I have a CNN architecture that works well on 32x32x3 images. Can I use that same architecture for a data set made up of 28x28x1 images? (Both data sets have 10 classes). If this is possible, what changes would I need to make to the architecture I have?


One way is to redesign your CNN architecture to fit your new data input. However, if you want to use the current model you have, you should:

(1) Retraining: Re-train the model with new data set images

(2) Reshape new images: You should add a pre-processing layer to convert 28x28x1 images to 32x32x3 images. This could be done by replicating a single channel in other two channels and adding boundaries to your images. This deformation process should be the same on all new input images.

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  • $\begingroup$ If I were to retrain the model with the new data set images I would have some tensor dimension errors right? $\endgroup$ – brubrudsi Nov 14 '19 at 2:24
  • $\begingroup$ That actually depends what all layers you have and how you have connected them as well imo. $\endgroup$ – Aditya Nov 14 '19 at 4:22
  • $\begingroup$ @brubrudsi If you retrained the model you need to change the input shape in the first layer to avoid such errors. $\endgroup$ – serali Nov 14 '19 at 8:59

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