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BLOCK WHICH LETS GET THIS ONE RUNNING SO THE SIMPLE PART IS JUST GO OVER THERE AND THEN | |
YOU HAVE THE RUN SELECTED CELL SO WE SELECT THAT ONE AND RUN IT SO WHILE IT RUNS YOU WOULD | |
SO THIS IS JUST A COMMENT SO IF YOU CAN CHOOSE TO RUN IT BUT IT DOESNT ACTUALLY DO ANYTHING | |
YOU WOULD BE MAKE BASICALLY CLASSIFYING THEM INTO THESE DIFFERENT KINDS OF CLASSES OVER | |
IMAGES ITSELF AND THESE ARE ALL COLOR RGB COLOR IMAGES SO THATS AVAILABLE DIRECTLY WITHIN | |
THE TORCH VISION DATA SETS SO NOW WE HAD IMPORTED TORCH VISION DATA SET OVER HERE | |
NOW I CAN GO INTO DATA SETS AND THEN FROM THERE I INPUT THE CIFAR TEN DATA SET NOW THE | |
POINT IS WHEN IT IMPORTS LOCALLY SO ITS ITS EITHER IMPORTED SOMEWHERE EARLIER AND THEN | |
IS BASICALLY ANOTHER FOLDER WHICH IS CREATED WITHIN MY LOCAL DIRECTORY SO YOU SEE YOUR | |
DIRECTORY ANYWAYS BECAUSE WE DID NOT UPLOAD THE DATA SET THATS A HUGE BULKY FILE TO BE | |
SO IF YOU HAVE IT ALREADY DOWNLOADED | |
PURPOSE OTHERWISE YOU NEED TO DOWNLOAD IT FROM SCRATCH SO HERE LIKE WHAT IT WOULD DO | |
IS IT JUST GOES OVER THERE AND SEES THAT FILES ARE ALREADY DOWNLOADED AND THEY ARE PERFECTLY | |
AND THEN WITHIN CIFAR TEN BATCHES IT WILL BE CREATING MY TRAINING AND TEST BATCHES OVER | |
HERE OK SO NOW ONCE THATS DONE SO WHAT I CAN DO IS I MOVE BACK ON TO MY MAIN DIRECTORY | |
OVER HERE AND LETS GO TO THE NEXT PART OF IT SO HERE WHAT I AM TRYING TO DO IS GET INTO | |
WHAT IS THE LENGTH OVER THERE AND THEN IT JUST CONVERTS IT TO A STRING AND PRINTS IT | |
TRAINING AND TESTING DATA SET IS OF TEN THOUSAND IMAGES NOW ONCE THATS DONE THE NEXT PART IS | |
WE COME DOWN OVER HERE WHICH IS FEATURE EXTRACTION ON A SINGLE IMAGE SO INITIALLY WHAT WE WILL | |
BE DOING IS LETS LETS SEE WHAT THESE IMAGES LOOK LIKE SO WHAT I AM DOING IS I TAKE DOWN | |
ONE OF THESE IMAGES WHICH IS AT THE ZERO , ZERO LOCATION SO THIS IS THE FIRST IMAGE PRESENT | |
FORMAT SO THAT WILL TYPICALLY BE COMING DOWN AS SOME SORT OF A CONTAINER WITH ME NOW THAT | |
ITS ITS REALLY FUZZY TO UNDERSTAND BUT THIS IS BASICALLY IF YOU LIKE REALLY GO FAR OFF | |
WHAT YOU ARE GOING TO DO IS YOU WOULD NEED THE MAIN IMAGE ARRAY SO THATS PRESENT OVER | |
IS BASICALLY THE NUMBER OF POINTS YOU WOULD BE TAKING AROUND THE CENTRAL POINT | |
SO YOU REMEMBER CLEARLY FROM OUR EARLIER DISCUSSIONS ON FROM IN THE LAST CLASS ON LBP WHERE YOU | |
YOU WOULD BE GETTING EIGHT SUCH NEIGHBORS ALONG THAT POINT WHICH ARE AT A DISTANCE SEPARATION | |
YOU WOULD DO NOW WHAT IT ALLOWS WITHIN THESE FUNCTIONS IS THAT YOU CAN CHOOSE DOWN ANY | |
NUMBER OF NEIGHBORS YOU CAN CHOOSE FOUR FIVE SIX SEVEN TYPICALLY FOR THE THREE CROSS THREE | |
THAT THAT WOULD NOT BE A UNIFORM PIXEL KIND OF A DISTRIBUTION BUT YOU CAN INTERPOLATE | |
AND GO DOWN TO THOSE KIND OF FORMS SO WHAT WE CHOOSE TO DO IS WE TAKE A CIRCULAR NEIGHBORHOOD | |
THIS LBP FEATURE ON A POINT TO POINT BASIS LOOKS LIKE SO WE COMPUTE THIS ONE AND THIS | |
HARD TO ACTUALLY FIND OUT WHETHER THERE IS A FROG OR SOMETHING OR NOT FROM SO MANY POINTS | |
