# Which combination of 3 hyperparameters to combat overfitting of a convolutional neural network?

I have a small dataset with which I want to train a CNN by using Data Augmentation. Since the CNN is overfitting due to the small data set, I would like to optimize some hyperparameters. However, since I would like to use GridSearchCV from Scikit-Learn for this and I would therefore like to optimize only 3 hyperparameters due to reducing computational time. Here the question arises which combination of hyperparameters should I use for grid search?

My current approach would be to optimize the learning rate, the dropout layer rate and the number of epochs.

I choose the learning rate because the book "Deep Learning" by Goodfellow recommends to always optimize the learning rate. But I'm not sure if my combination of hyperparameters for tuning is really good.

What combination would you recommend? Many thank for every hint

My previous architectur is as follows:

model = Sequential()



• and which library are you planning to use for the model? is Keras an option? – serali Dec 14 '19 at 18:05
• @serali yes, I use keras – Code Now Dec 14 '19 at 19:56