Machine learning workshop

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Introduction to
Machine Learning
and Tensorflow
Lakshya Sivaramakrishnan
Program Coordinator, Women Techmakers
@lakshyas90

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Human Learning
Features Labels
Colour Texture Taste Fruit
Red Smooth Sweet Apple
Orange Bumpy Sweet-Sour Orange

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Machine Learning
The ability to learn without being explicitly
programmed.
or
Algorithm or model that learns patterns
in data and then predicts similar
patterns in new data.
or
Learning from experiences and examples.
Algorithm InsightData

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When is the last
time you
experienced ML ?

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It’s with us 24*7
(day in and day out)

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Machine Learning
Artificial Intelligence
Neural Network
AI, ML, NN - Are all the same?

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Why is ML important?
To solve interesting use cases
● Making speech recognition and machine translation
possible.
● The new search feature in Google Photos, which
received broad acclaim.
● Recognizing pedestrians and other vehicles in
self-driving cars

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How Can You Get Started with ML?
Three ways, with varying complexity:
(1) Use a Cloud-based or Mobile API (Vision, Natural Language,
etc.)
(2) Use an existing model architecture, and retrain it or fine tune
on your dataset
(3) Develop your own machine learning models for new
problems
More
flexible,
but more
effort
required

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Review:What/Why/How of ML
WHAT
Algorithms that can generate insights by learning from data.
WHY
Because algorithms can learn faster, cheaper, and better than
humans.
HOW
By finding patterns in data.

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The Process
Algorithm
Model
New Data
Training
Predictions
Examples

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Workflow
Apple
Orange

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The processof ‘Learning’
SUPERVISED UNSUPERVISED REINFORCEMENT
Image Credits : https://goo.gl/xU5KCv, https://goo.gl/aDjjFR, https://goo.gl/3NGzuW

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SupervisedLearning
SUPERVISED
● Teach the machine using data that’s well labelled
● Has prior knowledge of output
● Data is labelled with class or value
● Task driven
● Goal : predict class or value label
● Neural network, support vector machines , decision
trees etc
Image Credits : https://goo.gl/xU5KCv

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UnsupervisedLearning
UNSUPERVISED
● Data set with no label
● Learning algo is not told what is being learnt
● No knowledge of output class of value
● Data driven
● Goal : determine patterns or grouping
● K-means, genetic algorithms, clustering
Image Credits : https://goo.gl/aDjjFR

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Reinforcement Learning
REINFORCEMENT
● Similar to unsupervised learning
● Uses unlabelled data
● Outcome is evaluated and reward is fed back to
change the algo
● Algo learns to act in a given environment to achieve
a goal
● Goal driven
Image Credits : https://goo.gl/3NGzuW

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Machine Learning use cases at Google
Search
Search ranking
Speech recognition
Android
Keyboard & speech input
Gmail
Smart reply
Spam classification
Drive
Intelligence in Apps
Chrome
Search by image
Assistant
Smart connections
across products
Maps
Parsing local search
Translate
Text, graphic and speech
translation
Cardboard
Smart stitching
Photos
Photos search

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Smart reply
in Inbox by Gmail
10%
of all responses
sent on mobile

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Neural Networks
● Interconnected web of nodes = neurons
● Receives a set of inputs, perform
calculations & use output to solve a
problem
● eg ) classification
● Multiple layer
● Use backpropagation to adjust the weights
Image Credits : https://goo.gl/H7mNnT

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A multidimensional array.
A graph of operations.

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● Fast, flexible, and scalable
open-source machine learning
library
● For research and production
● Runs on CPU, GPU, Android, iOS,
Raspberry Pi, Datacenters, Mobile
● Apache 2.0 license
https://research.googleblog.com/2016/11/celebrating-tensorflows-first-year.html

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Sharing our tools with researchers and developers
around the world
repository
for “machine
learning”
category on
GitHub
Released in
Nov. 2015

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What can we do with tensor flow?

