# CNN for subsets of a dataset - how to tune hyperparameters

I have a dataset and would like to train CNNs on subsets of different size of the dataset. I already have a CNN, which classifies very well if I use the entire dataset. Now the question arises if I should really try to additionally optimize the parameters of the CNN for the subsets, regardless of whether I do Data Augmentation or not? Does it really make sense if I try to change the CNN model for the subsets by using RandomizedSearchCV or GridSearchCV to optimize the number of convolutional layers, different learning rates, etc....?

In other words, suppose I found the perfect CNN model for a dataset. Is this model also the perfect model for subsets of this dataset?

I hope someone can give me a hint. For any help, I thank you in advance.

• The subsets are determined using train_test_split with stratify=y and random_state=0. Then the CNN should also work well for the subsets? – Code Now Sep 2 '19 at 12:53
• I chose stratify=y to preserve the proportions of the classes in the subsets. According to scikit-learn.org/stable/modules/generated/… train_test_splitsplits arrays or matrices into random train and test subsets. The param shuffle =True by default, so the data should be shuffle before splitting. If this still not random sampling, which method would be better? – Code Now Sep 2 '19 at 15:49