Introducing cricpy:A python package to analyze performances of cricketers


Full many a gem of purest ray serene,
The dark unfathomed caves of ocean bear;
Full many a flower is born to blush unseen,
And waste its sweetness on the desert air.

            Thomas Gray, An Elegy Written In A Country Churchyard
            

Introduction

It is finally here! cricpy, the python avatar , of my R package cricketr is now ready to rock-n-roll! My R package cricketr had its genesis about 3 and some years ago and went through a couple of enhancements. During this time I have always thought about creating an equivalent python package like cricketr. Now I have finally done it.

So here it is. My python package ‘cricpy!!!’

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

This post is also hosted on Rpubs at Introducing cricpy. You can also download the pdf version of this post at cricpy.pdf

Do check out my post on R package cricketr at Re-introducing cricketr! : An R package to analyze performances of cricketers

If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr  and Beaten by sheer pace-Cricket analytics with yorkr  A must read for any cricket lover! Check it out!!

1

 

This package uses the statistics info available in ESPN Cricinfo Statsguru. T

The cricpy package

The cricpy package has several functions that perform several different analyses on both batsman and bowlers. The package has functions that plot percentage frequency runs or wickets, runs likelihood for a batsman, relative run/strike rates of batsman and relative performance/economy rate for bowlers are available.

Other interesting functions include batting performance moving average, forecasting, performance of a player against different oppositions, contribution to wins and losses etc.

The data for a particular player can be obtained with the getPlayerData() function. To do this you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Rahul Dravid, Virat Kohli, Alastair Cook etc. This will bring up a page which have the profile number for the player e.g. for Rahul Dravid this would be http://www.espncricinfo.com/india/content/player/28114.html. Hence, Dravid’s profile is 28114. This can be used to get the data for Rahul Dravid as shown below

The cricpy package is almost a clone of my R package cricketr. The signature of all the python functions are identical with that of its R avatar namely  ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familiar with one of the languages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the cricpy package at Github cricpy

The following 2 examples show the similarity between cricketr and cricpy packages

1a.Importing cricketr – R

Importing cricketr in R

#install.packages("cricketr")
library(cricketr)

2a. Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy
# You could either do
#1.  
import cricpy.analytics as ca 
#ca.batsman4s("../dravid.csv","Rahul Dravid")
# Or
#2.
from cricpy.analytics import *
#batsman4s("../dravid.csv","Rahul Dravid")

I would recommend using option 1 namely ca.batsman4s() as I may add an advanced analytics module in the future to cricpy.

2 Invoking functions

You can seen how the 2 calls are identical for both the R package cricketr and the Python package cricpy

2a. Invoking functions with R package ‘cricketr’

library(cricketr)
batsman4s("../dravid.csv","Rahul Dravid")

2b. Invoking functions with Python package ‘cricpy’

import cricpy.analytics as ca 
ca.batsman4s("../dravid.csv","Rahul Dravid")

 

3a. Getting help from cricketr – R

#help("getPlayerData")

3b. Getting help from cricpy – Python

help(ca.getPlayerData)
## Help on function getPlayerData in module cricpy.analytics:
## 
## getPlayerData(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2], result=[1, 2, 4], create=True)
##     Get the player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerData(profile,opposition="",host="",dir="./data",file="player001.csv",
##     type="batting", homeOrAway=c(1,2),result=c(1,2,4))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Sachin Tendulkar this turns out to be http://www.espncricinfo.com/india/content/player/35320.html. Hence the profile for Sachin is 35320
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     host        
##     The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "./data". Default="./data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is a list with either 1,2 or both. 1 is for home 2 is for away
##     result      
##     This is a list that can take values 1,2,4. 1 - won match 2- lost match 4- draw
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp
##     
##     Examples
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,4])
##     
##     # Only away. Get data only for won and lost innings
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     kumble = getPlayerData(30176,dir=".",file="kumble1.csv",
##     type="bowling",homeOrAway=[1],result=[1,2])
##     
##     #Get the Tendulkar's Performance against Australia in Australia
##     tendulkar = getPlayerData(35320, opposition = 2,host=2,dir=".", 
##     file="tendulkarVsAusInAus.csv",type="batting")

The details below will introduce the different functions that are available in cricpy.

3. Get the player data for a player using the function getPlayerData()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. dravid.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerData for all subsequent analyses

import cricpy.analytics as ca
#dravid =ca.getPlayerData(28114,dir="..",file="dravid.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
#acook =ca.getPlayerData(11728,dir="..",file="acook.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
import cricpy.analytics as ca
#lara =ca.getPlayerData(52337,dir="..",file="lara.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])253802
#kohli =ca.getPlayerData(253802,dir="..",file="kohli.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])

4 Rahul Dravid’s performance – Basic Analyses

The 3 plots below provide the following for Rahul Dravid

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("../dravid.csv","Rahul Dravid")

ca.batsmanMeanStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanRunsRanges("../dravid.csv","Rahul Dravid") 

5. More analyses

import cricpy.analytics as ca
ca.batsman4s("../dravid.csv","Rahul Dravid")

ca.batsman6s("../dravid.csv","Rahul Dravid") 

ca.batsmanDismissals("../dravid.csv","Rahul Dravid")

6. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Dravid Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

7. Average runs at different venues

The plot below gives the average runs scored by Dravid at different grounds. The plot also the number of innings at each ground as a label at x-axis. It can be seen Dravid did great in Rawalpindi, Leeds, Georgetown overseas and , Mohali and Bangalore at home

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("../dravid.csv","Rahul Dravid")

 

8. Average runs against different opposing teams

This plot computes the average runs scored by Dravid against different countries. Dravid has an average of 50+ in England, New Zealand, West Indies and Zimbabwe.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("../dravid.csv","Rahul Dravid")

9 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Sachin is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Dravid’s  highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("../dravid.csv","Rahul Dravid")

10. A look at the Top 4 batsman – Rahul Dravid, Alastair Cook, Brian Lara and Virat Kohli

The following batsmen have been very prolific in test cricket and will be used for teh analyses

  1. Rahul Dravid :Average:52.31,100’s – 36, 50’s – 63
  2. Alastair Cook : Average: 45.35, 100’s – 33, 50’s – 57
  3. Brian Lara : Average: 52.88, 100’s – 34 , 50’s – 48
  4. Virat Kohli: Average: 54.57 ,100’s – 24 , 50’s – 19

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

11. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("../dravid.csv","Rahul Dravid")

ca.batsmanPerfBoxHist("../acook.csv","Alastair Cook")

ca.batsmanPerfBoxHist("../lara.csv","Brian Lara")


ca.batsmanPerfBoxHist("../kohli.csv","Virat Kohli")


12. Contribution to won and lost matches

The plot below shows the contribution of Dravid, Cook, Lara and Kohli in matches won and lost. It can be seen that in matches where India has won Dravid and Kohli have scored more and must have been instrumental in the win

For the 2 functions below you will have to use the getPlayerDataSp() function as shown below. I have commented this as I already have these files

import cricpy.analytics as ca
#dravidsp = ca.getPlayerDataSp(28114,tdir=".",tfile="dravidsp.csv",ttype="batting")
#acooksp = ca.getPlayerDataSp(11728,tdir=".",tfile="acooksp.csv",ttype="batting")
#larasp = ca.getPlayerDataSp(52337,tdir=".",tfile="larasp.csv",ttype="batting")
#kohlisp = ca.getPlayerDataSp(253802,tdir=".",tfile="kohlisp.csv",ttype="batting")
import cricpy.analytics as ca
ca.batsmanContributionWonLost("../dravidsp.csv","Rahul Dravid")

ca.batsmanContributionWonLost("../acooksp.csv","Alastair Cook")

ca.batsmanContributionWonLost("../larasp.csv","Brian Lara")

ca.batsmanContributionWonLost("../kohlisp.csv","Virat Kohli")


13. Performance at home and overseas

From the plot below it can be seen

Dravid has a higher median overseas than at home.Cook, Lara and Kohli have a lower median of runs overseas than at home.

This function also requires the use of getPlayerDataSp() as shown above

import cricpy.analytics as ca
ca.batsmanPerfHomeAway("../dravidsp.csv","Rahul Dravid")

ca.batsmanPerfHomeAway("../acooksp.csv","Alastair Cook")

ca.batsmanPerfHomeAway("../larasp.csv","Brian Lara")

ca.batsmanPerfHomeAway("../kohlisp.csv","Virat Kohli")

14 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Lara’s performance seems to have been quite good before his retirement(wonder why retired so early!). Kohli’s performance has been steadily improving over the years

import cricpy.analytics as ca
ca.batsmanMovingAverage("../dravid.csv","Rahul Dravid")

ca.batsmanMovingAverage("../acook.csv","Alastair Cook")

ca.batsmanMovingAverage("../lara.csv","Brian Lara")

ca.batsmanMovingAverage("../kohli.csv","Virat Kohli")

15 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Dravid averages around 48, Cook around 44, Lara around 50 and Kohli shows a steady improvement in his cumulative average. Kohli seems to be getting better with time.

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeAverageRuns("../acook.csv","Alastair Cook")

ca.batsmanCumulativeAverageRuns("../lara.csv","Brian Lara")

ca.batsmanCumulativeAverageRuns("../kohli.csv","Virat Kohli")

16 Cumulative Average strike rate of batsman in career

Lara has a terrific strike rate of 52+. Cook has a better strike rate over Dravid. Kohli’s strike rate has improved over the years.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeStrikeRate("../acook.csv","Alastair Cook")

ca.batsmanCumulativeStrikeRate("../lara.csv","Brian Lara")

ca.batsmanCumulativeStrikeRate("../kohli.csv","Virat Kohli")


17 Future Runs forecast

Here are plots that forecast how the batsman will perform in future. Currently ARIMA has been used for the forecast. (To do:  Perform Holt-Winters forecast!)

import cricpy.analytics as ca
ca.batsmanPerfForecast("../dravid.csv","Rahul Dravid")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:                 D.runs   No. Observations:                  284
## Model:                 ARIMA(5, 1, 0)   Log Likelihood               -1522.837
## Method:                       css-mle   S.D. of innovations             51.488
## Date:                Sun, 28 Oct 2018   AIC                           3059.673
## Time:                        09:47:39   BIC                           3085.216
## Sample:                    07-04-1996   HQIC                          3069.914
##                          - 01-24-2012                                         
## ================================================================================
##                    coef    std err          z      P>|z|      [0.025      0.975]
## --------------------------------------------------------------------------------
## const           -0.1336      0.884     -0.151      0.880      -1.867       1.599
## ar.L1.D.runs    -0.7729      0.058    -13.322      0.000      -0.887      -0.659
## ar.L2.D.runs    -0.6234      0.071     -8.753      0.000      -0.763      -0.484
## ar.L3.D.runs    -0.5199      0.074     -7.038      0.000      -0.665      -0.375
## ar.L4.D.runs    -0.3490      0.071     -4.927      0.000      -0.488      -0.210
## ar.L5.D.runs    -0.2116      0.058     -3.665      0.000      -0.325      -0.098
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5789           -1.1743j            1.3093           -0.1771
## AR.2            0.5789           +1.1743j            1.3093            0.1771
## AR.3           -1.3617           -0.0000j            1.3617           -0.5000
## AR.4           -0.7227           -1.2257j            1.4230           -0.3348
## AR.5           -0.7227           +1.2257j            1.4230            0.3348
## -----------------------------------------------------------------------------
##                 0
## count  284.000000
## mean    -0.306769
## std     51.632947
## min   -106.653589
## 25%    -33.835148
## 50%     -8.954253
## 75%     21.024763
## max    223.152901
## 
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.
##   elif issubdtype(paramsdtype, complex):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):

18 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following Range 30 – 100 innings – Lara leads followed by Dravid Range 100+ innings – Kohli races ahead of the rest

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

19. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

Brian Lara towers over the Dravid, Cook and Kohli. However you will notice that Kohli’s strike rate is going up

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

20. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

ca.battingPerf3d("../acook.csv","Alastair Cook")

ca.battingPerf3d("../lara.csv","Brian Lara")

ca.battingPerf3d("../kohli.csv","Virat Kohli")

21. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
dravid = ca.batsmanRunsPredict("../dravid.csv",newDF,"Dravid")
print(dravid)
##             BF        Mins        Runs
## 0    10.000000   30.000000    0.519667
## 1    37.857143   70.714286   13.821794
## 2    65.714286  111.428571   27.123920
## 3    93.571429  152.142857   40.426046
## 4   121.428571  192.857143   53.728173
## 5   149.285714  233.571429   67.030299
## 6   177.142857  274.285714   80.332425
## 7   205.000000  315.000000   93.634552
## 8   232.857143  355.714286  106.936678
## 9   260.714286  396.428571  120.238805
## 10  288.571429  437.142857  133.540931
## 11  316.428571  477.857143  146.843057
## 12  344.285714  518.571429  160.145184
## 13  372.142857  559.285714  173.447310
## 14  400.000000  600.000000  186.749436

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

22 Analysis of Top 3 wicket takers

The following 3 bowlers have had an excellent career and will be used for the analysis

  1. Glenn McGrath:Wickets: 563, Average = 21.64, Economy Rate – 2.49
  2. Kapil Dev : Wickets: 434, Average = 29.64, Economy Rate – 2.78
  3. James Anderson: Wickets: 564, Average = 28.64, Economy Rate – 2.88

How do Glenn McGrath, Kapil Dev and James Anderson compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

23. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#mcgrath =ca.getPlayerData(6565,dir=".",file="mcgrath.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#kapil =ca.getPlayerData(30028,dir=".",file="kapil.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#anderson =ca.getPlayerData(8608,dir=".",file="anderson.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])

24. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsFreqPercent("../kapil.csv","Kapil Dev")

ca.bowlerWktsFreqPercent("../anderson.csv","James Anderson")

25. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsRunsPlot("../kapil.csv","Kapil Dev")

ca.bowlerWktsRunsPlot("../anderson.csv","James Anderson")

26 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsGround("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsGround("../anderson.csv","James Anderson")

27 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsOpposition("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsOpposition("../anderson.csv","James Anderson")

28 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("../mcgrath.csv","Glenn McGrath")

ca.bowlerMovingAverage("../kapil.csv","Kapil Dev")

ca.bowlerMovingAverage("../anderson.csv","James Anderson")

29 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. mcGrath plateaus around 2.4 wickets, Kapil Dev’s performance deteriorates over the years. Anderson holds on rock steady around 2 wickets

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgWickets("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgWickets("../anderson.csv","James Anderson")

30 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. McGrath’s was very expensive early in his career conceding about 2.8 runs per over which drops to around 2.5 runs towards the end. Kapil Dev’s economy rate drops from 3.6 to 2.8. Anderson is probably more expensive than the other 2.

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgEconRate("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgEconRate("../anderson.csv","James Anderson")

31 Future Wickets forecast

import cricpy.analytics as ca
ca.bowlerPerfForecast("../mcgrath.csv","Glenn McGrath")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:              D.Wickets   No. Observations:                  236
## Model:                 ARIMA(5, 1, 0)   Log Likelihood                -480.815
## Method:                       css-mle   S.D. of innovations              1.851
## Date:                Sun, 28 Oct 2018   AIC                            975.630
## Time:                        09:28:32   BIC                            999.877
## Sample:                    11-12-1993   HQIC                           985.404
##                          - 01-02-2007                                         
## ===================================================================================
##                       coef    std err          z      P>|z|      [0.025      0.975]
## -----------------------------------------------------------------------------------
## const               0.0037      0.033      0.113      0.910      -0.061       0.068
## ar.L1.D.Wickets    -0.9432      0.064    -14.708      0.000      -1.069      -0.818
## ar.L2.D.Wickets    -0.7254      0.086     -8.469      0.000      -0.893      -0.558
## ar.L3.D.Wickets    -0.4827      0.093     -5.217      0.000      -0.664      -0.301
## ar.L4.D.Wickets    -0.3690      0.085     -4.324      0.000      -0.536      -0.202
## ar.L5.D.Wickets    -0.1709      0.064     -2.678      0.008      -0.296      -0.046
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5630           -1.2761j            1.3948           -0.1839
## AR.2            0.5630           +1.2761j            1.3948            0.1839
## AR.3           -0.8433           -1.0820j            1.3718           -0.3554
## AR.4           -0.8433           +1.0820j            1.3718            0.3554
## AR.5           -1.5981           -0.0000j            1.5981           -0.5000
## -----------------------------------------------------------------------------
##                 0
## count  236.000000
## mean    -0.005142
## std      1.856961
## min     -3.457002
## 25%     -1.433391
## 50%     -0.080237
## 75%      1.446149
## max      5.840050

32 Get player data special

As discussed above the next 2 charts require the use of getPlayerDataSp()

import cricpy.analytics as ca
#mcgrathsp =ca.getPlayerDataSp(6565,tdir=".",tfile="mcgrathsp.csv",ttype="bowling")
#kapilsp =ca.getPlayerDataSp(30028,tdir=".",tfile="kapilsp.csv",ttype="bowling")
#andersonsp =ca.getPlayerDataSp(8608,tdir=".",tfile="andersonsp.csv",ttype="bowling")

33 Contribution to matches won and lost

The plot below is extremely interesting Glenn McGrath has been more instrumental in Australia winning than Kapil and Anderson as seems to have taken more wickets when Australia won.

import cricpy.analytics as ca
ca.bowlerContributionWonLost("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerContributionWonLost("../kapilsp.csv","Kapil Dev")

ca.bowlerContributionWonLost("../andersonsp.csv","James Anderson")

34 Performance home and overseas

McGrath and Kapil Dev have performed better overseas than at home. Anderson has performed about the same home and overseas

import cricpy.analytics as ca
ca.bowlerPerfHomeAway("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerPerfHomeAway("../kapilsp.csv","Kapil Dev")

ca.bowlerPerfHomeAway("../andersonsp.csv","James Anderson")

35 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that McGrath has the best economy rate followed by Kapil Dev and then Anderson.

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

36 Relative Economy Rate against wickets taken

McGrath has been economical regardless of the number of wickets taken. Kapil Dev has been slightly more expensive when he takes more wickets

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlingER(frames,names)

37 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. Kapil’s leads Anderson till about 150 innings after which Anderson takes over

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Key insights

1. Brian Lara is head and shoulders above the rest in the overall strike rate
2. Kohli performance has been steadily improving over the years and with the way he is going he will shatter all records.
3. Kohli and Dravid have scored more in matches where India has won than the other two.
4. Dravid has performed very well overseas
5. The cumulative average runs has Kohli just edging out the other 3. Kohli is probably midway in his career but considering that his moving average is improving strongly, we can expect great things of him with the way he is going.
6. McGrath has had some great performances overseas
7. Mcgrath has the best economy rate and has contributed significantly to Australia’s wins.
8.In the cumulative average wickets race McGrath leads the pack. Kapil leads Anderson till about 150 matches after which Anderson takes over.

The code for cricpy can be accessed at Github at cricpy

Do let me know if you run into issues.

Conclusion

I have long wanted to make a python equivalent of cricketr and I have been able to make it. cricpy is still work in progress. I have add the necessary functions for ODI and Twenty20.  Go ahead give ‘cricpy’ a spin!!

Stay tuned!

Big Data-2: Move into the big league:Graduate from R to SparkR


This post is a continuation of my earlier post Big Data-1: Move into the big league:Graduate from Python to Pyspark. While the earlier post discussed parallel constructs in Python and Pyspark, this post elaborates similar and key constructs in R and SparkR. While this post just focuses on the programming part of R and SparkR it is essential to understand and fully grasp the concept of Spark, RDD and how data is distributed across the clusters. This post like the earlier post shows how if you already have a good handle of R, you can easily graduate to Big Data with SparkR

Note 1: This notebook has also been published at Databricks community site Big Data-2: Move into the big league:Graduate from R to SparkR

Note 2: You can download this RMarkdown file from Github at Big Data- Python to Pyspark and R to SparkR
1a. Read CSV- R

Note: To upload the CSV to databricks see the video Upload Flat File to Databricks Table

# Read CSV file
tendulkar= read.csv("/dbfs/FileStore/tables/tendulkar.csv",stringsAsFactors = FALSE,na.strings=c(NA,"-"))
#Check the dimensions of the dataframe
dim(tendulkar)
[1] 347  12
1b. Read CSV – SparkR
# Load the SparkR library
library(SparkR)
# Initiate a SparkR session
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

# Check the dimensions of the dataframe
dim(tendulkar1)
[1] 347  12
2a. Data frame shape – R
# Get the shape of the dataframe in R
dim(tendulkar)
[1] 347  12
2b. Dataframe shape – SparkR

The same ‘dim’ command works in SparkR too!

