Python Machine Learning - Second Edition

Summary

In this chapter, we covered in detail the key features and concepts of TensorFlow. We started with discussing TensorFlow's main features and advantages, and key TensorFlow concepts such as ranks and tensors. We then looked at TensorFlow's computation graphs, and discussed how to launch a graph in a session environment, and you learned about placeholders and variables. We then saw different ways to evaluate tensors and execute operators, using Python variables or by referring to them via their name in the graph.

We went further to explore some of the essential TensorFlow operators and functions for transforming tensors, such as tf.transpose, tf.reshape, tf.split, and tf.concat. Finally, we saw how to visualize a TensorFlow computation graph ...