Validate computer vision deep learning models

This code pattern is part of the Getting started with PowerAI Vision learning path. After a deep learning computer vision model is trained and deployed, it is often necessary to periodically (or continuously) evaluate the model with new test data. This developer code pattern provides a Jupyter Notebook that will take test images with known "ground-truth" categories and evaluate the inference results versus the truth. We will use a Jupyter Notebook to evaluate a PowerAI Vision image classification model. You can train a model using the provided example or test your own deployed model.