dim(tendulkar1)
[1] 347  12
3a . Dataframe columns – R
# Get the names
names(tendulkar) # Also colnames(tendulkar)
 [1] "Runs"       "Mins"       "BF"         "X4s"        "X6s"       
 [6] "SR"         "Pos"        "Dismissal"  "Inns"       "Opposition"
[11] "Ground"     "Start.Date"
3b. Dataframe columns – SparkR
names(tendulkar1)
 [1] "Runs"       "Mins"       "BF"         "4s"         "6s"        
 [6] "SR"         "Pos"        "Dismissal"  "Inns"       "Opposition"
[11] "Ground"     "Start Date"
4a. Rename columns – R
names(tendulkar)=c('Runs','Minutes','BallsFaced','Fours','Sixes','StrikeRate','Position','Dismissal','Innings','Opposition','Ground','StartDate')
names(tendulkar)
 [1] "Runs"       "Minutes"    "BallsFaced" "Fours"      "Sixes"     
 [6] "StrikeRate" "Position"   "Dismissal"  "Innings"    "Opposition"
[11] "Ground"     "StartDate"
4b. Rename columns – SparkR
names(tendulkar1)=c('Runs','Minutes','BallsFaced','Fours','Sixes','StrikeRate','Position','Dismissal','Innings','Opposition','Ground','StartDate')
names(tendulkar1)
 [1] "Runs"       "Minutes"    "BallsFaced" "Fours"      "Sixes"     
 [6] "StrikeRate" "Position"   "Dismissal"  "Innings"    "Opposition"
[11] "Ground"     "StartDate"
5a. Summary – R
summary(tendulkar)
     Runs              Minutes        BallsFaced         Fours       
 Length:347         Min.   :  1.0   Min.   :  0.00   Min.   : 0.000  
 Class :character   1st Qu.: 33.0   1st Qu.: 22.00   1st Qu.: 1.000  
 Mode  :character   Median : 82.0   Median : 58.50   Median : 4.000  
                    Mean   :125.5   Mean   : 89.75   Mean   : 6.274  
                    3rd Qu.:181.0   3rd Qu.:133.25   3rd Qu.: 9.000  
                    Max.   :613.0   Max.   :436.00   Max.   :35.000  
                    NA's   :18      NA's   :19       NA's   :19      
     Sixes          StrikeRate        Position     Dismissal        
 Min.   :0.0000   Min.   :  0.00   Min.   :2.00   Length:347        
 1st Qu.:0.0000   1st Qu.: 38.09   1st Qu.:4.00   Class :character  
 Median :0.0000   Median : 52.25   Median :4.00   Mode  :character  
 Mean   :0.2097   Mean   : 51.79   Mean   :4.24                     
 3rd Qu.:0.0000   3rd Qu.: 65.09   3rd Qu.:4.00                     
 Max.   :4.0000   Max.   :166.66   Max.   :7.00                     
 NA's   :18       NA's   :20       NA's   :18                       
    Innings       Opposition           Ground           StartDate        
 Min.   :1.000   Length:347         Length:347         Length:347        
 1st Qu.:1.000   Class :character   Class :character   Class :character  
 Median :2.000   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2.376                                                           
 3rd Qu.:3.000                                                           
 Max.   :4.000                                                           
 NA's   :1
5b. Summary – SparkR
summary(tendulkar1)
SparkDataFrame[summary:string, Runs:string, Minutes:string, BallsFaced:string, Fours:string, Sixes:string, StrikeRate:string, Position:string, Dismissal:string, Innings:string, Opposition:string, Ground:string, StartDate:string]
6a. Displaying details of dataframe with str() – R
str(tendulkar)
'data.frame':	347 obs. of  12 variables:
 $ Runs      : chr  "15" "DNB" "59" "8" ...
 $ Minutes   : int  28 NA 254 24 124 74 193 1 50 324 ...
 $ BallsFaced: int  24 NA 172 16 90 51 134 1 44 266 ...
 $ Fours     : int  2 NA 4 1 5 5 6 0 3 5 ...
 $ Sixes     : int  0 NA 0 0 0 0 0 0 0 0 ...
 $ StrikeRate: num  62.5 NA 34.3 50 45.5 ...
 $ Position  : int  6 NA 6 6 7 6 6 6 6 6 ...
 $ Dismissal : chr  "bowled" NA "lbw" "run out" ...
 $ Innings   : int  2 4 1 3 1 1 3 2 3 1 ...
 $ Opposition: chr  "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" ...
 $ Ground    : chr  "Karachi" "Karachi" "Faisalabad" "Faisalabad" ...
 $ StartDate : chr  "15-Nov-89" "15-Nov-89" "23-Nov-89" "23-Nov-89" ...
6b. Displaying details of dataframe with str() – SparkR
str(tendulkar1)
'SparkDataFrame': 12 variables:
 $ Runs      : chr "15" "DNB" "59" "8" "41" "35"
 $ Minutes   : chr "28" "-" "254" "24" "124" "74"
 $ BallsFaced: chr "24" "-" "172" "16" "90" "51"
 $ Fours     : chr "2" "-" "4" "1" "5" "5"
 $ Sixes     : chr "0" "-" "0" "0" "0" "0"
 $ StrikeRate: chr "62.5" "-" "34.3" "50" "45.55" "68.62"
 $ Position  : chr "6" "-" "6" "6" "7" "6"
 $ Dismissal : chr "bowled" "-" "lbw" "run out" "bowled" "lbw"
 $ Innings   : chr "2" "4" "1" "3" "1" "1"
 $ Opposition: chr "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan"
 $ Ground    : chr "Karachi" "Karachi" "Faisalabad" "Faisalabad" "Lahore" "Sialkot"
 $ StartDate : chr "15-Nov-89" "15-Nov-89" "23-Nov-89" "23-Nov-89" "1-Dec-89" "9-Dec-89"
7a. Head & tail -R
print(head(tendulkar),3)
print(tail(tendulkar),3)
 Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1   15      28         24     2     0      62.50        6    bowled       2
2  DNB      NA         NA    NA    NA         NA       NA             4
3   59     254        172     4     0      34.30        6       lbw       1
4    8      24         16     1     0      50.00        6   run out       3
5   41     124         90     5     0      45.55        7    bowled       1
6   35      74         51     5     0      68.62        6       lbw       1
  Opposition     Ground StartDate
1 v Pakistan    Karachi 15-Nov-89
2 v Pakistan    Karachi 15-Nov-89
3 v Pakistan Faisalabad 23-Nov-89
4 v Pakistan Faisalabad 23-Nov-89
5 v Pakistan     Lahore  1-Dec-89
6 v Pakistan    Sialkot  9-Dec-89
    Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
342   37     125         81     5     0      45.67        4    caught       2
343   21      71         23     2     0      91.30        4   run out       4
344   32      99         53     5     0      60.37        4       lbw       2
345    1       8          5     0     0      20.00        4       lbw       4
346   10      41         24     2     0      41.66        4       lbw       2
347   74     150        118    12     0      62.71        4    caught       2
       Opposition  Ground StartDate
342   v Australia  Mohali 14-Mar-13
343   v Australia  Mohali 14-Mar-13
344   v Australia   Delhi 22-Mar-13
345   v Australia   Delhi 22-Mar-13
346 v West Indies Kolkata  6-Nov-13
347 v West Indies  Mumbai 14-Nov-13
7b. Head – SparkR
head(tendulkar1,3)
  Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1   15      28         24     2     0       62.5        6    bowled       2
2  DNB       -          -     -     -          -        -         -       4
3   59     254        172     4     0       34.3        6       lbw       1
  Opposition     Ground StartDate
1 v Pakistan    Karachi 15-Nov-89
2 v Pakistan    Karachi 15-Nov-89
3 v Pakistan Faisalabad 23-Nov-89
8a. Determining the column types with sapply -R
sapply(tendulkar,class)
       Runs     Minutes  BallsFaced       Fours       Sixes  StrikeRate 
"character"   "integer"   "integer"   "integer"   "integer"   "numeric" 
   Position   Dismissal     Innings  Opposition      Ground   StartDate 
  "integer" "character"   "integer" "character" "character" "character"
8b. Determining the column types with printSchema – SparkR
printSchema(tendulkar1)
root
 |-- Runs: string (nullable = true)
 |-- Minutes: string (nullable = true)
 |-- BallsFaced: string (nullable = true)
 |-- Fours: string (nullable = true)
 |-- Sixes: string (nullable = true)
 |-- StrikeRate: string (nullable = true)
 |-- Position: string (nullable = true)
 |-- Dismissal: string (nullable = true)
 |-- Innings: string (nullable = true)
 |-- Opposition: string (nullable = true)
 |-- Ground: string (nullable = true)
 |-- StartDate: string (nullable = true)
9a. Selecting columns – R
library(dplyr)
df=select(tendulkar,Runs,BallsFaced,Minutes)
head(df,5)
  Runs BallsFaced Minutes
1   15         24      28
2  DNB         NA      NA
3   59        172     254
4    8         16      24
5   41         90     124
9b. Selecting columns – SparkR
library(SparkR)
Sys.setenv(SPARK_HOME="/usr/hdp/2.6.0.3-8/spark")
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
# Initiate a SparkR session
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")
df=SparkR::select(tendulkar1, "Runs", "BF","Mins")
head(SparkR::collect(df))
  Runs  BF Mins
1   15  24   28
2  DNB   -    -
3   59 172  254
4    8  16   24
5   41  90  124
6   35  51   74
10a. Filter rows by criteria – R
library(dplyr)
df=tendulkar %>% filter(Runs > 50)
head(df,5)
  Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1  DNB      NA         NA    NA    NA         NA       NA             4
2   59     254        172     4     0      34.30        6       lbw       1
3    8      24         16     1     0      50.00        6   run out       3
4   57     193        134     6     0      42.53        6    caught       3
5   88     324        266     5     0      33.08        6    caught       1
     Opposition     Ground StartDate
1    v Pakistan    Karachi 15-Nov-89
2    v Pakistan Faisalabad 23-Nov-89
3    v Pakistan Faisalabad 23-Nov-89
4    v Pakistan    Sialkot  9-Dec-89
5 v New Zealand     Napier  9-Feb-90
10b. Filter rows by criteria – SparkR
df=SparkR::filter(tendulkar1, tendulkar1$Runs > 50)
head(SparkR::collect(df))
  Runs Mins  BF 4s 6s    SR Pos Dismissal Inns     Opposition       Ground
1   59  254 172  4  0  34.3   6       lbw    1     v Pakistan   Faisalabad
2   57  193 134  6  0 42.53   6    caught    3     v Pakistan      Sialkot
3   88  324 266  5  0 33.08   6    caught    1  v New Zealand       Napier
4   68  216 136  8  0    50   6    caught    2      v England   Manchester
5  114  228 161 16  0  70.8   4    caught    2    v Australia        Perth
6  111  373 270 19  0 41.11   4    caught    2 v South Africa Johannesburg
  Start Date
1  23-Nov-89
2   9-Dec-89
3   9-Feb-90
4   9-Aug-90
5   1-Feb-92
6  26-Nov-92
11a. Unique values -R
unique(tendulkar$Runs)
  [1] "15"   "DNB"  "59"   "8"    "41"   "35"   "57"   "0"    "24"   "88"  
 [11] "5"    "10"   "27"   "68"   "119*" "21"   "11"   "16"   "7"    "40"  
 [21] "148*" "6"    "17"   "114"  "111"  "1"    "73"   "50"   "9*"   "165" 
 [31] "78"   "62"   "TDNB" "28"   "104*" "71"   "142"  "96"   "43"   "11*" 
 [41] "34"   "85"   "179"  "54"   "4"    "0*"   "52*"  "2"    "122"  "31"  
 [51] "177"  "74"   "42"   "18"   "61"   "36"   "169"  "9"    "15*"  "92"  
 [61] "83"   "143"  "139"  "23"   "148"  "13"   "155*" "79"   "47"   "113" 
 [71] "67"   "136"  "29"   "53"   "124*" "126*" "44*"  "217"  "116"  "52"  
 [81] "45"   "97"   "20"   "39"   "201*" "76"   "65"   "126"  "36*"  "69"  
 [91] "155"  "22*"  "103"  "26"   "90"   "176"  "117"  "86"   "12"   "193" 
[101] "16*"  "51"   "32"   "55"   "37"   "44"   "241*" "60*"  "194*" "3"   
[111] "32*"  "248*" "94"   "22"   "109"  "19"   "14"   "28*"  "63"   "64"  
[121] "101"  "122*" "91"   "82"   "56*"  "154*" "153"  "49"   "10*"  "103*"
[131] "160"  "100*" "105*" "100"  "106"  "84"   "203"  "98"   "38"   "214" 
[141] "53*"  "111*" "146"  "14*"  "56"   "80"   "25"   "81"   "13*"
11b. Unique values – SparkR
head(SparkR::distinct(tendulkar1[,"Runs"]),5)
  Runs
1 119*
2    7
3   51
4  169
5  32*
12a. Aggregate – Mean, min and max – R
library(dplyr)
library(magrittr)
a <- tendulkar$Runs != "DNB"
tendulkar <- tendulkar[a,]
dim(tendulkar)

# Remove rows with 'TDNB'
c <- tendulkar$Runs != "TDNB"
tendulkar <- tendulkar[c,]

# Remove rows with absent
d <- tendulkar$Runs != "absent"
tendulkar <- tendulkar[d,]
dim(tendulkar)

# Remove the "* indicating not out
tendulkar$Runs <- as.numeric(gsub("\\*","",tendulkar$Runs))
c <- complete.cases(tendulkar)

#Subset the rows which are complete
tendulkar <- tendulkar[c,]
print(dim(tendulkar))
df <-tendulkar %>%  group_by(Ground) %>% summarise(meanRuns= mean(Runs), minRuns=min(Runs), maxRuns=max(Runs)) 
#names(tendulkar)
head(df)
[1] 327  12
# A tibble: 6 x 4
  Ground       meanRuns minRuns maxRuns
                   
1 Adelaide        32.6       0.    153.
2 Ahmedabad       40.1       4.    217.
3 Auckland         5.00      5.      5.
4 Bangalore       57.9       4.    214.
5 Birmingham      46.8       1.    122.
6 Bloemfontein    85.0      15.    155.
12b. Aggregate- Mean, Min, Max – SparkR
sparkR.session()

tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

print(dim(tendulkar1))
tendulkar1 <-SparkR::filter(tendulkar1,tendulkar1$Runs != "DNB")
print(dim(tendulkar1))
tendulkar1<-SparkR::filter(tendulkar1,tendulkar1$Runs != "TDNB")
print(dim(tendulkar1))
tendulkar1<-SparkR::filter(tendulkar1,tendulkar1$Runs != "absent")
print(dim(tendulkar1))

# Cast the string type Runs to double
withColumn(tendulkar1, "Runs", cast(tendulkar1$Runs, "double"))
head(SparkR::distinct(tendulkar1[,"Runs"]),20)
# Remove the "* indicating not out
tendulkar1$Runs=SparkR::regexp_replace(tendulkar1$Runs, "\\*", "")
head(SparkR::distinct(tendulkar1[,"Runs"]),20)
df=SparkR::summarize(SparkR::groupBy(tendulkar1, tendulkar1$Ground), mean = mean(tendulkar1$Runs), minRuns=min(tendulkar1$Runs),maxRuns=max(tendulkar1$Runs))
head(df,20)
[1] 347  12
[1] 330  12
[1] 329  12
[1] 329  12
          Ground       mean minRuns maxRuns
1      Bangalore  54.312500       0      96
2       Adelaide  32.600000       0      61
3  Colombo (PSS)  37.200000      14      71
4   Christchurch  12.000000       0      24
5       Auckland   5.000000       5       5
6        Chennai  60.625000       0      81
7      Centurion  73.500000     111      36
8       Brisbane   7.666667       0       7
9     Birmingham  46.750000       1      40
10     Ahmedabad  40.125000     100       8
11 Colombo (RPS) 143.000000     143     143
12    Chittagong  57.800000     101      36
13     Cape Town  69.857143      14       9
14    Bridgetown  26.000000       0      92
15      Bulawayo  55.000000      36      74
16         Delhi  39.947368       0      76
17    Chandigarh  11.000000      11      11
18  Bloemfontein  85.000000      15     155
19 Colombo (SSC)  77.555556     104       8
20       Cuttack   2.000000       2       2
13a Using SQL with SparkR
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

# Register this SparkDataFrame as a temporary view.
createOrReplaceTempView(tendulkar1, "tendulkar2")

# SQL statements can be run by using the sql method
df=SparkR::sql("SELECT * FROM tendulkar2 WHERE Ground='Karachi'")

head(df)

  Runs Mins BF 4s 6s    SR Pos Dismissal Inns Opposition  Ground Start Date
1   15   28 24  2  0  62.5   6    bowled    2 v Pakistan Karachi  15-Nov-89
2  DNB    -  -  -  -     -   -         -    4 v Pakistan Karachi  15-Nov-89
3   23   49 29  5  0 79.31   4    bowled    2 v Pakistan Karachi  29-Jan-06
4   26   74 47  5  0 55.31   4    bowled    4 v Pakistan Karachi  29-Jan-06
Conclusion

This post discusses some of the key constructs in R and SparkR and how one can transition from R to SparkR fairly easily. I will be adding more constructs later. Do check back!

You may also like
1. Exploring Quantum Gate operations with QCSimulator
2. Deep Learning from first principles in Python, R and Octave – Part 4
3. A Bluemix recipe with MongoDB and Node.js
4. Practical Machine Learning with R and Python – Part 5
5. Introducing cricketr! : An R package to analyze performances of cricketers

To see all posts click Index of posts

My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon


The second edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($10.99) and kindle ($7.99/Rs449) versions.  This second edition includes more content,  extensive comments and formatting for better readability.

In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code.
1. Practical machine with R and Python: Second Edition – Machine Learning in Stereo(Paperback-$10.99)
2. Practical machine with R and Python Second Edition – Machine Learning in Stereo(Kindle- $7.99/Rs449)

This book is ideal both for beginners and the experts in R and/or Python. Those starting their journey into datascience and ML will find the first 3 chapters useful, as they touch upon the most important programming constructs in R and Python and also deal with equivalent statements in R and Python. Those who are expert in either of the languages, R or Python, will find the equivalent code ideal for brushing up on the other language. And finally,those who are proficient in both languages, can use the R and Python implementations to internalize the ML algorithms better.

Here is a look at the topics covered

Table of Contents
Preface …………………………………………………………………………….4
Introduction ………………………………………………………………………6
1. Essential R ………………………………………………………………… 8
2. Essential Python for Datascience ……………………………………………57
3. R vs Python …………………………………………………………………81
4. Regression of a continuous variable ……………………………………….101
5. Classification and Cross Validation ………………………………………..121
6. Regression techniques and regularization ………………………………….146
7. SVMs, Decision Trees and Validation curves ………………………………191
8. Splines, GAMs, Random Forests and Boosting ……………………………222
9. PCA, K-Means and Hierarchical Clustering ………………………………258
References ……………………………………………………………………..269

Pick up your copy today!!
Hope you have a great time learning as I did while implementing these algorithms!

My book “Deep Learning from first principles” now on Amazon


My 4th book(self-published), “Deep Learning from first principles – In vectorized Python, R and Octave” (557 pages), is now available on Amazon in both paperback ($16.99) and kindle ($6.65/Rs449). The book starts with the most primitive 2-layer Neural Network and works  its way to a generic L-layer Deep Learning Network, with all the bells and whistles.  The book includes detailed derivations and vectorized implementations in Python, R and Octave.  The code has been extensively  commented and has been included in the Appendix section.

Pick up your copy today!!!

My other books
1. Practical Machine Learning with R and Python
2. Beaten by sheer pace – Cricket analytics with yorkr
3. Cricket analytics with cricketr

Deep Learning from first principles in Python, R and Octave – Part 8


1. Introduction

You don’t understand anything until you learn it more than one way. Marvin Minsky
No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either. Marvin Minsky
A wealth of information creates a poverty of attention. Herbert Simon

This post, Deep Learning from first Principles in Python, R and Octave-Part8, is my final post in my Deep Learning from first principles series. In this post, I discuss and implement a key functionality needed while building Deep Learning networks viz. ‘Gradient Checking’. Gradient Checking is an important method to check the correctness of your implementation, specifically the forward propagation and the backward propagation cycles of an implementation. In addition I also discuss some tips for tuning hyper-parameters of a Deep Learning network based on my experience.

My post in this  ‘Deep Learning Series’ so far were
1. Deep Learning from first principles in Python, R and Octave – Part 1 In part 1, I implement logistic regression as a neural network in vectorized Python, R and Octave
2. Deep Learning from first principles in Python, R and Octave – Part 2 In the second part I implement a simple Neural network with just 1 hidden layer and a sigmoid activation output function
3. Deep Learning from first principles in Python, R and Octave – Part 3 The 3rd part implemented a multi-layer Deep Learning Network with sigmoid activation output in vectorized Python, R and Octave
4. Deep Learning from first principles in Python, R and Octave – Part 4 The 4th part deals with multi-class classification. Specifically, I derive the Jacobian of the Softmax function and enhance my L-Layer DL network to include Softmax output function in addition to Sigmoid activation
5. Deep Learning from first principles in Python, R and Octave – Part 5 This post uses the Softmax classifier implemented to classify MNIST digits using a L-layer Deep Learning network
6. Deep Learning from first principles in Python, R and Octave – Part 6 The 6th part adds more bells and whistles to my L-Layer DL network, by including different initialization types namely He and Xavier. Besides L2 Regularization and random dropout is added.
7. Deep Learning from first principles in Python, R and Octave – Part 7 The 7th part deals with Stochastic Gradient Descent Optimization methods including momentum, RMSProp and Adam
8. Deep Learning from first principles in Python, R and Octave – Part 8 – This post implements a critical function for ensuring the correctness of a L-Layer Deep Learning network implementation using Gradient Checking

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99) and in kindle version($6.65/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python- Machine Learning in stereo” available in Amazon in paperback($9.99) and Kindle($6.99) versions. This book is ideal for a quick reference of the various ML functions and associated measurements in both R and Python which are essential to delve deep into Deep Learning.

Gradient Checking is based on the following approach. One iteration of Gradient Descent computes and updates the parameters \theta by doing
\theta := \theta - \frac{d}{d\theta}J(\theta).
To minimize the cost we will need to minimize J(\theta)
Let g(\theta) be a function that computes the derivative \frac {d}{d\theta}J(\theta). Gradient Checking allows us to numerically evaluate the implementation of the function g(\theta) and verify its correctness.
We know the derivative of a function is given by
\frac {d}{d\theta}J(\theta) = lim->0 \frac {J(\theta +\epsilon) - J(\theta -\epsilon)} {2*\epsilon}
Note: The above derivative is based on the 2 sided derivative. The 1-sided derivative  is given by \frac {d}{d\theta}J(\theta) = lim->0 \frac {J(\theta +\epsilon) - J(\theta)} {\epsilon}
Gradient Checking is based on the 2-sided derivative because the error is of the order O(\epsilon^{2}) as opposed O(\epsilon) for the 1-sided derivative.
Hence Gradient Check uses the 2 sided derivative as follows.
g(\theta) = lim->0 \frac {J(\theta +\epsilon) - J(\theta -\epsilon)} {2*\epsilon}

In Gradient Check the following is done
A) Run one normal cycle of your implementation by doing the following
a) Compute the output activation by running 1 cycle of forward propagation
b) Compute the cost using the output activation
c) Compute the gradients using backpropation (grad)

B) Perform gradient check steps as below
a) Set \theta . Flatten all ‘weights’ and ‘bias’ matrices and vectors to a column vector.
b) Initialize \theta+ by bumping up \theta by adding \epsilon (\theta + \epsilon)
c) Perform forward propagation with \theta+
d) Compute cost with \theta+ i.e. J(\theta+)
e) Initialize  \theta- by bumping down \theta by subtracting \epsilon (\theta - \epsilon)
f) Perform forward propagation with \theta-
g) Compute cost with \theta- i.e.  J(\theta-)
h) Compute \frac {d} {d\theta} J(\theta) or ‘gradapprox’ as\frac {J(\theta+) - J(\theta-) } {2\epsilon} using the 2 sided derivative.
i) Compute L2norm or the Euclidean distance between ‘grad’ and ‘gradapprox’. If the
diference is of the order of 10^{-5} or 10^{-7} the implementation is correct. In the Deep Learning Specialization Prof Andrew Ng mentions that if the difference is of the order of 10^{-7} then the implementation is correct. A difference of 10^{-5} is also ok. Anything more than that is a cause of worry and you should look at your code more closely. To see more details click Gradient checking and advanced optimization

You can clone/download the code from Github at DeepLearning-Part8

After spending a better part of 3 days, I now realize how critical Gradient Check is for ensuring the correctness of you implementation. Initially I was getting very high difference and did not know how to understand the results or debug my implementation. After many hours of staring at the results, I  was able to finally arrive at a way, to localize issues in the implementation. In fact, I did catch a small bug in my Python code, which did not exist in the R and Octave implementations. I will demonstrate this below

1.1a Gradient Check – Sigmoid Activation – Python

import numpy as np
import matplotlib

exec(open("DLfunctions8.py").read())
exec(open("testcases.py").read())
#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
#Set layer dimensions
layersDimensions = [2,4,1]  
parameters = initializeDeepModel(layersDimensions)
#Perform forward prop
AL, caches, dropoutMat = forwardPropagationDeep(train_X, parameters, keep_prob=1, hiddenActivationFunc="relu",outputActivationFunc="sigmoid")
#Compute cost
cost = computeCost(AL, train_Y, outputActivationFunc="sigmoid") 
print("cost=",cost)
#Perform backprop and get gradients
gradients = backwardPropagationDeep(AL, train_Y, caches, dropoutMat, lambd=0, keep_prob=1,                                   hiddenActivationFunc="relu",outputActivationFunc="sigmoid")

epsilon = 1e-7
outputActivationFunc="sigmoid"

# Set-up variables
# Flatten parameters to a vector
parameters_values, _ = dictionary_to_vector(parameters)
#Flatten gradients to a vector
grad = gradients_to_vector(parameters,gradients)
num_parameters = parameters_values.shape[0]
#Initialize
J_plus = np.zeros((num_parameters, 1))
J_minus = np.zeros((num_parameters, 1))
gradapprox = np.zeros((num_parameters, 1))

# Compute gradapprox using 2 sided derivative
for i in range(num_parameters):
    # Compute J_plus[i]. 
    thetaplus = np.copy(parameters_values)                                   
    thetaplus[i][0] = thetaplus[i][0] + epsilon                                 
    AL, caches, dropoutMat = forwardPropagationDeep(train_X, vector_to_dictionary(parameters,thetaplus), keep_prob=1, 
                                              hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc)
    J_plus[i] = computeCost(AL, train_Y, outputActivationFunc=outputActivationFunc) 
    
    
    # Compute J_minus[i]. 
    thetaminus = np.copy(parameters_values)                                     
    thetaminus[i][0] = thetaminus[i][0] - epsilon                                     
    AL, caches, dropoutMat  = forwardPropagationDeep(train_X, vector_to_dictionary(parameters,thetaminus), keep_prob=1, 
                                              hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc)     
    J_minus[i] = computeCost(AL, train_Y, outputActivationFunc=outputActivationFunc)                            
       
    # Compute gradapprox[i]   
    gradapprox[i] = (J_plus[i] - J_minus[i])/(2*epsilon)

# Compare gradapprox to backward propagation gradients by computing difference. 
numerator = np.linalg.norm(grad-gradapprox)                                           
denominator = np.linalg.norm(grad) +  np.linalg.norm(gradapprox)                                         
difference =  numerator/denominator                                        

#Check the difference
if difference > 1e-5:
    print ("\033[93m" + "There is a mistake in the backward propagation! difference = " + str(difference) + "\033[0m")
else:
    print ("\033[92m" + "Your backward propagation works perfectly fine! difference = " + str(difference) + "\033[0m")
print(difference)
print("\n")    
# The technique below can be used to identify 
# which of the parameters are in error
# Covert grad to dictionary
m=vector_to_dictionary2(parameters,grad)
print("Gradients from backprop")
print(m)
print("\n")
# Convert gradapprox to dictionary
n=vector_to_dictionary2(parameters,gradapprox)
print("Gradapprox from gradient check")
print(n)
## (300, 2)
## (300,)
## cost= 0.6931455556341791
## [92mYour backward propagation works perfectly fine! difference = 1.1604150683743381e-06[0m
## 1.1604150683743381e-06
## 
## 
## Gradients from backprop
## {'dW1': array([[-6.19439955e-06, -2.06438046e-06],
##        [-1.50165447e-05,  7.50401672e-05],
##        [ 1.33435433e-04,  1.74112143e-04],
##        [-3.40909024e-05, -1.38363681e-04]]), 'db1': array([[ 7.31333221e-07],
##        [ 7.98425950e-06],
##        [ 8.15002817e-08],
##        [-5.69821155e-08]]), 'dW2': array([[2.73416304e-04, 2.96061451e-04, 7.51837363e-05, 1.01257729e-04]]), 'db2': array([[-7.22232235e-06]])}
## 
## 
## Gradapprox from gradient check
## {'dW1': array([[-6.19448937e-06, -2.06501483e-06],
##        [-1.50168766e-05,  7.50399742e-05],
##        [ 1.33435485e-04,  1.74112391e-04],
##        [-3.40910633e-05, -1.38363765e-04]]), 'db1': array([[ 7.31081862e-07],
##        [ 7.98472399e-06],
##        [ 8.16013923e-08],
##        [-5.71764858e-08]]), 'dW2': array([[2.73416290e-04, 2.96061509e-04, 7.51831930e-05, 1.01257891e-04]]), 'db2': array([[-7.22255589e-06]])}

1.1b Gradient Check – Softmax Activation – Python (Error!!)

