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Python Machine Learning - Code Examples

Chapter 13: Parallelizing Neural Network Training with TensorFlow

Chapter Outline

  • TensorFlow and training performance
    • Performance challenges
    • What is TensorFlow?
    • How we will learn TensorFlow
  • First steps with TensorFlow
    • Installing TensorFlow
    • Creating tensors in TensorFlow
    • Manipulating the data type and shape of a tensor
    • Applying mathematical operations to tensors
    • Split, stack, and concatenate tensors
    • Building input pipelines using tf.data – the TensorFlow Dataset API
      • Creating a TensorFlow Dataset from existing tensors
      • Combining two tensors into a joint dataset
      • Shuffle, batch, and repeat
      • Creating a dataset from files on your local storage disk
      • Fetching available datasets from the tensorflow_datasets library
  • Building an NN model in TensorFlow
    • The TensorFlow Keras API (tf.keras)
    • Building a linear regression model
    • Model training via the .compile() and .fit() methods
    • Building a multilayer perceptron for classifying flowers in the Iris dataset
    • Evaluating the trained model on the test dataset
    • Saving and reloading the trained model
  • Choosing activation functions for multilayer NNs
    • Logistic function recap
    • Estimating class probabilities in multiclass classification via the softmax function
    • Broadening the output spectrum using a hyperbolic tangent
    • Rectified linear unit activation
  • 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: ch13_part1.ipynb and ch13_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.