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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Deep learning framework comparisons

In this section, we will look at some of the most popular frameworks regarding deep learning. In short, almost all libraries provide the possibility of using the graphics processor to speed up the learning process, and are released under an open license and are the result of implementation by university research groups.Before starting the comparison, refer figure 13 which is one of the most complete charts of neural networks till date. If you see the URL and related papers, you will find that the idea of neural networks is pretty old and the software frameworks that we are going to compare below also adapts similar architecture during their framework development:


Figure 13: complete charts of neural networks (Source: http://www.asimovinstitute.org/neural-network-zoo/)

  • Theano: This is probably the most widespread library. Written in Python, one of the languages most used in the field of machine learning (also in TensorFlow), allows the calculation also GPU, coming to have 24x the performance of the CPU. It allows you to define, optimize, and evaluate complex mathematical expressions such as multidimensional arrays.
  • Caffe: This was developed primarily by Berkeley Vision and Learning Center (BVLC), and is a framework designed to stand out in terms of expression, speed, and modularity. Its unique architecture encourages application and innovation by allowing easier transition from calculation of CPU to GPU. The large community of users allowed for considerable development in recent times. It is written in Python, but the installation process can be long due to the numerous support libraries to compile.
  • Torch: This is a vast ecosystem for machine learning which offers a large number of algorithms and functions, including those for deep learning and processing of various types of multimedia, such as audio and video data, with particular attention to parallel computing. It provides an excellent interface for the C language and has a large community of users. Torch is a library that extends the scripting language Lua, and is intended to provide a flexible environment for designing and training machine learning systems. Torch is a self-contained and highly portable framework on each platform (for example, Windows, Mac, Linux, and Android) and scripts manage to run on these platforms without modification. The Torch package provides many useful features for different applications.

Finally, the following table provides a summary of each framework's salient features, including TensorFlow, which will be described (of course!!) in the coming chapters:

TensorFlow Torch Caffe Theano

Programming language

used

Python and C++ Lua C++ Python
GPU card support Yes Yes Yes By default, no
Pros
  • Computational graph abstraction, like Theano
  • Faster compile times than Theano
  • TensorBoard for visualization
  • Data and model parallelism
  • Easy to set up
  • Helpful error messages
  • Large amount of sample code and tutorials
  • Good for feedforward networks and image processing
  • Good for fine tuning existing networks
  • Python interface is pretty useful
  • Expressive Python syntax
  • Higher-level spin-off frameworks
  • Large amount of sample code and tutorials
Cons
  • Slower than other frameworks
  • Not many pre-trained models
  • Computational graph is pure Python, therefore slow
  • No commercial support
  • Can be somewhat difficult to set up in CentOS
  • Error messages are cryptic
  • Need to write C++ / CUDA for new GPU layers
  • Not good for recurrent networks
  • No commercial support
  • Error messages can be unhelpful
  • Large models can have long compile times
Deep learning framework comparison
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Deep Learning with TensorFlow
Published in: Apr 2017
Publisher: Packt
ISBN-13: 9781786469786
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