THERE FOR FOR THIS FROM THIS HISTOGRAM THEN THAT WOULD HELP YOU TO GET DOWN THE ENERGY | |
AND ENTROPY AS WELL | |
NOW ONCE YOU HAVE ALL OF THESE YOU CAN BASICALLY USE ENERGY AND ENTROPY AS TWO DIFFERENT DISTINCT | |
WHOLE IMAGE NEEDS TO BE REPRESENTED IN TERMS OF ONE SINGLE SCALAR VALUE AND A SET OF THOSE | |
MULTIPLE NUMBER OF SCALAR VALUES WHICH WILL BE YOUR FEATURES WHICH DESCRIBE THIS IMAGE | |
SO FOR THAT WHAT WE DO IS WE JUST EVALUATE THIS PART OVER HERE AND I GET DOWN THAT LBP | |
ENERGY OF THIS MUCH AND LBP ENTROPY OF THIS MUCH IS WHAT DEFINES ALL OF THIS TOGETHER | |
PRESENT IN THIS IMAGE OK NOW ONCE THAT GOES DOWN THE NEXT PART IS TO FIND IT OUT ON THE | |
CO OCCURRENCE MATRIX OK SO IN A CO OCCURRENCE MATRIX WHAT I NEED TO DO IS I NEED TO GET | |
THERE IS WHAT IS THE ORIENTATION OF YOUR VECTOR WHETHER ITS AT ZERO DEGREES FORTY FIVE DEGREE | |
NUMBER TWO FIFTY SIX IS BASICALLY THE NUMBER OF GRAY LEVELS YOU HAVE IN YOUR GRAY LEVEL | |
ARE BASICALLY TO SHOW DOWN HOW TO HANDLE DOWN THE BOUNDARY CONDITIONS PRESENT OVER THERE | |
ONE THE FIRST SCALAR VALUE IS BASICALLY TO GET DONE CONTRAST SECOND SCALAR VALUE IS TO | |
IN GETTING AND THIS ARE THE DIFFERENT MEASURES FOR THAT ONE PARTICULAR IMAGE NOW FROM THERE | |
THE NEXT ONE IS TO GET INTO WAVELETS AND DO IT SO FOR WE CHOOSE TO DO IT WITH GABOR FILTERS | |
NOW AS YOU REMEMBER FROM YOUR GABOR FILTERED EQUATIONS IN THE LAST CLASS SO THERE WOULD | |
DOWN OVER THERE AS WELL AS WHAT IS YOUR FREQUENCY AT WHICH YOU WOULD LIKE TO OPERATE | |
NOW THE OTHER PART IS WHAT IS THE ANGLE AT WHICH IT IS LOCATED AND WHAT ARE THE VARIABLES | |
ALSO CHOOSE TO GIVE THEM SO YOU CAN READ DOWN WITH WITHIN THE DETAILS MORE OVER THERE NOW | |
GIVEN THAT AT ANY POINT YOU WILL BE GETTING DOWN TO COMPONENTS OF YOUR WAVELET DECOMPOSITION | |
IMAGINARY PART AND THIS IS BASICALLY THE CONSOLIDATED MAGNITUDE RESPONSE OVER THERE THE NEXT PART | |
THAN THESE KIND OF MATRIX REPRESENTATION AND THEY ARE BASICALLY YOUR PROBABILITY ENERGY | |
NOW THIS IS TILL NOW WHAT WE HAVE DONE WAS JUST FOR ONE OF THESE IMAGES WHICH WAS AT | |
THE FIRST LOCATION WITHIN MY TRAINING DATA SET NOW IN ORDER TO DO IT FOR TRAINING I WOULD | |
DEFINE SOME SORT OF A MATRIX WHICH IS CALLED AS THE TRAINING FEATURES MATRIX SO THIS IS | |
A TWO D MATRIX WHICH IS THE NUMBER OF ROWS IN THIS MATRIX IS EQUAL TO THE LENGTH OF THE | |
TRAINING DATA SET THE NUMBER OF COLUMNS IS EQUAL TO THE LENGTH OF FEATURES NOW HOW MANY | |
FEATURES WE FOUND OUT WAS BASICALLY TWO plus FIVE plus TWO AND THAT MAKES IT NINE FEATURES | |
WHICH WE ARE GOING TO HAVE OVER HERE NOW FOR THIS PART WHAT WE DO IS WE WRITE DOWN FIRST | |
OVER THE WHOLE LENGTH OF THE TRAINING DATA SET ONCE YOU GET OVER THE WHOLE LENGTH OF | |
THE TRAINING DATA SET YOU NEED TO FIND OUT ONE FEATURE AT A TIME NOW ONCE YOU HAVE ONE | |
FEATURE AT A TIME COMING DOWN YOU NEED TO CALCULATE ALL OF THESE FEATURES ONE SORRY | |
FILTERS NOW ONCE YOU HAVE ALL OF THEM YOU NEED TO CONCATENATE THAT INTO ONE ROW MATRIX | |
AND THEN YOU KEEP ON CONCATENATING ONE BELOW THE OTHER AND YOU GET YOUR TWO D MATRIX COMING | |
