On November 9, 2015, Google released the artificial intelligence system TensorFlow and announced it as open source.

1. The concept of TensorFlow

TensorFlow is an open source software library that uses data flow graphs for numerical calculations. In other words, TensorFlow uses graphs to represent computing tasks. The nodes in the graph represent mathematical operations, and the edges represent multi-dimensional arrays (that is, tensors) used to communicate between operations. TensorFlow's flexible architecture allows you to deploy computing processes to one or more CPUs or GPUs.

TensorFlow was originally developed by researchers and engineers from the Google Brain Team and is primarily used by Google's Machine Intelligence research organization for machine learning and deep neural network research, but the system is also general in many other fields. .

#The abstract representation of the model is automatically handled by the framework, which makes TensorFlow and Theano particularly suitable for developing new models based on gradient optimization algorithms.

The main disadvantage of Theano is the long compilation time of the above symbolic model, and TensorFlow effectively improves this, that is, the compilation time is shorter.

Another advantage of TensorFlow is its supporting tool-TensorBoard, which visualizes the training process of the model. In this way, users can interactively examine the structure of the model and observe changes in parameters and model performance.

The open source version of TensorFlow can only run on one machine, but it supports parallel computing on multiple processors (CPUs or GPUs).

1.1 What is a data flow diagram?

Data flow diagrams use directed graphs to describe mathematical calculations, with points and edges in the graph.

Nodes usually perform mathematical calculations, but can also serve as endpoints to represent results, or read/write persistent variables.

Edges are used to describe the input/output relationships between nodes. These edges carry multidimensional arrays or tensors of variable size.

These nodes are assigned to computing devices and executed asynchronously. Once all tensors arrive at the same time, parallel calculations will be performed.

Due to the flow of tensors in the graph, we call it TensorFlow.

1.2 Features of TensorFlow

1. Deep Flexibility, flexibility. TensorFlow is not a rigid neural network library. As long as you can express your calculation process as a data flow graph, you can use TensorFlow. Defining a new calculation is as simple as writing a Python function and the cost is not high. If you can't see the underlying data operations you need, you can add them through C++.

2. True Portability, portability. TensorFlow runs on a CPU or GPU and can run on a laptop, server, or mobile computing platform. Models can be trained on mobile devices without special hardware or code changes, and computing can be performed on the cloud by encapsulating Docker and TensorFlow.

3. Connect Research and Production, both academic and industrial. At Google, scientists use TensorFlow to test new algorithms, and product teams use TensorFlow to train models to serve users.

4. Auto-Differentiation, automatic identification. TensorFlow's automatic discrimination capabilities are beneficial for implementing gradient-based machine learning algorithms. When you use TensorFlow, you define the calculation framework structure for your prediction model, plus the objective function. Then you only need to add data, and TensorFlow will calculate the derivative, that is, the gradient for you.

5. Language Options, language selection. TensorFlow can use the Python interface and the simple C++ interface to build and execute your computational graphs. If you are interested, you can also provide an interface in your favorite language - Lua, JavaScript, or R, etc.

6. Maximize Performance, maximize performance. TensorFlow allows you to take full advantage of the hardware you have available: 32 CPU cores and 4 GPU cards, threads, queues, asynchronous calculations, etc. You can freely allocate the computing elements in the TensorFlow graph to different devices and let TensorFlow handle them.

1.3 Who can use TensorFlow?

Students, researchers, enthusiasts, hackers, engineers, developers, inventors, innovators and more.

TensorFlow is currently not complete and it needs to be extended. Google only gave an initial version of the source code, and they hope to build an active open source community to make TensorFlow better and better.

Companies currently using TensorFlow include: Google, Uber, Twitter, DeepMind, JD.com, etc.

1.4 Why does Google open source TensorFlow?

"TensorFlow" is Google's internal machine learning system for many years. Today, Google is making this system an open source system and publishing the parameters of this system to industry engineers, scholars, and technicians with a large number of programming capabilities. What does this mean?

The answer is simple: Google believes that machine learning is a key component of future innovation. Research in this field is global and developing rapidly, but there is a lack of standard tools. Google engineers are using TensorFlow to develop user-oriented products and services. Google's research team also plans to share the implementation process of TensorFlow. They hope that TensorFlow can become one of the best tools for machine learning.

To use an inappropriate metaphor, Google’s current treatment of the TensorFlow system is somewhat similar to its treatment of its mobile operating system Android. If more data scientists start using Google's systems to conduct machine learning research, it will help Google gain more dominance over the growing machine learning industry.

1.5 TensorFlow and Computational Biology

There is a prerequisite for using current deep learning methods: having a data set with many samples.

Like identifying genes or methylation probes related to specific diseases, the sample set is very small, so it cannot be analyzed with deep learning. [Crying... I just want to use deep learning to identify disease genes...]

If you want to use a deep learning model, the first step is to solve the problem of how to represent the data. Such as: splice junctions, RNA-protein binding sites, or methylation. In other words, the number of your samples far exceeds the number of variables.

So, as long as this critical step is solved, TensorFlow will open the door to deep learning for you.

2. Tensorflow installation (based on pip)

Pip is a Python software package installation and management tool. Moreover, Tensorflow can only be installed in a Linux environment. For details on installing a Linux system under VMware, please refer to my other blog.

2.1 Install pip (need to install under super administrator privileges)

Note: The above interface appears indicating that pip has been installed successfully!

2.2 Install Tensorflow

##2.3 Test

2.4 Install IDE Download the Community version for Linux from Pycharm

official website and install it according to the steps of the official website tutorial.

Note: You can download it directly from the browser in Linux.

First, copy the file to the location where you want to install it.

cd

/home/andy

tar xfz pycharm-community-

2016.3.

tar.

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