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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
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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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Inception v3 in TensorFlow

You can follow along with the code in the Jupyter notebook ch-12c_InceptionV3_TensorFlow.

TensorFlow's Inception v3 is trained on 1,001 labels instead of 1,000. Also, the images used for training are pre-processed differently. We showed the preprocessing code in previous sections. Let us dive directly into restoring the Inception v3 model using TensorFlow.

Let us download the checkpoint file for the Inception v3:

# load the inception V3 model
model_name='inception_v3'
model_url='http://download.tensorflow.org/models/'
model_files=['inception_v3_2016_08_28.tar.gz']
model_home=os.path.join(models_root,model_name)

dsu.download_dataset(source_url=model_url,
source_files=model_files,
dest_dir = model_home,
force=False,
extract=True)

Define the common imports for inception module and variables:

### define common...
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