In this tutorial, I will demonstrate how I have managed to get the predictions after each training batch in Keras model.
Using Tensorflow training code it is pretty easy, since we implement the training loop, in which we call:
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In this tutorial, I will demonstrate how I have managed to get the predictions after each training batch in Keras model.
Using Tensorflow training code it is pretty easy, since we implement the training loop, in which we call:
One of the ways that I have better understood the usefulness of a Reactive Streams-based approach is how it simplifies a non-blocking IO call.
This post will be a quick walkthrough of the kind of code involved in making a synchronous remote call. Then, we will demonstrate how layering in non-blocking IO, though highly efficient in the use of resources (especially threads), introduces complications referred to as a callback hell and how a Reactive Streams-based approach simplifies the programming model.