In the code below I show, how I managed to spot a bug in your implementation

import numpy as np
exec(open("DLfunctions8.py").read())
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
#plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
layersDimensions = [2,3,3]
y1=y.reshape(-1,1).T
train_X=X.T
train_Y=y1

parameters = initializeDeepModel(layersDimensions)
#Compute forward prop
AL, caches, dropoutMat = forwardPropagationDeep(train_X, parameters, keep_prob=1, 
                                                hiddenActivationFunc="relu",outputActivationFunc="softmax")
#Compute cost
cost = computeCost(AL, train_Y, outputActivationFunc="softmax") 
print("cost=",cost)
#Compute gradients from backprop
gradients = backwardPropagationDeep(AL, train_Y, caches, dropoutMat, lambd=0, keep_prob=1, 
                                    hiddenActivationFunc="relu",outputActivationFunc="softmax")
# Note the transpose of the gradients for Softmax has to be taken
L= len(parameters)//2
print(L)
gradients['dW'+str(L)]=gradients['dW'+str(L)].T
gradients['db'+str(L)]=gradients['db'+str(L)].T
# Perform gradient check
gradient_check_n(parameters, gradients, train_X, train_Y, epsilon = 1e-7,outputActivationFunc="softmax")

cost= 1.0986187818144022
2
There is a mistake in the backward propagation! difference = 0.7100295155692544
0.7100295155692544


Gradients from backprop
{'dW1': array([[ 0.00050125,  0.00045194],
       [ 0.00096392,  0.00039641],
       [-0.00014276, -0.00045639]]), 'db1': array([[ 0.00070082],
       [-0.00224399],
       [ 0.00052305]]), 'dW2': array([[-8.40953794e-05, -9.52657769e-04, -1.10269379e-04],
       [-7.45469382e-04,  9.49795606e-04,  2.29045434e-04],
       [ 8.29564761e-04,  2.86216305e-06, -1.18776055e-04]]), 
     'db2': array([[-0.00253808],
       [-0.00505508],
       [ 0.00759315]])}


Gradapprox from gradient check
{'dW1': array([[ 0.00050125,  0.00045194],
       [ 0.00096392,  0.00039641],
       [-0.00014276, -0.00045639]]), 'db1': array([[ 0.00070082],
       [-0.00224399],
       [ 0.00052305]]), 'dW2': array([[-8.40960634e-05, -9.52657953e-04, -1.10268461e-04],
       [-7.45469242e-04,  9.49796908e-04,  2.29045671e-04],
       [ 8.29565305e-04,  2.86104473e-06, -1.18776100e-04]]), 
     'db2': array([[-8.46211989e-06],
       [-1.68487446e-05],
       [ 2.53108645e-05]])}

Gradient Check gives a high value of the difference of 0.7100295. Inspecting the Gradients and Gradapprox we can see there is a very big discrepancy in db2. After I went over my code I discovered that I my computation in the function layerActivationBackward for Softmax was

 
   # Erroneous code
   if activationFunc == 'softmax':
        dW = 1/numtraining * np.dot(A_prev,dZ)
        db = np.sum(dZ, axis=0, keepdims=True)
        dA_prev = np.dot(dZ,W)
instead of
   # Fixed code
   if activationFunc == 'softmax':
        dW = 1/numtraining * np.dot(A_prev,dZ)
        db = 1/numtraining *  np.sum(dZ, axis=0, keepdims=True)
        dA_prev = np.dot(dZ,W)

After fixing this error when I ran Gradient Check I get

1.1c Gradient Check – Softmax Activation – Python (Corrected!!)

import numpy as np
exec(open("DLfunctions8.py").read())
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
#plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
layersDimensions = [2,3,3]
y1=y.reshape(-1,1).T
train_X=X.T
train_Y=y1
#Set layer dimensions
parameters = initializeDeepModel(layersDimensions)
#Perform forward prop
AL, caches, dropoutMat = forwardPropagationDeep(train_X, parameters, keep_prob=1, 
                                                hiddenActivationFunc="relu",outputActivationFunc="softmax")
#Compute cost
cost = computeCost(AL, train_Y, outputActivationFunc="softmax") 
print("cost=",cost)
#Compute gradients from backprop
gradients = backwardPropagationDeep(AL, train_Y, caches, dropoutMat, lambd=0, keep_prob=1, 
                                    hiddenActivationFunc="relu",outputActivationFunc="softmax")
# Note the transpose of the gradients for Softmax has to be taken
L= len(parameters)//2
print(L)
gradients['dW'+str(L)]=gradients['dW'+str(L)].T
gradients['db'+str(L)]=gradients['db'+str(L)].T
#Perform gradient check
gradient_check_n(parameters, gradients, train_X, train_Y, epsilon = 1e-7,outputActivationFunc="softmax")
## cost= 1.0986193170234435
## 2
## [92mYour backward propagation works perfectly fine! difference = 5.268804859613151e-07[0m
## 5.268804859613151e-07
## 
## 
## Gradients from backprop
## {'dW1': array([[ 0.00053206,  0.00038987],
##        [ 0.00093941,  0.00038077],
##        [-0.00012177, -0.0004692 ]]), 'db1': array([[ 0.00072662],
##        [-0.00210198],
##        [ 0.00046741]]), 'dW2': array([[-7.83441270e-05, -9.70179498e-04, -1.08715815e-04],
##        [-7.70175008e-04,  9.54478237e-04,  2.27690198e-04],
##        [ 8.48519135e-04,  1.57012608e-05, -1.18974383e-04]]), 'db2': array([[-8.52190476e-06],
##        [-1.69954294e-05],
##        [ 2.55173342e-05]])}
## 
## 
## Gradapprox from gradient check
## {'dW1': array([[ 0.00053206,  0.00038987],
##        [ 0.00093941,  0.00038077],
##        [-0.00012177, -0.0004692 ]]), 'db1': array([[ 0.00072662],
##        [-0.00210198],
##        [ 0.00046741]]), 'dW2': array([[-7.83439980e-05, -9.70180603e-04, -1.08716369e-04],
##        [-7.70173925e-04,  9.54478718e-04,  2.27690089e-04],
##        [ 8.48520143e-04,  1.57018842e-05, -1.18973720e-04]]), 'db2': array([[-8.52096171e-06],
##        [-1.69964043e-05],
##        [ 2.55162558e-05]])}

1.2a Gradient Check – Sigmoid Activation – R

source("DLfunctions8.R")
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 

x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
#Set layer dimensions
layersDimensions = c(2,5,1)
parameters = initializeDeepModel(layersDimensions)
#Perform forward prop
retvals = forwardPropagationDeep(X, parameters,keep_prob=1, hiddenActivationFunc="relu",
                                 outputActivationFunc="sigmoid")
AL <- retvals[['AL']]
caches <- retvals[['caches']]
dropoutMat <- retvals[['dropoutMat']]
#Compute cost
cost <- computeCost(AL, Y,outputActivationFunc="sigmoid",
                    numClasses=layersDimensions[length(layersDimensions)])
print(cost)
## [1] 0.6931447
# Backward propagation.
gradients = backwardPropagationDeep(AL, Y, caches, dropoutMat, lambd=0, keep_prob=1, hiddenActivationFunc="relu",
                                    outputActivationFunc="sigmoid",numClasses=layersDimensions[length(layersDimensions)])
epsilon = 1e-07
outputActivationFunc="sigmoid"
#Convert parameter list to vector
parameters_values = list_to_vector(parameters)
#Convert gradient list to vector
grad = gradients_to_vector(parameters,gradients)
num_parameters = dim(parameters_values)[1]
#Initialize
J_plus = matrix(rep(0,num_parameters),
                nrow=num_parameters,ncol=1)
J_minus = matrix(rep(0,num_parameters),
                 nrow=num_parameters,ncol=1)
gradapprox = matrix(rep(0,num_parameters),
                    nrow=num_parameters,ncol=1)

# Compute gradapprox
for(i in 1:num_parameters){
    # Compute J_plus[i]. 
    thetaplus = parameters_values                                   
    thetaplus[i][1] = thetaplus[i][1] + epsilon                                 
    retvals = forwardPropagationDeep(X, vector_to_list(parameters,thetaplus), keep_prob=1, 
                                                hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc)
    
    AL <- retvals[['AL']]
    J_plus[i] = computeCost(AL, Y, outputActivationFunc=outputActivationFunc) 


   # Compute J_minus[i]. 
    thetaminus = parameters_values                                     
    thetaminus[i][1] = thetaminus[i][1] - epsilon                                     
    retvals  = forwardPropagationDeep(X, vector_to_list(parameters,thetaminus), keep_prob=1, 
                                                 hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc)     
    AL <- retvals[['AL']]
    J_minus[i] = computeCost(AL, Y, outputActivationFunc=outputActivationFunc)                            

    # Compute gradapprox[i]   
    gradapprox[i] = (J_plus[i] - J_minus[i])/(2*epsilon)
}
# Compare gradapprox to backward propagation gradients by computing difference.
#Compute L2Norm
numerator = L2NormVec(grad-gradapprox)                                           
denominator = L2NormVec(grad) +  L2NormVec(gradapprox)                                         
difference =  numerator/denominator 
if(difference > 1e-5){
    cat("There is a mistake, the difference is too high",difference)
} else{
    cat("The implementations works perfectly", difference)
}
## The implementations works perfectly 1.279911e-06
# This can be used to check
print("Gradients from backprop")
## [1] "Gradients from backprop"
vector_to_list2(parameters,grad)
## $dW1
##               [,1]          [,2]
## [1,] -7.641588e-05 -3.427989e-07
## [2,] -9.049683e-06  6.906304e-05
## [3,]  3.401039e-06 -1.503914e-04
## [4,]  1.535226e-04 -1.686402e-04
## [5,] -6.029292e-05 -2.715648e-04
## 
## $db1
##               [,1]
## [1,]  6.930318e-06
## [2,] -3.283117e-05
## [3,]  1.310647e-05
## [4,] -3.454308e-05
## [5,] -2.331729e-08
## 
## $dW2
##              [,1]         [,2]         [,3]        [,4]         [,5]
## [1,] 0.0001612356 0.0001113475 0.0002435824 0.000362149 2.874116e-05
## 
## $db2
##              [,1]
## [1,] -1.16364e-05
print("Grad approx from gradient check")
## [1] "Grad approx from gradient check"
vector_to_list2(parameters,gradapprox)
## $dW1
##               [,1]          [,2]
## [1,] -7.641554e-05 -3.430589e-07
## [2,] -9.049428e-06  6.906253e-05
## [3,]  3.401168e-06 -1.503919e-04
## [4,]  1.535228e-04 -1.686401e-04
## [5,] -6.029288e-05 -2.715650e-04
## 
## $db1
##               [,1]
## [1,]  6.930012e-06
## [2,] -3.283096e-05
## [3,]  1.310618e-05
## [4,] -3.454237e-05
## [5,] -2.275957e-08
## 
## $dW2
##              [,1]         [,2]         [,3]         [,4]        [,5]
## [1,] 0.0001612355 0.0001113476 0.0002435829 0.0003621486 2.87409e-05
## 
## $db2
##              [,1]
## [1,] -1.16368e-05

1.2b Gradient Check – Softmax Activation – R

source("DLfunctions8.R")
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)
layersDimensions = c(2, 3, 3)
parameters = initializeDeepModel(layersDimensions)
#Perform forward prop
retvals = forwardPropagationDeep(X, parameters,keep_prob=1, hiddenActivationFunc="relu",
                                 outputActivationFunc="softmax")
AL <- retvals[['AL']]
caches <- retvals[['caches']]
dropoutMat <- retvals[['dropoutMat']]
#Compute cost
cost <- computeCost(AL, Y,outputActivationFunc="softmax",
                    numClasses=layersDimensions[length(layersDimensions)])
print(cost)
## [1] 1.098618
# Backward propagation.
gradients = backwardPropagationDeep(AL, Y, caches, dropoutMat, lambd=0, keep_prob=1, hiddenActivationFunc="relu",
                                    outputActivationFunc="softmax",numClasses=layersDimensions[length(layersDimensions)])
# Need to take transpose of the last layer for Softmax
L=length(parameters)/2
gradients[[paste('dW',L,sep="")]]=t(gradients[[paste('dW',L,sep="")]])
gradients[[paste('db',L,sep="")]]=t(gradients[[paste('db',L,sep="")]])
#Perform gradient check
gradient_check_n(parameters, gradients, X, Y, 
                 epsilon = 1e-7,outputActivationFunc="softmax")
## The implementations works perfectly 3.903011e-07[1] "Gradients from backprop"
## $dW1
##              [,1]          [,2]
## [1,] 0.0007962367 -0.0001907606
## [2,] 0.0004444254  0.0010354412
## [3,] 0.0003078611  0.0007591255
## 
## $db1
##               [,1]
## [1,] -0.0017305136
## [2,]  0.0005393734
## [3,]  0.0012484550
## 
## $dW2
##               [,1]          [,2]          [,3]
## [1,] -3.515627e-04  7.487283e-04 -3.971656e-04
## [2,] -6.381521e-05 -1.257328e-06  6.507254e-05
## [3,] -1.719479e-04 -4.857264e-04  6.576743e-04
## 
## $db2
##               [,1]
## [1,] -5.536383e-06
## [2,] -1.824656e-05
## [3,]  2.378295e-05
## 
## [1] "Grad approx from gradient check"
## $dW1
##              [,1]          [,2]
## [1,] 0.0007962364 -0.0001907607
## [2,] 0.0004444256  0.0010354406
## [3,] 0.0003078615  0.0007591250
## 
## $db1
##               [,1]
## [1,] -0.0017305135
## [2,]  0.0005393741
## [3,]  0.0012484547
## 
## $dW2
##               [,1]          [,2]          [,3]
## [1,] -3.515632e-04  7.487277e-04 -3.971656e-04
## [2,] -6.381451e-05 -1.257883e-06  6.507239e-05
## [3,] -1.719469e-04 -4.857270e-04  6.576739e-04
## 
## $db2
##               [,1]
## [1,] -5.536682e-06
## [2,] -1.824652e-05
## [3,]  2.378209e-05

1.3a Gradient Check – Sigmoid Activation – Octave

source("DL8functions.m")
################## Circles
data=csvread("circles.csv");

X=data(:,1:2);
Y=data(:,3);
#Set layer dimensions
layersDimensions = [2 5  1]; #tanh=-0.5(ok), #relu=0.1 best!
[weights biases] = initializeDeepModel(layersDimensions);
#Perform forward prop
[AL forward_caches activation_caches droputMat] = forwardPropagationDeep(X', weights, biases,keep_prob=1, 
                 hiddenActivationFunc="relu", outputActivationFunc="sigmoid");
#Compute cost
cost = computeCost(AL, Y',outputActivationFunc=outputActivationFunc,numClasses=layersDimensions(size(layersDimensions)(2)));
disp(cost);
#Compute gradients from cost
[gradsDA gradsDW gradsDB] = backwardPropagationDeep(AL, Y', activation_caches,forward_caches, droputMat, lambd=0, keep_prob=1, 
                                 hiddenActivationFunc="relu", outputActivationFunc="sigmoid",
                                 numClasses=layersDimensions(size(layersDimensions)(2)));
epsilon = 1e-07;
outputActivationFunc="sigmoid";
# Convert paramter cell array to vector
parameters_values = cellArray_to_vector(weights, biases);
#Convert gradient cell array to vector
grad = gradients_to_vector(gradsDW,gradsDB);
num_parameters = size(parameters_values)(1);
#Initialize
J_plus = zeros(num_parameters, 1);
J_minus = zeros(num_parameters, 1);
gradapprox = zeros(num_parameters, 1);
# Compute gradapprox
for i = 1:num_parameters
    # Compute J_plus[i]. 
    thetaplus = parameters_values;                                   
    thetaplus(i,1) = thetaplus(i,1) + epsilon;      
    [weights1 biases1] =vector_to_cellArray(weights, biases,thetaplus);    
    [AL forward_caches activation_caches droputMat] = forwardPropagationDeep(X', weights1, biases1, keep_prob=1, 
                                              hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc);
    J_plus(i) = computeCost(AL, Y', outputActivationFunc=outputActivationFunc); 
        
    # Compute J_minus[i]. 
    thetaminus = parameters_values;                                  
    thetaminus(i,1) = thetaminus(i,1) - epsilon ;    
    [weights1 biases1] = vector_to_cellArray(weights, biases,thetaminus);    
    [AL forward_caches activation_caches droputMat]  = forwardPropagationDeep(X',weights1, biases1, keep_prob=1, 
                                              hiddenActivationFunc="relu",outputActivationFunc=outputActivationFunc);     
    J_minus(i) = computeCost(AL, Y', outputActivationFunc=outputActivationFunc);                            
      
    # Compute gradapprox[i]   
    gradapprox(i) = (J_plus(i) - J_minus(i))/(2*epsilon);

endfor

#Compute L2Norm
numerator = L2NormVec(grad-gradapprox);                                           
denominator = L2NormVec(grad) +  L2NormVec(gradapprox);                                         
difference =  numerator/denominator;
disp(difference);
#Check difference
if difference > 1e-04
   printf("There is a mistake in the implementation ");
   disp(difference);
else
   printf("The implementation works perfectly");
      disp(difference);
endif
[weights1 biases1] = vector_to_cellArray(weights, biases,grad);  
printf("Gradients from back propagation"); 
disp(weights1);
disp(biases1); 
[weights2 biases2] = vector_to_cellArray(weights, biases,gradapprox); 
printf("Gradients from gradient check");
disp(weights2);
disp(biases2); 
0.69315
1.4893e-005
The implementation works perfectly 1.4893e-005
Gradients from back propagation
{
[1,1] =
5.0349e-005 2.1323e-005
8.8632e-007 1.8231e-006
9.3784e-005 1.0057e-004
1.0875e-004 -1.9529e-007
5.4502e-005 3.2721e-005
[1,2] =
1.0567e-005 6.0615e-005 4.6004e-005 1.3977e-004 1.0405e-004
}
{
[1,1] =
-1.8716e-005
1.1309e-009
4.7686e-005
1.2051e-005
-1.4612e-005
[1,2] = 9.5808e-006
}
Gradients from gradient check
{
[1,1] =
5.0348e-005 2.1320e-005
8.8485e-007 1.8219e-006
9.3784e-005 1.0057e-004
1.0875e-004 -1.9762e-007
5.4502e-005 3.2723e-005
[1,2] =
[1,2] =
1.0565e-005 6.0614e-005 4.6007e-005 1.3977e-004 1.0405e-004
}
{
[1,1] =
-1.8713e-005
1.1102e-009
4.7687e-005
1.2048e-005
-1.4609e-005
[1,2] = 9.5790e-006
}

1.3b Gradient Check – Softmax Activation – Octave

source("DL8functions.m")
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);
# Set the layer dimensions
layersDimensions = [2 3  3]; 
[weights biases] = initializeDeepModel(layersDimensions);
# Run forward prop
[AL forward_caches activation_caches droputMat] = forwardPropagationDeep(X', weights, biases,keep_prob=1, 
                 hiddenActivationFunc="relu", outputActivationFunc="softmax");
# Compute cost
cost = computeCost(AL, Y',outputActivationFunc=outputActivationFunc,numClasses=layersDimensions(size(layersDimensions)(2)));
disp(cost);
# Perform backward prop
[gradsDA gradsDW gradsDB] = backwardPropagationDeep(AL, Y', activation_caches,forward_caches, droputMat, lambd=0, keep_prob=1, 
                                 hiddenActivationFunc="relu", outputActivationFunc="softmax",
                                 numClasses=layersDimensions(size(layersDimensions)(2)));

#Take transpose of last layer for Softmax                                
L=size(weights)(2);
gradsDW{L}= gradsDW{L}';
gradsDB{L}= gradsDB{L}';   
#Perform gradient check
difference= gradient_check_n(weights, biases, gradsDW,gradsDB, X, Y, epsilon = 1e-7,
                  outputActivationFunc="softmax",numClasses=layersDimensions(size(layersDimensions)(2)));
 1.0986
The implementation works perfectly  2.0021e-005
Gradients from back propagation
{
  [1,1] =
    -7.1590e-005  4.1375e-005
    -1.9494e-004  -5.2014e-005
    -1.4554e-004  5.1699e-005
  [1,2] =
    3.3129e-004  1.9806e-004  -1.5662e-005
    -4.9692e-004  -3.7756e-004  -8.2318e-005
    1.6562e-004  1.7950e-004  9.7980e-005
}
{
  [1,1] =
    -3.0856e-005
    -3.3321e-004
    -3.8197e-004
  [1,2] =
    1.2046e-006
    2.9259e-007
    -1.4972e-006
}
Gradients from gradient check
{
  [1,1] =
    -7.1586e-005  4.1377e-005
    -1.9494e-004  -5.2013e-005
    -1.4554e-004  5.1695e-005
    3.3129e-004  1.9806e-004  -1.5664e-005
    -4.9692e-004  -3.7756e-004  -8.2316e-005
    1.6562e-004  1.7950e-004  9.7979e-005
}
{
  [1,1] =
    -3.0852e-005
    -3.3321e-004
    -3.8197e-004
  [1,2] =
    1.1902e-006
    2.8200e-007
    -1.4644e-006
}

2.1 Tip for tuning hyperparameters

Deep Learning Networks come with a large number of hyper parameters which require tuning. The hyper parameters are

1. \alpha -learning rate
2. Number of layers
3. Number of hidden units
4. Number of iterations
5. Momentum – \beta – 0.9
6. RMSProp – \beta_{1} – 0.9
7. Adam – \beta_{1},\beta_{2} and \epsilon
8. learning rate decay
9. mini batch size
10. Initialization method – He, Xavier
11. Regularization

– Among the above the most critical is learning rate \alpha . Rather than just trying out random values, it may help to try out values on a logarithmic scale. So we could try out values -0.01,0.1,1.0,10 etc. If we find that the cost is between 0.01 and 0.1 we could use a technique similar to binary search or bisection, so we can try 0.01, 0.05. If we need to be bigger than 0.01 and 0.05 we could try 0.25  and then keep halving the distance etc.
– The performance of Momentum and RMSProp are very good and work well with values 0.9. Even with this, it is better to try out values of 1-\beta in the logarithmic range. So 1-\beta could 0.001,0.01,0.1 and hence \beta would be 0.999,0.99 or 0.9
– Increasing the number of hidden units or number of hidden layers need to be done gradually. I have noticed that increasing number of hidden layers heavily does not improve performance and sometimes degrades it.
– Sometimes, I tend to increase the number of iterations if I think I see a steady decrease in the cost for a certain learning rate
– It may also help to add learning rate decay if you see there is an oscillation while it decreases.
– Xavier and He initializations also help in a fast convergence and are worth trying out.

