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  • TIM DAVIS: Hi, my name's Tim Davis,

  • and I'm the product manager for TensorFlow Lite.

  • Here we are at TensorFlow World, and one

  • of the really exciting things that we have to demo today

  • is TensorFlow Lite on microcontrollers.

  • Microcontrollers are everywhere.

  • They're in all the things that you use.

  • They're very, very small circuits,

  • and now they can run machine learning.

  • MARK STUBBS: What we're demonstrating here

  • is TensorFlow Lite micro running on a Cortex-M4 from Ambiq.

  • So we're simulating an anomaly with an offset weight

  • on the motor.

  • When we increase the speed, vibration happens.

  • This turns red, that indicates there's an anomaly.

  • That's running from the TensorFlow Lite micro engine.

  • And then on the cloud, this will turn red as well.

  • TIM DAVIS: Check out tensorflow.org/lite.

  • There's lots of code, documentation and samples

  • available.

  • GAL OSHRI: So TensorBoard is TensorFlow's visualization

  • toolkit.

  • It enables you to track your training metrics like loss

  • and accuracy, visualize your model draft,

  • inspect the model parameters, and a lot more.

  • At TensorFlow World, we announced TensorBoard.dev,

  • which lets you easily upload your TensorBoard logs and get

  • back a link that you can send to anyone so you can include it

  • in your GitHub issues, your Stack Overflow questions,

  • or even your research papers.

  • You can go to TensorBoard.dev to learn more and try out

  • a collab that you can get started with really easily.

  • SANDEEP GUPTA: So TensorFlow JS is

  • a library for doing machine learning in JavaScript.

  • And it brings machine learning in the hands of web developers

  • and other JavaScript developers who don't necessarily now have

  • to use Python-based tools.

  • The library is full featured.

  • We have packaged a whole bunch of models

  • which let you bring machine learning into your application

  • straight out of the box.

  • And these are pre-trained models that

  • make it super easy to enhance your web applications with.

  • We are showcasing some new models here,

  • some faster versions and performance improvements

  • in some of these models.

  • And these are great for use cases

  • like accessibility, recognizing people, and gestures

  • and images, text classification in web interfaces,

  • as well as speech commands models

  • to recognize spoken words.

  • In addition to using these pre-trained models out

  • of the box that we packaged for you, often

  • you need to train a custom model for your application

  • on your own data.

  • And this can be pretty challenging.

  • So Google has a service called AutoML

  • which lets you bring your data to Google Cloud

  • and train a custom model.

  • And these models are optimized for your problem,

  • they give excellent accuracy and performance,

  • and then they're ready for deployment.

  • We are really excited to announce that we now

  • have integration of TensorFlow JS with this AutoML service.

  • So what that means is after training a custom image

  • classification model, you can export it

  • for use in a web application with the click of one button.

  • And we have early customer testimonials

  • who are showing the impressive gains

  • that they get in their workflow by using the service.

  • We are also showing some of the improvements

  • that we have on performance and platforms.

  • One of the things we are really excited about

  • is React Native integration.

  • So if you are a React Native developer who

  • is trying to build cross-platform native

  • applications, you can now use TensorFlow JS

  • directly from inside React Native

  • and you'll get the full power of WebGL acceleration.

  • We are seeing a lot our users here

  • who are giving exciting talks on applications that they are

  • building with TensorFlow JS.

  • One of our favorites is from Dr. Joseph Paul

  • Cohen, who is from the University of Montreal.

  • And he's showing how they're using

  • TensorFlow JS for scoring chest X-ray radiology

  • images in the browser.

  • And this has huge privacy implications

  • because patients' medical data stays

  • client-side in the browser, doesn't go to the server side,

  • and you can run powerful machine learning models directly

  • on the client side.

  • ROBERT CROWE: And we're here looking

  • at TensorFlow Extended, or TFX, including the great pipeline

  • that you can create to move your model to production with TFX.

  • Pipeline starts over here on the left where

  • you're ingesting your data, you're

  • creating TensorFlow examples with it,

  • then you're running statistics across your data, which

  • you normally would do.

  • And then you're looking for problems with your data

  • with ExampleValidator and doing feature engineering

  • with Transform.

  • Eventually, when you've got everything right,

  • you're going to train your model and then

  • do deep analysis with Evaluator, looking

  • not just at the top level metrics,

  • but at each individual slice of your data to make sure

  • that the performance is good across the whole data set.

  • Then if it is good, you're going to use

  • ModelValidator and Pusher to push your model to production.

  • But TFX is actually more than just a pipeline.

  • TFX is a whole suite of tools, including the What-If tool.

  • The What-If tool lets you do deep experimentation

  • of your data set to see if I make changes,

  • how does it affect the performance of my model?

  • If you want to learn more, go to tensorflow.org/tfx.

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B1 中級

TF World '19中的TensorFlow有哪些新內容? (What’s new in TensorFlow from TF World ‘19)

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    林宜悉 發佈於 2021 年 01 月 14 日
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