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Shared Research in TensorFlow
Inception https://research.googleblog.com/2016/08/improving-inception-and-image.html
Show and Tell https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html
Parsey McParseface https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html
Translation https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
Summarization https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
Pathology https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html

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BuildingModels
CONSTRUCTION PHASE
● Assembles the graph
● Define the computation graph
○ Input, Operations, Output
EXECUTION PHASE
● Executes operations in the graph
● Run Session
○ Execute graph and fetch output
Tensorflow programs are generally structured into two phases.

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Build a graph; then run it.
...
c = tf.add(a, b)
...
session = tf.Session()
value_of_c = session.run(c, {a=1, b=2})
add
a b
c
TensorFlow separates computation graph construction from execution.

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Let’s writesome code.

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Code Demos
Hello World Matrix
Multiplication
Character
recognition
Fun experiments

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Let’s dive deep into code - Hello World!
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print sess.run(hello)
Tell the compiler that you want to use all
functionalities that come with the
tensorflow package.
Create a constant op. This op is added as
a node to the default graph.
Start a session.
Run the operation and get the result.
Source : https://github.com/lakshya90/wwc-workshop/blob/master/hello_world.py

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Let’s try Matrix Multiplication in TF
matrix1 = tf.constant([[3, 3]])
matrix2 = tf.constant([[2],[2]])
product = tf.matmul(matrix1, matrix2)
Tell the compiler that you want to use all functionalities
that come with the tensorflow package.
Create a constant op that produces a 1x2 matrix. The op is
added as a node to the default graph.The value returned by
the constructor represents the output of the Constant op.
Create another constant that produces a 2x1 matrix.
Create a matmul op that takes 'matrix1' and 'matrix2' as
inputs. The returned value, 'product', represents the result
of the matrix multiplication.
CONSTRUCTION PHASE
Source : https://github.com/lakshya90/wwc-workshop/blob/master/mat_mul.py

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Trying Matrix Multiplication in TF
sess = tf.Session()
result = sess.run(product)
print(result)
sess.close()
Launch the default graph.
To run the matmul op we call the session 'run()' method,
passing 'product' which represents the output of the matmul op.
This indicates to the call that we want to get the output of the
matmul op back. All inputs needed by the op are run
automatically by the session. They typically are run in parallel.
The call 'run(product)' thus causes the execution of three ops in
the graph: the two constants and matmul.
The output of the op is returned in 'result' as a numpy `ndarray`
object. ==> [[ 12]]
Close the Session when we are done.
EXECUTION PHASE
Source : https://github.com/lakshya90/wwc-workshop/blob/master/mat_mul.py

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What do we know so far ?
● Represents computations as graphs.
● Executes graphs in the context of Sessions.
● Represents data as tensors.
● Maintains state with Variables.
● Uses feeds and fetches to get data into and out of arbitrary operations.

