Book Image

The Applied TensorFlow and Keras Workshop

By : Harveen Singh Chadha, Luis Capelo
Book Image

The Applied TensorFlow and Keras Workshop

By: Harveen Singh Chadha, Luis Capelo

Overview of this book

Machine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy. As a beginner, you’ll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools. The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you’ve understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you’ll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you’ll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you’ll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization. By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.
Table of Contents (6 chapters)

1. Introduction to Neural Networks and Deep Learning

Activity 1.01: Training a Neural Network with Different Hyperparameters

Solution:

  1. Using your Terminal, navigate to the directory cloned from https://packt.live/2ZVyf0C and execute the following command to start TensorBoard:
    $ tensorboard --logdir logs/fit

    The output is as follows:

    Figure 1.15: A screenshot of a Terminal after starting a TensorBoard instance

  2. Now, open the URL provided by TensorBoard in your browser. You should be able to see the TensorBoard SCALARS page:

    Figure 1.16: A screenshot of the TensorBoard SCALARS page

  3. On the TensorBoard page, click on the SCALARS page and enlarge the epoch_accuracy graph. Now, move the smoothing slider to 0.6.

    The accuracy graph measures how accurately the network was able to guess the labels of a test set. At first, the network guesses those labels completely incorrectly. This happens because we have initialized the weights and biases of our network with random values, so...