Bring your neural networks to the browser with TF.js - Simone Scardapane
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Bring your neural networks to the browser with TF.js - Simone Scardapane
- 1. Bring your neural networks to the browser with TF.js!
- 2. Something about myself Research fellow @ Sapienza researching on neural networks Strong passion for promoting ML in all its aspects
- 3. Not-so-easy question: what is TensorFlow?
- 4. TensorFlow is all about: 1. Neural networks 2. Python 3. Huge data & infrastructures
- 5. TensorFlow has grown impressively
- 6. TensorFlow Distributed Execution Engine CPU GPU TPU Mobile Embedded Neural Nets C++ K-Means Loss Functions, Metrics Linear Algebra Decision Trees SVM Gaussian Mixture Models Regression Random Forests Probabilistic Methods Signal Processing Lattice TensorFlow is a comprehensive ML framework Python Java Javascript
- 8. 1. No installation required 2. Privacy (GDPR, anyone?) 3. Sensors! (Lots of) 4. WebGL 5. The world runs on apps and web... Why TF on browsers?
- 9. 1. Train a model from scratch 2. Fine-tune a model 3. Import a trained model 3 use cases COMPLEXITY
- 11. https://blog.mgechev.com/2018/10/20/transfer-learning-tensorflow-js-data-augmentation-mobile-net/
- 14. TensorFlow.js
- 15. Core API (ex deeplearn.js) TensorFlow SavedModel Layers API Keras models Browser (WebGL) Node.js: everywhere! (GPUs, TPUs, …)
- 16. Inference Time (ms) of MobileNet 1.0_224 Average of 200 runs
- 17. Core concepts
- 18. Import TF.js in your page <html> <head> <!-- Load TensorFlow.js --> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.3/dist/tf.min.js" /> <script> <!-- Code here! --> ... </script> </head> ... </html>
- 19. Tensors // A 3x2 matrix of random numbers const x = tf.randomUniform([3, 2]); // Convert from Javascript arrays const y = tf.tensor1d([0.4, -0.2, 1.0]); // Matrix operations const z = x.transpose().matMul(y.reshape([3, 1]));
- 20. Auto-differencing // Function definition const axis = 1; const f = x => tf.sin(x).sum(axis); // Automatic gradient computation const fGrad = tf.grad(f);
- 21. Models and layers // Initialize the model const model = tf.sequential(); // Add layers model.add(tf.layers.dense({'units': 10, 'inputShape': 4})); model.add(tf.layers.dense({'units': 1})); // Define loss const loss = tf.losses.meanSquaredError // Optimize! const opt = tf.train.sgd({'lr': 0.1}); opt.minimize(() => loss(model.predict(xs), ys));
- 22. Simplified Keras API model.compile({'optimizer': 'sgd', loss: 'categoricalCrossentropy'}); await model.fit({x: xs, y: ys}); The training step is an async function!
- 23. Tidy can help you the code const sum = tf.tidy(() => { const a1 = tf.randomUniform([3, 2]); const a2 = tf.randomNormal([3, 2]); return a1.square().add(a2); });
- 24. Demo time!
- 25. ● Input: a (fixed-length) sequence. ● Output: the index of the largest element. Please don’t do this in real life. :-( Let’s learn… MAX!
- 26. Our RNN 0.14 0.81 0.64 0.0 LSTM 2% 95% 2.5% 0.5%
- 28. Where to from here?
- 29. Some resources 1. TensorFlow.js tutorials: https://js.tensorflow.org/tutorials/ 2. More examples: https://github.com/tensorflow/tfjs-examples/
- 30. Thanks for listening! «The future will be intensely data-driven. Creating a dynamic hub that brings together professionals, industries, and academics will be essential to achieve this vision.» http://www.iaml.it/member