To learn how to prepare image data for deep learning networks, see Preprocess Images for Deep Learning.
This topic presents part of a typical multilayer network workflow. For more information and other steps, see Multilayer Shallow Neural Networks and Backpropagation Training.
Before beginning the network design process, you first collect and prepare sample data. It is generally difficult to incorporate prior knowledge into a neural network, therefore the network can only be as accurate as the data that are used to train the network.
It is important that the data cover the range of inputs for which the network will be used. Multilayer networks can be trained to generalize well within the range of inputs for which they have been trained. However, they do not have the ability to accurately extrapolate beyond this range, so it is important that the training data span the full range of the input space.
After the data have been collected, there are two steps that need to be performed before the data are used to train the network: the data need to be preprocessed, and they need to be divided into subsets.