How do I do classification from feature vector with Deep Network Designer
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I want to use Deep Network Designer to design a network which inputs a feature vector and outputs a classification. Simple case:
Data: 10000 labeled observations each with 5 features i.e. available
data matrix: X = [observations , features] -> size(X) = [ 10000 , 5 ]
and
label vector: y = [label] -> size(y) = [ 10000 , 1 ].
Classes: A binary classification problem, say class 'A' and class 'B'
What I strugle with is how to use the Deep Network Designer to setup; correct input for the feature vector (only image and sequence input layers are available) and a trainable network with correct output.
Thank you for your time
\P
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Pruthvi Muppavarapu
2019년 2월 20일
My understanding is that you want to know how the Deep Network Designer can be used to create a network.
The following documentation might help you understand how the said app works:
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Pruthvi Muppavarapu
2019년 2월 21일
편집: Pruthvi Muppavarapu
2019년 2월 21일
You can use the ImageInputLayer when you have multi dimensional vector of features and the SequenceInputLayer when the vector of features is of single dimension (preferably a row vector).
And it gives error when you try to change the dimensions of a pre-trained model. Otherwise it should work as intended.
Hope this answers your question.
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