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.