Old training data not removed when changing dataset

[rachelglenn]

When I switch datasets, the preprocessing adds the new dataset to the old dataset directories
numpy/images
numpy/labels

If we can remove these directories to avoid this issue, when running the preprocessing, it would really help.

https://github.com/aecelaya/MIST/blob/9824ba8e6ff728586c14afe4e26992426a7e737b/mist-torch/runtime/utils.py#L30

def create_empty_dir(path):
if not (os.path.exists(path)):
os.makedirs(path)
else:
os.system("rm -rf " + path)
os.makedirs(path)

[aecelaya]

Can you give me a specific example of this please? When running on a new dataset, you can specify the directory where the preprocessed data is saved. Retaining the preprocessed data for a given dataset is useful when you want to run multiple iterations of training with different models and/or loss functions.