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TensorFlow 1.x and 2.x
You should see something similar to the following graph:
Figure 1: An example of a computational graph
This section provided an overview of TensorFlow 1.x programming paradigms.
Now, let's turn our attention to what is new in TensorFlow 2.x.
Understanding TensorFlow 2.x
As discussed, TensorFlow 2.x recommends using a high-level API such as tf.keras,
but leaves low-level APIs typical of TensorFlow 1.x for when there is a need to have
more control on internal details. tf.keras and TensorFlow 2.x come with some great
benefits. Let's review them.
Eager execution
TensorFlow 1.x defines static computational graphs. This type of declarative
programming might be confusing for many people. However, Python is typically
more dynamic. So, following the Python spirit, PyTorch, another popular deep
learning package, defines things in a more imperative and dynamic way: you
still have a graph, but you can define, change, and execute nodes on-the-fly, with
no special session interfaces or placeholders. This is what is called eager execution,
meaning that the model definitions are dynamic, and the execution is immediate.
Graphs and sessions should be considered as implementation details.
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