When picking a train test split for model training, does it choose an even number of samples from all class?
The test train loader will approximate the split, since it uses sampling with replacement each epoch to generate a test batch.
Let's say I have a CNN model to classify the handwritten numbers 1 to 10. I am using a dataset with 20,000 samples and I make a train test split of 50:50.
That leaves me with 10,000 for training and testing. Will, it automatically pick 1000 images from each class for testing/training, or will it approximate it?
I am trying a similar problem, (with different numbers of samples and classes) but I noticed that the testing data is not evenly split. For example, it has 1010 number ones being tested but only 990 number twos.
Is this normal? I couldn't find any documentation verifying this. My dataset is large enough that the small discrepancy is irrelevant, but I still would like to confirm.
Thanks!