Welcome back.
I hope you have installed get us an intensive float in your system now as a practice project.
We are going to create an image classifier we will be classifying this kind of images into 10 different
categories such as T-shirts trousers pullover dresses bags boots etc. For this we are going to use a
very famous database that is known as fashion eminence database.
Here we have our own seventy thousand great skill images of 10 different fashion categories objects
our training site will be off 60000 images which we are going to use to screen our model we have and
another set of 10000 images which we will be using as a basis to evaluate the performance of our model.
These images are in the form of printed by 28 pixel is squared and each pixel is represented on the
gray skin on a scale of 0 to 255.
The great thing about this database is that it is available within gave us and we can directly imported
from get us.
We don't have to upload a separate fight to access this database here you can see the 10 different objects
that are present in this database
now to access this database from gave us.
We are first creating a fashion underscored and this object where we are calling this database and after
that we are loading this database and our X and vice data sites.
We are calling our train dataset as X underscore xn underscore the full and via underscore train underscore
full and our test dataset as X underscore best and find the scored best.
So just load this database now we can use my library to view images in this database.
So for example if I want to excel the first image I can just write X underscore green underscore full
and we are accessing the first element that is the picture that is present at position 0.
You can see that this is our first image.
If you want to access the second image I'm just changing the location to 1 in this way you can access
the different images that are present in this database.
Now this is our X video.
This out of the pixels that we are going to use to predict the object.
Now 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 the Zero label we can refer to this about table here zero
sense for t shirt and tops.
One is sense or closer to sense for the lower and so on so this image is of a t shirt and the output
is also representing that.
This is a t shirt
V.
Just check this for the first element you can see that this is a boot and the white label is 9.
If you see the label line correspond to ankle boot so instead of referring people each time we can create
a list of last name where we have lists certain all the categories in the order of their labels so that
if I call the first element of this list it frenetically Give me the t shirt.
If I call the second element it will give closeups as an output.
So instead of calling the labels I can directly call the description of those labels.
Using this last name list so just check the image of object that this president look Sean Penn
to check the white label of this object.
I can ethically call last name and then the label of the pollution and you can see the last name.
Here it is.
These are total
few notice.
Here we are using my block to block the data that is stored in the extreme dataset not to view the content
of this data.
You can
can just write extra in full and then call the object.
So earlier I have mentioned that this images are of 28 white 28 grayscale format.
So here in the data you are seeing 28 in 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 that can be a draw here zero represent pure black and blue fifty five 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 mantle and to get the vibe and lose the actual category of this data you have to call white
grained 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 in 28 values that is 784 values and using these values we are going to predict the description
of this that solve for the data.
You have the raw data can view this data and you have the last names.
Now we have to create model on this data in the next lecture.
We will normalize our dataset and for divided our trained dataset into validation entering site thinking.