An Introduction to python and brief overview of TensorFlow

1. Introduction to Python
& TensorFlow
DSW Camp & Jam
December 3rd, 2016
Bayu Aldi Yansyah
Data Scientist @ Sale Stock
https://careers.salestock.io

2. - Understand the basic of Python
- Able to write and execute Python program
- Understand what is TensorFlow and how to use it
Our Goals
Overview

3. - You understand the basic of programming (What is variable, data types
etc)
I assume …
Overview

4. 1. Introduction to Python
- Why learn Python?
- Python basic
- Python data types
- Comparison operators
- Control Flow
- Function
- Class
- Module
Outline
Overview

5. 2. Introduction to Tensorflow
- What is TensorFlow?
- Programming model
- Use case: Forward propagation of hidden layers
in Feed-forward Neural Networks model
Outline
Overview

6. 1.
PYTHON
INTRODUCTION
- Python is:
1. A programming language created by Guido Van Rossum in
1991 and emphasizes productivity and code readability.
2. A general purpose language that is easy and intuitive.
3. A multi purpose language, it brings people with different
backgrounds together.
- In Python, everything is an Object.

7. 1.1.
WHY LEARN PYTHON?
MOTIVATION
- Easy to learn and intuitive.
- One of the most popular programming languages on Github.
- One of the best languages for data science. The important factor is the
Python community.
- Python is used by a bunch of cool companies like Google, Dropbox etc.
- It works very well with C and C++. For example: Sale Stock’s
fastText.py is written in Python and C++, this python package is
used/starred by folks from Baidu, Comcast, Facebook, Alibaba, and
Github. https://github.com/salestock/fastText.py

8. 2.
BASIC
WRITE & EXECUTE “HELLO WORLD” PROGRAM
print “Hello word”
hello.py
% python hello.py
Hello world
Terminal

9. 2.1.
BASIC
SYNTAX: INDENTATION
is_new = True
if is_new:
print "Is new!”
else:
print "Uh, it's old stuff"
indentation.py
% python indentation.py
Is new!
Terminal

10. Run:
2.1.
BASIC
SYNTAX: COMMENT
# Commented line is not executed
# print "Hello"
print "Hai"
"""
also this
print "hai hai"
"""
comment.py
% python comment.py
Hai
Terminal

11. Run:
2.2.
BASIC
READ-EVAL-PRINT LOOP
% python
Python 2.7.10 (default, Jul 30 2016, 18:31:42) [GCC 4.2.1 Compatible
Apple LLVM 8.0.0 (clang-800.0.34)] on darwinType "help", "copyright",
"credits" or "license" for more information.
>>> print "hello”
hello
>>> 2 + 5
7
>>> "hello".upper()
'HELLO’
>>> 3 in [1, 2, 3]
True
>>>
Terminal

12. 3.
DATA TYPES
INTRODUCTION
We will cover 6 data types in Python and their common
operations:
1. Numeric
2. Sequences
3. Sets
4. Dictionaries
5. Boolean

13. 3.1.
NUMERIC
INTRODUCTION
- There are 4 basic numeric types: Integer, Float, Long Integer and
Complex
- Common operations: Addition, Difference, Product, Quotient and
modulo
- Type conversion is required for some operation

14. 3.1.
NUMERIC
4 TYPES
# Integer
>>> 10
10
# Float
>>> 0.5
0.5
# Complex
>>> 10 + 5J
(10+5j)
# Long integer
>>> 10L
10L
PYTHON REPL

15. 3.1.
NUMERIC
OPERATORS
# Addition
>>> 10 + 6
16
# Difference
>>> 100 – 90
10
# Product
>>> 0.5 * 60
30.0
# Quotient
>>> 22.0/7.0
3.142857142857143
PYTHON REPL

