Using eager execution
When developing deep and complex neural networks, you need to continuously experiment with architectures and data. This proved difficult in TensorFlow 1.0 because you always need to run your code from the beginning to end in order to check whether it worked. TensorFlow 2.x works in eager execution mode as default, which means that you develop and check your code step by step as you progress into your project. This is great news; now we just have to understand how to experiment with eager execution, so we can use this TensorFlow 2.x feature to our advantage. This recipe will provide you with the basics to get started.
Getting ready
TensorFlow 1.x performed optimally because it executed its computations after compiling a static computational graph. All computations were distributed and connected into a graph as you compiled your network and that graph helped TensorFlow to execute computations, leveraging the available resources (multi-core CPUs of multiple...
