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