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Hands-On Neural Networks with TensorFlow 2.0

You're reading from  Hands-On Neural Networks with TensorFlow 2.0

Product type Book
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Pages 358 pages
Edition 1st Edition
Languages
Author (1):
Paolo Galeone Paolo Galeone
Profile icon Paolo Galeone

Table of Contents (15) Chapters

Preface Section 1: Neural Network Fundamentals
What is Machine Learning? Neural Networks and Deep Learning Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture TensorFlow 2.0 Architecture Efficient Data Input Pipelines and Estimator API Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub Introduction to Object Detection Semantic Segmentation and Custom Dataset Builder Generative Adversarial Networks Bringing a Model to Production Other Books You May Enjoy

Exercises

Please go through the following exercises and answer all questions carefully. This is the only way (by making exercises, via trial and error, and with a lot of struggle) you will be able to master the framework and become an expert:

  1. Define a classifier using the Sequential, Functional, and Subclassing APIs so that you can classify the fashion-MNIST dataset.
  2. Train the model using the Keras model's built-in methods and measure the prediction accuracy.
  3. Write a class that accepts a Keras model in its constructor and that it trains and evaluates.
    The API should work as follows:
# Define your model
trainer = Trainer(model)
# Get features and labels as numpy arrays (explore the dataset available in the keras module)
trainer.train(features, labels)
# measure the accuracy
trainer.evaluate(test_features, test_labels)
  1. Accelerate the training method using the @tf.function annotation...
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