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Python Machine Learning - Code Examples

Chapter 14: Going Deeper – The Mechanics of TensorFlow

Chapter Outline

  • The key features of TensorFlow
    • TensorFlow's computation graphs: migrating to TensorFlow v2
      • Understanding computation graphs
      • Creating a graph in TensorFlow v1.x
      • Migrating a graph to TensorFlow v2
      • Loading input data into a model: TensorFlow v1.x style
      • Loading input data into a model: TensorFlow v2 style
    • Improving computational performance with function decorators
    • TensorFlow Variable objects for storing and updating model parameters
    • Computing gradients via automatic differentiation and GradientTape
      • Computing the gradients of the loss with respect to trainable variables
      • Computing gradients with respect to nontrainable tensors
      • Keeping resources for multiple gradient computations
  • Simplifying implementations of common architectures via the Keras API
    • Solving an XOR classification problem
    • Making model building more flexible with Keras' functional API
    • Implementing models based on Keras' Model class
    • Writing custom Keras layers
  • TensorFlow Estimators
    • Working with feature columns
    • Machine learning with pre-made Estimators
    • Using Estimators for MNIST handwritten digit classification
    • Creating a custom Estimator from an existing Keras model
  • Summary

A note on using the code examples

The recommended way to interact with the code examples in this book is via Jupyter Notebook (the .ipynb files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.

Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:

conda install jupyter notebook

Then you can launch jupyter notebook by executing

jupyter notebook

A window will open up in your browser, which you can then use to navigate to the target directory that contains the .ipynb file you wish to open.

More installation and setup instructions can be found in the README.md file of Chapter 1.

(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: ch14_part1.ipynb and ch14_part2.ipynb)

In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.

When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (.py files) that can be viewed and edited in any plaintext editor.