Placeholder Image

字幕表 動画を再生する

  • [MUSIC PLAYING]

  • LAURENCE MORONEY: Hi, everybody.

  • I'm Laurence.

  • I'm an AI advocate on the TensorFlow team

  • and I'm here with my colleague and friend, Jason.

  • JASON MAYES: Hi there.

  • I'm Jason and I'm a developer advocate on TensorFlow.js.

  • LAURENCE MORONEY: So here we're going

  • to try to answer the questions that you post on social media

  • with the hashtag #AskTensorFlow, so

  • why don't we just get right down to it?

  • JASON MAYES: Sounds good to me.

  • First up, we have @adkumar who asked, how do we

  • extend Keras APIs, model subclassing,

  • and generally improve the ease of use?

  • LAURENCE MORONEY: Oh, wow.

  • That's a great question and it's one

  • that we get a lot because, if you are new to TensorFlow,

  • you probably are thinking it does everything

  • that I need to do and training models

  • and all of that kind of stuff.

  • But then once you get a little bit more

  • advanced into TensorFlow, you realize that, sometimes, you

  • are trusting some code that was written for you by somebody

  • else and you want to be able to subclass that.

  • You want to be able to override that.

  • You want to be able to customize it like custom training

  • loops and that kind of thing.

  • And that's a relatively advanced thing that you want to do,

  • but a very important one as you get more advanced,

  • particularly in research.

  • So we've realized that the training and the information

  • for this is kind of scattered all over the place.

  • There's lots of files.

  • I think the original question, he

  • mentioned that there are over 100 files.

  • JASON MAYES: Wow.

  • LAURENCE MORONEY: I haven't counted them,

  • but I'm sure there are quite a few.

  • So I'm actually, right now, hard at work

  • trying to put all of this together and produce a training

  • course for advanced TensorFlow and advanced Keras, so teaching

  • you some of that stuff such as subclassing some of the classes

  • that are there, creating custom training loops,

  • and all of that kind of thing so that you really--

  • I like to see it as driving stick instead of driving

  • automatic.

  • So we're working on that.

  • We're hoping to get it published in the next few months,

  • so just watch out and, hopefully, you'll

  • have something that you can enjoy.

  • So next question.

  • This is one for you, Jason.

  • JASON MAYES: Excellent.

  • LAURENCE MORONEY: So @io62898019--

  • that's a great handle--

  • asked, what about web developers, and particularly,

  • web developers who are new to AI and machine learning and they

  • want to get started with all of this,

  • but do they have a technology to help them?

  • JASON MAYES: Indeed, we do, and that's why I'm here.

  • We actually have TensorFlow.js, which

  • is our JavaScript implementation of TensorFlow,

  • and that means you can basically run it anywhere

  • that JavaScript can run.

  • So that might be client-side in the browser,

  • in Node.js on the server-side, on Internet

  • of Things like Raspberry Pi, and we've just

  • announced support for React Native as well,

  • so even in native app development,

  • which is pretty neat.

  • LAURENCE MORONEY: That's new to me.

  • I didn't know that.

  • Wow.

  • Cool.

  • JASON MAYES: And then, of course,

  • if you are really prone to using Python,

  • you can actually still use that.

  • And we have a command line converter

  • that allows you to convert the Python SavedModels

  • into a TensorFlow.js format which

  • you can then use in all of these environments as well.

  • LAURENCE MORONEY: I love how you say prone to using Python.

  • [LAUGHING]

  • But which is one of the really nice things that--

  • like with TensorFlow.js where you

  • can train it in the browser.

  • JASON MAYES: Yes.

  • LAURENCE MORONEY: It's not just for the data scientists

  • that do Python and all that kind of stuff.

  • So if you're a JavaScript developer,

  • you don't have to learn something new.

  • You can actually do your training in there.

  • JASON MAYES: That's right.

  • You can stick to what you're used to, I guess.

  • LAURENCE MORONEY: Yeah.

  • JASON MAYES: There are some caveats

  • if you're going client-side or server-side.

  • We've got smaller models.

