Welcome back.
Now hopefully you've had a chance to check out tense afloat hub.
If not that's okay.
That's exactly what we're going to check out now.
So let's go here.
Tense a flow hub search that up intensive low hub.
LET'S READ ABOUT IT intensive flow hub is a library for reusable machine learning models.
Okay.
So it has a flow hub is a library for the publication discovery and a consumption of reusable parts
of machine learning models.
The beautiful is a self-contained piece of tensor flow graph along with its weights and assets a.k.a.
patterns so the patterns of previous model has learned that can be used reused across different tasks
in a process known as transfer learning so transfer learning can train a model with a smaller dataset.
Wonderful.
Improved generalization and speed up the training.
That is what we are interested in look how quickly you can get started by using a tensor flow model.
How beautiful is that.
And so this is tensor flow hub and now tend to flow is one of the biggest deep learning frameworks that's
out there and it's the one that we're using right now.
But I want to also show you that is also apply to a job and model zoo and papers with code Don't worry
I'll link all of these so model zoo deep learning code and pre trained models pi torch if you're using
pi torch so let's just let all these load unlike socket learn with your Roomba.
We have the psychic learn machine learning that we go here so unlike psychic loan which has been pretty
established for a fairly long time using more traditional machine learning techniques we've seen this
map before in the psychic loan section one of these is kind of yet to be created for deep learning and
neural networks and remember neural network is just another type of machine learning module.
And so this is why we have these type of resources like pie to watch Harb tend to flow Harb model zoo
open source Deep Learning Code and pre train models.
Papers with code papers with code is like the latest trending research of the deep learning field and
oftentimes you'll have the paper.
So an academic paper and the code to go along with it on github which is absolutely amazing.
And so these are some of the resources you should be coming to when you're first getting started exactly
like what we're doing in dog vision where we're going to find out how to choose a U.R.L. and a second
but because the book on deep learning a machine learning is still being written.
Learning to build things from scratch when you first start is a great approach but instead the approach
we're taking is focusing on working on a project first and then if you wanted to dive deeper to get
anything like that's going on behind the scenes of what we're doing.
You can do that.
So don't let me control your zoom level as how far we're going in terms of depth.
Once you start to solve problems like we're doing with dog vision working end to end you won't be able
to help yourself you'll be compelled do you want to dive deep and figure out how to improve what we're
working on.
That's the best thing that I can kind of impart on you is rather than showing you how to do every single
thing which would take a whole documentation course it's four times as long as this one and a lot more.
It's better to just spark an interest and then on your own self.
Search and self discovery you find resources like these and try and implement them on your own problems.
So that's what we're focused on in dog vision is building an end to end project and then you can take
what you've learned in dog vision go to these resources here model zoo papers with code and try and
apply what you've learned to your own project.
And then once you do once you get that little taste of working into and on a project you'll really start
to want to go hey I need to figure out what's going on behind the scenes then and then your knowledge
will will just grow from within itself which is a beautiful thing.
So that's a kind of long winded philosophical speech on our training philosophy here.
Let's see what we can do with tensor flow hub so Hello and welcome to tensor flow hub.
Beautiful so there's a bunch of tutorials about how tensor flow works but you can browse by problem
domain.
This is what I was talking about.
This is why it's so important to be able to define your problem like way back up in the top of our notebook.
What's our problem.
Identify the breed of a dog given an image of a dog.
Okay.
Nice and simple then this is a beautiful thing about collab right.
You can just jump back and forth between your notebook.
So let's go to tensor flow hub image beautiful.
And what do we see here while this could look like a whole bunch of jargon.
What's happening here.
But let's pay attention to what's going on on the left.
We've got image augmentation.
Is any of these tags our problem augmentation.
No classification yes image feature vector generator detection image others are in an age.
Let's just look at image classification problems.
All right.
And so what's going on here.
Well this is kind of like the tensor flow version of the socket loan machine learning map again.
This is still in development phase.
So just a couple of years ago this thing didn't even exist.
