COMPARING TWO OPEN SOURCE DEEP LEARNING PLATFORMS THAT COME IN HANDY WHEN BUILDING DEEP LEARNING MODELS.

DEEP LEARNING IS THE NEW TREND .THE TECH GIANTS ARE INVESTING BIG . THIS HAS LED TO AN UPSURGE IN PEOPLE WANTING TO LEARN DEEP LEARNING TO STAY INDUSTRY RELEVANT . THERE ARE MANY DEEP LEARNING FRAMEWORKS TO WORK WITH . OF ALL , THREE PARTICULAR FRAMEWORKS HAVE GAINED IMMENSE IMPORTANCE SINCE THEIR RELEASE . THEY ARE PYTORCH , TENSORFLOW AND KERAS .IN THIS POST WE COMPARE PYTORCH AND TENSORFLOW . IF YOU WANT A COMPARISION BETWEEN KERAS AND TENSORFLOW CLICK HERE.

LETS START WITH A BRIEF INTRO TO PYTORCH AND TENSORFLOW .

NEURAL NETWORKS ARE BUILT USING PYTORCH AND TENSORFLOW FRAMEWORKS. TENSORFLOW IS AN OPEN SOURCE PLATFORM FOR MACHINE LEARNING . ITS WRITTEN IN PYTHON ,C++ AND CUDA . IT WAS DEVELOPED BY GOOGLE BRAIN TEAM AND WAS FIRST RELEASED ON 9TH SEPTEMBER 2015. PYTORCH IS ALSO A MACHINE LEARNING FRAMEWORK THAT WAS DEVELOPED BY FACEBOOK . NOW LETS COMPARE THESE TWO:

COMMUNITY

TENSORFLOW FOR SURE HAS SPREAD MORE OVER THE WORLD IN THE LAST FEW YEARS. PYTORCH ,THOUGH SLIGHTLY BEHIND IS GAINING MOMENTUM TOO . TONS OF TENSORFLOW GITHUB REPOSITORIES AND FORKS SHOW THAT IT IS BEING USED WIDELY . THIS DOESNT MEAN THAT TENSORFLOW IS A BETTER FRAMEWORK THAN PYTORCH . IN FACT TESLA , A HUGE TECH GIANT TRAINED ITS AUTOPILOT IN THE PYTORCH DEEP LEARNING FRAMEWORK. FRAMEWORK . CURRENTLY TENSORFLOW IS MORE FAMOUS AMONG ENGINEERS AND PYTORCH IS USED BY RESEARCHERS , UNIVERSITIES . THE REASON AS TO WHY PYTORCH IS USED EXTENSIVELY IN RESEARCHES IS BECAUSE IT ALLOWS CUSTOMISATION AT VERY DEEP LEVELS , BY ALLOWING TO DESIGN NETWORKS FROM SCRATCH . HENCE NOT EVERYONE IS COMFORTABLE WITH THAT . PEOPLE PREFER WORKING ON ALREADY WELL DEFINED LIBRARIES AND HENCE THE COMMUNITY SIZE OF TENSORFLOW.

BELOW YOU CAN COMPARE A FULL CNN MODEL IN A TENSORFLOW FRAMEWORK AND BELOW THAT YOU SEE ONE SIMPLE BACKPROPOGATION WEIGHT UPDATING CODE IN A PYTORCH DEEP LEARNING FRAMEWORK . SEE FOR YOUR SELF THE DIFFERENCE :

DYNAMIC NATURE OF PYTORCH

TENSORFLOW WORKS ON A STATIC GRAPH CONCEPT . THE USER NEEDS TO DEFINE THE GRAPH BEFOREHAND IN TENSORFLOW . ON THE OTHER HAND PYTORCH ALLOWS DYNAMIC GRAPHS THAT KEEPS UPDATING AS THE MODEL TRAINS . THIS DYNAMIC NATURE ALLOWS YOU TO VISUALIZE THE PROCESS WHILE ITS HAPPENING , YOU CAN DEFINE ,CHANGE AND EXECUTE NODES AS YOU GO . THIS IS NOT POSSIBLE WITH TENSORFLOW . SUCH DYNAMIC BEHAVIOUR MAKES IT A BETTER FRAMEWORK FOR TRAINING DYNAMIC NEURAL NETWORKS LIKE RNNS, TREE RNNS . SUCH AN APPROACH CAN HANDLE INPUTS OF DIFFERENT DIMENSIONS ON ITS OWN , IN CONTRAST TO TENSORFLOW WHERE YOU REQUIRE TO MAKE ALL THE INPUTS OF SAME DIMENSIONS.

DEBUGGING

EVEN HERE PYTORCH TAKES THE LEAD . PYTORCH DEEP LEARNING FRAMEWORK CAN MAKE USE OF STANDARD PYTHON FLOW CONTROL WHEREAS TENSORFLOW CANNOT . PYTORCH SUPPORTS PYTHON DEBUGGERS AND GUESS WHO DOESN’T , TENSORFLOW!!!! TENSORBOARD IS THE TOOL THAT HELPS VISUALIZE GRAPHS , GRADIENTS AND OTHER DATA .

DEPLOYEMENT

AS FAR AS DEPLOYEMENT IS CONCERNED TENSORFLOW SURELY HAS LEAD ON PYTORCH. PERFORMANCE OF TENSORFLOW DEPLOYED MODELS IS BETTER ,AS FOR NOW , THAN PYTORCH .

CONCLUSION

PYTORCH WILL SURELY MAKE YOUR UNDERSTANDING BETTER , IT IS CODED FROM SCRATCH , IS SIMILAR TO USING NUMPY AND YOU CAN PLAY /DO WHAT YOU WANT TO EXPERIMENT AT THE DEEPEST LEVEL . WHEN IT COMES TO ENGINEERING RATHER THAN RESEARCH , BUILDING MODELS USING LIBRARIES FOR EASY READIBILITY , QUICKLY EDITABLE AND USER FRIENDLY , TENSORFLOW GOES WELL!!!!!! .I WOULD STRONGLY SUGGEST BUILDING MODELS IN BOTH THE FRAMEWORKS FOR THE SAKE OF HAVING AN EXPERIENCE WITH BOTH . AND THAT WILL WILL ONLY MAKE YOU MORE SKILLED AT WHAT YOU DO . ITS A WIN -WIN SITUATION !!!!

VISIT PYTORCH / TENSORFLOW AND START EXPLORING THE VAST TUTORIAL OPTIONS .

HAPPY LEARNING!!