# Tips on preparing data for training on neural networks? [closed]

Still feeling a bit new to the world of neural networks. I am working with a CNN model right now (working with Keras), and would like to train it to identify certain types of objects from a dataset. I have read through a number of walk-throughs and tutorials on the subject, but I am having conceptual issues in understanding the key component of what it means to set up your training and test data.

In many of the walk-throughs (cat or dog/mnst/etc.), there is always a portion in the keras tutorials where training data is downloaded from the datasets package, and part of that are the labels.

from keras.datasets import cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()


I understand in these examples, the labels are already applied. But at the risk of highlighting how much more I need to learn, how would this be done with real-world data? I have datasets of numerous images, but how does one provide labels to my training data? I thought this was unsupervised and as such, this wouldn't need to be done.

• @Sycorax I think these are two entirely different questions. – Jan Kukacka Jun 27 '18 at 10:17
• Not sure I understand the question: if your real-world data are unlabeled then you use an unsupervised algorithm. If you have your own images and you want to classify them, then you need to create a labeled training set - that is, by adding your own labels. – Robert Long Jun 28 '18 at 5:41
• He might be looking for some unsupervised clustering, like that shown on slide 10 of this link. – EngrStudent Jun 28 '18 at 11:40