3.1 Final thoughts

As I come to a close in this Deep Learning Series from first principles in Python, R and Octave, I must admit that I learnt a lot in the process.

* Building a L-layer, vectorized Deep Learning Network in Python, R and Octave was extremely challenging but very rewarding
* One benefit of building vectorized versions in Python, R and Octave was that I was looking at each function that I was implementing thrice, and hence I was able to fix any bugs in any of the languages
* In addition since I built the generic L-Layer DL network with all the bells and whistles, layer by layer I further had an opportunity to look at all the functions in each successive post.
* Each language has its advantages and disadvantages. From the performance perspective I think Python is the best, followed by Octave and then R
* Interesting, I noticed that even if small bugs creep into your implementation, the DL network does learn and does generate a valid set of weights and biases, however this may not be an optimum solution. In one case of an inadvertent bug, I was not updating the weights in the final layer of the DL network. Yet, using all the other layers, the DL network was able to come with a reasonable solution (maybe like random dropout, remaining units can still learn the data!)
* Having said that, the Gradient Check method discussed and implemented in this post can be very useful in ironing out bugs.
Feel free to clone/download the code from Github at DeepLearning-Part8

Conclusion

These last couple of months when I was writing the posts and the also churning up the code in Python, R and Octave were  very hectic. There have been times when I found that implementations of some function to be extremely demanding and I almost felt like giving up. Other times, I have spent quite some time on an intractable DL network which would not respond to changes in hyper-parameters. All in all, it was a great learning experience. I would suggest that you start from my first post Deep Learning from first principles in Python, R and Octave-Part 1 and work your way up. Feel free to take the code apart and try out things. That is the only way you will learn.

Hope you had as much fun as I had. Stay tuned. I will be back!!!

Also see
1. My book ‘Practical Machine Learning with R and Python’ on Amazon
2. Revisiting crimes against women in India
3. Literacy in India – A deepR dive
4. Sixer – R package cricketr’s new Shiny avatar
5. Bend it like Bluemix, MongoDB using Auto-scale – Part 1!
6. Computer Vision: Ramblings on derivatives, histograms and contours
7. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
8. A closer look at “Robot Horse on a Trot” in Android

To see all post click Index of Posts

Deep Learning from first principles in Python, R and Octave – Part 7


Artificial Intelligence is the new electricity. – Prof Andrew Ng

Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI.  – Yann LeCun, March 14, 2016 (Facebook)

Introduction

In this post ‘Deep Learning from first principles with Python, R and Octave-Part 7’, I implement optimization methods used in Stochastic Gradient Descent (SGD) to speed up the convergence. Specifically I discuss and implement the following gradient descent optimization techniques

a.Vanilla Stochastic Gradient Descent
b.Learning rate decay
c. Momentum method
d. RMSProp
e. Adaptive Moment Estimation (Adam)

This post, further enhances my generic  L-Layer Deep Learning Network implementations in  vectorized Python, R and Octave to also include the Stochastic Gradient Descent optimization techniques. You can clone/download the code from Github at DeepLearning-Part7

Incidentally, a good discussion of the various optimizations methods used in Stochastic Gradient Optimization techniques can be seen at Sebastian Ruder’s blog

Note: In the vectorized Python, R and Octave implementations below only a  1024 random training samples were used. This was to reduce the computation time. You are free to use the entire data set (60000 training data) for the computation.

This post is largely based of on Prof Andrew Ng’s Deep Learning Specialization.  All the above optimization techniques for Stochastic Gradient Descent are based on the technique of exponentially weighted average method. So for example if we had some time series data \theta_{1},\theta_{2},\theta_{3}... \theta_{t} then we we can represent the exponentially average value at time ‘t’ as a sequence of the the previous value v_{t-1} and \theta_{t} as shown below
v_{t} = \beta v_{t-1} + (1-\beta)\theta_{t}

Here v_{t} represent the average of the data set over \frac {1}{1-\beta}  By choosing different values of \beta, we can average over a larger or smaller number of the data points.
We can write the equations as follows
v_{t} = \beta v_{t-1} + (1-\beta)\theta_{t}
v_{t-1} = \beta v_{t-2} + (1-\beta)\theta_{t-1}
v_{t-2} = \beta v_{t-3} + (1-\beta)\theta_{t-2}
and
v_{t-k} = \beta v_{t-(k+1))} + (1-\beta)\theta_{t-k}
By substitution we have
v_{t} = (1-\beta)\theta_{t} + \beta v_{t-1}
v_{t} = (1-\beta)\theta_{t} + \beta ((1-\beta)\theta_{t-1}) + \beta v_{t-2}
v_{t} = (1-\beta)\theta_{t} + \beta ((1-\beta)\theta_{t-1}) + \beta ((1-\beta)\theta_{t-2}+ \beta v_{t-3} )

Hence it can be seen that the v_{t} is the weighted sum over the previous values \theta_{k}, which is an exponentially decaying function.

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99) and in kindle version($6.65/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python- Machine Learning in stereo” available in Amazon in paperback($9.99) and Kindle($6.99) versions. This book is ideal for a quick reference of the various ML functions and associated measurements in both R and Python which are essential to delve deep into Deep Learning.

1.1a. Stochastic Gradient Descent (Vanilla) – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read the training data
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of 1024 random numbers.
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
# Set the layer dimensions  
layersDimensions=[784, 15,9,10] 
# Perform SGD with regular gradient descent
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="gd",
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig1.png")

1.1b. Stochastic Gradient Descent (Vanilla) – R

source("mnist.R")
source("DLfunctions7.R")
#Load and read MNIST data
load_mnist() 
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
# Set layer dimensions
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with regular gradient descent
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                            hiddenActivationFunc='tanh',
                            outputActivationFunc="softmax",
                            learningRate = 0.05,
                            optimizer="gd",
                            mini_batch_size = 512, 
                            num_epochs = 5000, 
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs no of epochs") + xlab("No of epochss") + ylab("Cost")

1.1c. Stochastic Gradient Descent (Vanilla) – Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with regular gradient descent
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.005,
 lrDecay=true, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="gd",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);

plotCostVsEpochs(5000,costs);

2.1. Stochastic Gradient Descent with Learning rate decay

Since in Stochastic Gradient Descent,with  each epoch, we use slight different samples, the gradient descent algorithm, oscillates across the ravines and wanders around the minima, when a fixed learning rate is used. In this technique of ‘learning rate decay’ the learning rate is slowly decreased with the number of epochs and becomes smaller and smaller, so that gradient descent can take smaller steps towards the minima.

There are several techniques employed in learning rate decay

a) Exponential decay: \alpha = decayRate^{epochNum} *\alpha_{0}
b) 1/t decay : \alpha = \frac{\alpha_{0}}{1 + decayRate*epochNum}
c) \alpha = \frac {decayRate}{\sqrt(epochNum)}*\alpha_{0}

In my implementation I have used the ‘exponential decay’. The code snippet for Python is shown below

if lrDecay == True:
   learningRate = np.power(decayRate,(num_epochs/1000)) * learningRate

2.1a. Stochastic Gradient Descent with Learning rate decay – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read the MNIST data
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
# Set layer dimensions
layersDimensions=[784, 15,9,10] 
# Perform SGD with learning rate decay
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",
                                   learningRate = 0.01 , lrDecay=True, decayRate=0.9999,
                                   optimizer="gd",
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig2.png")

2.1b. Stochastic Gradient Descent with Learning rate decay – R

source("mnist.R")
source("DLfunctions7.R")
# Read and load MNIST
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
# Set layer dimensions
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with Learning rate decay
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.05,
                                  lrDecay=TRUE,
                                  decayRate=0.9999,
                                  optimizer="gd",
                                  mini_batch_size = 512, 
                                  num_epochs = 5000, 
                                  print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

2.1c. Stochastic Gradient Descent with Learning rate decay – Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with regular Learning rate decay
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.01,
 lrDecay=true, 
 decayRate=0.999,
 lambd=0,
 keep_prob=1,
 optimizer="gd",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);
plotCostVsEpochs(5000,costs)

3.1. Stochastic Gradient Descent with Momentum

Stochastic Gradient Descent with Momentum uses the exponentially weighted average method discusses above and more generally moves faster into the ravine than across it. The equations are
v_{dW}^l = \beta v_{dW}^l + (1-\beta)dW^{l}
v_{db}^l = \beta v_{db}^l + (1-\beta)db^{l}
W^{l} = W^{l} - \alpha v_{dW}^l
b^{l} = b^{l} - \alpha v_{db}^l where
v_{dW} and v_{db} are the momentum terms which are exponentially weighted with the corresponding gradients ‘dW’ and ‘db’ at the corresponding layer ‘l’ The code snippet for Stochastic Gradient Descent with momentum in R is shown below

# Perform Gradient Descent with momentum
# Input : Weights and biases
#       : beta
#       : gradients
#       : learning rate
#       : outputActivationFunc - Activation function at hidden layer sigmoid/softmax
#output : Updated weights after 1 iteration
gradientDescentWithMomentum  <- function(parameters, gradients,v, beta, learningRate,outputActivationFunc="sigmoid"){

    L = length(parameters)/2 # number of layers in the neural network    
    # Update rule for each parameter. Use a for loop.
    for(l in 1:(L-1)){
        # Compute velocities
        # v['dWk'] = beta *v['dWk'] + (1-beta)*dWk
        v[[paste("dW",l, sep="")]] = beta*v[[paste("dW",l, sep="")]] + 
                   (1-beta) * gradients[[paste('dW',l,sep="")]]
        v[[paste("db",l, sep="")]] = beta*v[[paste("db",l, sep="")]] + 
            (1-beta) * gradients[[paste('db',l,sep="")]]
        
        parameters[[paste("W",l,sep="")]] = parameters[[paste("W",l,sep="")]] -
            learningRate* v[[paste("dW",l, sep="")]] 
        parameters[[paste("b",l,sep="")]] = parameters[[paste("b",l,sep="")]] -
            learningRate* v[[paste("db",l, sep="")]] 
    }    
    # Compute for the Lth layer
    if(outputActivationFunc=="sigmoid"){
        v[[paste("dW",L, sep="")]] = beta*v[[paste("dW",L, sep="")]] + 
            (1-beta) * gradients[[paste('dW',L,sep="")]]
        v[[paste("db",L, sep="")]] = beta*v[[paste("db",L, sep="")]] + 
            (1-beta) * gradients[[paste('db',L,sep="")]]
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate* v[[paste("dW",l, sep="")]]  
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate* v[[paste("db",l, sep="")]]
        
    }else if (outputActivationFunc=="softmax"){
        v[[paste("dW",L, sep="")]] = beta*v[[paste("dW",L, sep="")]] + 
            (1-beta) * t(gradients[[paste('dW',L,sep="")]])
        v[[paste("db",L, sep="")]] = beta*v[[paste("db",L, sep="")]] + 
            (1-beta) * t(gradients[[paste('db',L,sep="")]])       
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate* t(gradients[[paste("dW",L,sep="")]])
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate* t(gradients[[paste("db",L,sep="")]])
    }
    return(parameters)
}

3.1a. Stochastic Gradient Descent with Momentum- Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
# Read and load data
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
layersDimensions=[784, 15,9,10] 
# Perform SGD with momentum
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="momentum", beta=0.9,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig3.png")

3.1b. Stochastic Gradient Descent with Momentum- R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
# Perform SGD with momentum
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.05,
                                  optimizer="momentum",
                                  beta=0.9,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000, 
                                  print_cost = True)

#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

3.1c. Stochastic Gradient Descent with Momentum- Octave

source("DL7functions.m")
#Load and read MNIST
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 60K
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
# Perform SGD with Momentum
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.01,
 lrDecay=false, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="momentum",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=10^-8,
 mini_batch_size = 512, 
 num_epochs = 5000);

plotCostVsEpochs(5000,costs)

4.1. Stochastic Gradient Descent with RMSProp

Stochastic Gradient Descent with RMSProp tries to move faster towards the minima while dampening the oscillations across the ravine.
The equations are

s_{dW}^l = \beta_{1} s_{dW}^l + (1-\beta_{1})(dW^{l})^{2}
s_{db}^l = \beta_{1} s_{db}^l + (1-\beta_{1})(db^{l})^2
W^{l} = W^{l} - \frac {\alpha s_{dW}^l}{\sqrt (s_{dW}^l + \epsilon) }
b^{l} = b^{l} - \frac {\alpha s_{db}^l}{\sqrt (s_{db}^l + \epsilon) }
where s_{dW} and s_{db} are the RMSProp terms which are exponentially weighted with the corresponding gradients ‘dW’ and ‘db’ at the corresponding layer ‘l’

The code snippet in Octave is shown below

# Update parameters with RMSProp
# Input : parameters
#       : gradients
#       : s
#       : beta
#       : learningRate
#       : 
#output : Updated parameters RMSProp
function [weights biases] = gradientDescentWithRMSProp(weights, biases,gradsDW,gradsDB, sdW, sdB, beta1, epsilon, learningRate,outputActivationFunc="sigmoid")
    L = size(weights)(2); # number of layers in the neural network
    # Update rule for each parameter. 
    for l=1:(L-1)
        sdW{l} =  beta1*sdW{l} + (1 -beta1) * gradsDW{l} .* gradsDW{l};
        sdB{l} =  beta1*sdB{l} + (1 -beta1) * gradsDB{l} .* gradsDB{l};
        weights{l} = weights{l} - learningRate* gradsDW{l} ./ sqrt(sdW{l} + epsilon); 
        biases{l} = biases{l} -  learningRate* gradsDB{l} ./ sqrt(sdB{l} + epsilon);
    endfor
  
    if (strcmp(outputActivationFunc,"sigmoid"))
        sdW{L} =  beta1*sdW{L} + (1 -beta1) * gradsDW{L} .* gradsDW{L};
        sdB{L} =  beta1*sdB{L} + (1 -beta1) * gradsDB{L} .* gradsDB{L};
        weights{L} = weights{L} -learningRate* gradsDW{L} ./ sqrt(sdW{L} +epsilon); 
        biases{L} = biases{L} -learningRate* gradsDB{L} ./ sqrt(sdB{L} + epsilon);
     elseif (strcmp(outputActivationFunc,"softmax"))
        sdW{L} =  beta1*sdW{L} + (1 -beta1) * gradsDW{L}' .* gradsDW{L}';
        sdB{L} =  beta1*sdB{L} + (1 -beta1) * gradsDB{L}' .* gradsDB{L}';
        weights{L} = weights{L} -learningRate* gradsDW{L}' ./ sqrt(sdW{L} +epsilon); 
        biases{L} = biases{L} -learningRate* gradsDB{L}' ./ sqrt(sdB{L} + epsilon);
     endif   
end

4.1a. Stochastic Gradient Descent with RMSProp – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())

# Read and load MNIST
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T

print("X1=",X1.shape)
print("y1=",Y1.shape)

# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
  
layersDimensions=[784, 15,9,10] 
# Use SGD with RMSProp
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="rmsprop", beta1=0.7, epsilon=1e-8,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True,figure="fig4.png")

4.1b. Stochastic Gradient Descent with RMSProp – R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
#Perform SGD with RMSProp
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.001,
                                  optimizer="rmsprop",
                                  beta1=0.9,
                                  epsilon=10^-8,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000 , 
                                  print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

4.1c. Stochastic Gradient Descent with RMSProp – Octave

source("DL7functions.m")
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];
#Perform SGD with RMSProp
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
 hiddenActivationFunc='relu', 
 outputActivationFunc="softmax",
 learningRate = 0.005,
 lrDecay=false, 
 decayRate=1,
 lambd=0,
 keep_prob=1,
 optimizer="rmsprop",
 beta=0.9,
 beta1=0.9,
 beta2=0.999,
 epsilon=1,
 mini_batch_size = 512, 
 num_epochs = 5000);
plotCostVsEpochs(5000,costs)

5.1. Stochastic Gradient Descent with Adam

Adaptive Moment Estimate is a combination of the momentum (1st moment) and RMSProp(2nd moment). The equations for Adam are below
v_{dW}^l = \beta_{1} v_{dW}^l + (1-\beta_{1})dW^{l}
v_{db}^l = \beta_{1} v_{db}^l + (1-\beta_{1})db^{l}
The bias corrections for the 1st moment
vCorrected_{dW}^l= \frac {v_{dW}^l}{1 - \beta_{1}^{t}}
vCorrected_{db}^l= \frac {v_{db}^l}{1 - \beta_{1}^{t}}

Similarly the moving average for the 2nd moment- RMSProp
s_{dW}^l = \beta_{2} s_{dW}^l + (1-\beta_{2})(dW^{l})^2
s_{db}^l = \beta_{2} s_{db}^l + (1-\beta_{2})(db^{l})^2
The bias corrections for the 2nd moment
sCorrected_{dW}^l= \frac {s_{dW}^l}{1 - \beta_{2}^{t}}
sCorrected_{db}^l= \frac {s_{db}^l}{1 - \beta_{2}^{t}}

The Adam Gradient Descent is given by
W^{l} = W^{l} - \frac {\alpha vCorrected_{dW}^l}{\sqrt (s_{dW}^l + \epsilon) }
b^{l} = b^{l} - \frac {\alpha vCorrected_{db}^l}{\sqrt (s_{db}^l + \epsilon) }
The code snippet of Adam in R is included below

# Perform Gradient Descent with Adam
# Input : Weights and biases
#       : beta1
#       : epsilon
#       : gradients
#       : learning rate
#       : outputActivationFunc - Activation function at hidden layer sigmoid/softmax
#output : Updated weights after 1 iteration
gradientDescentWithAdam  <- function(parameters, gradients,v, s, t, 
                        beta1=0.9, beta2=0.999, epsilon=10^-8, learningRate=0.1,outputActivationFunc="sigmoid"){
    
    L = length(parameters)/2 # number of layers in the neural network
    v_corrected <- list()
    s_corrected <- list()
    # Update rule for each parameter. Use a for loop.
    for(l in 1:(L-1)){
        # v['dWk'] = beta *v['dWk'] + (1-beta)*dWk
        v[[paste("dW",l, sep="")]] = beta1*v[[paste("dW",l, sep="")]] + 
            (1-beta1) * gradients[[paste('dW',l,sep="")]]
        v[[paste("db",l, sep="")]] = beta1*v[[paste("db",l, sep="")]] + 
            (1-beta1) * gradients[[paste('db',l,sep="")]]
        
        
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",l, sep="")]] = v[[paste("dW",l, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",l, sep="")]] = v[[paste("db",l, sep="")]]/(1-beta1^t)
               
        # Element wise multiply of gradients
        s[[paste("dW",l, sep="")]] = beta2*s[[paste("dW",l, sep="")]] + 
            (1-beta2) * gradients[[paste('dW',l,sep="")]] * gradients[[paste('dW',l,sep="")]]
        s[[paste("db",l, sep="")]] = beta2*s[[paste("db",l, sep="")]] + 
            (1-beta2) * gradients[[paste('db',l,sep="")]] * gradients[[paste('db',l,sep="")]]
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",l, sep="")]] = s[[paste("dW",l, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",l, sep="")]] = s[[paste("db",l, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",l, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",l, sep="")]]+epsilon)        
                
        parameters[[paste("W",l,sep="")]] = parameters[[paste("W",l,sep="")]] -
            learningRate * v_corrected[[paste("dW",l, sep="")]]/d1
        parameters[[paste("b",l,sep="")]] = parameters[[paste("b",l,sep="")]] -
            learningRate*v_corrected[[paste("db",l, sep="")]]/d2
    }    
    # Compute for the Lth layer
    if(outputActivationFunc=="sigmoid"){
        v[[paste("dW",L, sep="")]] = beta1*v[[paste("dW",L, sep="")]] + 
            (1-beta1) * gradients[[paste('dW',L,sep="")]]
        v[[paste("db",L, sep="")]] = beta1*v[[paste("db",L, sep="")]] + 
            (1-beta1) * gradients[[paste('db',L,sep="")]]
                
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",L, sep="")]] = v[[paste("dW",L, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",L, sep="")]] = v[[paste("db",L, sep="")]]/(1-beta1^t)
                
        # Element wise multiply of gradients
        s[[paste("dW",L, sep="")]] = beta2*s[[paste("dW",L, sep="")]] + 
            (1-beta2) * gradients[[paste('dW',L,sep="")]] * gradients[[paste('dW',L,sep="")]]
        s[[paste("db",L, sep="")]] = beta2*s[[paste("db",L, sep="")]] + 
            (1-beta2) * gradients[[paste('db',L,sep="")]] * gradients[[paste('db',L,sep="")]]
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",L, sep="")]] = s[[paste("dW",L, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",L, sep="")]] = s[[paste("db",L, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",L, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",L, sep="")]]+epsilon)  
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate * v_corrected[[paste("dW",L, sep="")]]/d1
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate*v_corrected[[paste("db",L, sep="")]]/d2
        
    }else if (outputActivationFunc=="softmax"){
        v[[paste("dW",L, sep="")]] = beta1*v[[paste("dW",L, sep="")]] + 
            (1-beta1) * t(gradients[[paste('dW',L,sep="")]])
        v[[paste("db",L, sep="")]] = beta1*v[[paste("db",L, sep="")]] + 
            (1-beta1) * t(gradients[[paste('db',L,sep="")]])
                
        # Compute bias-corrected first moment estimate. 
        v_corrected[[paste("dW",L, sep="")]] = v[[paste("dW",L, sep="")]]/(1-beta1^t)
        v_corrected[[paste("db",L, sep="")]] = v[[paste("db",L, sep="")]]/(1-beta1^t)        
        
        # Element wise multiply of gradients
        s[[paste("dW",L, sep="")]] = beta2*s[[paste("dW",L, sep="")]] + 
            (1-beta2) * t(gradients[[paste('dW',L,sep="")]]) * t(gradients[[paste('dW',L,sep="")]])
        s[[paste("db",L, sep="")]] = beta2*s[[paste("db",L, sep="")]] + 
            (1-beta2) * t(gradients[[paste('db',L,sep="")]]) * t(gradients[[paste('db',L,sep="")]])
        
        # Compute bias-corrected second moment estimate. 
        s_corrected[[paste("dW",L, sep="")]] = s[[paste("dW",L, sep="")]]/(1-beta2^t)
        s_corrected[[paste("db",L, sep="")]] = s[[paste("db",L, sep="")]]/(1-beta2^t)
        
        # Update parameters. 
        d1=sqrt(s_corrected[[paste("dW",L, sep="")]]+epsilon)
        d2=sqrt(s_corrected[[paste("db",L, sep="")]]+epsilon) 
        
        parameters[[paste("W",L,sep="")]] = parameters[[paste("W",L,sep="")]] -
            learningRate * v_corrected[[paste("dW",L, sep="")]]/d1
        parameters[[paste("b",L,sep="")]] = parameters[[paste("b",L,sep="")]] -
            learningRate*v_corrected[[paste("db",L, sep="")]]/d2
    }
    return(parameters)
}

5.1a. Stochastic Gradient Descent with Adam – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions7.py").read())
exec(open("load_mnist.py").read())
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
lbls=[]
pxls=[]
print(len(training))
#for i in range(len(training)):
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T


# Create  a list of random numbers of 1024
permutation = list(np.random.permutation(2**10))
# Subset 16384 from the data
X2 = X1[:, permutation]
Y2 = Y1[:, permutation].reshape((1,2**10))
layersDimensions=[784, 15,9,10] 
#Perform SGD with Adam optimization
parameters = L_Layer_DeepModel_SGD(X2, Y2, layersDimensions, hiddenActivationFunc='relu', 
                                   outputActivationFunc="softmax",learningRate = 0.01 ,
                                   optimizer="adam", beta1=0.9, beta2=0.9, epsilon = 1e-8,
                                   mini_batch_size =512, num_epochs = 1000, print_cost = True, figure="fig5.png")

5.1b. Stochastic Gradient Descent with Adam – R

source("mnist.R")
source("DLfunctions7.R")
load_mnist()
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 1024 random samples from MNIST 
permutation = c(sample(2^10))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)
layersDimensions=c(784, 15,9, 10) 
#Perform SGD with Adam
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                                  hiddenActivationFunc='tanh',
                                  outputActivationFunc="softmax",
                                  learningRate = 0.005,
                                  optimizer="adam",
                                  beta1=0.7,
                                  beta2=0.9,
                                  epsilon=10^-8,
                                  mini_batch_size = 512, 
                                  num_epochs = 5000 , 
                                  print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvalsSGD$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs number of epochs") + xlab("No of epochs") + ylab("Cost")

5.1c. Stochastic Gradient Descent with Adam – Octave

source("DL7functions.m")
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 1024
permutation = randperm(1024);
disp(length(permutation));

# Use this 1024 as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);
# Set layer dimensions
layersDimensions=[784, 15, 9, 10];

# Note the high value for epsilon. 
#Otherwise GD with Adam does not seem to converge   
# Perform SGD with Adam         
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
                       hiddenActivationFunc='relu', 
                       outputActivationFunc="softmax",
                       learningRate = 0.1,
                       lrDecay=false, 
                       decayRate=1,
                       lambd=0,
                       keep_prob=1,
                       optimizer="adam",
                       beta=0.9,
                       beta1=0.9,
                       beta2=0.9,
                       epsilon=100,
                       mini_batch_size = 512, 
                       num_epochs = 5000);
plotCostVsEpochs(5000,costs)

Conclusion: In this post I discuss and implement several Stochastic Gradient Descent optimization methods. The implementation of these methods enhance my already existing generic L-Layer Deep Learning Network implementation in vectorized Python, R and Octave, which I had discussed in the previous post in this series on Deep Learning from first principles in Python, R and Octave. Check it out, if you haven’t already. As already mentioned the code for this post can be cloned/forked from Github at DeepLearning-Part7

Watch this space! I’ll be back!

