I would like to be able to use the trainNetwork function to train a deep neural network on a matrix. It is not sequence data or an image and I know the only available input layers the deep networks provide is the imageInputLayer and the sequenceInputLayer. Is there any way that I can input a matrix to classify rather than the other two? Thanks in advance.

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Same question :|
CLOUDY LU
CLOUDY LU 2020년 2월 12일
Same question, not solved yet.

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 채택된 답변

JESUS DAVID ARIZA ROYETH
JESUS DAVID ARIZA ROYETH 2020년 2월 15일

1 개 추천

The wonderful thing about Matlab is that almost everything is seen as matrices or vectors, in fact this is not a disadvantage but one of its greatest strengths, therefore, a grayscale image is a 2d matrix for Matlab, a picture Color is a 3D matrix. The answer then is yes, you can use imageInputLayer to train your matrices, in fact all the procedure that occurs within deep learning in the case of images are operations with matrices.
Maybe your matrices that you want to classify are images and you have not noticed! ,
try somehow graphing your matrices and maybe your your own brain will find patterns to classify
Now in the practical case For example, if you have a 50x28 matrix :
inputlayer = imageInputLayer ([50 28], 'Name', 'entry')
or 50,28 in InputSize in the Deep Network Designer
I hope that when you see my answer in the matrix of your screen, your convolutional networks classify me as a good contributor

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If I understood LMs question right, he has one m x n matrix containing m samples with n-1 features each and one target. (at least, this is my usecase)
Thus, matlab thinks I am inputing just one image. Even if I set the input layer to [n-1 1]. Probably, I need to reshape my matrix.
I have exacly the same problem!

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추가 답변 (1개)

Xiaoliang Tang
Xiaoliang Tang 2020년 11월 18일

1 개 추천

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Matt J
Matt J 2023년 4월 6일
편집: Matt J 2023년 4월 6일
Yes, but the featureInputLayer does not allow the features to be organized in multi-dimensional form. The InputSize property of the is necessarily a scalar.
The user could flatten the input, of course...

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