How to shape input data for training an Autoencoder
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
edited Nov 12 '18 at 19:12
Spara
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
add a comment |
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
edited Nov 12 '18 at 19:12
Spara
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
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
add a comment |
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
edited Nov 12 '18 at 19:12
Spara
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
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
networking machine-learning autoencoder
edited Nov 12 '18 at 19:12
Spara
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
edited Nov 12 '18 at 19:12
Spara
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
edited Nov 12 '18 at 19:12
Spara
1
edited Nov 12 '18 at 19:12
Spara
1
edited Nov 12 '18 at 19:12
Spara
1
1
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea Guidi
21
asked Nov 12 '18 at 16:36
Andrea GuidiAndrea 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
add a comment |
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
add a comment |
1 Answer
1
active
oldest
votes
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.
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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.
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
add a comment |
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.
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
add a comment |
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.
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
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.
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
answered Nov 12 '18 at 17:54
Matthieu BrucherMatthieu Brucher
13.5k32140
13.5k32140
add a comment |
add a comment |
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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