How to shape input data for training an Autoencoder












0














Good evening and nice to meet you all.
I've been asked for a project to use an autoencoder for anomaly detection purposes. The dataset (synthetic, created by me) consists of 9 fictitious sensor readings.



The problem is that the request is to have 90 neurons in the input layer of the autoencoder, so what I've been actually asked to do is to collect vectors of 10 samples per each sensor (10*9=90) in order to have a 90-dimensional feature vector as input to the net.



Do you have some hints?
Thank you










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  • The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
    – Andrea Guidi
    Nov 13 '18 at 19:05
















0














Good evening and nice to meet you all.
I've been asked for a project to use an autoencoder for anomaly detection purposes. The dataset (synthetic, created by me) consists of 9 fictitious sensor readings.



The problem is that the request is to have 90 neurons in the input layer of the autoencoder, so what I've been actually asked to do is to collect vectors of 10 samples per each sensor (10*9=90) in order to have a 90-dimensional feature vector as input to the net.



Do you have some hints?
Thank you










share|improve this question
























  • The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
    – Andrea Guidi
    Nov 13 '18 at 19:05














0












0








0







Good evening and nice to meet you all.
I've been asked for a project to use an autoencoder for anomaly detection purposes. The dataset (synthetic, created by me) consists of 9 fictitious sensor readings.



The problem is that the request is to have 90 neurons in the input layer of the autoencoder, so what I've been actually asked to do is to collect vectors of 10 samples per each sensor (10*9=90) in order to have a 90-dimensional feature vector as input to the net.



Do you have some hints?
Thank you










share|improve this question















Good evening and nice to meet you all.
I've been asked for a project to use an autoencoder for anomaly detection purposes. The dataset (synthetic, created by me) consists of 9 fictitious sensor readings.



The problem is that the request is to have 90 neurons in the input layer of the autoencoder, so what I've been actually asked to do is to collect vectors of 10 samples per each sensor (10*9=90) in order to have a 90-dimensional feature vector as input to the net.



Do you have some hints?
Thank you







networking machine-learning autoencoder






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edited Nov 12 '18 at 19:12









Spara

3,99311341




3,99311341










asked Nov 12 '18 at 16:36









Andrea Guidi

21




21












  • The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
    – Andrea Guidi
    Nov 13 '18 at 19:05


















  • The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
    – Andrea Guidi
    Nov 13 '18 at 19:05
















The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
– Andrea Guidi
Nov 13 '18 at 19:05




The problem is: he asked me to take a window of 10 samples per each sensor, stack it in a vector and feed it to a 90-Dim input layer. Does it make any sense at all?
– Andrea Guidi
Nov 13 '18 at 19:05












1 Answer
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Sample ten times your synthetic dataset.



The goal here is to have a mini time-serie, I suppose, so you want to have a 10-sample serie for each of your 9 inputs.






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    1 Answer
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    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

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    active

    oldest

    votes









    0














    Sample ten times your synthetic dataset.



    The goal here is to have a mini time-serie, I suppose, so you want to have a 10-sample serie for each of your 9 inputs.






    share|improve this answer


























      0














      Sample ten times your synthetic dataset.



      The goal here is to have a mini time-serie, I suppose, so you want to have a 10-sample serie for each of your 9 inputs.






      share|improve this answer
























        0












        0








        0






        Sample ten times your synthetic dataset.



        The goal here is to have a mini time-serie, I suppose, so you want to have a 10-sample serie for each of your 9 inputs.






        share|improve this answer












        Sample ten times your synthetic dataset.



        The goal here is to have a mini time-serie, I suppose, so you want to have a 10-sample serie for each of your 9 inputs.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 12 '18 at 17:54









        Matthieu Brucher

        12.7k22140




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