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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

Evaluating our image captioning deep learning model

Training a model and not evaluating its performance makes no sense at all. Hence, we will now be evaluating our deep learning model's performance on the test dataset, which has a total of 1,000 different images from the Flickr8K dataset. We start off by loading up the usual dependencies in case you don't already have them:

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
 
pd.options.display.max_colwidth = 500 

%matplotlib inline

Loading up data and models

The next steps include loading up the necessary data, model, and other assets from disk into memory. We first load up our test dataset and our trained deep learning models:

# load test...
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