Applied TensorFlow for Android apps

The video of this talk is now available: https://www.youtube.com/watch?v=B6Xx67liMCk

The slides are for a talk I gave at Droidcon NYC 2017: http://droidcon.nyc/

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Find out how to use machine learning on Android!

This talk is a hands-on guide to using TensorFlow in your Android app.

We cover:
- A brief overview of machine learning.
- How to use machine learning in Android apps:
- Machine learning REST APIs.
- Using a pre-trained TensorFlow model.
- Inspecting TensorFlow models in TensorBoard.
- Training and transfer learning with TensorFlow.

Transcript

1. Applied TensorFlow in Android apps Dan Jarvis @jarvisapps @daj
2. Agenda • Machine learning basics • Building things
3. Brief Overview of Machine Learning
4. https://www.youtube.com/watch?v=fX7eQ9oRp-U
5. 1. Supervised 2. Unsupervised 3. Reinforcement Types of Machine Learning
6. • Input data are already labelled • Model trains by iterating 1 - Supervised
7. • Input data are already labelled • Model trains by iterating • Common examples: • Regression 1 - Supervised
8. • Input data are already labelled • Model trains by iterating • Common examples: • Regression 1 - Supervised New input value
9. • Input data are already labelled • Model trains by iterating • Common examples: • Regression 1 - Supervised Prediction
10. • Input data are already labelled • Model trains by iterating • Common examples: • Regression • Classification 1 - Supervised
11. TensorFlow Android image classifier demo • Trained on 1000 image categories • Works offline!
12. Label Predictions TensorFlow Android image classifier demo
13. TensorFlow Android image classifier demo Confidence
14. • Input data are not labelled • Model deduces structures 2 - Unsupervised
15. • Input data are not labelled • Model deduces structures • Common examples: • Clustering 2 - Unsupervised
16. “Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not.”
17. “Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not.” -Quoc V. Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng (July 2012) https://arxiv.org/pdf/1112.6209.pdf
18. • Learn by using a reward function 3 - Reinforcement
19. Zero reward
20. More reward
21. Maximum reward
22. https://www.youtube.com/watch?v=Lt-KLtkDlh8
23. • Learn by using a reward function • Common examples: • Games 3 - Reinforcement http://karpathy.github.io/2016/05/31/rl/
24. • Learn by using a reward function • Common examples: • Games 3 - Reinforcement https://nihit.github.io/resources/spaceinvaders.pdf
25. 1. Supervised – labelled data 2. Unsupervised – data only 3. Reinforcement – reward function Types of Machine Learning - Recap
26. Training Overview https://upxacademy.com/introduction-machine-learning
29. Why TensorFlow? • Excellent tutorials, tools and demos • Great developer engagement • Cross platform • Windows, Mac, Linux, Android, iOS, Android Things! • Built to scale
30. • Training is done in Python or C++ • Bazel is sometimes used as a build tool • Using on Android requires NDK TensorFlow Basics
31. • Tutorials lag platform • Big model files • 10MB to 90MB for image classifiers • Can be shrunk (quantization, remove unused ops) TensorFlow Gotchas
32. How To Use Machine Learning
33. How To Use Machine Learning 1. Cloud APIs (MLaaS)
34. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model
35. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model 3. Follow instructions to train a model
36. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model 3. Follow instructions to train a model 4. Retraining a model
37. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model 3. Follow instructions to train a model 4. Retraining a model 5. Train your own model from scratch
38. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model 3. Follow instructions to train a model 4. Retraining a model 5. Train your own model from scratch 6. Train your model dynamically on the device
39. How To Use Machine Learning 1. Cloud APIs (MLaaS) 2. Use a pre-trained model 3. Follow instructions to train a model 4. Retraining a model 5. Train your own model from scratch 6. Train your model dynamically on the device
40. (1) Use Machine Learning Cloud APIs
41. Machine Learning APIs • Google Cloud https://cloud.google.com/products/machine-learning/ • IBM Watson https://www.ibm.com/watson/developercloud/discovery.html • Amazon AWS https://aws.amazon.com/machine-learning/ • Microsoft Azure https://docs.microsoft.com/en-us/rest/api/machinelearning/
42. Google Cloud APIs • Machine Learning Engine • Job Search • Video • Vision • Speech • Natural Language • Translation
