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This demo shows how to continuously creat a class activation mapping (CAM) during the traing process with a custom learning rate schedule with MATLAB.
Automatic differentiation enables you to customize CNN as you want. This example trains a network to classify data and simulteniously compute the CAM (Class Activation Mapping) of the validation data with the weights during the training. 
This demo can visualize how the CNNs get to focus on the region in the image to classify which leads to the reability of the network and helps a lot in education of CNNs. Further, if the CNN is over-tuned to the dataset, the process also can be visualized. 
The class activation mapping was done referring to the paper below. 
Zhou, Bolei, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Learning deep features for discriminative localization." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929. 2016.
This demo using the custom training loop was made with the official document below.
https://jp.mathworks.com/help/deeplearning/ug/train-network-using-custom-training-loop.html

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