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Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
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COCO animals dataset and pre-processing images

For our examples, we shall use the COCO animals dataset, which is a smaller subset of the COCO dataset made available by the researchers at the Stanford University at the following link: http://cs231n.stanford.edu/coco-animals.zip. The COCO animals dataset has 800 training images and 200 test images of 8 classes of animals: bear, bird, cat, dog, giraffe, horse, sheep, and zebra. The images are downloaded and pre-processed for the VGG16 and Inception models.

For the VGG model, the image size is 224 x 224 and the preprocessing steps are as follows:

  1. Images are resized to 224 x 224 with a function similar to the tf.image.resize_image_with_crop_or_pad function from TensorFlow. We implemented this function as follows:
def resize_image(self,in_image:PIL.Image, new_width, 
new_height, crop_or_pad=True):
img = in_image
if crop_or_pad...
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