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![# define the network
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
# define a training step
y_ = tf.placeholder(tf.float32, [None, 10])
xent = -tf.reduce_sum(y_*tf.log(y))
step =
tf.train.GradientDescentOptimizer(0.01).minimize(xent)](https://image.slidesharecdn.com/track202-170123091016/85/Track2-02-machine-intelligence-at-google-scale-google-kaz-sato-staff-developer-advocate-52-320.jpg)
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Track2 02. machine intelligence at google scale google, kaz sato, staff developer advocate
- 1. Machine Intelligence atGoogle Scale ML APIs, TensorFlow and Cloud ML
- 2. +Kazunori Sato @kazunori_279 Kaz Sato StaffDeveloper Advocate Tech Lead for Data & Analytics Cloud Platform, Google Inc.
- 3. What we’ll cover Whatis Neural Network and Deep Learning Machine Learning use cases at Google services Externalizing the power with ML APIs TensorFlow: the open source library for ML TensorFlow in the Wild Distributed training and prediction with Cloud ML
- 4. What is NeuralNetwork and Deep Learning
- 5. Neural Network isa function that can learn
- 6. xn > b? w1 wn x2 x1 Inspired bythe behavior of biological neurons
- 7. How do you classifythem?
- 8. weights bias (threshold) Programmers need tospecify the parameters
- 9. Let’s see howneural network solves the problem
- 10. The computer triesto find the best parameters A neuron classifies a data point into two kinds
- 11. Gradient Descent: adjustingthe params gradually to reduce errors
- 12. How do you classifythem?
- 13. What we seeWhat the computer “sees”
- 14. 28 x 28gray scale image = 784 numbers
- 15. input vector (pixel data) outputvector (probability)
- 16. How do you classifythem?
- 17. More neurons =More features to extract
- 18. Hidden Layers: mapping inputsto a feature space, classifying with a hyperplane From: Neural Networks, Manifolds, and Topology, colah's blog
- 19. From: Neural Networks,Manifolds, and Topology, colah's blog
- 20.
- 21. More hidden layers= More hierarchies of features
- 22.
- 23. We need togo deeper neural network From: Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Honglak Lee et al.
- 24. From: mNeuron: AMatlab Plugin to Visualize Neurons from Deep Models, Donglai Wei et. al.
- 25.
- 26. Machine Learning usecases at Google services
- 27. 27 signal for Search ranking, outof hundreds improvement to ranking quality in 2+ years #3 #1 Search machine learning for search engines RankBrain: a deep neural network for search ranking
- 29.
- 30.
- 31. 31 Smart reply in Inboxby Gmail 10% of all responses sent on mobile
- 32. Google Translate withNeural Machine Translation
- 33. Saved Data Centercooling energy for 40% Improved Power Usage Effectiveness (PUE) for 15%
- 34. 34 Android Apps Gmail Maps Photos Speech Search Translation YouTube and many others... Used across products: 2012 2013 2014 2015 Deep Learning usage at Google
- 35. Externalizing the power withML APIs
- 36. TensorFlow Cloud MachineLearning ML API Easy-to-Use, for non-ML engineers Customizable, for Data Scientists Machine Learning products from Google
- 37. Image analysis withpre-trained models No Machine Learning skill required REST API: receives an image and returns a JSON $1.50 per 1,000 units GA - cloud.google.com/vision Cloud Vision API
- 38. Confidential & ProprietaryGoogleCloud Platform 38 Faces Faces, facial landmarks, emotions OCR Read and extract text, with support for > 10 languages Label Detect entities from furniture to transportation Logos Identify product logos Landmarks & Image Properties Detect landmarks & dominant color of image Safe Search Detect explicit content - adult, violent, medical and spoof
- 39.
- 40. Pre-trained models. NoML skill required REST API: receives audio and returns texts Supports 80+ languages Streaming or non-streaming Public Beta - cloud.google.com/speech Cloud Speech API
- 41. Confidential & ProprietaryGoogleCloud Platform 41 Features Automatic Speech Recognition (ASR) powered by deep learning neural networking to power your applications like voice search or speech transcription. Recognizes over 80 languages and variants with an extensive vocabulary. Returns partial recognition results immediately, as they become available. Filter inappropriate content in text results. Audio input can be captured by an application’s microphone or sent from a pre-recorded audio file. Multiple audio file formats are supported, including FLAC, AMR, PCMU and linear-16. Handles noisy audio from many environments without requiring additional noise cancellation. Audio files can be uploaded in the request and, in future releases, integrated with Google Cloud Storage. Automatic Speech Recognition Global Vocabulary Inappropriate Content Filtering Streaming Recognition Real-time or Buffered Audio Support Noisy Audio Handling Integrated API
- 42.
- 43. Pre-trained models. NoML skill required REST API: receives text and returns analysis results Supports English, Spanish and Japanese GA - cloud.google.com/natural-language Cloud Natural Language API
- 44. Confidential & ProprietaryGoogleCloud Platform 44 Features Extract sentence, identify parts of speech and create dependency parse trees for each sentence. Identify entities and label by types such as person, organization, location, events, products and media. Understand the overall sentiment of a block of text. Syntax Analysis Entity Recognition Sentiment Analysis
- 45.