DOWN SO IF WE RUN THIS PART YOU SEE THIS VERBOSE COMMENTING COMING DOWN AND THEN IT KEEPS ON | |
RUNNING SO TOGETHER THAT WOULD FINISH IT OFF THERE MIGHT BE CERTAIN WARNINGS AT POSITIONS | |
IT OVER FIFTY THOUSAND OF THOSE BUT IF YOU LOOK THROUGH IT SO ITS ITS PRETTY MUCH FAST | |
SO TIDY SLOW AS WELL IN THE DURATION OF WHERE WE ARE SPEAKING YOU CAN ALREADY SEE THIS QUITE | |
GOING ON SO WE JUST HAVE A VERBOSE ,ND GIVEN DOWN OVER THERE SO IF YOU WOULD LIKE | |
TO GET RID OF THIS PART THEN THE SIMPLE TASK IS THAT YOU DONT KEEP ONE PRINTING THIS PART | |
TO SHOW DOWN HOW MANY OF THEM ARE DONE AND AND THEN YOU JUST JUST NEED TO WAIT TILL ITS | |
OUT ON YOUR TEST SET AS WELL | |
DO A BASIC REVISION IN THAT CASE SO WHAT I DID WAS I HAVE MY PRE DEFINED PRECURSOR COMING | |
THE TYPE OF THE DATA SET OR NOT BUT SAY IF YOU ARE WRITING A FULL FLEDGED CODE OVER THERE | |
ALL IMAGES IN YOUR DATA SET NOW IF YOU DONT WANT TO LOOK INTO WHATS GETTING EXTRACTED | |
STILL KEEPS ON RUNNING OVER HERE SO LETS SEE HOW FAR YEAH IT SHOULD BE QUITE CLOSE TO FINISHING | |
TIME NOW ONCE YOUR FEATURES ARE EXTRACTED THE NEXT PART OF YOUR CODE IS BASICALLY TO | |
ARE EXTRACTED THE NEXT PART IS TO GO DOWN ON YOUR TEST DATA SET AND ALSO EXTRACT OUT | |
FEATURES AND COMPLETELY SHOW IT AND AND THEN EVENTUALLY YOU CAN GO AND BASICALLY SAVE DOWN | |
YEAH SO NOW THIS IS OVER AND THE NEXT PART OF IT IS BASICALLY TO GET DOWN YOUR TESTING | |
OUT ALL THE FEATURES IS BASICALLY TO GET DOWN GET EACH FEATURE DYNAMICALLY VARYING WITHIN | |
TO BE APPLIED WITHIN YOUR TESTING SET OTHERWISE THE NATURE OF NORMALIZATIONS ARE GOING TO | |
FILE AND THEN JUST PRINT IT ALL SO ONCE THIS PART IS COMPLETE YOU NEED TO GET DOWN EXTRACT | |
FEATURES FOR YOUR TRAINING ONE AND FOR YOUR TESTING SET THEN RUN THE FEATURE NORMALIZATION | |
ON IMAGES SOME BASIC OPERATIONS USING THE CLASSICAL WAY SO AS YOU START WITH ANY KIND | |
YOU HAVE IN THAT BIG CORPUS OF PIXEL SPACE AVAILABLE TO YOU NOW FROM THAT WHEN WE EVENTUALLY | |
GO DOWN AS YOU HAVE SEEN THAT THERE ARE FEATURES WHICH YOU HAVE EXTRACTED OUT THE NEXT QUESTION | |
AS WHAT WE HAD DEFINED IN THE FIRST FEW LECTURES WAS THAT YOU NEED TO BE ABLE TO RELATE CERTAIN | |
CALLED AS A CLASSIFICATION PROBLEM OK | |
NOW IN ORDER TO MAKE IT EVEN SIMPLER SO WHAT IT WOULD ESSENTIALLY MEAN IS THAT IF I HAVE | |
THESE ARE ALL MAY BE SCALAR PARAMETERS NOW IF I ARRANGE THESE SCALAR PARAMETERS INTO | |
SORT OF A MATRIX THATS WHAT WE WOULD CALL DOWN AS A VECTOR OR IN THE STANDARD PARLANCE | |
OF OUR DEFINITIONS WE WOULD ALSO BE CALLING THIS AS A FEATURE VECTOR NOW ONCE YOU HAVE | |
THAT FEATURE VECTOR GIVEN TO YOU HOW DO I ASSOCIATE A FEATURE VECTOR TO ONE SINGLE CATEGORICAL | |
ITSELF AND NOW FROM THAT PERSPECTIVE HERE IS WHERE WE START DOWN SO WHAT TODAYS LECTURE | |
NEURON MODEL AND FROM THERE WE WILL GO DOWN TO AH THE NEURAL NETWORK FORMULATION AND THEN | |
WOULD DEFINE WHAT A NEURON IS SO AS IN A NEURAL NETWORK YOU WOULD ALWAYS HAVE A NEURON |
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