2. Presentation Outline
• Introduction
• History ofTensorFlow
• Features ofTensorFlow
• Working withTensorFlow
• Example : Iris Classification Problem
• Conclusion
• References
160414733094 Mohasin Ahamed
3. Introduction
• TensorFlow[1] is an open source software library for numerical computation using
data flow graphs.
• Nodes in the graph represent mathematical operations, while the graph edges
represent the multidimensional data arrays (tensors) communicated between
them.
• It is used by major companies all over the world, including Airbnb, EBay, Dropbox,
Snapchat,Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
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5. DistBeleif
• In 2011, Google Brain[2] built DistBelief as a proprietary machine learning system
based on deep learning neural networks.
• Its use grew rapidly across diverse Alphabet companies in both research and
commercial applications.
• Google assigned multiple computer scientists, including Jeff Dean, to simplify and
refactor the codebase of DistBelief into a faster, more robust application-grade
library, which becameTensorFlow.
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6. TensorFlow
• TensorFlow is Google Brain's second generation system.
• Version 1.0.0 was released on February 11, 2017.
• Fact: In June 2016, Dean stated that 1,500 repositories on GitHub mentioned
TensorFlow, of which only 5 were from Google
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7. Tensor Processing Unit
• In May 2016 Google announced itsTensor processing unit (TPU), an ASIC built
specifically for machine learning and tailored forTensorFlow.
• TPU is a programmable AI accelerator designed to provide high throughput of
low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models
rather than training them.
• In May 2017 Google announced the second-generation, as well as the availability
of theTPUs in Google Compute Engine.
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8. TensorFlow Lite
• In May 2017 Google announced a software stack specifically for Android
development,TensorFlow Lite, beginning with Android Oreo.
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9. Features ofTensorFlow
• Efficiently works with mathematical expressions involving multi-dimensional
arrays
• Good support of deep neural networks and machine learning concepts.
• GPU/CPU computing where the same code can be executed on both architectures
• High scalability of computation across machines and huge data sets
• Supported by a large community of developers
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10. Comparison of Different M/L Frameworks
Feature TensorFlow Theano[6] Torch[7] Caffe[8] Chainer[9] CNTK[9]
RunOn Single
Machine
Yes Yes Yes Yes Yes Yes
Run on Distributed
Machines
Yes No No No No Yes
Symbolic
differentiation
Yes Yes No No Yes No
Implemented by
C++
Yes No No Yes No No
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13. Tensors
• Tensors are the basic data structures inTensorFlow, and they represent the connecting
edges in a dataflow graph.
• A tensor simply identifies a multidimensional array or list.The tensor structure can be
identified with three parameters: rank, shape, and type.
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15. Dataflow Graphs
• InTensorFlow, computation is described using data flow graphs.
• Each node of the graph represents an instance of a mathematical operation (like
addition, division, or multiplication) and each edge is a multi-dimensional data set
(tensor) on which the operations are performed.
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17. Deep Learning
• Deep learning (also known as deep structured learning or hierarchical learning) is
part of a broader family of machine learning methods based on learning data
representations, as opposed to task-specific algorithms. Learning can be
supervised, semi-supervised or unsupervised.
• Deep learning models are loosely related to information processing and
communication patterns in a biological nervous system, such as neural coding that
attempts to define a relationship between various stimuli and associated neuronal
responses in the brain.
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20. • Estimators, which represent a complete model.The Estimator API provides
methods to train the model, to judge the model's accuracy, and to generate
predictions.
• Datasets, which build a data input pipeline.The Dataset API has methods to load
and manipulate data, and feed it into your model.The DatasetsAPI meshes well
with the Estimators API.
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21. InstallingTensorFlow
• Step 1: Head over to Python 3.5.x from python.org
• Step 2: Go to the Downloads page and Select the 3.5.2 download.
• Step 3: After that you will be brought to another page, where you will need to
select either the x86-64 or amd64 installer.The one I specifically recommend for
now is theWindows x86-64 executable installer.
• Step 4: Choose Add Python 3.5 to PATH.
• Step 5: Now you should be able to see a message saying Setup was successful.
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22. • Step 6: To installTensorFlow, start a terminal. Make sure that you run the cmd as
an administrator!
• Step 7: Once you’re done with that, now all you have to do is give just one simple
little command for you to finish installingTensorflow onto yourWindows. Just
enter this command:
pip3 install --upgrade tensorflow
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23. EXAMPLE: IRIS CLASSIFICATION
PROBLEM [13]
• Imagine you are a botanist seeking an automated way to classify each Iris flower
you find. Machine learning provides many ways to classify flowers.
• For instance, a sophisticated machine learning program could classify flowers
based on photographs.
• Our ambitions are more modest--we're going to classify Iris flowers based solely
on the length and width of their sepals and petals.
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24. • The Iris genus entails about 300 species, but our program will classify only the
following three:
a) Iris setosa
b) Iris versicolor
c) Iris virginica
Here's the representation:
• 0 represents setosa
• 1 represents versicolor
• 2 represents virginica
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25. Sepal length Sepal width Petal length Petal width Species
6.4 2.8 5.6 2.2 2
5.0 2.3 3.3 1.0 1
4.9 2.5 4.5 1.7 2
5.7 3.8 1.7 0.3 0
• The last column (species) is called the label; the first four columns are
called features. Features are characteristics of an example, while the
label is the thing we're trying to predict.
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26. Getting the Sample Program
• Before staring do the following:
1)InstallTensorFlow.
If you installedTensorFlow with virtualenv or Anaconda, activate your
TensorFlow environment.
2)Install or upgrade pandas by issuing the following command:
pip install pandas
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27. 3)Take the following steps to get the sample program:
• Clone theTensorFlow Models repository from GitHub by entering the following command:
git clone https://github.com/tensorflow/models
• Change directory within that branch to the location containing the examples used in this document:
cd models/samples/core/get_started/
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28. • You runTensorFlow programs as you would run any Python program.Therefore,
issue the following command from a command line to
run premade_estimators.py:
python premade_estimator.py
• Running the program should output a whole bunch of information ending with
three prediction lines like the following:
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30. CONCLUSION
• In the end we can say thatTensorFlow is the second machine learning framework
that Google created and used to design, build, and train deep learning models.
• You can use theTensorFlow library do to numerical computations, which in itself
doesn’t seem all too special, but these computations are done with data flow
graphs.
• And it is used by major companies all over the world, including Airbnb, EBay,
Dropbox, Snapchat,Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course,
Google!
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