Also see
1.My book ‘Practical Machine Learning with R and Python’ on Amazon
2. Deep Learning from first principles in Python, R and Octave – Part 3
3. Experiments with deblurring using OpenCV
3. Design Principles of Scalable, Distributed Systems
4. Natural language processing: What would Shakespeare say?
5. yorkr crashes the IPL party! – Part 3!
6. cricketr flexes new muscles: The final analysis

To see all post click Index of posts

Deep Learning from first principles in Python, R and Octave – Part 6


“Today you are You, that is truer than true. There is no one alive who is Youer than You.”
Dr. Seuss

“Explanations exist; they have existed for all time; there is always a well-known solution to every human problem — neat, plausible, and wrong.”
H L Mencken

Introduction

In this 6th instalment of ‘Deep Learning from first principles in Python, R and Octave-Part6’, I look at a couple of different initialization techniques used in Deep Learning, L2 regularization and the ‘dropout’ method. Specifically, I implement “He initialization” & “Xavier Initialization”. My earlier posts in this series of Deep Learning included

1. Part 1 – In the 1st part, I implemented logistic regression as a simple 2 layer Neural Network
2. Part 2 – In part 2, implemented the most basic of Neural Networks, with just 1 hidden layer, and any number of activation units in that hidden layer. The implementation was in vectorized Python, R and Octave
3. Part 3 -In part 3, I derive the equations and also implement a L-Layer Deep Learning network with either the relu, tanh or sigmoid activation function in Python, R and Octave. The output activation unit was a sigmoid function for logistic classification
4. Part 4 – This part looks at multi-class classification, and I derive the Jacobian of a Softmax function and implement a simple problem to perform multi-class classification.
5. Part 5 – In the 5th part, I extend the L-Layer Deep Learning network implemented in Part 3, to include the Softmax classification. I also use this L-layer implementation to classify MNIST handwritten digits with Python, R and Octave.

The code in Python, R and Octave are identical, and just take into account some of the minor idiosyncrasies of the individual language. In this post, I implement different initialization techniques (random, He, Xavier), L2 regularization and finally dropout. Hence my generic L-Layer Deep Learning network includes these additional enhancements for enabling/disabling initialization methods, regularization or dropout in the algorithm. It already included sigmoid & softmax output activation for binary and multi-class classification, besides allowing relu, tanh and sigmoid activation for hidden units.

This R Markdown file and the code for Python, R and Octave can be cloned/downloaded from Github at DeepLearning-Part6

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99) and in kindle version($6.65/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python:Second Edition- Machine Learning in stereo” available in Amazon in paperback($10.99) and Kindle($7.99/Rs449) versions. This book is ideal for a quick reference of the various ML functions and associated measurements in both R and Python which are essential to delve deep into Deep Learning.

1. Initialization techniques

The usual initialization technique is to generate Gaussian or uniform random numbers and multiply it by a small value like 0.01. Two techniques which are used to speed up convergence is the He initialization or Xavier. These initialization techniques enable gradient descent to converge faster.

1.1 a Default initialization – Python

This technique just initializes the weights to small random values based on Gaussian or uniform distribution

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network with random initialization
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid",learningRate = 0.6, num_iterations = 9000, initType="default", print_cost = True,figure="fig1.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig2.png")

1.1 b He initialization – Python

‘He’ initialization attributed to He et al, multiplies the random weights by
\sqrt{\frac{2}{dimension\ of\ previous\ layer}}

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network with He  initialization
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid", learningRate =0.6,    num_iterations = 10000,initType="He",print_cost = True,                           figure="fig3.png")

plt.clf()
plt.close()
# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig4.png")


1.1 c Xavier initialization – Python

Xavier  initialization multiply the random weights by
\sqrt{\frac{1}{dimension\ of\ previous\ layer}}

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]
 
# Train a L layer Deep Learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid",
                            learningRate = 0.6,num_iterations = 10000, initType="Xavier",print_cost = True,
                            figure="fig5.png")

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig6.png")


1.2a Default initialization – R

source("DLfunctions61.R")
#Load the data
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
#Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 8000, 
                            initType="default",
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,8000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",lr=0.5)

1.2b He initialization – R

The code for ‘He’ initilaization in R is included below

# He Initialization model for L layers
# Input : List of units in each layer
# Returns: Initial weights and biases matrices for all layers
# He initilization multiplies the random numbers with sqrt(2/layerDimensions[previouslayer])
HeInitializeDeepModel <- function(layerDimensions){
    set.seed(2)
    
    # Initialize empty list
    layerParams <- list()
    
    # Note the Weight matrix at layer 'l' is a matrix of size (l,l-1)
    # The Bias is a vectors of size (l,1)
    
    # Loop through the layer dimension from 1.. L
    # Indices in R start from 1
    for(l in 2:length(layersDimensions)){
        # Initialize a matrix of small random numbers of size l x l-1
        # Create random numbers of size  l x l-1
        w=rnorm(layersDimensions[l]*layersDimensions[l-1])
        
        # Create a weight matrix of size l x l-1 with this initial weights and
        # Add to list W1,W2... WL
        # He initialization - Divide by sqrt(2/layerDimensions[previous layer])
        layerParams[[paste('W',l-1,sep="")]] = matrix(w,nrow=layersDimensions[l],
                                                      ncol=layersDimensions[l-1])*sqrt(2/layersDimensions[l-1])
        layerParams[[paste('b',l-1,sep="")]] = matrix(rep(0,layersDimensions[l]),
                                                      nrow=layersDimensions[l],ncol=1)
    }
    return(layerParams)
}

The code in R below uses He initialization to learn the data

source("DLfunctions61.R")
# Load the data
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
# Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 9000, 
                            initType="He",
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5,lr=0.5)

1.2c Xavier initialization – R

## Xav initialization 
# Set the layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            numIterations = 9000, 
                            initType="Xav",
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5)

1.3a Default initialization – Octave

source("DL61functions.m")
# Read the data
data=csvread("circles.csv");

X=data(:,1:2);
Y=data(:,3);
# Set the layer dimensions
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0, 
                               keep_prob=1,
                               numIterations = 10000,
                               initType="default");
# Plot cost vs iterations
plotCostVsIterations(10000,costs)  
#Plot decision boundary                            
plotDecisionBoundary(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu")

 

1.3b He initialization – Octave

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
# Set the layer dimensions
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0, 
                               keep_prob=1,
                               numIterations = 8000,
                               initType="He");
plotCostVsIterations(8000,costs)   
#Plot decision boundary                              
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

1.3c Xavier initialization – Octave

The code snippet for Xavier initialization in Octave is shown below

source("DL61functions.m")
# Xavier Initialization for L layers
# Input : List of units in each layer
# Returns: Initial weights and biases matrices for all layers
function [W b] = XavInitializeDeepModel(layerDimensions)
    rand ("seed", 3);
    # note the Weight matrix at layer 'l' is a matrix of size (l,l-1)
    # The Bias is a vectors of size (l,1)
    
    # Loop through the layer dimension from 1.. L
    # Create cell arrays for Weights and biases

    for l =2:size(layerDimensions)(2)
         W{l-1} = rand(layerDimensions(l),layerDimensions(l-1))* sqrt(1/layerDimensions(l-1)); #  Multiply by .01 
         b{l-1} = zeros(layerDimensions(l),1);       
   
    endfor
end

The Octave code below uses Xavier initialization

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
#Set layer dimensions
layersDimensions = [2 11 1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='relu',
outputActivationFunc="sigmoid",
learningRate = 0.5,
lambd=0,
keep_prob=1,
numIterations = 8000,
initType="Xav");

plotCostVsIterations(8000,costs)
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")



 

2.1a Regularization : Circles data – Python

The cross entropy cost for Logistic classification is given as J = \frac{1}{m}\sum_{i=1}^{m}y^{i}log((a^{L})^{(i)}) - (1-y^{i})log((a^{L})^{(i)}) The regularized L2 cost is given by J = \frac{1}{m}\sum_{i=1}^{m}y^{i}log((a^{L})^{(i)}) - (1-y^{i})log((a^{L})^{(i)}) + \frac{\lambda}{2m}\sum \sum \sum W_{kj}^{l}

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())

#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu',  
                               outputActivationFunc="sigmoid",learningRate = 0.6, lambd=0.1, num_iterations = 9000, 
                               initType="default", print_cost = True,figure="fig7.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), train_X, train_Y,str(0.6),figure1="fig8.png")


plt.clf()
plt.close()
#Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T,keep_prob=0.9), train_X, train_Y,str(2.2),"fig8.png",)

2.1 b Regularization: Spiral data  – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.clf()
plt.close() 
#Set layer dimensions 
layersDimensions = [2,100,3]
y1=y.reshape(-1,1).T
# Train a deep learning network
parameters = L_Layer_DeepModel(X.T, y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",
                           learningRate = 1,lambd=1e-3, num_iterations = 5000, print_cost = True,figure="fig9.png")

plt.clf()
plt.close()  
W1=parameters['W1']
b1=parameters['b1']
W2=parameters['W2']
b2=parameters['b2']
plot_decision_boundary1(X, y1,W1,b1,W2,b2,figure2="fig10.png")

 

2.2a Regularization: Circles data  – R

source("DLfunctions61.R")
#Load data
df=read.csv("circles.csv",header=FALSE)
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
#Set layer dimensions
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            lambd=0.1,
                            numIterations = 9000, 
                            initType="default",
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
    ggtitle("Costs vs iterations") + xlab("No of iterations") + ylab("Cost")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.5)

2.2b Regularization:Spiral data – R

# Read the spiral dataset
#Load the data
source("DLfunctions61.R")
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)
layersDimensions = c(2, 100, 3)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
hiddenActivationFunc='relu',
outputActivationFunc="softmax",
learningRate = 0.5,
lambd=0.01,
numIterations = 9000,
print_cost = True)
print_cost = True)
parameters<-retvals$parameters
plotDecisionBoundary1(Z,parameters)


2.3a Regularization: Circles data – Octave

source("DL61functions.m")
#Load data
data=csvread("circles.csv");
X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0.2,
                               keep_prob=1,
                               numIterations = 8000,
                               initType="default");

plotCostVsIterations(8000,costs)  
#Plot decision boundary                              
plotDecisionBoundary(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")

2.3b Regularization:Spiral data  2 – Octave

source("DL61functions.m")
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 100 3]
# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="softmax",
                               learningRate = 0.6,
                               lambd=0.2,
                               keep_prob=1,
                               numIterations = 10000);
                              
plotCostVsIterations(10000,costs)
#Plot decision boundary
plotDecisionBoundary1(data,weights, biases,keep_prob=1,hiddenActivationFunc="relu")  

3.1 a Dropout: Circles data – Python

The ‘dropout’ regularization technique was used with great effectiveness, to prevent overfitting  by Alex Krizhevsky, Ilya Sutskever and Prof Geoffrey E. Hinton in the Imagenet classification with Deep Convolutional Neural Networks

The technique of dropout works by dropping a random set of activation units in each hidden layer, based on a ‘keep_prob’ criteria in the forward propagation cycle. Here is the code for Octave. A ‘dropoutMat’ is created for each layer which specifies which units to drop Note: The same ‘dropoutMat has to be used which computing the gradients in the backward propagation cycle. Hence the dropout matrices are stored in a cell array.

 for l =1:L-1  
    ...      
    D=rand(size(A)(1),size(A)(2));
    D = (D < keep_prob) ;
    # Zero out some hidden units
    A= A .* D;    
    # Divide by keep_prob to keep the expected value of A the same                                  
    A = A ./ keep_prob; 
    # Store D in a dropoutMat cell array
    dropoutMat{l}=D;
    ...
 endfor

In the backward propagation cycle we have

    for l =(L-1):-1:1
          ...
          D = dropoutMat{l};  
          # Zero out the dAl based on same dropout matrix       
          dAl= dAl .* D;   
          # Divide by keep_prob to maintain the expected value                                       
          dAl = dAl ./ keep_prob;
          ...
    endfor 
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
#Load the data
train_X, train_Y, test_X, test_Y = load_dataset()
# Set the layers dimensions
layersDimensions = [2,7,1]

# Train a deep learning network
parameters = L_Layer_DeepModel(train_X, train_Y, layersDimensions, hiddenActivationFunc='relu',  
                               outputActivationFunc="sigmoid",learningRate = 0.6, keep_prob=0.7, num_iterations = 9000, 
                               initType="default", print_cost = True,figure="fig11.png")

# Clear the plot
plt.clf()
plt.close()

# Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T,keep_prob=0.7), train_X, train_Y,str(0.6),figure1="fig12.png")

3.1b Dropout: Spiral data – Python

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import sklearn.linear_model
import pandas as pd
import sklearn
import sklearn.datasets
exec(open("DLfunctions61.py").read())
# Create an input data set - Taken from CS231n Convolutional Neural networks,
# http://cs231n.github.io/neural-networks-case-study/
               

N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j


# Plot the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.clf()
plt.close()  
layersDimensions = [2,100,3]
y1=y.reshape(-1,1).T
# Train a deep learning network
parameters = L_Layer_DeepModel(X.T, y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",
                           learningRate = 1,keep_prob=0.9, num_iterations = 5000, print_cost = True,figure="fig13.png")

plt.clf()
plt.close()  
W1=parameters['W1']
b1=parameters['b1']
W2=parameters['W2']
b2=parameters['b2']
#Plot decision boundary
plot_decision_boundary1(X, y1,W1,b1,W2,b2,figure2="fig14.png")

3.2a Dropout: Circles data – R

source("DLfunctions61.R")
#Load data
df=read.csv("circles.csv",header=FALSE)
z <- as.matrix(read.csv("circles.csv",header=FALSE)) 

x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
layersDimensions = c(2,11,1)
# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.5,
                            keep_prob=0.8,
                            numIterations = 9000, 
                            initType="default",
                            print_cost = True)
# Plot the decision boundary
plotDecisionBoundary(z,retvals,keep_prob=0.6, hiddenActivationFunc="relu",0.5)

3.2b Dropout: Spiral data – R

# Read the spiral dataset
source("DLfunctions61.R")
# Load data
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)

# Train a deep learning network
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="softmax",
                            learningRate = 0.1,
                            keep_prob=0.90,
                            numIterations = 9000, 
                            print_cost = True)

parameters<-retvals$parameters
#Plot decision boundary
plotDecisionBoundary1(Z,parameters)

3.3a Dropout: Circles data – Octave

data=csvread("circles.csv");

X=data(:,1:2);
Y=data(:,3);
layersDimensions = [2 11  1]; #tanh=-0.5(ok), #relu=0.1 best!

# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.5,
                               lambd=0,
                               keep_prob=0.8,
                               numIterations = 10000,
                               initType="default");
plotCostVsIterations(10000,costs) 
#Plot decision boundary
plotDecisionBoundary1(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu") 

3.3b Dropout  Spiral data – Octave

source("DL61functions.m")
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);

layersDimensions = [numFeats numHidden  numOutput];  
# Train a deep learning network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="softmax",
                               learningRate = 0.1,
                               lambd=0,
                               keep_prob=0.8,
                               numIterations = 10000); 

plotCostVsIterations(10000,costs)    
#Plot decision boundary                            
plotDecisionBoundary1(data,weights, biases,keep_prob=1, hiddenActivationFunc="relu")  

Note: The Python, R and Octave code can be cloned/downloaded from Github at DeepLearning-Part6
Conclusion
This post further enhances my earlier L-Layer generic implementation of a Deep Learning network to include options for initialization techniques, L2 regularization or dropout regularization

References
1. Deep Learning Specialization
2. Neural Networks for Machine Learning

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
3. My book ‘Practical Machine Learning with R and Python’ on Amazon
4. Simulating a Web Joint in Android
5. Inswinger: yorkr swings into International T20s
6. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
7. Computer Vision: Ramblings on derivatives, histograms and contours
8. Bend it like Bluemix, MongoDB using Auto-scale – Part 1!
9. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon

To see all posts click Index of posts

Deep Learning from first principles in Python, R and Octave – Part 5


Introduction

a. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
b. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
c. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

      Isaac Asimov's Three Laws of Robotics 

Any sufficiently advanced technology is indistinguishable from magic.

      Arthur C Clarke.   

In this 5th part on Deep Learning from first Principles in Python, R and Octave, I solve the MNIST data set of handwritten digits (shown below), from the basics. To do this, I construct a L-Layer, vectorized Deep Learning implementation in Python, R and Octave from scratch and classify the  MNIST data set. The MNIST training data set  contains 60000 handwritten digits from 0-9, and a test set of 10000 digits. MNIST, is a popular dataset for running Deep Learning tests, and has been rightfully termed as the ‘drosophila’ of Deep Learning, by none other than the venerable Prof Geoffrey Hinton.

The ‘Deep Learning from first principles in Python, R and Octave’ series, so far included  Part 1 , where I had implemented logistic regression as a simple Neural Network. Part 2 implemented the most elementary neural network with 1 hidden layer, but  with any number of activation units in that layer, and a sigmoid activation at the output layer.

This post, ‘Deep Learning from first principles in Python, R and Octave – Part 5’ largely builds upon Part3. in which I implemented a multi-layer Deep Learning network, with an arbitrary number of hidden layers and activation units per hidden layer and with the output layer was based on the sigmoid unit, for binary classification. In Part 4, I derive the Jacobian of a Softmax, the Cross entropy loss and the gradient equations for a multi-class Softmax classifier. I also  implement a simple Neural Network using Softmax classifications in Python, R and Octave.

In this post I combine Part 3 and Part 4 to to build a L-layer Deep Learning network, with arbitrary number of hidden layers and hidden units, which can do both binary (sigmoid) and multi-class (softmax) classification.

The generic, vectorized L-Layer Deep Learning Network implementations in Python, R and Octave can be cloned/downloaded from GitHub at DeepLearning-Part5. This implementation allows for arbitrary number of hidden layers and hidden layer units. The activation function at the hidden layers can be one of sigmoid, relu and tanh (will be adding leaky relu soon). The output activation can be used for binary classification with the ‘sigmoid’, or multi-class classification with ‘softmax’. Feel free to download and play around with the code!

I thought the exercise of combining the two parts(Part 3, & Part 4)  would be a breeze. But it was anything but. Incorporating a Softmax classifier into the generic L-Layer Deep Learning model was a challenge. Moreover I found that I could not use the gradient descent on 60,000 training samples as my laptop ran out of memory. So I had to implement Stochastic Gradient Descent (SGD) for Python, R and Octave. In addition, I had to also implement the numerically stable version of Softmax, as the softmax and its derivative would result in NaNs.

Numerically stable Softmax

The Softmax function S_{j} =\frac{e^{Z_{j}}}{\sum_{i}^{k}e^{Z_{i}}} can be numerically unstable because of the division of large exponentials.  To handle this problem we have to implement stable Softmax function as below

S_{j} =\frac{e^{Z_{j}}}{\sum_{i}^{k}e^{Z_{i}}}
S_{j} =\frac{e^{Z_{j}}}{\sum_{i}^{k}e^{Z_{i}}} = \frac{Ce^{Z_{j}}}{C\sum_{i}^{k}e^{Z_{i}}} = \frac{e^{Z_{j}+log(C)}}{\sum_{i}^{k}e^{Z_{i}+log(C)}}
Therefore S_{j}  = \frac{e^{Z_{j}+ D}}{\sum_{i}^{k}e^{Z_{i}+ D}}
Here ‘D’ can be anything. A common choice is
D=-max(Z_{1},Z_{2},... Z_{k})

Here is the stable Softmax implementation in Python

# A numerically stable Softmax implementation
def stableSoftmax(Z):  
    #Compute the softmax of vector x in a numerically stable way.
    shiftZ = Z.T - np.max(Z.T,axis=1).reshape(-1,1)
    exp_scores = np.exp(shiftZ)
    # normalize them for each example
    A = exp_scores / np.sum(exp_scores, axis=1, keepdims=True) 
    cache=Z
    return A,cache

While trying to create a L-Layer generic Deep Learning network in the 3 languages, I found it useful to ensure that the model executed correctly on smaller datasets.  You can run into numerous problems while setting up the matrices, which becomes extremely difficult to debug. So in this post, I run the model on 2 smaller data for sets used in my earlier posts(Part 3 & Part4) , in each of the languages, before running the generic model on MNIST.

Here is a fair warning. if you think you can dive directly into Deep Learning, with just some basic knowledge of Machine Learning, you are bound to run into serious issues. Moreover, your knowledge will be incomplete. It is essential that you have a good grasp of Machine and Statistical Learning, the different algorithms, the measures and metrics for selecting the models etc.It would help to be conversant with all the ML models, ML concepts, validation techniques, classification measures  etc. Check out the internet/books for background.

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99) and in kindle version($6.65/Rs449).

You may also like my companion book “Practical Machine Learning with R and Python:Second Edition- Machine Learning in stereo” available in Amazon in paperback($10.99) and Kindle($7.99/Rs449) versions. This book is ideal for a quick reference of the various ML functions and associated measurements in both R and Python which are essential to delve deep into Deep Learning.

1. Random dataset with Sigmoid activation – Python

This random data with 9 clusters, was used in my post Deep Learning from first principles in Python, R and Octave – Part 3 , and was used to test the complete L-layer Deep Learning network with Sigmoid activation.

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import make_classification, make_blobs
exec(open("DLfunctions51.py").read()) # Cannot import in Rmd.
# Create a random data set with 9 centeres
X1, Y1 = make_blobs(n_samples = 400, n_features = 2, centers = 9,cluster_std = 1.3, random_state =4)
                       
#Create 2 classes
Y1=Y1.reshape(400,1)
Y1 = Y1 % 2
X2=X1.T
Y2=Y1.T
# Set the dimensions of L -layer DL network
layersDimensions = [2, 9, 9,1] #  4-layer model
# Execute DL network with hidden activation=relu and sigmoid output function
parameters = L_Layer_DeepModel(X2, Y2, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="sigmoid",learningRate = 0.3,num_iterations = 2500, print_cost = True)

2. Spiral dataset with Softmax activation – Python

The Spiral data was used in my post Deep Learning from first principles in Python, R and Octave – Part 4 and was used to test the complete L-layer Deep Learning network with multi-class Softmax activation at the output layer

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import make_classification, make_blobs
exec(open("DLfunctions51.py").read())

# Create an input data set - Taken from CS231n Convolutional Neural networks
# http://cs231n.github.io/neural-networks-case-study/
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j

X1=X.T
Y1=y.reshape(-1,1).T
numHidden=100 # No of hidden units in hidden layer
numFeats= 2 # dimensionality
numOutput = 3 # number of classes
# Set the dimensions of the layers
layersDimensions=[numFeats,numHidden,numOutput]
parameters = L_Layer_DeepModel(X1, Y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",learningRate = 0.6,num_iterations = 9000, print_cost = True)
## Cost after iteration 0: 1.098759
## Cost after iteration 1000: 0.112666
## Cost after iteration 2000: 0.044351
## Cost after iteration 3000: 0.027491
## Cost after iteration 4000: 0.021898
## Cost after iteration 5000: 0.019181
## Cost after iteration 6000: 0.017832
## Cost after iteration 7000: 0.017452
## Cost after iteration 8000: 0.017161

3. MNIST dataset with Softmax activation – Python

In the code below, I execute Stochastic Gradient Descent on the MNIST training data of 60000. I used a mini-batch size of 1000. Python takes about 40 minutes to crunch the data. In addition I also compute the Confusion Matrix and other metrics like Accuracy, Precision and Recall for the MNIST data set. I get an accuracy of 0.93 on the MNIST test set. This accuracy can be improved by choosing more hidden layers or more hidden units and possibly also tweaking the learning rate and the number of epochs.