43. Vision API https://cloud.google.com/vision/
44. https://cloud.google.com/vision/
47. Natural Language API https://cloud.google.com/natural-language/
51. (2) Using a Pre-trained Model
52. https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample
55. https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample
56. • 814k handwritten character images • 128x128 pixels per character NIST
57. • Subset of NIST • 60k for training • 10k for testing • Normalized MNIST - http://yann.lecun.com/exdb/mnist/
58. https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample
59. https://jalammar.github.io/Supercharging-android-apps-using-tensorflow/
61. Interfacing With TensorFlow 1. Add dependency: compile 'org.tensorflow:tensorflow-android:+' 2. Use TensorFlowInferenceInterface to use your model
62. buildscript { repositories { jcenter() } } dependencies { compile 'org.tensorflow:tensorflow-android:1.2.0' }
68. 1. Init
69. 1. Init 2. Feed input
70. 1. Init 2. Feed input 3. Run inference
71. 1. Init 2. Feed input 3. Run inference 4. Fetch output
72. 1. Init 2. Feed input 3. Run inference 4. Fetch output 5. Close
74. inferenceInterface = new TensorFlowInferenceInterface( getAssets(), file:///android_asset/mnist_model_graph.pb);
75. public TensorFlowInferenceInterface( AssetManager assetManager, String modelFile) { System.loadLibrary("tensorflow_inference"); InputStream file = assetManager.open(modelFile); this.loadGraph(file, new Graph()); }
76. public TensorFlowInferenceInterface( AssetManager assetManager, String modelFile) { System.loadLibrary("tensorflow_inference"); InputStream file = assetManager.open(modelFile); this.loadGraph(file, new Graph()); }
77. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
78. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
79. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
80. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
81. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
82. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
83. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
84. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
85. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
86. Viewing TensorFlow Models In TensorBoard
88. Run TensorBoard • Easy to run: tensorboard --logdir=/tmp/tensorflow_logdir • Open http://localhost:6006 in your browser
89. Install TensorBoard Two easiest options: 1. Standalone pip install tensorboard 2. TensorFlow Docker container docker run -p 6006:6006 -it gcr.io/tensorflow/tensorflow:1.2.0 bash
90. Install TensorBoard Two easiest options: 1. Standalone pip install tensorboard 2. TensorFlow Docker container docker run -p 6006:6006 -it gcr.io/tensorflow/tensorflow:1.2.0 bash
91. Import Existing Model • Get import_pb_to_tensorboard.py from TensorFlow GitHub • Run it: python import_pb_to_tensorboard.py \ --model_dir /tmp/mnist_model_graph.pb \ --log_dir /tmp/tensorflow_logdir https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/import_pb_to_tensorboard.py
92. Import Existing Model • Get import_pb_to_tensorboard.py from TensorFlow GitHub • Run it: python import_pb_to_tensorboard.py \ --model_dir /tmp/mnist_model_graph.pb \ --log_dir /tmp/tensorflow_logdir https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/import_pb_to_tensorboard.py
93. $ python import_pb_to_tensorboard.py --model_dir /tmp/mnist_model_graph.pb --log_dir /tmp/tensorflow_logdir <snip> Model Imported. Visualize by running: tensorboard -- logdir=/tmp/tensorflow_logdir
94. $ python import_pb_to_tensorboard.py --model_dir /tmp/mnist_model_graph.pb --log_dir /tmp/tensorflow_logdir <snip> Model Imported. Visualize by running: tensorboard -- logdir=/tmp/tensorflow_logdir
103. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
104. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( inputName, pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
105. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
106. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{outputName}); // Copy the output data inferenceInterface.fetch(outputName, outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
107. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{"output"}); // Copy the output data inferenceInterface.fetch("output", outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
108. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{"output"}); // Copy the output data inferenceInterface.fetch("output", outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
109. // Output shape is [N, NUM_CLASSES], where N is the batch size int numClasses = (int) inferenceInterface.graph(). operation(outputName).output(0).shape().size(1); outputs = new float[numClasses];
110. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{"output"}); // Copy the output data inferenceInterface.fetch("output", outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
111. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{inputSize * inputSize}); // Run the inference inferenceInterface.run(new String[]{"output"}); // Copy the output data inferenceInterface.fetch("output", outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
112. @Override public List<Recognition> recognizeImage(final float[] pixels) { // Copy the input data into TensorFlow inferenceInterface.feed( "input", pixels, new long[]{28 * 28}); // Run the inference inferenceInterface.run(new String[]{"output"}); // Copy the output data inferenceInterface.fetch("output", outputs); // Find the best classifications for (int i = 0; i < outputs.length; ++i) { <snip> } return recognitions; }
113. https://github.com/daj/AndroidTensorFlowMNISTExample
114. https://github.com/daj/AndroidTensorFlowMNISTExample 28x28 inputs 5 5 3 3
116. File Sizes Built: • APK = 32MB Internals: • tensorflow.aar = 19MB (compressed) • libtensorflow_mnist.so = 11MB to 17MB per architecture • mnist_model_graph.pb = 13MB
117. Performance • Android build = <30 seconds • Classification = ~70ms first time, 10-20ms thereafter (Nexus 5)
118. (3) Training TensorFlow Models For Beginners
119. Training TensorFlow Models 1. Install TensorFlow 2. Find, download and run a training script
120. 1 - Install TensorFlow Install Docker from www.docker.com Download and start the TensorFlow binary image: docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash
122. $ docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash
123. $ docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash root@d3db3849abfa:/notebooks#
124. $ docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash root@d3db3849abfa:/notebooks#
125. $ docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash root@d3db3849abfa:/notebooks# $ docker ps CONTAINER ID IMAGE ... COMMAND d3db3849abfa gcr.io/tensorflow/tensorflow:1.2.0 ... "bash"
126. $ docker run -it gcr.io/tensorflow/tensorflow:1.2.0 bash root@d3db3849abfa:/notebooks# $ docker ps CONTAINER ID IMAGE ... COMMAND d3db3849abfa gcr.io/tensorflow/tensorflow:1.2.0 ... "bash"
127. Docker Gotchas • Changes are lost unless you commit them • Only certain local folders can be mounted (e.g. $HOME) • Weird workarounds to avoid out of memory errors • Tricky to clean up leftover containers and images
128. 2(a) - Find Training Script
129. 2(a) - Find Training Script
131. 2(b) - Download Training Script From in our Docker container: curl -O https://raw.githubusercontent.com/MindorksOpenSource/Andr oidTensorFlowMNISTExample/master/mnist.py
132. 2(c) - Run Training Script python mnist.py
133. # python mnist.py Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting MNIST_data/train-images-idx3-ubyte.gz ...
134. # python mnist.py Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting MNIST_data/train-images-idx3-ubyte.gz ...
135. # python mnist.py Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting MNIST_data/train-images-idx3-ubyte.gz Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Extracting MNIST_data/train-labels-idx1-ubyte.gz Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting MNIST_data/t10k-images-idx3-ubyte.gz Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
136. ... Iter 1280, Minibatch Loss= 32370.832031, Training Accuracy= 0.21875 Iter 2560, Minibatch Loss= 15398.544922, Training Accuracy= 0.43750 Iter 3840, Minibatch Loss= 10572.830078, Training Accuracy= 0.54688 Iter 5120, Minibatch Loss= 5175.708008, Training Accuracy= 0.72656 Iter 6400, Minibatch Loss= 4093.027100, Training Accuracy= 0.80469 Iter 7680, Minibatch Loss= 7343.525391, Training Accuracy= 0.73438 Iter 8960, Minibatch Loss= 2532.883789, Training Accuracy= 0.82812 Iter 10240, Minibatch Loss= 2786.300293, Training Accuracy= 0.79688 Iter 11520, Minibatch Loss= 2092.962158, Training Accuracy= 0.91406 Iter 12800, Minibatch Loss= 2265.324707, Training Accuracy= 0.85938 ...
137. ... Iter 1280, Minibatch Loss= 32370.832031, Training Accuracy= 0.21875 Iter 2560, Minibatch Loss= 15398.544922, Training Accuracy= 0.43750 Iter 3840, Minibatch Loss= 10572.830078, Training Accuracy= 0.54688 Iter 5120, Minibatch Loss= 5175.708008, Training Accuracy= 0.72656 Iter 6400, Minibatch Loss= 4093.027100, Training Accuracy= 0.80469 Iter 7680, Minibatch Loss= 7343.525391, Training Accuracy= 0.73438 Iter 8960, Minibatch Loss= 2532.883789, Training Accuracy= 0.82812 Iter 10240, Minibatch Loss= 2786.300293, Training Accuracy= 0.79688 Iter 11520, Minibatch Loss= 2092.962158, Training Accuracy= 0.91406 Iter 12800, Minibatch Loss= 2265.324707, Training Accuracy= 0.85938 ...
