All right, now, let's begin our second lesson in the deep learning part of this course, where we're
going to build a scene CNN, we just built and pay to watch.
We're now going to use cameras with TensorFlow back end.
So open the site Python file, which I already have done here.
And let's take a look at this lesson now what we're going to do.
We're going to first load the data inspected.
Visualize it, reprocess it.
Build our model, train our model, then plot our training logs, then save a on model, then test on
all of us and tested it.
A lot of these things are what we did in PyTorch in the previous lesson.
However, not going to take a look at how we do it in Keros with TensorFlow 2.0, and you'll see that
it's actually a lot simpler in a way.
I hope this doesn't confuse you because getting a grasp of Python, which is actually a little bit difficult
for beginners and TensorFlow, while easier, can sometimes confuse you and how you learn things.
However, I think it's a very, very good idea to learn both of these libraries at the same time because
it sort of gives you a bit better.
And it's sort of like Ryan reinforces how you hold the concepts of how you actually learned the concepts.
So without talking too much, let's just move ahead with this lesson.
So firstly, in the Python lesson, we loaded our data using the Torch Vision Data Sets package and
carries with TensorFlow.
I'll use those terms interchangeably.
In fact, to may decide to use Keros going forward because it's a bit easier to say.
So with carries what we can do, we can import datasets similarly, and it can take a look and see what
datasets are here.
Not as much as the pie touch, but a few of the important ones here.
These three other important ones I'll be using, as well as the F fashion list.
So let's go back to this.
So it's important and this all we do is just from TensorFlow.
Don't US datasets import endless this?
This allows us to load the amnesty to set.
So we do amnesty of lotito and it puts the training data, the training labels, the test data and the
test labels into these variables that will be declared here.
So from now on, if you want to access to data, we can get it here directly.
So importantly, it's good to check to see if we're using the GPU like we did in the previous lesson.
So let me just firstly run this function here.
It may take a couple of seconds to download the data.
So while we wait for that, we'll talk about this line here so intensively.
In order to see if you're using the GPU, you can use this quick little function here.
Import sorry from TensorFlow to Python, the client and plot device level, and then you can use device
level list looking devices.
And this tells you what devices you're using.
You can see it's a GPU is is being.
If it's available, it will be displayed here in this part of the code here.
So this is this was done pretty quickly with download the amnesty to set 50000 churning images at 10000
test images.
Let's run this code to see if you as enabled.
If it's not, we will have to change it in the settings and it is available and to change it in the
settings.
It wasn't just going to edit notebook settings and change your hardware accelerated to you.
So now let's stop there and what we'll do.
We'll take a look at inspecting our data in the next section.
Thank you.