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import math
from sklearn.datasets import make_classification, make_blobs
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
exec(open("DLfunctions51.py").read())
exec(open("load_mnist.py").read())
# Read the MNIST training and test sets
training=list(read(dataset='training',path=".\\mnist"))
test=list(read(dataset='testing',path=".\\mnist"))
# Create labels and pixel arrays
lbls=[]
pxls=[]
print(len(training))
#for i in range(len(training)):
for i in range(60000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)       
y=labels.reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)
X1=X.T
Y1=y.T
# Set the dimensions of the layers. The MNIST data is 28x28 pixels= 784
# Hence input layer is 784. For the 10 digits the Softmax classifier
# has to handle 10 outputs
layersDimensions=[784, 15,9,10] # Works very well,lr=0.01,mini_batch =1000, total=20000
np.random.seed(1)
costs = []  
# Run Stochastic Gradient Descent with Learning Rate=0.01, mini batch size=1000
# number of epochs=3000
parameters = L_Layer_DeepModel_SGD(X1, Y1, layersDimensions, hiddenActivationFunc='relu', outputActivationFunc="softmax",learningRate = 0.01 ,mini_batch_size =1000, num_epochs = 3000, print_cost = True)

# Compute the Confusion Matrix on Training set
# Compute the training accuracy, precision and recall
proba=predict_proba(parameters, X1,outputActivationFunc="softmax")
#A2, cache = forwardPropagationDeep(X1, parameters)
#proba=np.argmax(A2, axis=0).reshape(-1,1)
a=confusion_matrix(Y1.T,proba)
print(a)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(Y1.T, proba)))
print('Precision: {:.2f}'.format(precision_score(Y1.T, proba,average="micro")))
print('Recall: {:.2f}'.format(recall_score(Y1.T, proba,average="micro")))

# Read the test data
lbls=[]
pxls=[]
print(len(test))
for i in range(10000):
       l,p=test[i]
       lbls.append(l)
       pxls.append(p)
testLabels= np.array(lbls)
testPixels=np.array(pxls)       
ytest=testLabels.reshape(-1,1)
Xtest=testPixels.reshape(testPixels.shape[0],-1)
X1test=Xtest.T
Y1test=ytest.T

# Compute the Confusion Matrix on Test set
# Compute the test accuracy, precision and recall
probaTest=predict_proba(parameters, X1test,outputActivationFunc="softmax")
#A2, cache = forwardPropagationDeep(X1, parameters)
#proba=np.argmax(A2, axis=0).reshape(-1,1)
a=confusion_matrix(Y1test.T,probaTest)
print(a)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(Y1test.T, probaTest)))
print('Precision: {:.2f}'.format(precision_score(Y1test.T, probaTest,average="micro")))
print('Recall: {:.2f}'.format(recall_score(Y1test.T, probaTest,average="micro")))
##1.  Confusion Matrix of Training set
       0     1    2    3    4    5    6    7    8    9
## [[5854    0   19    2   10    7    0    1   24    6]
##  [   1 6659   30   10    5    3    0   14   20    0]
##  [  20   24 5805   18    6   11    2   32   37    3]
##  [   5    4  175 5783    1   27    1   58   60   17]
##  [   1   21    9    0 5780    0    5    2   12   12]
##  [  29    9   21  224    6 4824   18   17  245   28]
##  [   5    4   22    1   32   12 5799    0   43    0]
##  [   3   13  148  154   18    3    0 5883    4   39]
##  [  11   34   30   21   13   16    4    7 5703   12]
##  [  10    4    1   32  135   14    1   92  134 5526]]

##2. Accuracy, Precision, Recall of  Training set
## Accuracy: 0.96
## Precision: 0.96
## Recall: 0.96

##3. Confusion Matrix of Test set
       0     1    2    3    4    5    6    7    8    9
## [[ 954    1    8    0    3    3    2    4    4    1]
##  [   0 1107    6    5    0    0    1    2   14    0]
##  [  11    7  957   10    5    0    5   20   16    1]
##  [   2    3   37  925    3   13    0    8   18    1]
##  [   2    6    1    1  944    0    7    3    4   14]
##  [  12    5    4   45    2  740   24    8   42   10]
##  [   8    4    4    2   16    9  903    0   12    0]
##  [   4   10   27   18    5    1    0  940    1   22]
##  [  11   13    6   13    9   10    7    2  900    3]
##  [   8    5    1    7   50    7    0   20   29  882]]
##4. Accuracy, Precision, Recall of  Training set
## Accuracy: 0.93
## Precision: 0.93
## Recall: 0.93

4. Random dataset with Sigmoid activation – R code

This is the random data set used in the Python code above which was saved as a CSV. The code is used to test a L -Layer DL network with Sigmoid Activation in R.

source("DLfunctions5.R")
# Read the random data set
z <- as.matrix(read.csv("data.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X <- t(x)
Y <- t(y)
# Set the dimensions of the  layer
layersDimensions = c(2, 9, 9,1)

# Run Gradient Descent on the data set with relu hidden unit activation 
# sigmoid activation unit in the output layer
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="sigmoid",
                            learningRate = 0.3,
                            numIterations = 5000, 
                            print_cost = True)
#Plot the cost vs iterations
iterations <- seq(0,5000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs iterations") + xlab("Iterations") + ylab("Loss")

5. Spiral dataset with Softmax activation – R

The spiral data set used in the Python code above, is reused to test  multi-class classification with Softmax.

source("DLfunctions5.R")
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X <- t(X)
Y <- t(y)

# Initialize number of features, number of hidden units in hidden layer and
# number of classes
numFeats<-2 # No features
numHidden<-100 # No of hidden units
numOutput<-3 # No of classes

# Set the layer dimensions
layersDimensions = c(numFeats,numHidden,numOutput)

# Perform gradient descent with relu activation unit for hidden layer
# and softmax activation in the output
retvals = L_Layer_DeepModel(X, Y, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="softmax",
                            learningRate = 0.5,
                            numIterations = 9000, 
                            print_cost = True)
#Plot cost vs iterations
iterations <- seq(0,9000,1000)
costs=retvals$costs
df=data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() + geom_line(color="blue") +
 ggtitle("Costs vs iterations") + xlab("Iterations") + ylab("Costs")

6. MNIST dataset with Softmax activation – R

The code below executes a L – Layer Deep Learning network with Softmax output activation, to classify the 10 handwritten digits from MNIST with Stochastic Gradient Descent. The entire 60000 data set was used to train the data. R takes almost 8 hours to process this data set with a mini-batch size of 1000.  The use of ‘for’ loops is limited to iterating through epochs, mini batches and for creating the mini batches itself. All other code is vectorized. Yet, it seems to crawl. Most likely the use of ‘lists’ in R, to return multiple values is performance intensive. Some day, I will try to profile the code, and see where the issue is. However the code works!

Having said that, the Confusion Matrix in R dumps a lot of interesting statistics! There is a bunch of statistical measures for each class. For e.g. the Balanced Accuracy for the digits ‘6’ and ‘9’ is around 50%. Looks like, the classifier is confused by the fact that 6 is inverted 9 and vice-versa. The accuracy on the Test data set is just around 75%. I could have played around with the number of layers, number of hidden units, learning rates, epochs etc to get a much higher accuracy. But since each test took about 8+ hours, I may work on this, some other day!

source("DLfunctions5.R")
source("mnist.R")
#Load the mnist data
load_mnist()
show_digit(train$x[2,])
#Set the layer dimensions
layersDimensions=c(784, 15,9, 10) # Works at 1500
x <- t(train$x)
X <- x[,1:60000]
y <-train$y
y1 <- y[1:60000]
y2 <- as.matrix(y1)
Y=t(y2)

# Subset 32768 random samples from MNIST 
permutation = c(sample(2^15))
# Randomly shuffle the training data
X1 = X[, permutation]
y1 = Y[1, permutation]
y2 <- as.matrix(y1)
Y1=t(y2)

# Execute Stochastic Gradient Descent on the entire training set
# with Softmax activation
retvalsSGD= L_Layer_DeepModel_SGD(X1, Y1, layersDimensions,
                            hiddenActivationFunc='relu',
                            outputActivationFunc="softmax",
                            learningRate = 0.05,
                            mini_batch_size = 512, 
                            num_epochs = 1, 
                            print_cost = True)

# Compute the Confusion Matrix
library(caret)
library(e1071)
predictions=predictProba(retvalsSGD[['parameters']], X,hiddenActivationFunc='relu',
                   outputActivationFunc="softmax")
confusionMatrix(predictions,Y)
# Confusion Matrix on the Training set
> confusionMatrix(predictions,Y)
Confusion Matrix and Statistics

          Reference
Prediction    0    1    2    3    4    5    6    7    8    9
         0 5738    1   21    5   16   17    7   15    9   43
         1    5 6632   21   24   25    3    2   33   13  392
         2   12   32 5747  106   25   28    3   27   44 4779
         3    0   27   12 5715    1   21    1   20    1   13
         4   10    5   21   18 5677    9   17   30   15  166
         5  142   21   96  136   93 5306 5884   43   60  413
         6    0    0    0    0    0    0    0    0    0    0
         7    6    9   13   13    3    4    0 6085    0   55
         8    8   12    7   43    1   32    2    7 5703   69
         9    2    3   20   71    1    1    2    5    6   19

Overall Statistics
                                          
               Accuracy : 0.777           
                 95% CI : (0.7737, 0.7804)
    No Information Rate : 0.1124          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.7524          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
Sensitivity           0.96877   0.9837  0.96459  0.93215  0.97176  0.97879  0.00000
Specificity           0.99752   0.9903  0.90644  0.99822  0.99463  0.87380  1.00000
Pos Pred Value        0.97718   0.9276  0.53198  0.98348  0.95124  0.43513      NaN
Neg Pred Value        0.99658   0.9979  0.99571  0.99232  0.99695  0.99759  0.90137
Prevalence            0.09872   0.1124  0.09930  0.10218  0.09737  0.09035  0.09863
Detection Rate        0.09563   0.1105  0.09578  0.09525  0.09462  0.08843  0.00000
Detection Prevalence  0.09787   0.1192  0.18005  0.09685  0.09947  0.20323  0.00000
Balanced Accuracy     0.98314   0.9870  0.93551  0.96518  0.98319  0.92629  0.50000
                     Class: 7 Class: 8  Class: 9
Sensitivity            0.9713  0.97471 0.0031938
Specificity            0.9981  0.99666 0.9979464
Pos Pred Value         0.9834  0.96924 0.1461538
Neg Pred Value         0.9967  0.99727 0.9009521
Prevalence             0.1044  0.09752 0.0991500
Detection Rate         0.1014  0.09505 0.0003167
Detection Prevalence   0.1031  0.09807 0.0021667
Balanced Accuracy      0.9847  0.98568 0.5005701
# Confusion Matrix on the Training set xtest <- t(test$x) Xtest <- xtest[,1:10000] ytest <-test$y ytest1 <- ytest[1:10000] ytest2 <- as.matrix(ytest1) Ytest=t(ytest2)

Confusion Matrix and Statistics

          Reference
Prediction    0    1    2    3    4    5    6    7    8    9
         0  950    2    2    3    0    6    9    4    7    6
         1    3 1110    4    2    9    0    3   12    5   74
         2    2    6  965   21    9   14    5   16   12  789
         3    1    2    9  908    2   16    0   21    2    6
         4    0    1    9    5  938    1    8    6    8   39
         5   19    5   25   35   20  835  929    8   54   67
         6    0    0    0    0    0    0    0    0    0    0
         7    4    4    7   10    2    4    0  952    5    6
         8    1    5    8   14    2   16    2    3  876   21
         9    0    0    3   12    0    0    2    6    5    1

Overall Statistics
                                          
               Accuracy : 0.7535          
                 95% CI : (0.7449, 0.7619)
    No Information Rate : 0.1135          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.7262          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
Sensitivity            0.9694   0.9780   0.9351   0.8990   0.9552   0.9361   0.0000
Specificity            0.9957   0.9874   0.9025   0.9934   0.9915   0.8724   1.0000
Pos Pred Value         0.9606   0.9083   0.5247   0.9390   0.9241   0.4181      NaN
Neg Pred Value         0.9967   0.9972   0.9918   0.9887   0.9951   0.9929   0.9042
Prevalence             0.0980   0.1135   0.1032   0.1010   0.0982   0.0892   0.0958
Detection Rate         0.0950   0.1110   0.0965   0.0908   0.0938   0.0835   0.0000
Detection Prevalence   0.0989   0.1222   0.1839   0.0967   0.1015   0.1997   0.0000
Balanced Accuracy      0.9825   0.9827   0.9188   0.9462   0.9733   0.9043   0.5000
                     Class: 7 Class: 8  Class: 9
Sensitivity            0.9261   0.8994 0.0009911
Specificity            0.9953   0.9920 0.9968858
Pos Pred Value         0.9577   0.9241 0.0344828
Neg Pred Value         0.9916   0.9892 0.8989068
Prevalence             0.1028   0.0974 0.1009000
Detection Rate         0.0952   0.0876 0.0001000
Detection Prevalence   0.0994   0.0948 0.0029000
Balanced Accuracy      0.9607   0.9457 0.4989384

7. Random dataset with Sigmoid activation – Octave

The Octave code below uses the random data set used by Python. The code below implements a L-Layer Deep Learning with Sigmoid Activation.


source("DL5functions.m")
# Read the data
data=csvread("data.csv");

X=data(:,1:2);
Y=data(:,3);
#Set the layer dimensions
layersDimensions = [2 9 7  1]; #tanh=-0.5(ok), #relu=0.1 best!
# Perform gradient descent 
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="sigmoid",
                               learningRate = 0.1,
                               numIterations = 10000);
# Plot cost vs iterations
plotCostVsIterations(10000,costs);       

8. Spiral dataset with Softmax activation – Octave

The  code below uses the spiral data set used by Python above. The code below implements a L-Layer Deep Learning with Softmax Activation.

# Read the data
data=csvread("spiral.csv");

# Setup the data
X=data(:,1:2);
Y=data(:,3);

# Set the number of features, number of hidden units in hidden layer and number of classess
numFeats=2; #No features
numHidden=100; # No of hidden units
numOutput=3; # No of  classes
# Set the layer dimensions
layersDimensions = [numFeats numHidden  numOutput];  
#Perform gradient descent with softmax activation unit
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
                               hiddenActivationFunc='relu', 
                               outputActivationFunc="softmax",
                               learningRate = 0.1,
                               numIterations = 10000); 

9. MNIST dataset with Softmax activation – Octave

The code below implements a L-Layer Deep Learning Network in Octave with Softmax output activation unit, for classifying the 10 handwritten digits in the MNIST dataset. Unfortunately, Octave can only index to around 10000 training at a time,  and I was getting an error ‘error: out of memory or dimension too large for Octave’s index type error: called from…’, when I tried to create a batch size of 20000.  So I had to come with a work around to create a batch size of 10000 (randomly) and then use a mini-batch of 1000 samples and execute Stochastic Gradient Descent. The performance was good. Octave takes about 15 minutes, on a batch size of 10000 and a mini batch of 1000.

I thought if the performance was not good, I could iterate through these random batches and refining the gradients as follows

# Pseudo code that could be used since Octave only allows 10K batches
# at a time
# Randomly create weights
[weights biases] = initialize_weights()
for i=1:k
    # Create a random permutation and create a random batch
    permutation = randperm(10000);
    X=trainX(permutation,:);
    Y=trainY(permutation,:);
    # Compute weights from SGD and update weights in the next batch update
    [weights biases costs]=L_Layer_DeepModel_SGD(X,Y,mini_bactch=1000,weights, biases,...);
    ...
endfor
# Load the MNIST data
load('./mnist/mnist.txt.gz'); 
#Create a random permutatation from 60K
permutation = randperm(10000);
disp(length(permutation));

# Use this 10K as the batch
X=trainX(permutation,:);
Y=trainY(permutation,:);

# Set layer dimensions
layersDimensions=[784, 15, 9, 10];

# Run Stochastic Gradient descent with batch size=10K and mini_batch_size=1000
[weights biases costs]=L_Layer_DeepModel_SGD(X', Y', layersDimensions,
                       hiddenActivationFunc='relu', 
                       outputActivationFunc="softmax",
                       learningRate = 0.01,
                       mini_batch_size = 2000, num_epochs = 5000);   

9. Final thoughts

Here are some of my final thoughts after working on Python, R and Octave in this series and in other projects
1. Python, with its highly optimized numpy library, is ideally suited for creating Deep Learning Models, which have a lot of matrix manipulations. Python is a real workhorse when it comes to Deep Learning computations.
2. R is somewhat clunky in comparison to its cousin Python in handling matrices or in returning multiple values. But R’s statistical libraries, dplyr, and ggplot are really superior to the Python peers. Also, I find R handles  dataframes,  much better than Python.
3. Octave is a no-nonsense,minimalist language which is very efficient in handling matrices. It is ideally suited for implementing Machine Learning and Deep Learning from scratch. But Octave has its problems and cannot handle large matrix sizes, and also lacks the statistical libaries of R and Python. They possibly exist in its sibling, Matlab

Feel free to clone/download the code from  GitHub at DeepLearning-Part5.

Conclusion

Building a Deep Learning Network from scratch is quite challenging, time-consuming but nevertheless an exciting task.  While the statements in the different languages for manipulating matrices, summing up columns, finding columns which have ones don’t take more than a single statement, extreme care has to be taken to ensure that the statements work well for any dimension.  The lessons learnt from creating L -Layer Deep Learning network  are many and well worth it. Give it a try!

Hasta la vista! I’ll be back, so stick around!
Watch this space!

References
1. Deep Learning Specialization
2. Neural Networks for Machine Learning
3. CS231 Convolutional Neural Networks for Visual Recognition
4. Eli Bendersky’s Website – The Softmax function and its derivative

Also see
1. My book ‘Practical Machine Learning with R and Python’ on Amazon
2. Presentation on Wireless Technologies – Part 1
3. Exploring Quantum Gate operations with QCSimulator
4. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
5. TWS-4: Gossip protocol: Epidemics and rumors to the rescue
6. cricketr plays the ODIs!
7. “Is it an animal? Is it an insect?” in Android
8. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon
9. Deblurring with OpenCV: Weiner filter reloaded
10. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

To see all posts click Index of Posts

 

Deep Learning from first principles in Python, R and Octave – Part 4


In this 4th post of my series on Deep Learning from first principles in Python, R and Octave – Part 4, I explore the details of creating a multi-class classifier using the Softmax activation unit in a neural network. The earlier posts in this series were

1. Deep Learning from first principles in Python, R and Octave – Part 1. In this post I implemented logistic regression as a simple Neural Network in vectorized Python, R and Octave
2. Deep Learning from first principles in Python, R and Octave – Part 2. This 2nd part implemented the most elementary neural network with 1 hidden layer and any number of activation units in the hidden layer with sigmoid activation at the output layer
3. Deep Learning from first principles in Python, R and Octave – Part 3. The 3rd implemented a multi-layer Deep Learning network with an arbitrary number if hidden layers and activation units per hidden layer. The output layer was for binary classification which was based on the sigmoid unit. This multi-layer deep network was implemented in vectorized Python, R and Octave.

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99 ) and in kindle version($6.65/Rs449).

This 4th part takes a swing at multi-class classification and uses the Softmax as the activation unit in the output layer. Inclusion of the Softmax activation unit in the activation layer requires us to compute the derivative of Softmax, or rather the “Jacobian” of the Softmax function, besides also computing the log loss for this Softmax activation during back propagation. Since the derivation of the Jacobian of a Softmax and the computation of the Cross Entropy/log loss is very involved, I have implemented a basic neural network with just 1 hidden layer with the Softmax activation at the output layer. I also perform multi-class classification based on the ‘spiral’ data set from CS231n Convolutional Neural Networks Stanford course, to test the performance and correctness of the implementations in Python, R and Octave. You can clone download the code for the Python, R and Octave implementations from Github at Deep Learning – Part 4

The Softmax function takes an N dimensional vector as input and generates a N dimensional vector as output.
The Softmax function is given by
S_{j}= \frac{e_{j}}{\sum_{i}^{N}e_{k}}
There is a probabilistic interpretation of the Softmax, since the sum of the Softmax values of a set of vectors will always add up to 1, given that each Softmax value is divided by the total of all values.

As mentioned earlier, the Softmax takes a vector input and returns a vector of outputs.  For e.g. the Softmax of a vector a=[1, 3, 6]  is another vector S=[0.0063,0.0471,0.9464]. Notice that vector output is proportional to the input vector.  Also, taking the derivative of a vector by another vector, is known as the Jacobian. By the way, The Matrix Calculus You Need For Deep Learning by Terence Parr and Jeremy Howard, is very good paper that distills all the main mathematical concepts for Deep Learning in one place.

