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Intelligent Projects Using Python

You're reading from   Intelligent Projects Using Python 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788996921
Length 342 pages
Edition 1st Edition
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Author (1):
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Santanu Pattanayak Santanu Pattanayak
Author Profile Icon Santanu Pattanayak
Santanu Pattanayak
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Table of Contents (12) Chapters Close

Preface 1. Foundations of Artificial Intelligence Based Systems FREE CHAPTER 2. Transfer Learning 3. Neural Machine Translation 4. Style Transfer in Fashion Industry using GANs 5. Video Captioning Application 6. The Intelligent Recommender System 7. Mobile App for Movie Review Sentiment Analysis 8. Conversational AI Chatbots for Customer Service 9. Autonomous Self-Driving Car Through Reinforcement Learning 10. CAPTCHA from a Deep-Learning Perspective 11. Other Books You May Enjoy

Processing the labelled captions of the video

The corpus.csv file contains the description of the videos in the form of text captions (see Figure 5.5). A snippet of the data is shown in the following screenshot. We can remove a few [VideoID,Start,End] combination records and treat these as test files for evaluation later on:

Figure 5.5: A snapshot of the format of the captions file

The VideoID, Start and End columns combine to form the video name in the following format: VideoID_Start_End.avi. Based on the video name, the features from the convolutional neural network VGG16 has been stored as VideoID_Start_End.npy. Illustrated in the following code block is the function to process the text captions for the video and create the path cross reference to the video image features from VGG16:

def get_clean_caption_data(self,text_path,feat_path):
text_data = pd.read_csv(text_path...
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