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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Automated image captioning in action!

Evaluating on our test dataset was a good way to test the model's performance, but how do we start using the model in the real world and caption completely new photos? This is where we need some knowledge of building an end-to-end system, which takes in any image as an input and gives us a free-text natural-language caption as the output.

Here are the major components and functions for our automated caption generator:

  • Caption model and metadata initializer
  • Image feature extraction model initializer
  • Transfer learning-based feature extractor
  • Caption generator

To make this generic, we built a class that makes use of several utility functions we mentioned in the previous sections:

from keras.preprocessing import image 
from keras.applications.vgg16 import preprocess_input as preprocess_vgg16_input 
from keras.applications import vgg16 ...
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