One of the goals of incremental learning is to train a model continuously from streaming data. Incremental learning from streaming data means you don’t need all the data in memory at once, and the model is as up-to-date as possible, which can matter for real-time use cases. The third driver for incremental learning that I mentioned in the previous blog is when there is concept drift in the data itself—but we’ll ignore this aspect for the time being.
In the last blog we demonstrated batch training with TensorFlow, and mentioned that TensorFlow, being a neural network framework, has the potential for incremental learning—just like animals and people do. In this blog, we will set ourselves the task of using TensorFlow to demonstrate incremental learning from the same static drone delivery data set of busy/not busy shops that we used in the last blog.
Read on to see the code, results, and warnings.