- The standard method of resolving this error is to reduce the size of training images. However, as you are looking for other ways of tackling the issue, one way is to try decreasing the “hiddenSize” parameter so that the number of learnable parameters of the model reduces, thereby decreasing the need for higher memory requirement.
- You can also try decreasing the parameter “MaxEpochs”.
- Also, try to increase the GPU resources allocated for training the model.
Insufficient memory capacity when using autoencoder
1 view (last 30 days)
Show older comments
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1007575/image.png)
When I use 6000 images of 64X64 size for training, the following error message appears. Is there any other way besides using smaller size images for training?
0 Comments
Answers (1)
Sai Pavan
on 5 Oct 2023
Hi ChiaWei,
I understand that you are trying to resolve the out of memory error raised while training an autoencoder in MATLAB.
Please refer to the below documentation to learn more about the different parameters that can be tweaked in the autoencoder model: https://www.mathworks.com/help/deeplearning/ref/trainautoencoder.html
Hope it helps.
Regards,
Sai Pavan
0 Comments
See Also
Categories
Find more on Image Data Workflows in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!