TensorFlow - Tutorialspoint
TensorFlow
i
TensorFlow
About the Tutorial
TensorFlow is an open source machine learning framework for all developers. It is used
for implementing machine learning and deep learning applications. To develop and
research on fascinating ideas on artificial intelligence, Google team created TensorFlow.
TensorFlow is designed in Python programming language, hence it is considered an easy
to understand framework.
Audience
This tutorial has been prepared for python developers who focus on research and
development with various machine learning and deep learning algorithms. The aim of this
tutorial is to describe all TensorFlow objects and methods.
Prerequisites
Before proceeding with this tutorial, you need to have a basic knowledge of any Python
programming language. Knowledge of artificial intelligence concepts will be a plus point.
Copyright & Disclaimer
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TensorFlow
Table of Contents
About the Tutorial ............................................................................................................................................ i
Audience ........................................................................................................................................................... i
Prerequisites ..................................................................................................................................................... i
Copyright & Disclaimer ..................................................................................................................................... i
Table of Contents ............................................................................................................................................ ii
1.
TensorFlow ¡ª Introduction ...................................................................................................................... 1
Why is TensorFlow So Popular? ...................................................................................................................... 1
2.
TensorFlow ¡ª Installation ........................................................................................................................ 3
3.
TensorFlow ¡ª Understanding Artificial Intelligence ................................................................................. 8
Supervised Learning ........................................................................................................................................ 9
Unsupervised Learning .................................................................................................................................... 9
4.
TensorFlow ¡ª Mathematical Foundations.............................................................................................. 11
Vector ............................................................................................................................................................ 11
Mathematical Computations ......................................................................................................................... 12
5.
TensorFlow ¡ª Machine Learning and Deep Learning .............................................................................. 15
Machine Learning .......................................................................................................................................... 15
Deep Learning................................................................................................................................................ 15
Difference between Machine Learning and Deep learning ........................................................................... 16
Applications of Machine Learning and Deep Learning .................................................................................. 17
6.
TensorFlow ¡ª Basics............................................................................................................................... 19
Tensor Data Structure ................................................................................................................................... 19
Various Dimensions of TensorFlow ............................................................................................................... 20
Two dimensional Tensors .............................................................................................................................. 21
Tensor Handling and Manipulations ............................................................................................................. 23
7.
TensorFlow ¡ª Convolutional Neural Networks....................................................................................... 25
Convolutional Neural Networks .................................................................................................................... 25
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TensorFlow
TensorFlow Implementation of CNN ............................................................................................................. 27
8.
TensorFlow ¡ª Recurrent Neural Networks ............................................................................................. 31
Recurrent Neural Network Implementation with TensorFlow ...................................................................... 32
9.
TensorFlow ¡ª TensorBoard Visualization ............................................................................................... 36
10. TensorFlow ¡ª Word Embedding ............................................................................................................. 38
Word2vec ...................................................................................................................................................... 38
11. TensorFlow ¡ª Single Layer Perceptron ................................................................................................... 42
Single Layer Perceptron ................................................................................................................................. 43
12. TensorFlow ¡ª Linear Regression ............................................................................................................ 47
Steps to design an algorithm for linear regression ........................................................................................ 48
13. TensorFlow ¡ª TFLearn and its installation .............................................................................................. 50
14. TensorFlow ¡ª CNN and RNN Difference ................................................................................................. 52
15. TensorFlow ¡ª Keras ............................................................................................................................... 53
16. TensorFlow ¡ª Distributed Computing .................................................................................................... 56
17. TensorFlow ¡ª Exporting with TensorFlow .............................................................................................. 58
18. TensorFlow ¡ª Multi-Layer Perceptron Learning ..................................................................................... 59
19. TensorFlow ¡ª Hidden Layers of Perceptron ........................................................................................... 63
20. TensorFlow ¡ª Optimizers in TensorFlow ................................................................................................ 67
21. TensorFlow ¡ª XOR Implementation ....................................................................................................... 68
22. TensorFlow ¡ª Gradient Descent Optimization ....................................................................................... 71
23. TensorFlow ¡ª Forming Graphs ............................................................................................................... 73
24. TensorFlow ¡ª Image Recognition using TensorFlow ............................................................................... 77
25. TensorFlow ¡ª Recommendations for Neural Network Training ............................................................. 82
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1. TensorFlow ¡ª Introduction
TensorFlow
TensorFlow is a software library or framework, designed by the Google team to implement
machine learning and deep learning concepts in the easiest manner. It combines the
computational algebra of optimization techniques for easy calculation of many
mathematical expressions.
The official website of TensorFlow is mentioned below:
Let us now consider the following important features of TensorFlow:
?
It includes a feature of that defines, optimizes and calculates mathematical
expressions easily with the help of multi-dimensional arrays called tensors.
?
It includes a programming support of deep neural networks and machine learning
techniques.
?
It includes a high scalable feature of computation with various data sets.
?
TensorFlow uses GPU computing, automating management. It also includes a
unique feature of optimization of same memory and the data used.
Why is TensorFlow So Popular?
TensorFlow is well-documented and includes plenty of machine learning libraries. It offers
a few important functionalities and methods for the same.
TensorFlow is also called a ¡°Google¡± product. It includes a variety of machine learning and
deep learning algorithms. TensorFlow can train and run deep neural networks for
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