Skip to main content

Introduction to TensorFlow

Here you will get TensorFlow introduction.

TensorFlow is a framework which is used for machine learning and deep learning applications like neural network. It is used for data flow programming as an open source software library over various range of tasks. It is symbolic  math library used in research and production both.

Tensorflow is used for general purpose computing on graphics processing units that are able to run on multiple CPUs and GPUs unlike the reference implementation that runs on single devices. It is available on Linux, macOS, windows with 64-bit configuration and also on mobile computing platforms like Android and IOS. As the neural network performs operations on multidimensional data arrays that derives from tensorflow and these arrays are called tensors.

Introduction to TensorFlow

Image Source

Also Read: Introduction to Deep Learning

Also Read: Introduction to Neural Networks

Tensorflow has multiple application programmable interfaces. The lowest level application programmable interface is tensorflow core that provides complete programming control. Generally tensorflow core is used by machine learning researchers and those who require control of fine levels over their models. The higher level application programmable interface unit makes task in repetition with easy task and maintain consistent between different users.

The higher level APIs are easier to use than tensorflow core and built on top of tensor flow core.

Tensors

Tensor is the central unit of data in tensorflow and it comprises of primitive values set shaped as an array of multi-dimension. Tensorflow is a framework with generalized tensor of vectors and matrices of higher dimensions. Tensors are represented as an n-dimensional array with n as rank of tensor of base data types. In tensor flow programming, tf.tensor is the main object that can be manipulated and passed around. The tf.tensor is an object that will produce value by partial computation. Firstly tf.tensor graph is built that describes detailed description about each tensor.

Tf.tensor has a data type and a shape as its properties. The element of tensor has same known data type and partially known shape i.e. the dimension.

Interaction of GPUs on TensorFlow System

Tensorflow by default maps almost all the GPU memory visible to the process. This process is efficiently used by reducing memory fragmentation of precious GPU memory resources on the devices. Tensorflow gives two configurations on the session to control the growth of memory usage, it only allocate a subset of memory as is needed by the process.

The first configuration option is “allow_growth” which allows to allocate only GPU memory based on runtime allocations. In starting it allocates very less amount of memory and thereafter as per the requirement they extent the GPU memory needed by tensorflow process. However it does not release memory automatically and this scenario leads to memory fragmentation. To release memory automatically we have to turn on this option by setting the option in configProto by:-

config=tf.ConfigProto()
config.gpu_options.allow_growth=true
session=tf.Session(config=config,...)

The second configuration method is “per process GPU memory fraction option”, it determines the amount of memory that the particular GPU should allocate.

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
session = tf.Session(config=config, ...)

Comment below if you have any queries related to above introduction to tensorflow.

The post Introduction to TensorFlow appeared first on The Crazy Programmer.



from The Crazy Programmer https://www.thecrazyprogrammer.com/2017/12/introduction-to-tensorflow.html
Labels:

Comments

Post a Comment

Popular posts from this blog

dotnet sdk list and dotnet sdk latest

Can someone make .NET Core better with a simple global command? Fanie Reynders did and he did it in a simple and elegant way. I'm envious, in fact, because I spec'ed this exact thing out in a meeting a few months ago but I could have just done it like he did and I would have used fewer keystrokes! Last year when .NET Core was just getting started, there was a "DNVM" helper command that you could use to simplify dealing with multiple versions of the .NET SDK on one machine. Later, rather than 'switching global SDK versions,' switching was simplified to be handled on a folder by folder basis. That meant that if you had a project in a folder with no global.json that pinned the SDK version, your project would use the latest installed version. If you liked, you could create a global.json file and pin your project's folder to a specific version. Great, but I would constantly have to google to remember the format for the global.json file, and I'd constan...
Post a Comment
Read more

15 Web Design Trends to Watch in 2018

The modern world is full of extraordinary things that influence our imagination and mood. Our soul needs a perfect atmosphere and impressive spots. To apply such things in practice, we have submitted the list of the web trends that deserve your attention. Robert frost design analysis will meet all your wishes and expectations. Image Source Web Design Trends to Watch in 2018 1. More Organic Shapes Until this year, web design, as well as mobile design, were based on the right-angled and sharp-edged shapes. However, it seems that this year will bring some significant changes in the field of web design. The recent trends will offer the absolute rounded corners. In addition, the web design of 2018 will make the real things look like the cartoonish ones. 2.   Bold Minimalism Although some of you may think that this web design trend will not attract the Internet users. Indeed, the notion of minimalism is often associated with boredom and dullness. However, in this case, bold ...
Post a Comment
Read more

R vs Python for Machine Learning

There are so many things to learn before to choose which language is good for Machine Learning. We will discuss each and everything about R as well as Python and the situation or problem in which situation we have to use which language. Let’s start Python and R are the two most Commonly used Programming Languages for Machine Learning and because of the popularity of both the languages Novice or you can say fresher are getting confused, whether they should choose R or Python language to commence their career in the Machine learning domain. Don’t worry guys through this article we will discuss R vs Python for Machine Learning. So, without exaggerating this article let’s get started. We will start it from the very Basics things or definitions. R vs Python for Machine Learning Introduction R is a programming language made by statisticians and data miners for statistical analysis and graphics supported by R foundation for statistical computing. R also provides high-quality graphics and...
1 comment
Read more