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How to improve validation loss and accuracy for CNN?
If the size of the images is too big, consider the possiblity of rescaling them before training the CNN. If possible, remove one Max-Pool layer. Lower dropout, that looks too high IMHO (but other people might disagree with me on this).
Why is CNN accuracy not improving with Keras?
Any idea what I’m missing. As in the github repo we can see, it gives 72% accuracy for the same dataset (Training -979, Validation -171). Why its not working for me. I tried his code from the github link on my machine but it hanged up while training the dataset (I waited for more than 8 hours), so changed the approach, but still no luck so far.
Which is better for validation accuracy dropout or L2?
I have tried different values of dropout and L1/L2 for both the convolutional and FC layers, but validation accuracy is never better than a coin toss. I understand that my data set is very small, but even getting a small increase in validation would be acceptable as long as my model seems correct, which it doesn’t at this point.
How to improve model validation accuracy in Python?
Remove batchnorm while testing. Reduce dropout while testing. Test with ELU (Exponential Linear Unit) activation. Last, use more FC layers with more neurons. Try using glorot (commonly known as Xavier) initializer. Tell if it helps.
What’s the accuracy of the CNN machine learning model?
The model predicts all articles in the validation set as Uninteresting [0]. The accuracy is 97.44% which is same as the ratio of Uninteresting articles in the validation set. I have tried variations of this architecture but still, the issue exists.
How can I tell if my CNNs is overfitting?
You can identify this visually by plotting your loss and accuracy metrics and seeing where the performance metrics converge for both datasets.
How to tackle the problem of constant Val accuracy in CNN model training?
1. Reduce network complexity 2. Use drop out ( more dropout in last layers) 3. Regularise 4. Use batch norms 5. Increase the tranning dataset size. I agree with Mohammad Deeb. This link is useful: https://stackoverflow.com/questions/52356068/validation-accuracy-constant-in-keras-cnn-for-multiclass-image-classification