- Lower the learning rate
- Use of regularization technique
- Make sure each set (train, validation and test) has sufficient samples like 60%, 20%, 20% or 70%, 15%, 15% split for training, validation and test sets respectively.
- Perform k-fold cross validation
- Randomly shuffle the data before doing the spit, this will make sure that data distribution is nearly the same. If your data is in datastore you can use 'shuffle' function else you can use "randperm" function.
cnn validation accuracy not increasing
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I am not able to increase validation accuracy after 70s. The traing curve is not too smooth. The loss is not saturating! the 2nd image 1001.png has increasing loss after some iteration. I am using laky relu, what to do to reduce the error, saturate the loss curve and increase accuracy. What to do, any suggestions highy appreciated.
Prince Kumar on 6 Apr 2022
Edited: Prince Kumar on 6 Apr 2022
According to the attached screenshot, the model is overfitting. This generally happens when your model is learning the data instead of learning the pattern.
Following few thing can be trieds:
Hope this helps!