Let us take a simple 2 layered neural network with just 2 activation units in the hidden layer is shown below

Z_{1}^{1} =W_{11}^{1}x_{1} + W_{21}^{1}x_{2} + b_{1}^{1}
Z_{2}^{1} =W_{12}^{1}x_{1} + W_{22}^{1}x_{2} + b_{2}^{1}
and
A_{1}^{1} = g'(Z_{1}^{1})
A_{2}^{1} = g'(Z_{2}^{1})
where g'() is the activation unit in the hidden layer which can be a relu, sigmoid or a
tanh function

Note: The superscript denotes the layer. The above denotes the equation for layer 1
of the neural network. For layer 2 with the Softmax activation, the equations are
Z_{1}^{2} =W_{11}^{2}x_{1} + W_{21}^{2}x_{2} + b_{1}^{2}
Z_{2}^{2} =W_{12}^{2}x_{1} + W_{22}^{2}x_{2} + b_{2}^{2}
and
A_{1}^{2} = S(A_{1}^{1})
A_{2}^{2} = S(A_{2}^{1})
where S() is the Softmax activation function
S=\begin{pmatrix}  S(Z_{1}^{2})\\ S(Z_{2}^{2})  \end{pmatrix}
S=\begin{pmatrix}  \frac{e^{Z1}}{e^{Z1}+e^{Z2}}\\ \frac{e^{Z2}}{e^{Z1}+e^{Z2}}  \end{pmatrix}

The Jacobian of the softmax ‘S’ is given by
\begin{pmatrix}  \frac {\partial S_{1}}{\partial Z_{1}} & \frac {\partial S_{1}}{\partial Z_{2}}\\  \frac {\partial S_{2}}{\partial Z_{1}} & \frac {\partial S_{2}}{\partial Z_{2}}  \end{pmatrix}
\begin{pmatrix}  \frac{\partial}{\partial Z_{1}} \frac {e^{Z1}}{e^{Z1}+ e^{Z2}} & \frac{\partial}{\partial Z_{2}} \frac {e^{Z1}}{e^{Z1}+ e^{Z2}}\\  \frac{\partial}{\partial Z_{1}} \frac {e^{Z2}}{e^{Z1}+ e^{Z2}} & \frac{\partial}{\partial Z_{2}} \frac {e^{Z2}}{e^{Z1}+ e^{Z2}}  \end{pmatrix}     – (A)

Now the ‘division-rule’  of derivatives is as follows. If u and v are functions of x, then
\frac{d}{dx} \frac {u}{v} =\frac {vdu -udv}{v^{2}}
Using this to compute each element of the above Jacobian matrix, we see that
when i=j we have
\frac {\partial}{\partial Z1}\frac{e^{Z1}}{e^{Z1}+e^{Z2}} =  \frac {\sum e^{Z1} - e^{Z1^{2}}}{\sum ^{2}}
and when i \neq j
\frac {\partial}{\partial Z1}\frac{e^{Z2}}{e^{Z1}+e^{Z2}} =  \frac {0 - e^{z1}e^{Z2}}{\sum ^{2}}
This is of the general form
\frac {\partial S_{j}}{\partial z_{i}} = S_{i}( 1-S_{j})  when i=j
and
\frac {\partial S_{j}}{\partial z_{i}} = -S_{i}S_{j}  when i \neq j
Note: Since the Softmax essentially gives the probability the following
notation is also used
\frac {\partial p_{j}}{\partial z_{i}} = p_{i}( 1-p_{j}) when i=j
and
\frac {\partial p_{j}}{\partial z_{i}} = -p_{i}p_{j} when i \neq j
If you throw the “Kronecker delta” into the equation, then the above equations can be expressed even more concisely as
\frac {\partial p_{j}}{\partial z_{i}} = p_{i} (\delta_{ij} - p_{j})
where \delta_{ij} = 1 when i=j and 0 when i \neq j

This reduces the Jacobian of the simple 2 output softmax vectors  equation (A) as
\begin{pmatrix}  p_{1}(1-p_{1}) & -p_{1}p_{2} \\  -p_{2}p_{1} & p_{2}(1-p_{2})  \end{pmatrix}
The loss of Softmax is given by
L = -\sum y_{i} log(p_{i})
For the 2 valued Softmax output this is
\frac {dL}{dp1} = -\frac {y_{1}}{p_{1}}
\frac {dL}{dp2} = -\frac {y_{2}}{p_{2}}
Using the chain rule we can write
\frac {\partial L}{\partial w_{pq}} = \sum _{i}\frac {\partial L}{\partial p_{i}} \frac {\partial p_{i}}{\partial w_{pq}} (1)
and
\frac {\partial p_{i}}{\partial w_{pq}} = \sum _{k}\frac {\partial p_{i}}{\partial z_{k}} \frac {\partial z_{k}}{\partial w_{pq}} (2)
In expanded form this is
\frac {\partial L}{\partial w_{pq}} = \sum _{i}\frac {\partial L}{\partial p_{i}} \sum _{k}\frac {\partial p_{i}}{\partial z_{k}} \frac {\partial z_{k}}{\partial w_{pq}}
Also
\frac {\partial L}{\partial Z_{i}} =\sum _{i} \frac {\partial L}{\partial p} \frac {\partial p}{\partial Z_{i}}
Therefore
\frac {\partial L}{\partial Z_{1}} =\frac {\partial L}{\partial p_{1}} \frac {\partial p_{1}}{\partial Z_{1}} +\frac {\partial L}{\partial p_{2}} \frac {\partial p_{2}}{\partial Z_{1}}
\frac {\partial L}{\partial z_{1}}=-\frac {y1}{p1} p1(1-p1) - \frac {y2}{p2}*(-p_{2}p_{1})
Since
\frac {\partial p_{j}}{\partial z_{i}} = p_{i}( 1-p_{j}) when i=j
and
\frac {\partial p_{j}}{\partial z_{i}} = -p_{i}p_{j} when i \neq j
which simplifies to
\frac {\partial L}{\partial Z_{1}} = -y_{1} + y_{1}p_{1} + y_{2}p_{1} =
p_{1}\sum (y_{1} + y_2) - y_{1}
\frac {\partial L}{\partial Z_{1}}= p_{1} - y_{1}
Since
\sum_{i} y_{i} =1
Similarly
\frac {\partial L}{\partial Z_{2}} =\frac {\partial L}{\partial p_{1}} \frac {\partial p_{1}}{\partial Z_{2}} +\frac {\partial L}{\partial p_{2}} \frac {\partial p_{2}}{\partial Z_{2}}
\frac {\partial L}{\partial z_{2}}=-\frac {y1}{p1}*(p_{1}p_{2}) - \frac {y2}{p2}*p_{2}(1-p_{2})
y_{1}p_{2} + y_{2}p_{2} - y_{2}
\frac {\partial L}{\partial Z_{2}} =p_{2}\sum (y_{1} + y_2) - y_{2}\\ = p_{2} - y_{2}
In general this is of the form
\frac {\partial L}{\partial z_{i}} = p_{i} -y_{i}
For e.g if the probabilities computed were p=[0.1, 0.7, 0.2] then this implies that the class with probability 0.7 is the likely class. This would imply that the ‘One hot encoding’ for  yi  would be yi=[0,1,0] therefore the gradient pi-yi = [0.1,-0.3,0.2]

<strong>Note: Further, we could extend this derivation for a Softmax activation output that outputs 3 classes
S=\begin{pmatrix}  \frac{e^{z1}}{e^{z1}+e^{z2}+e^{z3}}\\ \frac{e^{z2}}{e^{z1}+e^{z2}+e^{z3}} \\ \frac{e^{z3}}{e^{z1}+e^{z2}+e^{z3}}  \end{pmatrix}

We could derive
\frac {\partial L}{\partial z1}= \frac {\partial L}{\partial p_{1}} \frac {\partial p_{1}}{\partial z_{1}} +\frac {\partial L}{\partial p_{2}} \frac {\partial p_{2}}{\partial z_{1}} +\frac {\partial L}{\partial p_{3}} \frac {\partial p_{3}}{\partial z_{1}} which similarly reduces to
\frac {\partial L}{\partial z_{1}}=-\frac {y1}{p1} p1(1-p1) - \frac {y2}{p2}*(-p_{2}p_{1}) - \frac {y3}{p3}*(-p_{3}p_{1})
-y_{1}+ y_{1}p_{1} + y_{2}p_{1} + y_{3}p1 =  p_{1}\sum (y_{1} + y_2 + y_3) - y_{1}  = p_{1} - y_{1}
Interestingly, despite the lengthy derivations the final result is simple and intuitive!

As seen in my post ‘Deep Learning from first principles with Python, R and Octave – Part 3 the key equations for forward and backward propagation are

Forward propagation equations layer 1
Z_{1} = W_{1}X +b_{1}     and  A_{1} = g(Z_{1})
Forward propagation equations layer 1
Z_{2} = W_{2}A_{1} +b_{2}  and  A_{2} = S(Z_{2})

Using the result (A) in the back propagation equations below we have
Backward propagation equations layer 2
\partial L/\partial W_{2} =\partial L/\partial Z_{2}*A_{1}=(p_{2}-y_{2})*A_{1}
\partial L/\partial b_{2} =\partial L/\partial Z_{2}=p_{2}-y_{2}
\partial L/\partial A_{1} = \partial L/\partial Z_{2} * W_{2}=(p_{2}-y_{2})*W_{2}
Backward propagation equations layer 1
\partial L/\partial W_{1} =\partial L/\partial Z_{1} *A_{0}=(p_{1}-y_{1})*A_{0}
\partial L/\partial b_{1} =\partial L/\partial Z_{1}=(p_{1}-y_{1})

2.0 Spiral data set

As I mentioned earlier, I will be using the ‘spiral’ data from CS231n Convolutional Neural Networks to ensure that my vectorized implementations in Python, R and Octave are correct. Here is the ‘spiral’ data set.

import numpy as np
import matplotlib.pyplot as plt
import os
os.chdir("C:/junk/dl-4/dl-4")
exec(open("././DLfunctions41.py").read())

# Create an input data set - Taken from CS231n Convolutional Neural networks
# http://cs231n.github.io/neural-networks-case-study/
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j
# Plot the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.savefig("fig1.png", bbox_inches='tight')


The implementations of the vectorized Python, R and Octave code are shown diagrammatically below

2.1 Multi-class classification with Softmax – Python code

A simple 2 layer Neural network with a single hidden layer , with 100 Relu activation units in the hidden layer and the Softmax activation unit in the output layer is used for multi-class classification. This Deep Learning Network, plots the non-linear boundary of the 3 classes as shown below

import numpy as np
import matplotlib.pyplot as plt
import os
os.chdir("C:/junk/dl-4/dl-4")
exec(open("././DLfunctions41.py").read())

# Read the input data
N = 100 # number of points per class
D = 2 # dimensionality
K = 3 # number of classes
X = np.zeros((N*K,D)) # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8') # class labels
for j in range(K):
  ix = range(N*j,N*(j+1))
  r = np.linspace(0.0,1,N) # radius
  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta
  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
  y[ix] = j
  
# Set the number of features, hidden units in hidden layer and number of classess
numHidden=100 # No of hidden units in hidden layer
numFeats= 2 # dimensionality
numOutput = 3 # number of classes

# Initialize the model
parameters=initializeModel(numFeats,numHidden,numOutput)
W1= parameters['W1']
b1= parameters['b1']
W2= parameters['W2']
b2= parameters['b2']

# Set the learning rate
learningRate=0.6 

# Initialize losses
losses=[]
# Perform Gradient descent
for i in range(10000):
    # Forward propagation through hidden layer with Relu units
    A1,cache1= layerActivationForward(X.T,W1,b1,'relu')
    
    # Forward propagation through output layer with Softmax
    A2,cache2 = layerActivationForward(A1,W2,b2,'softmax')
    
    # No of training examples
    numTraining = X.shape[0]
    # Compute log probs. Take the log prob of correct class based on output y
    correct_logprobs = -np.log(A2[range(numTraining),y])
    # Conpute loss
    loss = np.sum(correct_logprobs)/numTraining
    
    # Print the loss
    if i % 1000 == 0:
        print("iteration %d: loss %f" % (i, loss))
        losses.append(loss)

    dA=0

    # Backward  propagation through output layer with Softmax
    dA1,dW2,db2 = layerActivationBackward(dA, cache2, y, activationFunc='softmax')
    # Backward  propagation through hidden layer with Relu unit
    dA0,dW1,db1 = layerActivationBackward(dA1.T, cache1, y, activationFunc='relu')
    
    #Update paramaters with the learning rate
    W1 += -learningRate * dW1
    b1 += -learningRate * db1
    W2 += -learningRate * dW2.T
    b2 += -learningRate * db2.T

#Plot losses vs iterations  
i=np.arange(0,10000,1000)
plt.plot(i,losses)

plt.xlabel('Iterations')
plt.ylabel('Loss')
plt.title('Losses vs Iterations')
plt.savefig("fig2.png", bbox="tight")

#Compute the multi-class Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# We need to determine the predicted values from the learnt data
# Forward propagation through hidden layer with Relu units
A1,cache1= layerActivationForward(X.T,W1,b1,'relu')
    
# Forward propagation through output layer with Softmax
A2,cache2 = layerActivationForward(A1,W2,b2,'softmax')
#Compute predicted values from weights and biases
yhat=np.argmax(A2, axis=1)

a=confusion_matrix(y.T,yhat.T)
print("Multi-class Confusion Matrix")
print(a)
## iteration 0: loss 1.098507
## iteration 1000: loss 0.214611
## iteration 2000: loss 0.043622
## iteration 3000: loss 0.032525
## iteration 4000: loss 0.025108
## iteration 5000: loss 0.021365
## iteration 6000: loss 0.019046
## iteration 7000: loss 0.017475
## iteration 8000: loss 0.016359
## iteration 9000: loss 0.015703
## Multi-class Confusion Matrix
## [[ 99   1   0]
##  [  0 100   0]
##  [  0   1  99]]

Check out my compact and minimal book  “Practical Machine Learning with R and Python:Second edition- Machine Learning in stereo”  available in Amazon in paperback($10.99) and kindle($7.99) versions. My book includes implementations of key ML algorithms and associated measures and metrics. The book is ideal for anybody who is familiar with the concepts and would like a quick reference to the different ML algorithms that can be applied to problems and how to select the best model. Pick your copy today!!

2.2 Multi-class classification with Softmax – R code

The spiral data set created with Python was saved, and is used as the input with R code. The R Neural Network seems to perform much,much slower than both Python and Octave. Not sure why! Incidentally the computation of loss and the softmax derivative are identical for both R and Octave. yet R is much slower. To compute the softmax derivative I create matrices for the One Hot Encoded yi and then stack them before subtracting pi-yi. I am sure there is a more elegant and more efficient way to do this, much like Python. Any suggestions?

library(ggplot2)
library(dplyr)
library(RColorBrewer)
source("DLfunctions41.R")
# Read the spiral dataset
Z <- as.matrix(read.csv("spiral.csv",header=FALSE)) 
Z1=data.frame(Z)
#Plot the dataset
ggplot(Z1,aes(x=V1,y=V2,col=V3)) +geom_point() + 
  scale_colour_gradientn(colours = brewer.pal(10, "Spectral"))

# Setup the data
X <- Z[,1:2]
y <- Z[,3]
X1 <- t(X)
Y1 <- t(y)

# Initialize number of features, number of hidden units in hidden layer and
# number of classes
numFeats<-2 # No features
numHidden<-100 # No of hidden units
numOutput<-3 # No of classes

# Initialize model
parameters <-initializeModel(numFeats, numHidden,numOutput)

W1 <-parameters[['W1']]
b1 <-parameters[['b1']]
W2 <-parameters[['W2']]
b2 <-parameters[['b2']]

# Set the learning rate
learningRate <- 0.5
# Initialize losses
losses <- NULL
# Perform gradient descent
for(i in 0:9000){

# Forward propagation through hidden layer with Relu units
retvals <- layerActivationForward(X1,W1,b1,'relu')
A1 <- retvals[['A']]
cache1 <- retvals[['cache']]
forward_cache1 <- cache1[['forward_cache1']]
activation_cache <- cache1[['activation_cache']]

# Forward propagation through output layer with Softmax units
retvals = layerActivationForward(A1,W2,b2,'softmax')
A2 <- retvals[['A']]
cache2 <- retvals[['cache']]
forward_cache2 <- cache2[['forward_cache1']]
activation_cache2 <- cache2[['activation_cache']]

# No oftraining examples
numTraining <- dim(X)[1]
dA <-0

# Select the elements where the y values are 0, 1 or 2 and make a vector
a=c(A2[y==0,1],A2[y==1,2],A2[y==2,3])
# Take log
correct_probs = -log(a)
# Compute loss
loss= sum(correct_probs)/numTraining

if(i %% 1000 == 0){
sprintf("iteration %d: loss %f",i, loss)
print(loss)
}
# Backward propagation through output layer with Softmax units
retvals = layerActivationBackward(dA, cache2, y, activationFunc='softmax')
dA1 = retvals[['dA_prev']]
dW2= retvals[['dW']]
db2= retvals[['db']]
# Backward propagation through hidden layer with Relu units
retvals = layerActivationBackward(t(dA1), cache1, y, activationFunc='relu')
dA0 = retvals[['dA_prev']]
dW1= retvals[['dW']]
db1= retvals[['db']]

# Update parameters
W1 <- W1 - learningRate * dW1
b1 <- b1 - learningRate * db1
W2 <- W2 - learningRate * t(dW2)
b2 <- b2 - learningRate * t(db2)
}
## [1] 1.212487
## [1] 0.5740867
## [1] 0.4048824
## [1] 0.3561941
## [1] 0.2509576
## [1] 0.7351063
## [1] 0.2066114
## [1] 0.2065875
## [1] 0.2151943
## [1] 0.1318807

 

#Create iterations
iterations <- seq(0,10)
#df=data.frame(iterations,losses)
ggplot(df,aes(x=iterations,y=losses)) + geom_point() + geom_line(color="blue") +
    ggtitle("Losses vs iterations") + xlab("Iterations") + ylab("Loss")

plotDecisionBoundary(Z,W1,b1,W2,b2)



Multi-class Confusion Matrix

library(caret)
library(e1071)

# Forward propagation through hidden layer with Relu units
retvals <- layerActivationForward(X1,W1,b1,'relu')
A1 <- retvals[['A']]

# Forward propagation through output layer with Softmax units
retvals = layerActivationForward(A1,W2,b2,'softmax')
A2 <- retvals[['A']]
yhat <- apply(A2, 1,which.max) -1
Confusion Matrix and Statistics
          Reference
Prediction  0  1  2
         0 97  0  1
         1  2 96  4
         2  1  4 95

Overall Statistics                                        
               Accuracy : 0.96            
                 95% CI : (0.9312, 0.9792)
    No Information Rate : 0.3333          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.94            
 Mcnemar's Test P-Value : 0.5724          
Statistics by Class:

                     Class: 0 Class: 1 Class: 2
Sensitivity            0.9700   0.9600   0.9500
Specificity            0.9950   0.9700   0.9750
Pos Pred Value         0.9898   0.9412   0.9500
Neg Pred Value         0.9851   0.9798   0.9750
Prevalence             0.3333   0.3333   0.3333
Detection Rate         0.3233   0.3200   0.3167
Detection Prevalence   0.3267   0.3400   0.3333
Balanced Accuracy      0.9825   0.9650   0.9625

My book “Practical Machine Learning with R and Python” includes the implementation for many Machine Learning algorithms and associated metrics. Pick up your copy today!

2.3 Multi-class classification with Softmax – Octave code

A 2 layer Neural network with the Softmax activation unit in the output layer is constructed in Octave. The same spiral data set is used for Octave also

source("DL41functions.m")
# Read the spiral data
data=csvread("spiral.csv");
# Setup the data
X=data(:,1:2);
Y=data(:,3);
# Set the number of features, number of hidden units in hidden layer and number of classes
numFeats=2; #No features
numHidden=100; # No of hidden units
numOutput=3; # No of classes
# Initialize model
[W1 b1 W2 b2] = initializeModel(numFeats,numHidden,numOutput);
# Initialize losses
losses=[]
#Initialize learningRate
learningRate=0.5;
for k =1:10000
# Forward propagation through hidden layer with Relu units
[A1,cache1 activation_cache1]= layerActivationForward(X',W1,b1,activationFunc ='relu');
# Forward propagation through output layer with Softmax units
[A2,cache2 activation_cache2] =
layerActivationForward(A1,W2,b2,activationFunc='softmax');
# No of training examples
numTraining = size(X)(1);
# Select rows where Y=0,1,and 2 and concatenate to a long vector
a=[A2(Y==0,1) ;A2(Y==1,2) ;A2(Y==2,3)];
#Select the correct column for log prob
correct_probs = -log(a);
#Compute log loss
loss= sum(correct_probs)/numTraining;
if(mod(k,1000) == 0)
disp(loss);
losses=[losses loss];
endif
dA=0;
# Backward propagation through output layer with Softmax units
[dA1 dW2 db2] = layerActivationBackward(dA, cache2, activation_cache2,Y,activationFunc='softmax');
# Backward propagation through hidden layer with Relu units
[dA0,dW1,db1] = layerActivationBackward(dA1', cache1, activation_cache1, Y, activationFunc='relu');
#Update parameters
W1 += -learningRate * dW1;
b1 += -learningRate * db1;
W2 += -learningRate * dW2';
b2 += -learningRate * db2';
endfor
# Plot Losses vs Iterations
iterations=0:1000:9000
plotCostVsIterations(iterations,losses)
# Plot the decision boundary
plotDecisionBoundary( X,Y,W1,b1,W2,b2)

The code for the Python, R and Octave implementations can be downloaded from Github at Deep Learning – Part 4

Conclusion

In this post I have implemented a 2 layer Neural Network with the Softmax classifier. In Part 3, I implemented a multi-layer Deep Learning Network. I intend to include the Softmax activation unit into the generalized multi-layer Deep Network along with the other activation units of sigmoid,tanh and relu.

Stick around, I’ll be back!!
Watch this space!

References
1. Deep Learning Specialization
2. Neural Networks for Machine Learning
3. CS231 Convolutional Neural Networks for Visual Recognition
4. Eli Bendersky’s Website – The Softmax function and its derivative
5. Cross Validated – Backpropagation with Softmax / Cross Entropy
6. Stackoverflow – CS231n: How to calculate gradient for Softmax loss function?
7. Math Stack Exchange – Derivative of Softmax
8. The Matrix Calculus for Deep Learning

You may like
1.My book ‘Practical Machine Learning with R and Python’ on Amazon
2. My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI)
3. Deblurring with OpenCV: Weiner filter reloaded
4. A method to crowd source pothole marking on (Indian) roads
5. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
6. Sea shells on the seashore
7. Design Principles of Scalable, Distributed Systems

To see all post click Index of posts

Deep Learning from first principles in Python, R and Octave – Part 3


“Once upon a time, I, Chuang Tzu, dreamt I was a butterfly, fluttering hither and thither, to all intents and purposes a butterfly. I was conscious only of following my fancies as a butterfly, and was unconscious of my individuality as a man. Suddenly, I awoke, and there I lay, myself again. Now I do not know whether I was then a man dreaming I was a butterfly, or whether I am now a butterfly dreaming that I am a man.”
from The Brain: The Story of you – David Eagleman

“Thought is a great big vector of neural activity”
Prof Geoffrey Hinton

Introduction

This is the third part in my series on Deep Learning from first principles in Python, R and Octave. In the first part Deep Learning from first principles in Python, R and Octave-Part 1, I implemented logistic regression as a 2 layer neural network. The 2nd part Deep Learning from first principles in Python, R and Octave-Part 2, dealt with the implementation of 3 layer Neural Networks with 1 hidden layer to perform classification tasks, where the 2 classes cannot be separated by a linear boundary. In this third part, I implement a multi-layer, Deep Learning (DL) network of arbitrary depth (any number of hidden layers) and arbitrary height (any number of activation units in each hidden layer). The implementations of these Deep Learning networks, in all the 3 parts, are based on vectorized versions in Python, R and Octave. The implementation in the 3rd part is for a L-layer Deep Netwwork, but without any regularization, early stopping, momentum or learning rate adaptation techniques. However even the barebones multi-layer DL, is a handful and has enough hyperparameters to fine-tune and adjust.

Checkout my book ‘Deep Learning from first principles- In vectorized Python, R and Octave’. My book starts with the implementation of a simple 2-layer Neural Network and works its way to a generic L-Layer Deep Learning Network, with all the bells and whistles. The derivations have been discussed in detail. The code has been extensively commented and included in its entirety in the Appendix sections. My book is available on Amazon as paperback ($16.99) and in kindle version($6.65/Rs449).

The implementation of the vectorized L-layer Deep Learning network in Python, R and Octave were both exhausting, and exacting!! Keeping track of the indices, layer number and matrix dimensions required quite bit of focus. While the implementation was demanding, it was also very exciting to get the code to work. The trick was to be able to shift gears between the slight quirkiness between the languages. Here are some of challenges I faced.

1. Python and Octave allow multiple return values to be unpacked in a single statement. With R, unpacking multiple return values from a list, requires the list returned, to be unpacked separately. I did see that there is a package gsubfn, which does this.  I hope this feature becomes a base R feature.
2. Python and R allow dissimilar elements to be saved and returned from functions using dictionaries or lists respectively. However there is no real equivalent in Octave. The closest I got to this functionality in Octave, was the ‘cell array’. But the cell array can be accessed only by the index, and not with the key as in a Python dictionary or R list. This makes things just a bit more difficult in Octave.
3. Python and Octave include implicit broadcasting. In R, broadcasting is not implicit, but R has a nifty function, the sweep(), with which we can broadcast either by columns or by rows
4. The closest equivalent of Python’s dictionary, or R’s list, in Octave is the cell array. However I had to manage separate cell arrays for weights and biases and during gradient descent and separate gradients dW and dB
5. In Python the rank-1 numpy arrays can be annoying at times. This issue is not present in R and Octave.

Though the number of lines of code for Deep Learning functions in Python, R and Octave are about ~350 apiece, they have been some of the most difficult code I have implemented. The current vectorized implementation supports the relu, sigmoid and tanh activation functions as of now. I will be adding other activation functions like the ‘leaky relu’, ‘softmax’ and others, to the implementation in the weeks to come.

While testing with different hyper-parameters namely i) the number of hidden layers, ii) the number of activation units in each layer, iii) the activation function and iv) the number iterations, I found the L-layer Deep Learning Network to be very sensitive to these hyper-parameters. It is not easy to tune the parameters. Adding more hidden layers, or more units per layer, does not help and mostly results in gradient descent getting stuck in some local minima. It does take a fair amount of trial and error and very close observation on how the DL network performs for logical changes. We then can zero in on the most the optimal solution. Feel free to download/fork my code from Github DeepLearning-Part 3 and play around with the hyper-parameters for your own problems.