16. 3.1.
NUMERIC
OPERATORS (CONTINUED)
# Modulo
>>> 4 % 3
1
PYTHON REPL

17. Type conversion:
- int(x) : x to integer
- float(x): x to float
- long(x) : x to long integer
3.1.
NUMERIC
TYPE CONVERSIONS
# Without conversion
>>> 12/100
0
# Convert to float first
>>> float(12)/100
0.12 PYTHON REPL

18. 3.2.
SEQUENCES
INTRODUCTION
- To store multiple values in an organized and efficient fashion.
- There are three kinds of sequences in Python:
1. Strings
2. Lists
3. Tuples

19. 3.2.1.
SEQUENCES
STRINGS: INTRO
- Define new string by simply by enclosing characters in single or double
quotes.
- Slice: A[0] = ‘H’
- Range Slice: A[1:3] = ‘el’
A[a:b] is all of A[i] where a <= i < b
- Common operations are concatenation, repetition, membership
checking and formatting.
A H e l l o
index 0 1 2 3 4

20. 3.2.1.
SEQUENCES
STRINGS: DEFINE A NEW STRING
# Single line
company_name = 'Sale Stock’
# Multiline
description = ''’
Sale Stock Pte, Ltd is a fast-growing multinational
tech start up company that is currently specialising
in mobile-commerce.
''’
mission = ('Giving access to affordable,'
' high-quality clothes to everyone’
‘ who needs it.')
string.py

21. 3.2.1.
SEQUENCES
STRINGS: OPERATORS
# Concatenation
>>> 'Hello ' + 'There'
'Hello There’
# Repetition
>>> 'hello' * 3
'hellohellohello’
# Slice
>>> 'hello'[0]
'h’
# Range Slice
>>> 'hello'[0:2]
'he’
PYTHON REPL

22. # Membership checking
>>> 'h' in 'hello’
True
>>> ‘h' not in 'hello’
False
# Formatting
>>> 'Hello, %s' % 'DSW!’
'Hello, DSW!’
>>> 'My number is %d' % 11
’My number is 11’
>>> 'pi = %f' % (22.0/7.0)
‘pi = 3.142857'
3.2.1.
SEQUENCES
STRINGS: OPERATORS (CONTINUED)
PYTHON REPL

23. 3.2.2.
SEQUENCES
LISTS: INTRO
- Each element of a list is assigned an index number. The index starts
from zero.
- Slice: B[0] = 12
- Range Slice: B[1:3] = [3,4]
B[a:b] is all of B[i] where a <= i < b
B 12 3 4 5 15
index 0 1 2 3 4

24. 3.2.2.
SEQUENCES
LISTS: DEFINE NEW LIST
# Define a list of number
>>> numbers = [1, 4, 12, 1]
>>> numbers
[1, 4, 12, 1]
# Define a list of string
>>> words = ['hey', 'there', '!']
>>> words
['hey', 'there', '!’]
PYTHON REPL

25. 3.2.2.
SEQUENCES
LISTS: SLICING
# Slicing
>>> words[0]
'hey’
>>> words[0:]
['hey', 'there', '!']
>>> words[2:]
['!']
>>> numbers[3]
1
>>> numbers[:3]
[1, 4, 12]
PYTHON REPL

26. 3.2.2.
SEQUENCES
LISTS: OPERATORS
# Concatenation
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
# Membership checking
>>> 1 in [1, 2, 3]
True
>>> 1 not in [1, 2, 3]
False
# Repetition
>>> ['Repeat'] * 3
['Repeat', 'Repeat', 'Repeat’]
PYTHON REPL

27. 3.2.2.
SEQUENCES
LISTS: INSERT, DELETE & UPDATE
>>> scores = [0.1, 0.4, 0.5]
# Insert new element
>>> scores.append(0.2)
>>> scores
[0.1, 0.4, 0.5, 0.2]
# Delete element
>>> scores.pop(0)
0.1
>>> scores
[0.4, 0.5, 0.2]
PYTHON REPL

