Machine learning with TensorFlow

Priya Ravindran is M.Sc (electronics) from VIT University, Vellore, Tamil Nadu. She loves to explore new avenues and is passionate about writing

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The graph also includes series collapsing—names with number indexes are condensed to a single line on the graph, which can be expanded at your will. There are also special icons for different types of nodes to help you distinguish these easily. You can dump statistical information like time and resource usage data if you need.

Other than viewing the graphs pictorially, TensorFlow lets you plot quantitative metrics about the execution of your graphs and also shows additional data like images that pass through it. The base for TensorBoard is the event file generated while running the tool. This file contains summary data from nodes that you select for generating a summary.

Embedding visualisation.

Embedding Projector, the built-in visualiser of TensorFlow, aids interactive visualisation and analysis of high-dimensional data like embeddings. The projector reads the embeddings from a model checkpoint file and loads any 2D tensor or embeddings.

Begin with the smallest unit, the tensor

Data in TensorFlow is based on its central unit, a tensor. A tensor is an array of a primitive set of values, and can have any number of dimensions.

Loading data.

Sending data into a TensorFlow program can be done in three different ways. It can be fed directly via Python code, read from input files or preloaded into a constant or variable. The last comes of use for small data sets. Choose the method most suitable for your purpose.

Graphs and statistics on TensorFlow
Fig. 3: Graphs and statistics on TensorFlow

Large-scale numerical computation made easy

TensorFlow offers powerful support for implementing and training deep neural networks, owing to its highly-efficient C++ backend. The support this software offers has acted as the foundation stone for many other developmental projects. DeepDream is an automated image-captioning software based on TensorFlow.

Another application is RankBrain, which was built to replace and supplement static algorithm based search query results. RankBrain is the brainchild of Google.

Google also went on to build Tensor Processing Unit, a custom application-specific integrated circuit for machine learning. The unit is a programmable accelerator for AI based projects and is tailored for TensorFlow. Google announced that they had been running these inside their data centres for over a year and had achieved better results for machine learning applications.

Download the latest version of the software


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