  • They work very well on the client-side

  • and you got other features like privacy and such as well.

  • And if you want to train larger models,

  • then Node.js works just as well as Python

  • does and can do all that hard, heavy lifting for you too.

  • LAURENCE MORONEY: Nice.

  • And one of the things I really liked about it,

  • when I started playing with it, was

  • that, if I'm a JavaScript developer who hasn't learned

  • Python but I have teammates who've

  • been building models in Python for a long time,

  • there's a converter.

  • JASON MAYES: Yes.

  • LAURENCE MORONEY: Right?

  • It converts the model into a JSON Object Notation.

  • And then I can start using that, load my interpreter,

  • and off I go--

  • JASON MAYES: Totally.

  • LAURENCE MORONEY: --which is kind of cool.

  • JASON MAYES: And another fun fact, if you are using Node.js,

  • you can actually run SavedModels without conversion in node.

  • LAURENCE MORONEY: Ooh.

  • JASON MAYES: But you can't use them on the front end.

  • So if you are just sticking to server-side in Node.js,

  • then you can run Python models without conversion now.

  • And they actually run slightly faster

  • because of a just-in-time compiler, which is very cool.

  • LAURENCE MORONEY: Very nice.

  • Cool.

  • I'm learning a lot today.

  • Thanks, Jason.

  • JASON MAYES: Awesome.

  • LAURENCE MORONEY: And also one thing that--

  • just a shameless self-plug.

  • I hope you don't mind.

  • But a lot of the stuff around TensorFlow.js,

  • when I got excited about it I decided to put together

  • a course to teach.

  • JASON MAYES: Of course, yeah.

  • LAURENCE MORONEY: And I'm one of those who's prone to Python,

  • so I did it from that perspective for people

  • who are not necessarily just JavaScript developers

  • but people who are Python developers who

  • were used to building ML just to see how easy it

  • is to do stuff in the browser.

  • And we've got some cool projects that you can play with.

  • JASON MAYES: Awesome.

  • LAURENCE MORONEY: And it's all on Coursera

  • if they want to check it out.

  • JASON MAYES: I should have to take that one as well.

  • LAURENCE MORONEY: Yes, please.

  • Yes.

  • I need all the students I can get.

  • So actually another question for you.

  • This one comes from @isbilen_erdem, and they ask,

  • are there any TensorFlow.js transfer learning

  • examples, in particular, for object detection?

  • JASON MAYES: That's a really good question.

  • That's something I was looking into when I first joined

  • the team a little while ago.

  • Before I answer that though, I just

  • want to talk a little bit about object detection

  • and what that is versus image detection.

  • So object detection is essentially the ability

  • to recognize one or multiple objects in a given image

  • and also find their locations.

  • You have little bounding boxes.

  • That's different to image detection, which essentially

  • allows you to know if something is in an image

  • but not where it is, and also typically for one thing only.

  • So now we know that.

  • How do we do it in TensorFlow.js?

  • Well, I think the easiest way to do this is actually

  • to use Cloud AutoML, which now supports exporting the custom

  • train models you make on there to TensorFlow.js format.

  • LAURENCE MORONEY: OK.

  • JASON MAYES: And of course, with that, you

  • can then use that anywhere, as we discussed before.

  • And you can check out the documentation

  • to get started on that online.

  • But essentially, all you need to do

  • is have a folder full of images like cat images

  • and then a CSV file that has the coordinates

  • of the bounding boxes for each image showing

  • where the cat is in each image.

  • And that is then used as the training data

  • to retrain the model to then work with your data.

  • You then download that and then use it as you need to.

  • LAURENCE MORONEY: OK.

  • JASON MAYES: Yeah.

  • LAURENCE MORONEY: That's pretty cool.

  • So instead of you building a transfer learn model yourself,

  • you're using an existing online model

  • and having Cloud AutoML retrain that.

  • JASON MAYES: Exactly, yeah.

  • Doing this in TensorFlow.js for something like COCO-SSD

  • might be a little tricky unless you have access

  • to the full model, the original model, if you will.