138. ... Iter 98560, Minibatch Loss= 66.527161, Training Accuracy= 0.99219 Iter 99840, Minibatch Loss= 513.522278, Training Accuracy= 0.95312 Iter 101120, Minibatch Loss= 552.358948, Training Accuracy= 0.96094 Iter 102400, Minibatch Loss= 825.438721, Training Accuracy= 0.93750 ...
139. ... Iter 194560, Minibatch Loss= 80.143494, Training Accuracy= 0.98438 Iter 195840, Minibatch Loss= 150.199615, Training Accuracy= 0.97656 Iter 197120, Minibatch Loss= 18.043427, Training Accuracy= 0.99219 Iter 198400, Minibatch Loss= 62.561752, Training Accuracy= 0.98438 Iter 199680, Minibatch Loss= 91.177673, Training Accuracy= 0.98438 Optimization Finished! Testing Accuracy: 0.988281 check accuracy 0.9719
147. (4) Retraining TensorFlow Models With Transfer Learning
153. Image Classifier Model - Inception-v3 • https://github.com/tensorflow/models/tree/master/inception • Trained on 1000 IMAGENET categories from 2012 http://medium.com/towards-data-science/transfer-learning-using-keras-d804b2e04ef8
154. http://medium.com/towards-data-science/transfer-learning-using-keras-d804b2e04ef8
155. http://medium.com/towards-data-science/transfer-learning-using-keras-d804b2e04ef8 https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/
157. Transfer Learning • https://www.tensorflow.org/tutorials/image_retraining • Retraining a classifier is easy! http://medium.com/towards-data-science/transfer-learning-using-keras-d804b2e04ef8
163. docker run -it -v $HOME/tf_files:/tf_files danjarvis/tensorflow-android:1.0.0 Retraining The Model
164. docker run -it -v $HOME/tf_files:/tf_files danjarvis/tensorflow-android:1.0.0 Retraining The Model
165. docker run -it -v $HOME/tf_files:/tf_files danjarvis/tensorflow-android:1.0.0 Retraining The Model
167. cd /tensorflow Retraining The Model
168. cd /tensorflow python tensorflow/examples/image_retraining/retrain.py --bottleneck_dir=/tf_files/bottlenecks --how_many_training_steps 500 --model_dir=/tf_files/inception --output_graph=/tf_files/retrained_graph.pb --output_labels=/tf_files/retrained_labels.txt --image_dir /tf_files/photos Retraining The Model
169. ... 2017-09-04 20:38:03.254191: Step 499: Train accuracy = 100.0% 2017-09-04 20:38:03.254272: Step 499: Cross entropy = 0.007678 2017-09-04 20:38:03.763952: Step 499: Validation accuracy = 100.0% (N=100) Final test accuracy = 91.7% (N=12) Converted 2 variables to const ops.
173. bazel build --local_resources 4096,4.0,1.0 -j 1 tensorflow/python/tools:strip_unused Prepare The Model
174. bazel build --local_resources 4096,4.0,1.0 -j 1 tensorflow/python/tools:strip_unused Prepare The Model
175. bazel build --local_resources 4096,4.0,1.0 -j 1 tensorflow/python/tools:strip_unused bazel-bin/tensorflow/python/tools/strip_unused --input_graph=/tf_files/retrained_graph.pb --output_graph=/tf_files/stripped_retrained_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true Prepare The Model
176. bazel build --local_resources 4096,4.0,1.0 -j 1 tensorflow/python/tools:strip_unused bazel-bin/tensorflow/python/tools/strip_unused --input_graph=/tf_files/retrained_graph.pb --output_graph=/tf_files/stripped_retrained_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true Prepare The Model
177. Testing The Model
178. curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/ma ster/tensorflow/examples/image_retraining/label_image.py Testing The Model
179. curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/ma ster/tensorflow/examples/image_retraining/label_image.py python label_image.py --image <input_image> --graph retrained_graph.pb --labels retrained_labels.txt Testing The Model
180. curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/ma ster/tensorflow/examples/image_retraining/label_image.py python label_image.py --image <input_image> --graph retrained_graph.pb --labels retrained_labels.txt Testing The Model
181. hot dogs (score = 0.89646) legs (score = 0.10354)
182. legs (score = 0.59678) hot dogs (score = 0.40322)
183. legs (score = 0.91612) hot dogs (score = 0.08388)
184. legs (score = 0.50959) hot dogs (score = 0.49041)
185. Recap
186. 1. Use machine learning cloud APIs 2. Embed pre-trained models 3. Follow instructions to train a model 4. Use transfer learning to customize a pre-trained model How To Use Machine Learning
187. Blog posts: https://medium.com/@daj Slides: https://speakerdeck.com/daj Legs or hot dogs images: https://github.com/daj/legs-or-hotdogs-images Docker container: https://hub.docker.com/r/danjarvis/ Links @jarvisapps @daj