Welcome to Ask TensorFlow where we answer the questions

that you submitted with the hashtag #AskTensorFlow.

I'm Paige and I'm a developer advocate on the TensorFlow team.

And I'm Alex and I'm an engineer.

So for the first question today, Codigo asked,

"What will be the support model for stand-alone Keras?

Excellent, so that's a phenomenal question, Codigo

and we're really excited to answer it.

Keras has been integrated as part of TensorFlow 2.0

as tf.keras.

But stand-alone Keras still certainly exists.

A number of the optimizations that we've included

for tf.keras haven't yet made their way back

to the stand-alone package but we are setting up

a special interest group specifically for Keras users

for TensorFlow.

So if you have any questions, comments, concerns,

feel free to join the SIG Keras group and you can find that

on the TensorFlow community section.

Yeah, thanks for the question, Codigo.

Our second question is from Gurjeet, who asked,

"Does tf.keras include everything that stand-alone Keras includes?"

That's a great question, Gurjeet, we get that one a lot,

and I understand where you're coming from.

The simple answer is no.

There are lots of things in stand-alone Keras

that TensorFlow has no business having,

like the Theano backend or the CNTK backend.

But all the useful pieces, all of the optimizers, the metrics,

the losses, the layers, the model building API,

all the things that you need to use Keras

to train your models, to deploy your models,

they're all a part of tf.keras.

Absolutely, and if you're familiar with stand-alone Keras,

the syntax is very similar, if not identical using tf.keras.

For the next question, Clement asked,

"What will TensorFlow 2.0 change to stand-alone Keras?"

That's a great question.

And the answer is that that's still yet to be defined.

I mentioned before that we have a special interest group,

it's going to be specifically focused on Keras

and that will include a lot of the interplay

between tf.keras and stand-alone Keras.

The ideal is that all of the great optimizations

that we've made for tf.keras

will find their way back to the open source community

and we would love to have your help with that.

So if you have interest, join the SIG Keras group.

Yeah, you can find all the TensorFlow SIGs

in github.com/tensorflow/community.

Our next question is also from Gurjeet, who asked,

"Is there support for Bayesian layers in tf.keras?"

So in a way, while tf.keras itself

does not ship with any Bayesian layers.

TensorFlow Probability does.

They have a full set of Bayesian layers,

you can do all sorts of cool things with it,

including turning your deep neural network

into a Gaussian process with just one line of code change.

They also have made a port for Bayesian methods for hackers

and it's available on their website.

So go and take a look.

So for our next question, from Siby,

"Can I create custom layers through tf.keras?"

And yes, absolutely you can.

Do you want to go a little bit more into that, Alex?

Yeah, so you just do the same thing you'd do

if you were using keras-team/keras

So you can inherit from the layer class and make your own layer.

Same way you can inherit from the metric class

and make your own metric.

Or the same thing for the losses.

So shipping with a default set of layers, losses, metrics, optimizers, models--

all the things that you're already used to from Keras--

but we also make it easy to extend, right?

Absolutely.

So if you want to add your own custom loss function,

your own custom layer, you can contribute it

to the open source community through TensorFlow add-ons

and we would love to have you submit it as a PR.

Yeah, thanks for the question, Siby.

Our next question, from Sharavsambuu, is

"Will the Keras namespace be removed in future releases of TF 2.0?"

Please tell me no.

Of course not.

TF 2.0 has a stable public API.

Not just Keras, but any symbol that is in TF 2.0 API

is going to stay there until at least TF 3.0.

And this even includes the symbols that we removed from TF 1.0

that are still available in tf.compat.v1

So if you're using anything from TF 2.0 now,

you can keep using it,

for as long as there is a TF 2.X at least.

Absolutely, and I am super delighted to hear that.

Yeah, thanks for the question, Sharavsambuu.

And out next question is also from Siby, who asked,

"Can we use SavedModel for a Keras model?"

Yes.

You can take a Keras model and export it to the SavedModel format

and then use it with the entire rest of the TensorFlow ecosystem

like TF Lite, TFJS, TF Serving.

If you have a Keras model that was saved to SavedModel,

you can load it back into Python and get a full Keras model back

with exactly the same API.

So it's completely seamless and fun to use.

Absolutely, and SavedModel really is a first-class citizen

as part of TensorFlow 2.0.

So we're trying to make it as easy as possible

for you to interact with your models and to export them

to any location that you would like to have them,

to any sort of platform, any kind of device.

Yeah, so thanks for the great question, Siby.

So if you have anymore questions, please use #AskTensorFlow

on social media and we'll answer it for you

in a future video.

Excellent, thank you so much to everyone who submitted

the questions that we had for this episode

and we'll see you next time.

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