Next, we do one-hot encoding of labels stored in trainy and testy using the following code:

# One-hot encoding
trainy <- to_categorical(trainy, 10)
testy <- to_categorical(testy, 10)
head(trainy)
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    0    0    0    0    0    0    0    0    0     1
[2,]    1    0    0    0    0    0    0    0    0     0
[3,]    1    0    0    0    0    0    0    0    0     0
[4,]    0    0    0    1    0    0    0    0    0     0
[5,]    1    0    0    0    0    0    0    0    0     0
[6,]    0    0    1    0    0    0    0    0    0     0

After one-hot encoding, the first row for the train data indicates a value of 1 for the tenth category (ankle boot). Similarly, the second row for the train data indicates a value of 1 for the first category (t-shirt/top). After completing the changes mentioned previously, now the fashion-MNIST data is ready for developing an image recognition and classification model.