Welcome back.
I hope you have installed get us and intensive flow in your system.
Now, as a practice project, we are going to create an inmate classifier.
We will be classifying this kind of images into ten different categories, such as T-shirts, trousers,
pullover dresses, bags, boots, et cetera.
For this, we are going to use a very famous database that is known as Fashion Emmis Database.
Here we have our own 70000 great scale images of ten different fashion categories, objects.
A training site will be off.
60000 images which we are going to use to screen over more than.
We have another site of 10000 images which we will be using as a test site to evaluate the performance
of our model.
These images are in the form of twenty eight by twenty eight, pixel is square and each pixel is represented
on a gray skin.
On a scale of zero to 255.
The great thing about this database is that it is available within us and we can directly imported from
caite us.
We don't have to upload a separate file to access this database here.
You can see the ten different objects that are present in this database.
Now, to access this database from Get US, we are first creating a fashion underscored amnesty object
where we are calling this database and after that we are loading this database into our X and via datasets.
We are calling Overtrain dataset as X, underscore green, underscore the full and via underscore craine,
underscore full and our best dataset as X underscore best and Švanda scored best.
So just load this database.
No, we can use my library to view images in this database.
So, for example, if I want to access the first image, I can just write Xander score green under score
full.
And we're accessing the first element.
That is the picture that is present at position zero.
You can see that this is our first image if we want to exit the second image.
I'm just changing the location to one.
In this way, you can access the different images that are present in this database.
Now, this is our X variable.
This out of the pixels that we are going to use to predict the object.
Not to view the actual category of this object.
We have to call the wide screen dataset so you can just call the element that is present at the first
position.
So if I run this.
You can see that the output is zero.
To view the category that is responding to this zero label, we can refer this about table here, zero
sense for t shirt and tops.
One is sense for Krosa, who stands for lower and so on.
So this image is of a T-shirt and the output is also representing that this is a T-shirt.
We just checked this for the first segment.
You can see that this is a boot and the vital label is nine.
If you see the labor line correspond to ankle boot.
So instead of referring people each time, we can create a list of class name.
We are.
We have this search all look at degrees in the order of their labels.
So that if I call the first sentiment of this list, it pretty likely give me the T-shirt, if I call
the second element.
It will give closeups as an old port.
So instead of calling the labels, I can medically call.
The description of those labels using this last name list.
So just check the image off object that is present location.
Ben?
Who checked the white label of this object?
I can medically call last name and then the label of the pollution pan.
You can see the last name here is Shirt Thoughtful.
Few.
Here we are using my plot to plot the data that is stored in the extreme dataset, not to view the content
of this data.
You can.
You can just write extra in full and then call the object.
So earlier I mentioned that this images are of 28 by 28, greyscale, formic.
So here in the data, you are seeing 28 to 28 pixel values.
These are the pixels that are present at the first rule.
These are the pixels that are present.
The second row and so on for the 20th through here.
Zero to present pure black and 255 represents white.
So the location of first pixel, that is the first row and the first pixel.
You can see it's pure black.
That's why we are getting zero.
So our data is present in this form to view the data.
You have to use.
I am sure, mentored and to get the vibe, lose the actual category of this data.
You have to call wide-screen data and you can also use last name list to directly get the descriptions
set off label.
So this is the data that we are going to use for each record.
We have 28 include 28 values, that is 784 values.
And using these values, we are going to predict the description of this.
So that's all for the data.
You have the raw data can view this data.
And you have the last names.
Now, we have to create more than on this data.
In the next lecture, we will normalize our dataset and further divided our train dataset into validation
and train set.
Thank you.