CNN model validation accuracy is not improving

Question

I am currently working on a CNN model for classification, I have to predict words on a wav file. I encountered a problem with my validation accuracy that stays (almost) the same, first I was thinking of overfitting but that does not seem to be the problem. Below you can see a photo with the result at the different epochs:

I am building a CNN model with Keras and using the 'adam' optimizer and 'categorical_crossentropy' for the loss. I already have tried to increase the number of epochs until 1000 and changed the batch size.

Answer 1

Your training loss seems to be decreasing but val_loss is increasing while val_accuracy is approximately same. This is standard case of overfitting. Why do you think that's not the case?

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Increasing the training epochs or batch size is not helpful as you're just changing the number of times the model sees the data or the quantity of data it sees in one epoch.

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For current scenario, the best model is created till the point both val_loss and train_loss continues to decrease before it becomes saturated. To address the problem, you need to add noise in the training data so that the model generalizes better, generalize the examples better, create balanced categories in terms of training data volume.

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Secondly, you can increase your validation dataset to see if it continues to have the same issue. If it's there then the model is definitely overfitting. ALso please update your question about what kind of validation set and technique you're using. If possible, add the code snippet of your validation set and loss function