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‫Welcome back.

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‫I hope you have installed keras and tensorflow in your system.

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‫Now, as a practice project, we are going to create an image classifier.

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‫We will be classifying this kind of images into ten different categories, such as T-shirts, trousers,

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‫pullover dresses, bags, boots, et cetera.

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‫For this, we are going to use a very famous database that is known as Fashion emnist Database.

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‫Here we have around 70000 grey scale images of ten different fashion categories, objects.

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‫Our training set will be of

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‫60000 images which we are going to use to train our model.

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‫We have an another set of 10000 images which we will be using as a test set to evaluate the performance

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‫of our model.

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‫These images are in a form of twenty eight by twenty eight pixel  square and each pixel is represented

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‫on a gray scale.

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‫On a scale of 0 to 255.

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‫The great thing about this database is that it is available within keras and we can directly import it from

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‫Keras.

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‫We don't have to upload a separate file to access this database. Here

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‫You can see the ten different objects that are present in this database.

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‫Now, to access this database from keras, we are first creating a fashion underscored mnist object

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‫where we are calling this database and after that we are loading this database into our X and y datasets.

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‫We are calling our train dataset as X underscore tarin underscore  full and y underscore train

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‫underscore full and our test dataset as X underscore test and y underscore test.

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‫So just load this database.

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‫No, we can use macplot library to view images in this database.

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‫So, for example, if I want to access the first image, I can just write x underscore train underscore

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‫full.

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‫And we're accessing the first element.

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‫That is the picture that is present at position zero.

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‫You can see that this is our first image if we want to access the second image.

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‫I'm just changing the location to one.

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‫In this way, you can access the different images that are present in this database.

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‫Now, this is our X variable.

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‫This are of the pixels that we are going to use to predict the object.

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‫Not to view the actual category of this object.

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‫We have to call the y train dataset so you can just call the element that is present at the first

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‫position.

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‫So if I run this.

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‫You can see that the output is zero.

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‫To view the category that is responding to this zero label, we can refer this above table here, zero

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‫Stands for t shirt and tops.

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‫One is stands for trouser, who stands for pullover and so on.

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‫So this image is of a T-shirt and the output is also representing that this is a T-shirt.

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‫We just checked this for the first element.

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‫You can see that this is a boot and the y label is nine.

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‫If you see label 9 correspond to ankle boot.

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‫So instead of referring table each time, we can create a list of class name.

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‫Where

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‫We have listed all the categories  in the order of their labels.

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‫So that if I call the first element of this list, it will directly give me the T-shirt, if I call

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‫the second element.

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‫It will give trousers as an output.

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‫So instead of calling the labels, I can directly call

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‫The description of those labels using this class name list.

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‫So just check the image of object that is present location

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‫10

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‫Who check  the y label of this object

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‫I can directly call class name and then the label of the position 10.

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‫You can see the class name here tshirt or top.

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‫If you notice

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‫Here we are using matplot to plot the data that is stored in the x train dataset, now to view the content

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‫of this data.

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‫You can

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‫You can just write x train full and then call the object.

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‫So earlier I have mentioned that this images are of 28 by 28, greyscale format.

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‫So here in the data, you are seeing 28 into 28 pixel values.

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‫These are the pixels that are present at the first row.

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‫These are the pixels that are present at

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‫The second row and so on for the 20th row. Here

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‫Zero represent pure black and 255 represents white.

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‫So the location of first pixel, that is the first row and the first pixel.

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‫You can see it's pure black.

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‫That's why we are getting zero.

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‫So our data is present in this form to view the data.

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‫You have to use

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‫Imshow method and to get the y values the actual category of this data.

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‫You have to call y train data and you can also use class name list to directly get the descriptions

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‫Instead  of label.

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‫So this is the data that we are going to use for each record.

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‫We have 28 into 28 values, that is 784 values.

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‫And using these values, we are going to predict the description of this.

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‫So that's all for the data.

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‫You have the raw data can view this data.

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‫And you have the class names.

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‫Now, we have to create model on this data.

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‫In the next lecture, we will normalize our dataset and further divided our train dataset into validation

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‫and train set.

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‫Thank you.