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convolutional 1d net

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kaare
kaare on 21 Mar 2017
Commented: Parisa Yas on 12 May 2021 at 9:28
I am trying to reproduce the convolution network described in https://arxiv.org/abs/1610.01683 .
Overview of their setup:
However, I seem to run into an obstacle when trying to combine results from different filters. In the paper, the authors have "stacking" layer, where 20 different filtered 1D signals are stacked, to create a sort of spectrogram, which is then fed to another convolutional layer. How does one do a similar thing in matlab? Below is what I have tried, and the error message that I get:
Input:
inputLayer=imageInputLayer([1 6000]);
c1=convolution2dLayer([1 200],20,'stride',1);
p1=maxPooling2dLayer([1 20],'stride',10);
c2=convolution2dLayer([20 30],400,'numChannels',20);
p2=maxPooling2dLayer([1 10],'stride',[1 2]);
f1=fullyConnectedLayer(500);
f2=fullyConnectedLayer(500);
s1=softmaxLayer;
outputLayer=classificationLayer;
convnet=[inputLayer; c1; p1; c2; p2; f1; f2; s1;outputLayer]
opts = trainingOptions('sgdm');
convnet = trainNetwork(allData',labels,convnet,opts);
Output:
convnet =
9x1 Layer array with layers:
1 '' Image Input 1x6000x1 images with 'zerocenter' normalization
2 '' Convolution 20 1x200 convolutions with stride [1 1] and padding [0 0]
3 '' Max Pooling 1x20 max pooling with stride [10 10] and padding [0 0]
4 '' Convolution 400 20x30 convolutions with stride [1 1] and padding [0 0]
5 '' Max Pooling 1x10 max pooling with stride [1 2] and padding [0 0]
6 '' Fully Connected 500 fully connected layer
7 '' Fully Connected 500 fully connected layer
8 '' Softmax softmax
9 '' Classification Output cross-entropy
Error using nnet.cnn.layer.Layer>iInferSize (line 261)
Layer 5 is expected to have a different size.
Error in nnet.cnn.layer.Layer.inferParameters (line 53)
layers = iInferSize(layers, i, inputSize);
Error in trainNetwork (line 61)
layers = nnet.cnn.layer.Layer.inferParameters(layers);
The error message is for layer 5, but I suspect it has to do with layer 4, where the "stacking" takes place. Thoughts?
  2 Comments
keshav kumar
keshav kumar on 22 Aug 2020
How can we implement CNN on iris data?

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Accepted Answer

Joss Knight
Joss Knight on 28 Mar 2017
Edited: Joss Knight on 28 Mar 2017
The 20 filters of c1 are stacked along dim 3, not dim 1. You need to specify a c2 filter size of [1 30], with dim 3 being inferred from the input:
c2 = convolution2dlayer([1 30],400);
Size 20 in dim 3 is inferred here to make the sizes correct, but you can explicitly set 'numChannels' to be certain.
This is the output I got:
9x1 Layer array with layers:
1 'imageinput' Image Input 1x6000x1 images with 'zerocenter' normalization
2 'conv' Convolution 20 1x200x1 convolutions with stride [1 1] and padding [0 0]
3 'maxpool' Max Pooling 1x20 max pooling with stride [10 10] and padding [0 0]
4 'conv_1' Convolution 400 1x30x20 convolutions with stride [1 1] and padding [0 0]
5 'maxpool_1' Max Pooling 1x10 max pooling with stride [1 2] and padding [0 0]
6 'fc' Fully Connected 500 fully connected layer
7 'fc_1' Fully Connected 500 fully connected layer
8 'softmax' Softmax softmax
9 'classoutput' Classification Output cross-entropy with '1', '2', and 498 other classes
  2 Comments
Joss Knight
Joss Knight on 26 Apr 2017
Okay, so the help text for trainNetwork says
For an image input layer, X is a numeric array of images arranged so
that the first three dimensions are the width, height and channels,
and the last dimension indexes the individual images.
So to treat 1-D data like a stack of colour images, you need to arrange the data along one of the spatial dimensions (rows or columns), and the observations along the 4th dimension.
In other words, your data should be 1600-by-1-by-1-by-4990, or 1-by-1600-by-1-by-4990. You can achieve this using reshape.
The number of classes must match the size of the output of your final layer, which is typically done by having at least one final Fully Connected layer with that many outputs. In your code the last FC layer had 500 outputs, which is what I replicated in my code. It sounds like you want to change that layer to have 4 outputs.
All of this might benefit from reading a few introductory articles on deep learning to help give you some familiarity with the way they work.

