Overfitting problem in convolutional neural Network and deciding the parameters of convolution and dence layer
Batch normalization has been shown to help in many cases but is not always optimal. I found that it depends where it resides in your model architecture and what you are trying to achieve. I have done a lot with different GAN CNNs and found that often BN is not needed and can even degrade performance. It's purpose is to help the model generalize faster but sometimes it increases training times. If I am trying to replicate images, I skip BN entirely. I don't understand what you mean with regards to the accuracy. Do you mean it achieved 83% accuracy with the training data but dropped to 15% accuracy on the validation data? What was the validation accuracy without the BN? In general, the validation accuracy is the more important metric. If you have a high training accuracy and a low validation accuracy, you are indeed overfitting. If you have several convolution layers, you may want to apply BN after each. If you still over-fit, try increasing your strides and kernel size. If that doesn't work you might need to look at the data again and make sure you have enough and that it is somewhat diverse. Assuming you are working with image data, are you creating samples where you rotate your images, crop them, etc. Consider synthetic data to augment your real data to help combat overfiiting.
I applied batch normalization technique to increase the accuracy of my cnn model.The accuracy of model without batch Normalization was only 46 % but after applying batch normalization it crossed 83% but a here arisen a bif overfitting problem that the model was giving validation Accuracy only 15%. Also please tell me how to decide no of filters strides in convolution layer and no of units in dence layer