‫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.