Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Who's Afraid Of Machine Learning? & first steps...
Search
Britt Barak
April 23, 2018
Technology
5
850
Who's Afraid Of Machine Learning? & first steps with TensorFlow
Chicago Roboto & Android Makers 2018
Britt Barak
April 23, 2018
Tweet
Share
More Decks by Britt Barak
See All by Britt Barak
[Vonage] Introducing Conversations
brittbarak
1
120
Kids, Play Nice! Kotlin-Java Interop In Mind
brittbarak
2
350
Sharing is Caring- Getting Started with Kotlin Multiplatform
brittbarak
2
1.9k
Between JOMO and FOMO: You are reshaping communication.
brittbarak
2
1.2k
Build Apps For The Ones You Love
brittbarak
1
110
What an ML-ful World! MLKit for Android dev.
brittbarak
0
130
Make your app dance with MotionLayout
brittbarak
8
1.3k
Who's afraid of ML? V2 : First steps with MlKit
brittbarak
1
440
Oh, the places you'll go! Cracking Navigation on Android
brittbarak
0
460
Other Decks in Technology
See All in Technology
AWSへのNIST SP800-171管理策 導入に向けての整備/20240930 Mitsutoshi Matsuo
shift_evolve
0
150
つよつよリーダーが 抜けたらどうする? 〜ナビタイムのAgile⽀援組織の変遷〜
navitimejapan
PRO
22
13k
LINEヤフー新卒採用 コーディングテスト解説 実装問題編
lycorp_recruit_jp
1
12k
Perlで始めるeBPF: 自作Loaderの作り方 / Getting started with eBPF in Perl_How to create your own Loader
takehaya
1
210
【shownet.conf_】多様化するネットワーク環境を柔軟に統合するルーティングテクノロジー
shownet
PRO
0
280
[JAWS-UG GameTech] 第6回 各種事例紹介_18TRIPにおけるAWSサービスを活用した負荷テスト・障害テスト
naoto_yasuda
0
120
Efficient zero-copy networking using io_uring
ennael
PRO
0
280
【インフラエンジニアbooks】30分でわかる「AWS継続的セキュリティ実践ガイド」
hssh2_bin
4
1.1k
AI時代のアジャイル開発(XP祭り2024版) / Agile Development in the AI Era in XPJUG
takaking22
13
3.5k
いまからでも遅くない! コンテナでWebアプリケーションを 動かしてみよう(2-1)WebAPI座学
nomu
0
140
「ばん・さく・つき・たー!」にならないためにSHIROBAKOから 学んだこと
ysknsid25
3
440
Authenticator のエミュレーションによる パスキーのログインテスト/nikkei-tech-talk-25
nikkei_engineer_recruiting
0
140
Featured
See All Featured
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
249
21k
Fantastic passwords and where to find them - at NoRuKo
philnash
50
2.8k
Fashionably flexible responsive web design (full day workshop)
malarkey
403
65k
It's Worth the Effort
3n
183
27k
jQuery: Nuts, Bolts and Bling
dougneiner
61
7.5k
Keith and Marios Guide to Fast Websites
keithpitt
408
22k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
504
140k
The Power of CSS Pseudo Elements
geoffreycrofte
71
5.3k
StorybookのUI Testing Handbookを読んだ
zakiyama
26
5.1k
A Modern Web Designer's Workflow
chriscoyier
692
190k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
26
1.3k
Fireside Chat
paigeccino
32
2.9k
Transcript
Who’s afraid of Machine Learning? Britt Barak
Britt Barak Google Developer Expert - Android Women Techmakers Israel
Britt Barak @brittBarak
None
None
None
None
None
None
None
None
In a machine...
None
Strawberry Not Strawberry
Input Red Seeds pattern Top leaves 0.64 0.75 0.4
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input 0.5 0.8 0.3 Red Seeds pattern
Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4 ___________ 1.04
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4 ___________ 1.04 + 0.7
0.64 0.75 0.4 1.74 0.5 * 0.64 + 0.8 *
0.75 + 0.3 * 0.4 ___________ 1.04 + 0.7 ___________ 1.74 Input Red Seeds pattern Top leaves 0.5 0.8 0.3
0.64 0.75 0.4 1.02 1.74 Input Red Seeds pattern Top
leaves 0.97
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 1.02
1.74 0.97
0.64 0.75 0.4 Output Strawberry Not Strawberry Input Red Seeds
pattern Top leaves 1.02 1.74 0.97 0.87 0.13
0.64 0.75 0.4 0.87 0.13 Strawberry Not Strawberry Output Input
Red Seeds pattern Top leaves 1.02 1.74 0.97
None
0.7 0.03 0.01 3.72 0.89 1.92 Strawberry Not Strawberry Output
Input Red Seeds pattern Top leaves 0.2 0.8
0.7 0.03 0.01 3.72 0.89 1.92 Strawberry Not Strawberry Output
Input Red Seeds pattern Top leaves 0.2 0.8
0.7 0.03 0.01 3.72 0.89 1.92 0.2 0.8 Strawberry Not
Strawberry Output Input Red Seeds pattern Top leaves
0.5 * 0.64 + 0.8 * 0.75 + 0.3 *
0.4 ___________ 1.04 + 0.7 ___________ 1.74 Strawberry Not Not Strawberry Not Not Strawberry Not Not
Training TRAINING
0.64 0.75 0.4 1.02 1.74 0.97 0.89 0.11 Strawberry Not
Strawberry Output Input Red Seeds pattern Top leaves
Strawberry Not Strawberry Output Input Hidden Red Seeds pattern Top
leaves
None
Data science
We get a trained model !
