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
2 件のコメント
Pruthvi Muppavarapu
2019 年 2 月 20 日
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:
回答 (1 件)
Pruthvi Muppavarapu
2019 年 2 月 21 日
編集済み: Pruthvi Muppavarapu
2019 年 2 月 21 日
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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