28. 3.2.2.
SEQUENCES
LISTS: INSERT, DELETE & UPDATE (CONTINUED)
>>> scores
[0.4, 0.5, 0.2]
# Update element
>>> scores[0] = 0.6
>>> scores
[0.6, 0.5, 0.2]
PYTHON REPL

29. 3.2.3.
SEQUENCES
TUPLES: INTRO
- Tuples are similar to lists, except they are immutable.
- You can’t add, change, or remove elements of a tuple

30. 3.2.3.
SEQUENCES
TUPLES: DEFINE NEW TUPLE
# Define a tuple
>>> tuple1 = (1, 2, 3)
>>> tuple1
(1, 2, 3)
>>> tuple2 = 1, 2, 3
>>> tuple2
(1, 2, 3)
PYTHON REPL

31. 3.2.3.
SEQUENCES
TUPLES: SLICING
# Slicing
>>> tuple1[0]
1
>>> tuple1[1]
2
>>> tuple1[0:2]
(1, 2)
PYTHON REPL

32. 3.2.3.
SEQUENCES
TUPLES: OPERATORS
# Concatenation
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
# Membership checking
>>> 1 in (1, 2, 3)
True
>>> 1 not in (1, 2, 3)
False
# Repetition
>>> ('Re', 'peat') * 3
('Re', 'peat', 'Re', 'peat', 'Re', 'peat')
PYTHON REPL

33. 3.3.
SETS
INTRODUCTION
- Just like lists except that Sets are unordered and the value is unique.
- We can’t do slice and range slice operation on Sets.
- Sets performs faster for element insertion, deletion, and membership
checking than lists and tuples.
- Sets support mathematical set operations such as testing for subsets
and finding the union or intersection of two sets.

34. 3.3.
SETS
DEFINE NEW SET
# Define a set of number
>>> numbers = set([1, 4, 12, 1])
>>> numbers
set([1, 4, 12])
# Define a set of string
>>> words = set(['hey', 'there', '!'])
>>> words
set(['hey', 'there', '!’])
PYTHON REPL

35. 3.3.
SETS
OPERATORS
# Define set a and b
>>> a = set([1, 2, 3])
>>> b = set([1, 4, 5])
# Perform union
>>> a.union(b)
set([1, 2, 3, 4, 5])
# Perform Intersection
>>> a.intersection(b)
set([1])
# Perform Difference
>>> a.difference(b)
set([2, 3])
PYTHON REPL

36. 3.3.
SETS
INSERT & DELETE
>>> scores = set([0.1, 0.4, 0.5])
# Insert new element
>>> scores.add(0.2)
>>> scores
set([0.5, 0.2, 0.4, 0.1])
# Delete element
>>> scores.remove(0.5)
>>> scores
set([0.2, 0.4, 0.1])
PYTHON REPL

37. 3.4.
DICTIONARIES
INTRODUCTION
- Dictionaries is a associative array. Collection of (key, value) pairs where
the key is unique and only map to one value.
- We can add, change, and remove value from a dictionary by their key.

38. 3.4.
DICTIONARIES
DEFINE NEW DICTIONARY
# Define an empty dictionary
>>> empty_dict = {}
# Define a dictionary
>>> data = {‘name’: ‘DSW’, ‘type’: ‘camp’}
>>> data
{'type': 'camp', 'name': 'DSW'}
# Access value by key
>>> data['name']
'DSW'
PYTHON REPL

39. 3.4.
DICTIONARIES
INSERT, UPDATE & DELETE
>>> d = {'name': 'D', 'order': 4}
# Insert new key-value pairs
>>> d['last_order'] = 6
>>> d
{'last_order': 6, 'name': 'D', 'order': 4}
# Update the value
>>> d['name'] = 'D D’
>>> d
{'last_order': 6, 'name': 'D D', 'order': 4}
PYTHON REPL

40. 3.4.
DICTIONARIES
INSERT, UPDATE & DELETE (CONTINUED)
# Delete the key and value
>>> del d['order']
>>> d
{'last_order': 6, 'name': 'D D'}
PYTHON REPL

41. 3.5.
BOOLEAN
INTRODUCTION
- Represent the truth value.
- Values that considered as False: None, False, zero of any numeric type,
any empty sequences and any empty dictionaries.