  • So if you don't have access to that,

  • you can just leverage Cloud AutoML instead.

  • LAURENCE MORONEY: So check out to Cloud AutoML documentation

  • on that for details.

  • JASON MAYES: Definitely.

  • Sounds good to me.

  • LAURENCE MORONEY: Cool.

  • Should we go to the next question?

  • JASON MAYES: Let's do it.

  • OK, so next up we have @conradwt who asks,

  • does TensorFlow leverage Metal when running on macOS?

  • LAURENCE MORONEY: I take it they mean iOS and not macOS.

  • JASON MAYES: Ah, yes.

  • LAURENCE MORONEY: So there are a number of ways

  • that you use Metal in iOS.

  • The simple answer is yes, you can.

  • So with TensorFlow Lite, there's a thing called the GPU delegate

  • that allows you that-- some mobile devices have access

  • to GPUs, some do not.

  • On iOS, of course, it's more common to have access

  • to the GPU.

  • So with the GPU delegate in TFLite,

  • you can actually effectively access

  • Metal, which gives you the ability

  • to run inference using the GPU so you

  • can have much faster inference on the device itself.

  • JASON MAYES: Very useful, yeah.

  • LAURENCE MORONEY: Super useful.

  • Faster inference means you're using less battery life,

  • means you're more responsive at your application,

  • and stuff like that.

  • It's pretty cool.

  • It's a bit complex to go over all of that here in this video,

  • but I would say check out the tensorflow.org/lite site

  • or search for GPU on that site as well and you'll see all

  • the details.

  • There's a whole bunch of stuff including

  • some sample apps showing you how you can enable it

  • and how you can use the GPU delegate so that you can just

  • have a little bit of fun using your GPU

  • to do faster inference on mobile, not just Metal in iOS,

  • but also things like the Neural Network API on Android.

  • JASON MAYES: Nice.

  • Very cool.

  • LAURENCE MORONEY: So the next question that came in

  • was from @rishabh16, and they asked,

  • what's the best way that someone like a high school student

  • could engage with TensorFlow and could learn maybe

  • some of the basics around ML and start doing some real things,

  • and then from that, then be able to move

  • onto the different components and submodules

  • for their projects and their work.

  • JASON MAYES: Sounds good.

  • LAURENCE MORONEY: What's a good way for a high-schooler to get

  • started?

  • JASON MAYES: So I guess, when you're starting out,

  • you want to kind of try something

  • a little more graphical to get started to just learn

  • how things need certain amounts of training data

  • and what biases might come into this kind of situation.

  • So I recommend checking out a website

  • called Teachable Machine, which is made by Google.

  • And it allows you to simply point your webcam

  • at various objects, or maybe use a microphone, whatever you want

  • to use, and train on that data.

  • And within about one minute, you can

  • have a machine learning model that

  • can classify speech or objects and even poses,

  • which is pretty cool.

  • LAURENCE MORONEY: Right.

  • So very quickly, within the browser,

  • being able to put something together

  • so they can just see how machine learning models work?

  • JASON MAYES: Exactly.

  • And they can try it live after the model's trained.

  • The webcam would be fired up and you can then

  • re-point it at the things you're training on

  • and see the class it predicts right

  • there in the browser in real time, super low latency.

  • And if you like it, if it's actually useful to you,

  • you can then download that model.

  • It's just a JSON file essentially,

  • which you can then reuse on any website you wanted

  • to play that on essentially.

  • LAURENCE MORONEY: Nice.

  • What a great way to get started.

  • JASON MAYES: Very cool.

  • I wish we had that back in my day.

  • LAURENCE MORONEY: Like last year.

  • JASON MAYES: Yeah, yeah.

  • [LAUGHING]

  • So next up, we have @1amarvind who asks,

  • what are TensorFlow Records and why are they needed for input?

  • LAURENCE MORONEY: That's a great question.

  • And to understand why you need TensorFlow records,

  • you have to kind of double click a level above that

  • and think about data.

  • Data is really the lifeblood in training any kind of machine

  • learning application.

