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Who's Afraid Of Machine Learning? & first steps with TensorFlow
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Britt Barak
April 23, 2018
Technology
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Who's Afraid Of Machine Learning? & first steps with TensorFlow
Chicago Roboto & Android Makers 2018
Britt Barak
April 23, 2018
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Transcript
Who’s afraid of Machine Learning? Britt Barak
Britt Barak Google Developer Expert - Android Women Techmakers Israel
Britt Barak @brittBarak
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In a machine...
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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