- 46. Pre-trained models. NoML skill required REST API: receives text and returns translated text 8 languages: English to Chinese, French, German, Japanese, Korean, Portuguese, Spanish, Turkish Public Beta - cloud.google.com/translate Cloud Translation API Premium
- 47.
- 48. TensorFlow: An open sourcelibrary for Machine Intelligence
- 49. Google's open sourcelibrary for machine intelligence tensorflow.org launched in Nov 2015 Used by many production ML projects What is TensorFlow?
- 50. 50 Sharing our toolswith researchers and developers around the world repository for “machine learning” category on GitHub #1 Released in Nov. 2015 From: http://deliprao.com/archives/168
- 51. 51 Before Hire Data Scientists ↓ Understandthe math model ↓ Impl with programming code ↓ Train with single GPU ↓ Build a GPU cluster ↓ Train with the GPU cluster ↓ Build a prediction server or Impl mobile/IoT prediction After Easy network design and impl ↓ Train with single machine ↓ Train on the cloud ↓ Prediction on the cloud or mobile/IoT devices many people stuck here
- 52. # define thenetwork import tensorflow as tf x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) # define a training step y_ = tf.placeholder(tf.float32, [None, 10]) xent = -tf.reduce_sum(y_*tf.log(y)) step = tf.train.GradientDescentOptimizer(0.01).minimize(xent)
- 53.
- 54. Portable and Scalable Trainingon: Mac/Windows GPU server GPU cluster / Cloud Prediction on: Android and iOS RasPi and TPU
- 55. Distributed Training withTensorFlow
- 56. TensorFlow in theWild (or democratization of deep learning)
- 58. TensorFlow powered CucumberSorter From: http://workpiles.com/2016/02/tensorflow-cnn-cucumber/
- 59.
- 60. TensorFlow powered Fried Chicken Nugget Server From:http://www.rt-net.jp/karaage1/
- 61. TV popstar face generatorwith DCGAN From: http://memo.sugyan.com/entry/2016/10/12/084751
- 62. TensorFlow + Drones for countingtrucks From: http://www.brainpad.co.jp/news/2016/09/02/3454
- 63. TensorFlow＋ RasPi for sorting garbages From:https://techcrunch.com/2016/09/13/auto- trash-sorts-garbage-automatically-at-the-techcrunch- disrupt-hackathon/
- 64. Autonomous Driving of RasPi car withInception 3 on TensorFlow From: https://github.com/zxzhijia/GoPiGo- Driven-by-Tensorflow
- 65. From: http://otoro.net/ Generative Artswith TensorFlow
- 66. Distributed Training and Predictionwith Cloud ML
- 67. From: Andrew Ng TheBigger, The Better
- 68. The Challenge: ComputingPower DNN requires large training datasets Large models doesn't fit into a GPU Requires try-and-errors to find the best design, configs and params ↓ Need to spend a few days or weeks to finish a training
- 69. GPUs run atnanoseconds GPU cluster needs microsec network
- 71. Enterprise Google Cloud is TheDatacenter as a Computer
- 72. Jupiter network 10 GbEx 100 K = 1 Pbps Consolidates servers with microsec latency
- 73. Borg No VMs, purecontainers 10K - 20K nodes per Cell DC-scale job scheduling CPUs, mem, disks and IO
- 74. Distributed Training with TensorFlow bydata parallelism split data, share model
- 75. CPU/GPU scheduling Communications Local, RPC,RDMA 32/16/8 bit quantization Cost-based optimization Distributed Systems for Large Neural Network
- 76. Distributed Training withTensorFlow on Google Cloud "Large Scale Distributed Systems for Training Neural Networks", NIPS 2015 Inception / ImageNet: 40x with 50 GPUs RankBrain: 300x with 500 nodes
- 77. Fully managed distributedtraining and prediction Supports custom TensorFlow graphs HyperTune for hyper-parameter tuning automation Integrated with Cloud Dataflow and Cloud Datalab Public Beta - cloud.google.com/ml Cloud Machine Learning (Cloud ML)
- 78. Cloud ML atWork: AUCNET The largest real-time car auction service in Japan For 30K used car dealers The auction volume overs $3.7B every year Problem: auction entry is time consuming task for dealers Classifying parts of car for thousands of photos Identifying the exact car model
- 79. Solution: Custom ImageClassification with TensorFlow/Cloud ML Used 5,000 training images for 500 car models Inception v3 + Transfer Learning Cloud ML: increased training performance for 6x faster
- 80. Predicting "large loss"cases in car insurance Old method (Random Forest): 38% accuracy New method (TensorFlow): 73% accuracy A Global Insurance Firm
- 81.
- 82. 8282 Ready to useMachine Learning models Use your own data to train models Cloud Vision API Cloud Speech API Cloud Translation API Cloud Machine Learning Develop - Model - Test Google BigQuery Cloud Storage Cloud Datalab Beta GA BetaGA Beta GA GA GA Cloud Natural Language API
- 83. Tensor Processing Unit ASICfor TensorFlow Designed by Google 10x better perf / watt latency and efficiency bit quantization
- 84. TPU on Production RankBrain AlphaGo GooglePhotos Speech and more
- 85.