TensorFlow - Open source - Widely used - Flexible for
scale: - 1 or more CPUs / GPUs - desktop, server, mobile device
Strawberry
Strawberry
Strawberry • Bandwidth • Performance • Latency • Network •
Security • Privacy • …
TensorFlow Mobile - Speech Recognition - Image Recognition - Object
Localization - Gesture Recognition - Translation - Text Classification - Voice Synthesis
Lightweight Fast Cross platform
MobileNet Inception-V3 SmartReply Models
None
Image Classifier classifier .classify(bitmap) label
1. Add Assets
None
labels.txt strawberry orange lemon fig pineapple banana jackfruit custard apple
pomegranate hay carbonara chocolate sauce dough meat loaf
2. Add TensorFlow Lite
repositories { maven { url 'https://google.bintray.com/tensorflow' } } dependencies
{ // ... implementation 'org.tensorflow:tensorflow-lite:+' } build.gradle
android { aaptOptions { noCompress "tflite" } } build.gradle
3. Create ImageClassifier.java
Image Classifier
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter();
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model);
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH);
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH); FileInputStream inputStream = new
FileInputStream(descriptor.getFileDescriptor()); FileChannel channel = inputStream.getChannel();
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH); FileInputStream inputStream = new
FileInputStream(descriptor.getFileDescriptor()); FileChannel channel = inputStream.getChannel(); long start = descriptor.getStartOffset(); long length = descriptor.getDeclaredLength(); return channel.map(FileChannel.MapMode.READ_ONLY, start, length); }
Image Classifier [strawberry, apple, ... ] labels.txt
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList();
labels.txt strawberry orange lemon fig pineapple banana jackfruit custard apple
pomegranate hay carbonara chocolate sauce dough meat loaf
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
}
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); }
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); List<String> labelList = new ArrayList<>(); String line; while ((line = reader.readLine()) != null) { labelList.add(line); } }
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); List<String> labelList = new ArrayList<>(); String line; while ((line = reader.readLine()) != null) { labelList.add(line); } reader.close(); return labelList; }
Image Classifier [ [0..6] , [ 0.1 ] , ...
] [strawberry, apple, ... ] probArray labels.txt
probArray = { [0.7], [0.3], [0], [0], } labelList =
{ strawberry, apple, pineapple, banana, } 0.3
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList(); probArray = new float[1][labelList.size()];
Image Classifier [......] [ [0..6] , [ 0.1 ] ,
... ] [strawberry, apple, ... ] ByteBuffer probArray labels.txt
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList(); probArray = new float[1][labelList.size()]; imgData = ByteBuffer.allocateDirect( DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE); imgData.order(ByteOrder.nativeOrder());
4. Run the model / classify
classifier .classify(bitmap) Image Classifier [......] [ [0..6] , [ 0.1
] , ... ] [strawberry, apple, ... ] ByteBuffer probArray labels.txt
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap);
}
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); }
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); int pixel = 0; for (int i = 0; i < DIM_IMG_SIZE_X; ++i) { for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) { final int val = intValues[pixel++]; imgData.put((byte) ((val >> 16) & 0xFF)); imgData.put((byte) ((val >> 8) & 0xFF)); imgData.put((byte) (val & 0xFF)); } } }
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); int pixel = 0; for (int i = 0; i < DIM_IMG_SIZE_X; ++i) { for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) { final int val = intValues[pixel++]; imgData.put((byte) ((val >> 16) & 0xFF)); imgData.put((byte) ((val >> 8) & 0xFF)); imgData.put((byte) (val & 0xFF)); } } }
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap); tflite.run(imgData,
probArray); }
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap); tflite.run(imgData,
probArray); String textToShow = getTopLabels(); return textToShow; }
Strawberry - 0.87 Apple - 0.13 Tomato - 0.01
Machine Learning is a new world
Links - Tensorflow - https://www.tensorflow.org/ - Tensorflow lite - https://www.tensorflow.org/mobile/tflite/
- Codes labs - codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/ - Google’s Machine Learning Crash Course - developers.google.com/machine-learning/crash-course/ - [Dr. Joe Dispenza]
Thank you! Keep in touch! Britt Barak @brittBarak