  • But data comes in all shapes and sizes.

  • JASON MAYES: That's very true.

  • LAURENCE MORONEY: That might be a zip file over here

  • with images.

  • There might be CSV files if you are

  • inclined to be a JSON person.

  • Might be JSON files, those kind of things.

  • And without having a lot of skills

  • and being able to understand this,

  • it becomes a huge learning curve for people

  • to say, OK, which one am I going to use?

  • How am I going to use it?

  • How do I unzip?

  • How do I use JSON?

  • And when I've seen a lot of people building models,

  • you might have this much code for building a model, but this,

  • much is the model architecture--

  • JASON MAYES: Sure, yeah.

  • LAURENCE MORONEY: --and this much is downloading the data,

  • figuring it out, putting it into formats like--

  • I'm Python inclined, and putting it into NumPy format so that I

  • can do training, or if it's .js, putting it into JSON sort

  • of Tensors so that I can do training.

  • And it's a whole amount of calories

  • that I have to burn before I can even get started.

  • JASON MAYES: That's very true.

  • LAURENCE MORONEY: So the idea behind TFRecord and something

  • called TensorFlow Data Services and TensorFlow

  • Datasets is to try and make that as easy as possible.

  • So what we've done is we've taken

  • a whole bunch of different data sets and put them into an API

  • so that, with one or two lines of code,

  • you have everything that you need to start training.

  • JASON MAYES: Very cool.

  • LAURENCE MORONEY: So now, instead

  • of going from this with this much for your data,

  • you're going from this to this with only

  • this much for your data--

  • JASON MAYES: Very nice.

  • LAURENCE MORONEY: --if that visualization works OK.

  • So things like that, and then the core of that

  • is the TFRecord.

  • So you need to have one kind of base class

  • from which you can do everything.

  • And then, when you're doing that,

  • there's all of these different optimizations for training

  • like if you're doing distributed training

  • and you want to manage pipelines.

  • And I always like to think about it as, say-- take for example,

  • you have a CPU and a GPU and you're

  • going to do your training on the GPU

  • but you do all your data pre-processing on the CPU.

  • So the CPU is grabbing the data and handing it to the GPU.

  • And then while the GPU is working,

  • the CPU also has to be doing something,

  • and to get the two of these to work in parallel

  • can be very difficult.

  • JASON MAYES: Sure.

  • LAURENCE MORONEY: So there's a lot of pipelining technology

  • in TensorFlow, and that is built to use TFRecord to be

  • able to manage all that data.

  • JASON MAYES: I see.

  • Very neat.

  • LAURENCE MORONEY: It seems like one small thing.

  • And you might think, well, why on earth would

  • I want to use this when I've got CSV or I've got databases

  • or all of that kind of thing?

  • But once you start using it in that way,

  • you'll see it has great benefits for your training.

  • JASON MAYES: Awesome.

  • LAURENCE MORONEY: So that's it.

  • Great questions.

  • It was a lot of fun answering them too, right?

  • JASON MAYES: It was indeed.

  • LAURENCE MORONEY: So don't forget, on social media--

  • YouTube, Twitter, wherever you like-- hashtag #AskTensorFlow

  • and we'll do our best to answer those questions.

  • And Jason, I think some of the stuff

  • you spoke about today you'll have online demos for.

  • JASON MAYES: Indeed.

  • We've got some live demos, and we're

  • going to publish those for you guys

  • to see at home because it all runs in the web browser

  • after all.

  • LAURENCE MORONEY: That's one of the nice things

  • about JavaScript.

  • JASON MAYES: Indeed.

  • LAURENCE MORONEY: All right.

  • So thank you, and we'll see you around.

  • [MUSIC PLAYING]

[MUSIC PLAYING]

字幕と単語

ワンタップで英和辞典検索 単語をクリックすると、意味が表示されます

A2 初級

最新のTensorFlowの質問にお答えします!#AskTensorFlow (Answering your latest TensorFlow questions! #AskTensorFlow)

  • 3 0
    林宜悉 に公開 2021 年 01 月 14 日
動画の中の単語