Derivation of a Multi Layer Deep Learning Network

Lets take a simple 3 layer Neural network with 3 hidden layers and an output layer

In the forward propagation cycle the equations are

Z_{1} = W_{1}A_{0} +b_{1}  and  A_{1} = g(Z_{1})
Z_{2} = W_{2}A_{1} +b_{2}  and  A_{2} = g(Z_{2})
Z_{3} = W_{3}A_{2} +b_{3}  and A_{3} = g(Z_{3})

The loss function is given by
L = -(ylogA3 + (1-y)log(1-A3))
and dL/dA3 = -(Y/A_{3} + (1-Y)/(1-A_{3}))

For a binary classification the output activation function is the sigmoid function given by
A_{3} = 1/(1+ e^{-Z3}). It can be shown that
dA_{3}/dZ_{3} = A_{3}(1-A_3) see equation 2 in Part 1

\partial L/\partial Z_{3} = \partial L/\partial A_{3}* \partial A_{3}/\partial Z_{3} = A3-Y see equation (f) in  Part 1
and since
\partial L/\partial A_{2} = \partial L/\partial Z_{3} * \partial Z_{3}/\partial A_{2} = (A_{3} -Y) * W_{3} because \partial Z_{3}/\partial A_{2} = W_{3} -(1a)
and \partial L/\partial Z_{2} =\partial L/\partial A_{2} * \partial A_{2}/\partial Z_{2} = (A_{3} -Y) * W_{3} *g'(Z_{2}) -(1b)
\partial L/\partial W_{2} = \partial L/\partial Z_{2} * A_{1} -(1c)
since \partial Z_{2}/\partial W_{2} = A_{1}
and
\partial L/\partial b_{2} = \partial L/\partial Z_{2} -(1d)
because
\partial Z_{2}/\partial b_{2} =1

Also

\partial L/\partial A_{1} =\partial L/\partial Z_{2} * \partial Z_{2}/\partial A_{1} = \partial L/\partial Z_{2} * W_{2}     – (2a)
\partial L/\partial Z_{1} =\partial L/\partial A_{1} * \partial A_{1}/\partial Z_{1} = \partial L/\partial A_{1} * W_{2} *g'(Z_{1})          – (2b)
\partial L/\partial W_{1} = \partial L/\partial Z_{1} * A_{0} – (2c)
\partial L/\partial b_{1} = \partial L/\partial Z_{1} – (2d)

Inspecting the above equations (1a – 1d & 2a-2d), our ‘Uber deep, bottomless’ brain  can easily discern the pattern in these equations. The equation for any layer ‘l’ is of the form
Z_{l} = W_{l}A_{l-1} +b_{l}     and  A_{l} = g(Z_{l})
The equation for the backward propagation have the general form
\partial L/\partial A_{l} = \partial L/\partial Z_{l+1} * W^{l+1}
\partial L/\partial Z_{l}=\partial L/\partial A_{l} *g'(Z_{l})
\partial L/\partial W_{l} =\partial L/\partial Z_{l} *A^{l-1}
\partial L/\partial b_{l} =\partial L/\partial Z_{l}

Some other important results The derivatives of the activation functions in the implemented Deep Learning network
g(z) = sigmoid(z) = 1/(1+e^{-z}) = a g’(z) = a(1-a) – See Part 1
g(z) = tanh(z) = a g’(z) = 1 - a^{2}
g(z) = relu(z) = z  when z>0 and 0 when z 0 and 0 when z <= 0
While it appears that there is a discontinuity for the derivative at 0 the small value at the discontinuity does not present a problem

The implementation of the multi layer vectorized Deep Learning Network for Python, R and Octave is included below. For all these implementations, initially I create the size and configuration of the the Deep Learning network with the layer dimennsions So for example layersDimension Vector ‘V’ of length L indicating ‘L’ layers where

V (in Python)= [v_{0}, v_{1}, v_{2}, … v_{L-1}]
V (in R)= c(v_{1}, v_{2}, v_{3} , … v_{L})
V (in Octave)= [ v_{1} v_{2} v_{3}v_{L}]

In all of these implementations the first element is the number of input features to the Deep Learning network and the last element is always a ‘sigmoid’ activation function since all the problems deal with binary classification.

The number of elements between the first and the last element are the number of hidden layers and the magnitude of each v_{i} is the number of activation units in each hidden layer, which is specified while actually executing the Deep Learning network using the function L_Layer_DeepModel(), in all the implementations Python, R and Octave

1a. Classification with Multi layer Deep Learning Network – Relu activation(Python)

In the code below a 4 layer Neural Network is trained to generate a non-linear boundary between the classes. In the code below the ‘Relu’ Activation function is used. The number of activation units in each layer is 9. The cost vs iterations is plotted in addition to the decision boundary. Further the accuracy, precision, recall and F1 score are also computed

import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
import sklearn.linear_model

from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification, make_blobs
from matplotlib.colors import ListedColormap
import sklearn
import sklearn.datasets

#from DLfunctions import plot_decision_boundary
execfile("./DLfunctions34.py") # 
os.chdir("C:\\software\\DeepLearning-Posts\\part3")

# Create clusters of 2 classes
X1, Y1 = make_blobs(n_samples = 400, n_features = 2, centers = 9,
                       cluster_std = 1.3, random_state = 4)
#Create 2 classes
Y1=Y1.reshape(400,1)
Y1 = Y1 % 2
X2=X1.T
Y2=Y1.T
# Set the dimensions of DL Network 
#  Below we have 
#  2 - 2 input features
#  9,9 - 2 hidden layers with 9 activation units per layer and
#  1 - 1 sigmoid activation unit in the output layer as this is a binary classification
# The activation in the hidden layer is the 'relu' specified in L_Layer_DeepModel

layersDimensions = [2, 9, 9,1] #  4-layer model
parameters = L_Layer_DeepModel(X2, Y2, layersDimensions,hiddenActivationFunc='relu', learning_rate = 0.3,num_iterations = 2500, fig="fig1.png")
#Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), X2,Y2,str(0.3),"fig2.png")

# Compute the confusion matrix
yhat = predict(parameters,X2)
from sklearn.metrics import confusion_matrix
a=confusion_matrix(Y2.T,yhat.T)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(Y2.T, yhat.T)))
print('Precision: {:.2f}'.format(precision_score(Y2.T, yhat.T)))
print('Recall: {:.2f}'.format(recall_score(Y2.T, yhat.T)))
print('F1: {:.2f}'.format(f1_score(Y2.T, yhat.T)))
## Accuracy: 0.90
## Precision: 0.91
## Recall: 0.87
## F1: 0.89

For more details on metrics like Accuracy, Recall, Precision etc. used in classification take a look at my post Practical Machine Learning with R and Python – Part 2. More details about these and other metrics besides implementation of the most common machine learning algorithms are available in my book My book ‘Practical Machine Learning with R and Python’ on Amazon

1b. Classification with Multi layer Deep Learning Network – Relu activation(R)

In the code below, binary classification is performed on the same data set as above using the Relu activation function. The DL network is same as above

library(ggplot2)
# Read the data
z <- as.matrix(read.csv("data.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X1 <- t(x)
Y1 <- t(y)

# Set the dimensions of the Deep Learning network
# No of input features =2, 2 hidden layers with 9 activation units and 1 output layer
layersDimensions = c(2, 9, 9,1)
# Execute the Deep Learning Neural Network
retvals = L_Layer_DeepModel(X1, Y1, layersDimensions,
                               hiddenActivationFunc='relu', 
                               learningRate = 0.3,
                               numIterations = 5000, 
                               print_cost = True)
library(ggplot2)
source("DLfunctions33.R")
# Get the computed costs
costs <- retvals[['costs']]
# Create a sequence of iterations
numIterations=5000
iterations <- seq(0,numIterations,by=1000)
df <-data.frame(iterations,costs)
# Plot the Costs vs number of iterations
ggplot(df,aes(x=iterations,y=costs)) + geom_point() +geom_line(color="blue") +
    xlab('No of iterations') + ylab('Cost') + ggtitle("Cost vs No of iterations")

# Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="relu",0.3)

library(caret)
# Predict the output for the data values
yhat <-predict(retvals$parameters,X1,hiddenActivationFunc="relu")
yhat[yhat==FALSE]=0
yhat[yhat==TRUE]=1
# Compute the confusion matrix
confusionMatrix(yhat,Y1)
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 201  10
##          1  21 168
##                                           
##                Accuracy : 0.9225          
##                  95% CI : (0.8918, 0.9467)
##     No Information Rate : 0.555           
##     P-Value [Acc > NIR] : < 2e-16         
##                                           
##                   Kappa : 0.8441          
##  Mcnemar's Test P-Value : 0.07249         
##                                           
##             Sensitivity : 0.9054          
##             Specificity : 0.9438          
##          Pos Pred Value : 0.9526          
##          Neg Pred Value : 0.8889          
##              Prevalence : 0.5550          
##          Detection Rate : 0.5025          
##    Detection Prevalence : 0.5275          
##       Balanced Accuracy : 0.9246          
##                                           
##        'Positive' Class : 0               
## 

1c. Classification with Multi layer Deep Learning Network – Relu activation(Octave)

Included below is the code for performing classification. Incidentally Octave does not seem to have implemented the confusion matrix,  but confusionmat is available in Matlab.
# Read the data
data=csvread("data.csv");
X=data(:,1:2);
Y=data(:,3);
# Set layer dimensions
layersDimensions = [2 9 7 1] #tanh=-0.5(ok), #relu=0.1 best!
# Execute Deep Network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='relu',
learningRate = 0.1,
numIterations = 10000);
plotCostVsIterations(10000,costs);
plotDecisionBoundary(data,weights, biases,hiddenActivationFunc="tanh")


2a. Classification with Multi layer Deep Learning Network – Tanh activation(Python)

Below the Tanh activation function is used to perform the same classification. I found the Tanh activation required a simpler Neural Network of 3 layers.

# Tanh activation
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
import sklearn.linear_model

from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification, make_blobs
from matplotlib.colors import ListedColormap
import sklearn
import sklearn.datasets

#from DLfunctions import plot_decision_boundary
os.chdir("C:\\software\\DeepLearning-Posts\\part3")
execfile("./DLfunctions34.py") 
# Create the dataset
X1, Y1 = make_blobs(n_samples = 400, n_features = 2, centers = 9,
                       cluster_std = 1.3, random_state = 4)
#Create 2 classes
Y1=Y1.reshape(400,1)
Y1 = Y1 % 2
X2=X1.T
Y2=Y1.T
# Set the dimensions of the Neural Network
layersDimensions = [2, 4, 1] #  3-layer model
# Compute the DL network
parameters = L_Layer_DeepModel(X2, Y2, layersDimensions, hiddenActivationFunc='tanh', learning_rate = .5,num_iterations = 2500,fig="fig3.png")
#Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), X2,Y2,str(0.5),"fig4.png")

2b. Classification with Multi layer Deep Learning Network – Tanh activation(R)

R performs better with a Tanh activation than the Relu as can be seen below

 #Set the dimensions of the Neural Network
layersDimensions = c(2, 9, 9,1)
library(ggplot2)
# Read the data
z <- as.matrix(read.csv("data.csv",header=FALSE)) 
x <- z[,1:2]
y <- z[,3]
X1 <- t(x)
Y1 <- t(y)
# Execute the Deep Model
retvals = L_Layer_DeepModel(X1, Y1, layersDimensions,
                            hiddenActivationFunc='tanh', 
                            learningRate = 0.3,
                            numIterations = 5000, 
                            print_cost = True)
# Get the costs
costs <- retvals[['costs']]
iterations <- seq(0,numIterations,by=1000)
df <-data.frame(iterations,costs)
# Plot Cost vs number of iterations
ggplot(df,aes(x=iterations,y=costs)) + geom_point() +geom_line(color="blue") +
    xlab('No of iterations') + ylab('Cost') + ggtitle("Cost vs No of iterations")

#Plot the decision boundary
plotDecisionBoundary(z,retvals,hiddenActivationFunc="tanh",0.3)

2c. Classification with Multi layer Deep Learning Network – Tanh activation(Octave)

The code below uses the   Tanh activation in the hidden layers for Octave
# Read the data
data=csvread("data.csv");
X=data(:,1:2);
Y=data(:,3);
# Set layer dimensions
layersDimensions = [2 9 7 1] #tanh=-0.5(ok), #relu=0.1 best!
# Execute Deep Network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='tanh',
learningRate = 0.1,
numIterations = 10000);
plotCostVsIterations(10000,costs);
plotDecisionBoundary(data,weights, biases,hiddenActivationFunc="tanh")


3. Bernoulli’s Lemniscate

To make things  more interesting, I create a 2D figure of the Bernoulli’s lemniscate to perform non-linear classification. The Lemniscate is given by the equation
(x^{2} + y^{2})^{2} = 2a^{2}*(x^{2}-y^{2})

3a. Classifying a lemniscate with Deep Learning Network – Relu activation(Python)

import os
import numpy as np 
import matplotlib.pyplot as plt
os.chdir("C:\\software\\DeepLearning-Posts\\part3")
execfile("./DLfunctions33.py") 
x1=np.random.uniform(0,10,2000).reshape(2000,1)
x2=np.random.uniform(0,10,2000).reshape(2000,1)

X=np.append(x1,x2,axis=1)
X.shape

# Create a subset of values where squared is <0,4. Perform ravel() to flatten this vector
# Create the equation
# (x^{2} + y^{2})^2 - 2a^2*(x^{2}-y^{2}) <= 0
a=np.power(np.power(X[:,0]-5,2) + np.power(X[:,1]-5,2),2)
b=np.power(X[:,0]-5,2) - np.power(X[:,1]-5,2)
c= a - (b*np.power(4,2)) <=0
Y=c.reshape(2000,1)
# Create a scatter plot of the lemniscate
plt.scatter(X[:,0], X[:,1], c=Y, marker= 'o', s=15,cmap="viridis")
Z=np.append(X,Y,axis=1)
plt.savefig("fig50.png",bbox_inches='tight')
plt.clf()

# Set the data for classification
X2=X.T
Y2=Y.T
# These settings work the best
# Set the Deep Learning layer dimensions for a Relu activation
layersDimensions = [2,7,4,1]
#Execute the DL network
parameters = L_Layer_DeepModel(X2, Y2, layersDimensions, hiddenActivationFunc='relu', learning_rate = 0.5,num_iterations = 10000, fig="fig5.png")
#Plot the decision boundary
plot_decision_boundary(lambda x: predict(parameters, x.T), X2, Y2,str(2.2),"fig6.png")

# Compute the Confusion matrix
yhat = predict(parameters,X2)
from sklearn.metrics import confusion_matrix
a=confusion_matrix(Y2.T,yhat.T)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(Y2.T, yhat.T)))
print('Precision: {:.2f}'.format(precision_score(Y2.T, yhat.T)))
print('Recall: {:.2f}'.format(recall_score(Y2.T, yhat.T)))
print('F1: {:.2f}'.format(f1_score(Y2.T, yhat.T)))
## Accuracy: 0.93
## Precision: 0.77
## Recall: 0.76
## F1: 0.76

We could get better performance by tuning further. Do play around if you fork the code.
Note:: The lemniscate data is saved as a CSV and then read in R and also in Octave. I do this instead of recreating the lemniscate shape

3b. Classifying a lemniscate with Deep Learning Network – Relu activation(R code)

The R decision boundary for the Bernoulli’s lemniscate is shown below

Z <- as.matrix(read.csv("lemniscate.csv",header=FALSE))
Z1=data.frame(Z)
# Create a scatter plot of the lemniscate
ggplot(Z1,aes(x=V1,y=V2,col=V3)) +geom_point()
#Set the data for the DL network
X=Z[,1:2]
Y=Z[,3]

X1=t(X)
Y1=t(Y)

# Set the layer dimensions for the tanh activation function
layersDimensions = c(2,5,4,1)
# Execute the Deep Learning network with Tanh activation
retvals = L_Layer_DeepModel(X1, Y1, layersDimensions, 
                               hiddenActivationFunc='tanh', 
                               learningRate = 0.3,
                               numIterations = 20000, print_cost = True)
# Plot cost vs iteration
costs <- retvals[['costs']]
numIterations = 20000
iterations <- seq(0,numIterations,by=1000)
df <-data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() +geom_line(color="blue") +
    xlab('No of iterations') + ylab('Cost') + ggtitle("Cost vs No of iterations")

#Plot the decision boundary
plotDecisionBoundary(Z,retvals,hiddenActivationFunc="tanh",0.3)

3c. Classifying a lemniscate with Deep Learning Network – Relu activation(Octave code)

Octave is used to generate the non-linear lemniscate boundary.

# Read the data
data=csvread("lemniscate.csv");
X=data(:,1:2);
Y=data(:,3);
# Set the dimensions of the layers
layersDimensions = [2 9 7 1]
# Compute the DL network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='relu',
learningRate = 0.20,
numIterations = 10000);
plotCostVsIterations(10000,costs);
plotDecisionBoundary(data,weights, biases,hiddenActivationFunc="relu")


4a. Binary Classification using MNIST – Python code

Finally I perform a simple classification using the MNIST handwritten digits, which according to Prof Geoffrey Hinton is “the Drosophila of Deep Learning”.

The Python code for reading the MNIST data is taken from Alex Kesling’s github link MNIST.

In the Python code below, I perform a simple binary classification between the handwritten digit ‘5’ and ‘not 5’ which is all other digits. I will perform the proper classification of all digits using the  Softmax classifier some time later.

import os
import numpy as np 
import matplotlib.pyplot as plt
os.chdir("C:\\software\\DeepLearning-Posts\\part3")
execfile("./DLfunctions34.py") 
execfile("./load_mnist.py")
training=list(read(dataset='training',path="./mnist"))
test=list(read(dataset='testing',path="./mnist"))
lbls=[]
pxls=[]
print(len(training))

# Select the first 10000 training data and the labels
for i in range(10000):
       l,p=training[i]
       lbls.append(l)
       pxls.append(p)
labels= np.array(lbls)
pixels=np.array(pxls)   

#  Sey y=1  when labels == 5 and 0 otherwise
y=(labels==5).reshape(-1,1)
X=pixels.reshape(pixels.shape[0],-1)

# Create the necessary feature and target variable
X1=X.T
Y1=y.T

# Create the layer dimensions. The number of features are 28 x 28 = 784 since the 28 x 28
# pixels is flattened to single vector of length 784.
layersDimensions=[784, 15,9,7,1] # Works very well
parameters = L_Layer_DeepModel(X1, Y1, layersDimensions, hiddenActivationFunc='relu', learning_rate = 0.1,num_iterations = 1000, fig="fig7.png")

# Test data
lbls1=[]
pxls1=[]
for i in range(800):
       l,p=test[i]
       lbls1.append(l)
       pxls1.append(p)
 
testLabels=np.array(lbls1)
testData=np.array(pxls1)

ytest=(testLabels==5).reshape(-1,1)
Xtest=testData.reshape(testData.shape[0],-1)
Xtest1=Xtest.T
Ytest1=ytest.T

yhat = predict(parameters,Xtest1)
from sklearn.metrics import confusion_matrix
a=confusion_matrix(Ytest1.T,yhat.T)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy: {:.2f}'.format(accuracy_score(Ytest1.T, yhat.T)))
print('Precision: {:.2f}'.format(precision_score(Ytest1.T, yhat.T)))
print('Recall: {:.2f}'.format(recall_score(Ytest1.T, yhat.T)))
print('F1: {:.2f}'.format(f1_score(Ytest1.T, yhat.T)))

probs=predict_proba(parameters,Xtest1)
from sklearn.metrics import precision_recall_curve

precision, recall, thresholds = precision_recall_curve(Ytest1.T, probs.T)
closest_zero = np.argmin(np.abs(thresholds))
closest_zero_p = precision[closest_zero]
closest_zero_r = recall[closest_zero]
plt.xlim([0.0, 1.01])
plt.ylim([0.0, 1.01])
plt.plot(precision, recall, label='Precision-Recall Curve')
plt.plot(closest_zero_p, closest_zero_r, 'o', markersize = 12, fillstyle = 'none', c='r', mew=3)
plt.xlabel('Precision', fontsize=16)
plt.ylabel('Recall', fontsize=16)
plt.savefig("fig8.png",bbox_inches='tight')

## Accuracy: 0.99
## Precision: 0.96
## Recall: 0.89
## F1: 0.92

In addition to plotting the Cost vs Iterations, I also plot the Precision-Recall curve to show how the Precision and Recall, which are complementary to each other vary with respect to the other. To know more about Precision-Recall, please check my post Practical Machine Learning with R and Python – Part 4.

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4b. Binary Classification using MNIST – R code

In the R code below the same binary classification of the digit ‘5’ and the ‘not 5’ is performed. The code to read and display the MNIST data is taken from Brendan O’ Connor’s github link at MNIST

source("mnist.R")
load_mnist()
#show_digit(train$x[2,]
layersDimensions=c(784, 7,7,3,1) # Works at 1500
x <- t(train$x)
# Choose only 5000 training data
x2 <- x[,1:5000]
y <-train$y
# Set labels for all digits that are 'not 5' to 0
y[y!=5] <- 0
# Set labels of digit 5 as 1
y[y==5] <- 1
# Set the data
y1 <- as.matrix(y)
y2 <- t(y1)
# Choose the 1st 5000 data
y3 <- y2[,1:5000]

#Execute the Deep Learning Model
retvals = L_Layer_DeepModel(x2, y3, layersDimensions, 
                               hiddenActivationFunc='tanh', 
                               learningRate = 0.3,
                               numIterations = 3000, print_cost = True)
# Plot cost vs iteration
costs <- retvals[['costs']]
numIterations = 3000
iterations <- seq(0,numIterations,by=1000)
df <-data.frame(iterations,costs)
ggplot(df,aes(x=iterations,y=costs)) + geom_point() +geom_line(color="blue") +
    xlab('No of iterations') + ylab('Cost') + ggtitle("Cost vs No of iterations")

# Compute probability scores
scores <- computeScores(retvals$parameters, x2,hiddenActivationFunc='relu')
a=y3==1
b=y3==0

# Compute probabilities of class 0 and class 1
class1=scores[a]
class0=scores[b]

# Plot ROC curve
pr <-pr.curve(scores.class0=class1,
        scores.class1=class0,
       curve=T)

plot(pr)

The AUC curve hugs the top left corner and hence the performance of the classifier is quite good.

4c. Binary Classification using MNIST – Octave code

This code to load MNIST data was taken from Daniel E blog.
Precision recall curves are available in Matlab but are yet to be implemented in Octave’s statistics package.

load('./mnist/mnist.txt.gz'); % load the dataset
# Subset the 'not 5' digits
a=(trainY != 5);
# Subset '5'
b=(trainY == 5);
#make a copy of trainY
#Set 'not 5' as 0 and '5' as 1
y=trainY;
y(a)=0;
y(b)=1;
X=trainX(1:5000,:);
Y=y(1:5000);
# Set the dimensions of layer
layersDimensions=[784, 7,7,3,1];
# Compute the DL network
[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,
hiddenActivationFunc='relu',
learningRate = 0.1,
numIterations = 5000);

Conclusion

It was quite a challenge coding a Deep Learning Network in Python, R and Octave. The Deep Learning network implementation, in this post,is the base Deep Learning network, without any of the regularization methods included. Here are some key learning that I got while playing with different multi-layer networks on different problems

a. Deep Learning Networks come with many levers, the hyper-parameters,
– learning rate
– activation unit
– number of hidden layers
– number of units per hidden layer
– number of iterations while performing gradient descent
b. Deep Networks are very sensitive. A change in any of the hyper-parameter makes it perform very differently
c. Initially I thought adding more hidden layers, or more units per hidden layer will make the DL network better at learning. On the contrary, there is a performance degradation after the optimal DL configuration
d. At a sub-optimal number of hidden layers or number of hidden units, gradient descent seems to get stuck at a local minima
e. There were occasions when the cost came down, only to increase slowly as the number of iterations were increased. Probably early stopping would have helped.
f. I also did come across situations of ‘exploding/vanishing gradient’, cost went to Inf/-Inf. Here I would think inclusion of ‘momentum method’ would have helped

I intend to add the additional hyper-parameters of L1, L2 regularization, momentum method, early stopping etc. into the code in my future posts.
Feel free to fork/clone the code from Github Deep Learning – Part 3, and take the DL network apart and play around with it.

I will be continuing this series with more hyper-parameters to handle vanishing and exploding gradients, early stopping and regularization in the weeks to come. I also intend to add some more activation functions to this basic Multi-Layer Network.
Hang around, there are more exciting things to come.

Watch this space!

References
1. Deep Learning Specialization
2. Neural Networks for Machine Learning
3. Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville
4. Neural Networks: The mechanics of backpropagation
5. Machine Learning

Also see
1.My book ‘Practical Machine Learning with R and Python’ on Amazon
2. My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI)
3. Designing a Social Web Portal
4. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables
4. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
6. Presentation on “Intelligent Networks, CAMEL protocol, services & applications
7. Design Principles of Scalable, Distributed Systems

To see all posts see Index of posts