42. 4.
COMPARISON OPERATORS
INTRODUCTION
- Operator that compare two or more objects.
- The result of this operator is boolean value.
- There are 3 basic comparison operators:
- Logical Comparison
- Identity Comparison
- Arithmetic Comparison
.

43. 4.1.
COMPARISON OPERATORS
LOGICAL
# Define the boolean value
>>> a = True; b = False
# Logical and
>>> a and a
True
>>> a and b
False
# Logical or
>>> a or b
True
>>> b or b
False
PYTHON REPL

44. 4.1.
COMPARISON OPERATORS
LOGICAL (CONTINUED)
# Compound
>>> ((a and a) or b)
True
>>> ((a and b) and a)
False
# Negation
>>> not a
False
PYTHON REPL

45. 4.2.
COMPARISON OPERATORS
IDENTITY
>>> a = 1
# Identity
>>> a is 1
True
# Non-Identity
>>> a is ‘1’
False
PYTHON REPL

46. 4.3.
COMPARISON OPERATORS
ARITHMETIC
>>> 1 < 2
True
>>> 1 >= 5
False
>>> 1 == 1
True
>>> 1 != 2
True
PYTHON REPL

47. 5.
CONTROL FLOW
INTRODUCTION
- Just like any other programming language, Python also have a basic
control flow such as if-else, for loop and while loop.
- Unlike any other programming language, we can create an easy-to-
understand control flow in python without hasle. Thanks to the nice
syntax.
- We can use break to stop the for-loop or while-loop.

48. 5.1.
CONTROL FLOW
IF-ELSE
is_exists = True
if is_exists:
print 'Exists: true’
else:
print 'Exists: false'
if_else.py

49. 5.1.
CONTROL FLOW
IF-ELSE (CONTINUED)
% python if_else.py
Exists: true
TERMINAL

50. 5.2.
CONTROL FLOW
FOR-LOOP
# Basic
for i in xrange(2):
print 'index:', I
# Iterate on sequences
scores = [0.2, 0.5]
for score in scores:
print 'score:', score
for_loop.py

51. 5.2.
CONTROL FLOW
FOR-LOOP (CONTINUED)
# Iterate on dictionaries
data = {'name': 'DSW', 'type': 'camp'}
for key in data:
value = data[key]
print key, ':', value
for_loop.py

52. 5.2.
CONTROL FLOW
FOR-LOOP (CONTINUED)
% python for_loop.py
index: 0
index: 1
score: 0.2
score: 0.5
type : camp
name : DSW
TERMINAL

53. 5.3.
CONTROL FLOW
WHILE-LOOP
- Just like for-loop that do iteration, but while-loop is accept boolean value
as their condition instead of iterator.
- If condition is false, the loop is stopped

54. 5.3.
CONTROL FLOW
WHILE-LOOP (CONTINUED)
# Stop the loop if i>=10
i = 0
while i < 10:
print 'i:', i
i += 1
while_loop.py

55. 5.3.
CONTROL FLOW
WHILE-LOOP (CONTINUED)
% python while_loop.py
i: 0
i: 1
i: 2
i: 3
i: 4
i: 5
i: 6
i: 7
i: 8
i: 9
TERMINAL

56. 6.
FUNCTION
INTRODUCTION
- Function in Python is defined using the following syntax:
def function_name(arg1, optional_arg=default_value):
# do some operation here
# Or return some value

57. 6.
FUNCTION
EXAMPLE
# Define a sum function
def sum(a, b):
return a + b
# Use the function
print sum(12, 12)
sum.py

58. 6.
FUNCTION
EXAMPLE
# Define a sum function
def sum(a, b):
return a + b
# Use the function
print sum(12, 12)
sum.py

