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• tf.data can be used to load models in a very efficient way.
Chapter 2
• tf.keras and Estimators are high-level libraries where the power of
TensorFlow 1.x is still accessible via tf.* lower-level libraries. tf.keras
supports eager computation while still retaining the performance of lowerlevel
computational graphs via tf.function. tf.hub is a nice collection of
pretrained models that can be used immediately.
• Distribution Strategies allow training to be run on CPUs, GPUs, and TPUs.
• SavedModel can be served on multiple platforms.
In this chapter we have discussed the main differences between TensorFlow 1.x
and 2.x and reviewed the powerful new features available in 2.x. The key topics
discussed in this chapter were: the computational graph in TensorFlow 1.x, and the
advantages of TensorFlow 2.x, such as support for eager execution, distribution, and
TPU training. The next chapter will introduce Regression a quite powerful tool for
mathematical modelling, classification and prediction.
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