>Step 2, should ideally look at each point of interest, and figure out which one is the &quot;true&quot; one, based on its local and global environment in the encoded input tensor.</p>
  <p>I hope this makes sense, I am not a deep learning expert by any means, but I have done some work in the past with CNNs, so please let me know if any additional information would be helpful.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-05T03:02:22.290Z
Comments.Text: For the output layer size, is it fixed? or is it varied based on the previous output?
Comments.UserDisplayName: malioboro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-05T22:12:23.633Z
Comments.Text: That is a great question, and something I neglected to include. The output is varied. The output _corresponds_ to an input element. So, it's not a 'classification' problem in the sense that there are 12 possible categories, but rather a variable number of categories depending on the input. I want to select the index of the input.
Comments.UserDisplayName: Vranvs
Tags: <neural-networks><deep-learning><convolutional-neural-networks>
Title: Can I use zero-padded input and output layers in a 1D convnet to predict an element of interest from a variable-length input sequence?
UserDisplayName: Vranvs
ThreadText: # Can I use zero-padded input and output layers in a 1D convnet to predict an element of interest from a variable-length input sequence?
  
  (Asked by: username_0 on 2023-03-05)
  
  I have developed a small encoding algorithm that accepts a time series of `n = 750` samples and `m = 1` feature from a scientific instrument, and encodes/transforms it into a new ordered sequence with an `n` that can vary between `1` and `12`, and `m = 3`.
  
  I want to build a neural network that can read the sequence of elements (likely via 1D convolution and subsequent dense layers?), and make a prediction as to which element is the 'element of interest', which corresponds to a particular position in the original time series.
  
  I am not sure if I am going about structure this the right way, so I wanted to ask if this looks roughly correct. The network should ideally read this input tensor, consider each element's features in the context of adjacent elements' features and then associate a fractional probability that a particular element (say, the green one, in the image) is the one I care about.
  
  [![enter image description here](https://i.sstatic.net/NMKmn.png)](https://i.sstatic.net/NMKmn.png)
  
  Step 1 takes a time series, finds point of interests, and associated features with those points. This step may find anywhere between 1 and 12 points of interest.
  
  Step 2, should ideally look at each point of interest, and figure out which one is the "true" one, based on its local and global environment in the encoded input tensor.
  
  I hope this makes sense, I am not a deep learning expert by any means, but I have done some work in the past with CNNs, so please let me know if any additional information would be helpful.
  
  **Tags:** neural-networks, deep-learning, convolutional-neural-networks
  > **Comments:**
  > **username_1**: For the output layer size, is it fixed? or is it varied based on the previous output?  
  > **username_0**: That is a great question, and something I neglected to include. The output is varied. The output _corresponds_ to an input element. So, it's not a 'classification' problem in the sense that there are 12 possible categories, but rather a variable number of categories depending on the input. I want to select the index of the input.  
  
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  ## 0 answers
  
  