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?