tensorflow 2
TRANSCRIPT
TENSORFLOWPART 2
SOUBHI HADRI
COMPANIES USING TENSORFLOW
• GOOGLE • OPENAI • DEEPMIND • SNAPCHAT • UBER • AIRBUS • EBAY • DROPBOX • A BUNCH OF STARTUPS
SOME COOL PROJECTS USING TENSORFLOW
SOME COOL PROJECTS USING TENSORFLOW
SOME COOL PROJECTS USING TENSORFLOW
COLORNET
SOME COOL PROJECTS USING TENSORFLOWFor More visit Awesome Tensorflow
A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.
Tutorials, Libraries, Videos, PapersBlog posts, Community, BooksModels/Projects Powered by TensorFlow
https://github.com/jtoy/awesome-tensorflow
EXAMPLE 1: MINST.
• EACH IMAGE IS A 28X28 ARRAY, FLATTENED OUT TO BE A 1-D TENSOR OF SIZE 784
EXAMPLE 1: MINST.
• STEP 1: PROCESS DATA
EXAMPLE 1: MINST.
• STEP 2: CREATE PLACEHOLDERS FOR INPUTS AND LABELS
EXAMPLE 1: MINST.
• STEP 3: CREATE WEIGHT AND BIAS
EXAMPLE 1: MINST.
• STEP 4: BUILD MODEL TO PREDICT Y
EXAMPLE 1: MINST.
• STEP 5: SPECIFY LOSS FUNCTION
EXAMPLE 1: MINST.
• STEP 6: CREATE OPTIMIZER
EXAMPLE 1: MINST.
• STEP 7 : TRAIN OUR MODEL
EXAMPLE 1: MINST.
• TEST ACCURACY : 0.9172.
NOT ENOUGH GOOD!
EXAMPLE 1: MINST.USIGN CNN
• FIRST:CON->POOL
EXAMPLE 1: MINST.USIGN CNN
• SECOND:CON2->POOL2
EXAMPLE 1: MINST.USIGN CNN
• THIRD:FULL CONNECTED
EXAMPLE 1: MINST.USIGN CNN
• FOURTH:DROPOUT->FC2
EXAMPLE 1: MINST.USIGN CNN
• FINALLY:ADAM OPTIMIZER.
• 50 BATCH SIZE
• TEST ACCURACY : 0.9927.
HOW DOES TENSORFLOW KNOW WHAT VARIABLES TO UPDATE?
Session looks at all trainable variables that loss depends on and update them OR you can use the argument var_list (Optional list of Variable objects to update to minimize loss).
VISUALIZE IT WITH TENSORBOARD:
IN GOOGLE’S OWN WORDS: “THE COMPUTATIONS YOU'LL USE TENSORFLOW FOR - LIKE TRAINING A MASSIVE DEEP NEURAL NETWORK - CAN BE COMPLEX AND CONFUSING. TO MAKE IT EASIER TO UNDERSTAND, DEBUG, AND OPTIMIZE TENSORFLOW PROGRAMS, WE'VE INCLUDED A SUITE OF VISUALIZATION TOOLS CALLED TENSORBOARD.”
VISUALIZE IT WITH TENSORBOARD:
VISUALIZE IT WITH TENSORBOARD:
• WHEN A USER PERFORM CERTAIN OPERATIONS IN A TENSORBOARD-ACTIVATED TENSORFLOW PROGRAM, THESE OPERATIONS ARE EXPORTED TO AN EVENT FILE. TENSORBOARD IS ABLE TO CONVERT THESE EVENT FILES TO GRAPHS THAT CAN GIVE INSIGHT INTO A MODEL’S BEHAVIOR. LEARNING TO USE TENSORBOARD EARLY AND OFTEN WILL MAKE WORKING WITH TENSORFLOW THAT MUCH MORE ENJOYABLE AND PRODUCTIVE.
HTTPS://WWW.TENSORFLOW.ORG/API_GUIDES/PYTHON/SUMMARY
VISUALIZE IT WITH TENSORBOARD:
• EXAMPLE-CODECREATE A FILEWRITER OBJECT TO WRITE YOUR GRAPH TO EVENT FILES.
• TERMINAL
• BROWSER
• LEARN TO USE TENSORBOARD WELL AND OFTEN. IT WILL HELP A LOT WHEN YOU BUILD COMPLICATED MODELS.
VISUALIZE IT WITH TENSORBOARD:
• EXPLICITLY NAME THE VARIABLES
• TERMINAL
• BROWSER
• LEARN TO USE TENSORBOARD WELL AND OFTEN. IT WILL HELP A LOT WHEN YOU BUILD COMPLICATED MODELS.
MINST WITH CNN GRAPH
NOTES:
• BEWARE WHEN USING NUMPY ARRAYS BECAUSE NUMPY AND TENSORFLOW MIGHT BECOME NOT SO COMPATIBLE IN THE FUTURE!
• CONSTANTS ARE STORED IN THE GRAPH DEFINITION. THIS MAKES LOADING GRAPHS EXPENSIVE WHEN CONSTANTS ARE BIG. ONLY USE CONSTANTS FOR PRIMITIVE TYPES. USE VARIABLES OR READERS FOR MORE DAT
• EACH SESSION MAINTAINS ITS OWN COPY OF VARIABLE A THAT REQUIRES MORE MEMORY. SO TRY NOT TO OPEN MORE THAN ONE SESSION IN THE SAME TIME!
• LEARN TO USE TENSORBOARD WELL AND OFTEN. IT WILL HELP A LOT WHEN YOU BUILD COMPLICATED MODELS.
WHY TENSORFLOW
• TENSORFLOW COMMUNITY APPEARS TO MOVE VERY FAST. THE COMMUNITY IS STRANGELY ACTIVE.
• TENSORBOARD, CREATING A POWERFUL SET OF VISUALIZATIONS FOR BOTH NETWORK TOPOLOGY AND PERFORMANCE.
• TENSORFLOW OPERATES EASILY WITH MULTIPLE GPUS.
AWESOME NN DEMO USING TESNSORFLOW
• HTTP://PLAYGROUND.TENSORFLOW.ORG/
RESOURCES:
• OFFICIAL WEBSITE : HTTPS://WWW.TENSORFLOW.ORG• STANFORD CS 20SI.