PYTORCH VS TENSORFLOW DEEP LEARNING

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!!