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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

Monitoring the generator and the discriminator loss

The loss can be monitored in the TensorBoard dashboard. The TensorBoard dashboard can be invoked as follows:

  1. From the terminal, run the following command:
      tensorboard --logdir=./logs

./logs is the destination where the Tensorboard logs specific to the program are stored and should be defined in the program as follows:

      self.writer = tf.summary.FileWriter("./logs", self.sess.graph)
  1. Once the command in step 1 is executed, navigate to the localhost:6006 site for TensorBoard:

Illustrated in the following screenshot are a few traces of the generator and discriminator losses as viewed in TensorBoard during the training of the DiscoGAN implemented in the project:

Figure 4.2: Tensorboard Scalars section containing the traces for different losses

The following screenshot shows the loss components of the discriminator...

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