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More Answers (6)

Kaushik Sardeshpande
Kaushik Sardeshpande on 8 May 2018
Edited: Kaushik Sardeshpande on 12 May 2018
Dear all,
I have a solution for using 1-D Convoluional Neural Network in Matlab. Well while importing your 1-D data to the network, you need to convert your 1-D data into a 4-D array and then accordingly you need to provide the Labels for your data in the categorical form, as the trainNetwork command accepts data in 4-D array form and can accept the Labels manually, if the dataset doesn't contains the labels, as in imageDatastore labels are imported in the dataset only, but we can input the labels manually also to CNN.
My dataset was an array of [A x 15000], containing A no. of signals with 15000 samples fo each. Which I converted into a 4-D array of size [1 x 15000 x 1 x A] using reshape command. Now you must sure that your label vector must be of size [A x 1]. To create label vector acceptable by trainNetwork, firstly you must create a cell array and then convert it to the categorical using the command categorical.
I'm sharing piece of my code for reference and it worked...!!! Hope it guide you all, who are working on 1-D CNN.
Train_data = reshape(Train_dataset, [1 15000 1 A]); % Dataset is ready %
%Now create the labels %
label(1:(A-30),:) = {'ABC'}; % 1st Label %
label((A-29):A,:) = {'XYZ'}; % 2nd Label %
Train_Labels = categorical(label); % Label vector is ready %
% Now input this data-set and labels to the network %
net = trainNetwork(Train_data, Train_Labels, layers, train_options);
  17 Comments
Amirah Nabilah Azman
Amirah Nabilah Azman on 8 Apr 2021
Hello sir, I am also trying to do 1D CNN. Can you kindly send me the code? (amirahnabilahazman@gmail.com)

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Edwar Macias
Edwar Macias on 28 Mar 2017
Dear Kaare, i'm working in something similar, i'm traying to run 1-D CNN but i couldn't done it, Could you do it?
Best regards
  2 Comments
SHILPA K
SHILPA K on 5 Feb 2019
can you explain the implementation in python

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Edwar Macias
Edwar Macias on 6 Apr 2017
Dear Kaare,
What a pity! I wanted to try it, anyway i'll try again, if i can find some result i'll tell you! Cheers!
  2 Comments
SAM
SAM on 21 Nov 2017
I tried to do it but it only accepts 4D image input

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Diego Alonso
Diego Alonso on 20 Sep 2017
Edited: Diego Alonso on 20 Sep 2017
Hi everyone!
I am trying to apply deep learning to NILM data following this thesis: http://lemt.ufrj.br/pdf/pedro.pdf
In summmary, I have an aggregated power signal and I want to disaggregate it. I also have the disaggregate consumption of each appliance so I have to use as input the aggregate signal and as target the disaggregated consumption of an appliance. How can I do this with a CNN?
Thanks
Diego

Kaushik Sardeshpande
Kaushik Sardeshpande on 25 Apr 2018
Hello Kaare,
I'm facing the same problem while implementing signal(1-D) classification using CNN. Can you please guide me how you created the database which is acceptable by trainNetwork . I'm converting my entire data into a 4-D array and then feeding it to the network with labels, but still its giving me error as X and Y must have same number of observations.
net = trainNetwork(dataset, Labels, layers, train_options);
Invalid training data. X and Y must have the same number of observations.
  2 Comments
Parisa Yas
Parisa Yas on 12 May 2021 at 9:28
Dear Kaushik
Did you solve your problem?
Iget this error like you

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prash
prash on 28 Jun 2018
Hello Kaushik
I do not understand how you are setting up your 4D array. If you have A signals, each signal has lets say N time history. You have A X N matrix for 1 sample. Like this you will have multiple samples 15000. So you have A X N X 15000 array. Now how did you reshape it? How does you "Train_dataset" array looks? Can you send snapshot?

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