So this is how beautiful the field is getting now we're starting to go okay.
These are all machine learning models.
I just gave that away that spoiler and these are ones that work pretty good.
So rather than retrain a machine learning model from scratch every time and take up a bunch of computing
power and a bunch of time and a bunch of resources let's just publish all the patents that we find in
previously built machine learning models so that other people can apply them to their own problem what
could we find.
Let's have a look at architecture.
Architecture is another word for machine learning model or neural network architecture with no networks.
You'll probably find that they have some pretty fancy names like Inception resonate V2.
Sounds like a crazy movie resonate v 150 resonant V2 50 and now I want to show you something about resonate.
We're actually not going to use this one but I want to just show you the power of what it's like.
So if we click on here this is gonna give us a link copied to clipboard that's as easy as it is using
a pretty trained resonant model we can copy that you're RL.
Or we could download the model.
But if we go here this is how we create a resonant model.
The reason why I want to show you resonate especially resonate 50 is that Tesla use resonate 50 backbones.
This is why transfer learning is so important to understand is because if we go here this is resonant
V2 50.
I took this slide here from a torque called Pi tortured Tesla by Andre capacity.
You can watch it now I'll be sure to link it but their computer vision systems in their Tesla cars use
resonate 50 like diluted backbones.
Of course they'll have custom layers and whatnot but what I'm saying is is that we have access to very
similar machine learning models as what are you being used in production at companies like Tesla.
So that's why it's important to be aware of these resources that are available to us.
And I know that this says pie to watch but the beautiful thing is if you come up to pi to a hub you
could probably find it they go resonate 1 0 1 all that sort of stuff.
Now we're working tend to flow.
Let's go back to here.
I have the model in mind that we want to use.
So we go architecture.
This is gonna take a little bit of exploring on your behalf but what you can essentially assume here
is that the higher the number the better the model but also the tradeoff there is the higher the number
the longer it takes to to train.
So mobile Net This is your homework for this video is to Google mobile Net just look it up and just
read about it.
So we're gonna use this one here image classification so if we have a read about this do you have to
save model.
This is a save model intensive low to format it requires using tensor flow to or one point one five
and higher.
So that's what we're using tensor flow to beautiful.
So mobile Net v2 is a family of neural network architectures for efficient on device image classification
and related task.
Originally published by so that's was originally published.
Now remember right back at the start what our problem is.
I was sitting at the cafe the other day this beautiful dog came walking pass and the friend I was with
said that's an Australian terrier and I was like wow I wish I could take a photo of that dog on my phone
and figure out what kind of dog it was.
So this is where we come to mobile Net where it's efficient on device image classification.
That's exactly what I want to do with dog vision.
Beautiful.
So we can come here we can see it's got some guidelines of what we can do.
This is your homework as well is to read through this even if you don't understand it at first go.
Just have a read through it and we'll leave this link in the resources section.
By the way this is where we got that batch input shape.
There we go.
2 2 4 2 2 4 3.
Little number on the end here.
But if you were reading through this that's where you would figure that out.
So let's copy this to the clipboard come back and this is our model U.R.L..
We're going to change that into a string and we're going to put it in here from tensor flow hub and
then we'll put a link to tensor flow hub.
Wonderful.
Come back to dog vision.
There we go.
So what have we got set up now.
So if we come back to our keynote this is what we've set up we've picked a model from tensor flow hub
to suit our problem.
Remember we went through our problem domain and we're working with images.
What we're focused on is our inputs we've done that we've got our data ready and we turned it into tenses
and we've chosen our machine learning algorithm.
Wonderful and now we've got our output shape correct.
Now we've got these three key criteria we have to work out how we can use them all together.
The next process is putting them together in the form of a function that's going to create a model for
us and return it so that we can fit it to our data set.
So let's get started on that in the next video.
But remember for now your homework is to read up about tensor flow hub and to checkout mobile Net V2
132 to 4.
All link both of these and these other resources in the resources section.