Image from https://github.com/mnielsen/neural-networks-and-deep-learning
?
Hello Computer Vision World
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What we see What the computer “sees”
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Difficult without Machine Learning
def classify (pixels):
# handwritten rules ...
# ...
# ...
return 8
An image of a
handwritten digit
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Machine Learning approach
Data
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mnist = tf.contrib.learn.datasets.load_dataset('mnist')
Images
Labels 5 0 4 1 9 2
MNIST
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Training data Testing data
training_data = mnist.train.images
training_labels = mnist.train.labels
testing_data = mnist.test.images
testing_labels = mnist.test.labels
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Classifier
classifier = tf.learn.LinearClassifier(n_classes=10)
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...
...
0 1 2 9
28x28
pixels
784 pixels
Connect each input
to each output
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...
...
0 1 2 9
Weights
w1,0 w1,1 w1,2 w1,9
28x28
pixels
784 pixels
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Fully connected
...
...
0 1 2 9
28x28
pixels
784 pixels
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...
...
0 1 2 9
28x28
pixels
784 pixels
WeightInput
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classifier.fit(mnist.train.images, mnist.train.labels)
...
...
0 1 2 9
28x28
pixels
784 pixels
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Backpropagation
Error
Start
Gradient descent
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Output: a prediction “1”
Input:
an image
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Output: “8”
Input:
an image
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score = metrics.accuracy_score(mnist.test.labels,
classifier.predict(mnist.test.images))
...
...
0 1 2 9
28x28
pixels
784 pixels actual label
predicted label
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...
...
0 1 2 9
92% accuracy
Is this good?
28x28
pixels
784 pixels
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Using TensorFlow on MNIST
import numpy as np
import tf.contrib.learn as learn
mnist = learn.datasets.load_dataset('mnist')
data = mnist.train.images
labels = np.asarray(mnist.train.labels, dtype=np.int32)
test_data = mnist.test.images
test_labels = np.asarray(mnist.test.labels, dtype=np.int32)
feature_columns = learn.infer_real_valued_columns_from_input(data)
classifier = learn.LinearClassifier(feature_columns=feature_columns, n_classes=10)
classifier.fit(data, labels, batch_size=100, steps=1000)
classifier.evaluate(test_data, test_labels)
print classifier.evaluate(test_data, test_labels)["accuracy"]
Import required libraries
Import the dataset
Fit a linear classifier
Evaluate accuracy
Source : https://github.com/lakshya90/wwc-workshop/blob/master/mnist.py

...
...
0 1 2 9
{Hidden
layers
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mnist = tf.contrib.learn.datasets.load_dataset('mnist')
classifier = tf.learn.DNNClassifier(n_classes=10,
hidden_units[1024,512,256)
classifier.fit(mnist.train.images, mnist.train.labels)
print('Accuracy: {0:f}'.format(score))
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Using TensorFlow on MNIST
import numpy as np
import tf.contrib.learn as learn
mnist = learn.datasets.load_dataset('mnist')
data = mnist.train.images
labels = np.asarray(mnist.train.labels, dtype=np.int32)
test_data = mnist.test.images
test_labels = np.asarray(mnist.test.labels, dtype=np.int32)
feature_columns = learn.infer_real_valued_columns_from_input(data)
classifier = learn.DNNClassifier(feature_columns=feature_columns, n_classes=10,
hidden_units = [1024,512,256])
classifier.fit(data, labels, batch_size=100, steps=1000)
classifier.evaluate(test_data, test_labels)
print classifier.evaluate(test_data, test_labels)["accuracy"]
Import required libraries
Import the dataset
Fit a linear classifier
Evaluate accuracy
Source : https://github.com/lakshya90/wwc-workshop/blob/master/mnist_DNN.py

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Fun experiments
+ = ?

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What Does A.I. Have To Do With This Selfie?
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● Step 1 : Get the content and style image.
● Step 2 : Import the neural_style.py, stylize.py and vgg.py file. Ensure the mat file is
present in your top level directory.
○ https://github.com/lakshya90/wwc-workshop, https://goo.gl/2hck1z
● Step 3 : Get your style transferred image.
○ python neural_style.py --content <content-file> --styles <style-file> --output <output-file>
Try them out - goo.gl/fyDxhC
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Source : https://github.com/lakshya90/wwc-workshop/tree/master/style_transfer

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Challenge
Push your code to GITHUB
https://guides.github.com/activities/hello-world/
http://rogerdudler.github.io/git-guide/
RESOURCES :
https://github.com/lakshya90/wwc-workshop

Lots of tutorials at tensorflow.org
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Codelab - goo.gl/xGsB9d Video - goo.gl/B2zYWN
TensorFlow for Poets
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tensorflow.org
github.com/tensorflow
Want to learn more?
Udacity class on Deep Learning, goo.gl/iHssII
Guides, codelabs, videos
MNIST for Beginners, goo.gl/tx8R2b
TF Learn Quickstart, goo.gl/uiefRn
TensorFlow for Poets, goo.gl/bVjFIL
ML Recipes, goo.gl/KewA03
TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7
Next steps
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Thank You!
Lakshya Sivaramakrishnan
@lakshyas90

Machine learning workshop

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