CODEWITHSUNDEEP
pythontensorflowconv-neural-network
navid p
navid p

Reputation: 1

how to use 1D-convolutional neural network for non-image data

I have a dataset that I have loaded as a data frame in Python. It consists of 21392 rows (the data instances, each row is one sample) and 79 columns (the features). The last column i.e. column 79 has string type labels. I would like to use a CNN to classify the data in this case and predict the target labels using the available features. This is a somewhat unconventional approach though it seems possible. However, I am very confused on how the methodology should be as I could not find any sample code/ pseudo code guiding on using CNN for Classifying non-image data, either in Tensorflow or Keras. Any help in this regard will be highly appreciated. Cheers!

Upvotes: 0

Views: 674

Answers (1)

youngseok jeon
youngseok jeon

Reputation: 116

You first have to know, if it is sensible to use CNN for your dataset. You could use sliding 1D-CNN if the features are sequential eg) ECG, DNA, AUDIO. However I doubt that this is not the case for you. Using a Fully Connected Neural Net would be a better choice.

Upvotes: 1

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