59. 6.
FUNCTION
EXAMPLE OUTPUT
% python sum.py
24
TERMINAL

60. 7.
CLASS
INTRODUCTION
- We can use Class to encapsulate object and their logic.
- Class can be defined using the following syntax:
class ClassName:
def __init__(self, arg1, arg2):
# Set property
self.property_name= arg1
# Define a method or function that read or
# update the property
def method_name(self, arg…):
# Define here

61. 7.
CLASS
EXAMPLE
class Book:
def __init__(self, title):
self.name = title
self.is_borrowed = False
def borrow(self):
self.is_borrowed = True
book.py

62. 7.
CLASS
EXAMPLE (CONTINUED)
if __name__ == '__main__':
b = Book('Hunger games')
print 'Book title:', b.title
print 'Book status: borrowed=’, b.is_borrowed
# We change the state of the object
print 'Borrow the book.'
b.borrow()
print 'Book title:', b.title
print 'Book status: borrowed=', b.is_borrowed
book.py

63. 7.
CLASS
EXAMPLE OUTPUT
% python book.py
Book title: Hunger games
Book status: borrowed= False
Borrow the book.
Book title: Hunger games
Book status: borrowed= True
TERMINAL

64. 8.
MODULE
INTRODUCTION
- Python module is just a file.
- We can use module to group all related variable, constant, function and
class in one file.
- This allow us to do a modular programming.
- Recall our book.py on previous section, we will use that as an example.

65. 8.
MODULE
EXAMPLE
# Import Book class from module book.py
from book import Book
if __name__ == '__main__':
books = []
for i in xrange(10):
title = 'Book #%s' % i
book = Book(title)
books.append(book)
# Show list of available books
for b in books:
print 'Book title:', b.title
store.py

66. 8.
MODULE
EXAMPLE OUTPUT
% python store.py
Book title: Book #0
Book title: Book #1
Book title: Book #2
Book title: Book #3
Book title: Book #4
Book title: Book #5
Book title: Book #6
Book title: Book #7
Book title: Book #8
Book title: Book #9
store.py

67. Introduction

68. 9.
TENSORFLOW
INTRODUCTION
- TensorFlow is an interface for expressing machine learning algorithms,
and an implementation for executing such algorithms.
- TensorFlow is available as Python package.
- Allows team of data scientist to express the ideas in shared
understanding concept.

69. 10.
TENSORFLOW
PROGRAMMING MODEL
- TensorFlow express a numeric computation as a graph.
- Graph nodes are operations which have any number of inputs and
outputs.
- Graph edges are tensors which flow between nodes.

70. 10.
TENSORFLOW
PROGRAMMING MODEL
- Suppose we have a Neural networks with the following hidden layer:
- We can represent this as a the computation graph:
𝑓𝜃
𝑙
𝑖
= tanh(𝑊 𝑙𝑇
𝑥𝑖 + 𝑏 𝑙
)
𝑊 𝑙𝑇
𝑥𝑖
Matrix
Multiplication
Addition
tanh
𝑏 𝑙

71. 11.
TENSORFLOW
IMPLEMENTATION IN TENSORFLOW
import numpy as np
import tensorflow as tf
# Initialize required variables
x_i = np.random.random(size=(32, 256))
# Create the computation graph
b = tf.Variable(tf.zeros((100,)))
W = tf.Variable(tf.random_uniform(shape=(256, 100), minval=-1,
maxval=1))
x = tf.placeholder(tf.float32, (None, 256))
h_i = tf.tanh(tf.matmul(x, W) + b)
forward_prop.py

72. 11.
TENSORFLOW
IMPLEMENTATION IN TENSORFLOW
# Run the computation graph within new session
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Fetch h_i and feed x_i
sess.run(h_i, {x: x_i})
forward_prop.py

73. bay@artificialintelligence.id
Notes available here: https://github.com/pyk/talks