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Hands-On Deep Learning Architectures with Python

You're reading from   Hands-On Deep Learning Architectures with Python Create deep neural networks to solve computational problems using TensorFlow and Keras

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Length 316 pages
Edition 1st Edition
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Authors (2):
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Saransh Mehta Saransh Mehta
Author Profile Icon Saransh Mehta
Saransh Mehta
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (15) Chapters Close

Preface 1. Section 1: The Elements of Deep Learning
2. Getting Started with Deep Learning FREE CHAPTER 3. Deep Feedforward Networks 4. Restricted Boltzmann Machines and Autoencoders 5. Section 2: Convolutional Neural Networks
6. CNN Architecture 7. Mobile Neural Networks and CNNs 8. Section 3: Sequence Modeling
9. Recurrent Neural Networks 10. Section 4: Generative Adversarial Networks (GANs)
11. Generative Adversarial Networks 12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13. New Trends of Deep Learning 14. Other Books You May Enjoy

Comparing the two MobileNets

MobileNetV2 has introduced significant changes in the architecture of MobileNet. Were the changes worth making? How much better is MobileNetV2 than MobileNet in regards to performance? We can compare the models in terms of the number of multiplication operations required for one inference, which is commonly known as MACs (number of multiply-accumulates). The higher the MAC value, the heavier the network is. We can also compare the models in terms of the number of parameters in the model. The following table shows the MACs and the number of parameters for both MobileNet and MobileNetV2:

Network Number of Parameters MACs/ MAdds
MobileNet V1 4.2M 575M
MobileNet V2 3.4M 300M

 

We can also compare the models in terms of memory that's required for the different number of channels and resolution. The following table provides...

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