For the one-hot encoding of the response variables, we use the following code:
# Labels
trainy <- c(0,0,0,1,1,1,2,2,2)
validy <- c(0,1,2)
testy <- c(0,0,1,1,2,2)
# One-hot encoding
trainLabels <- to_categorical(trainy)
validLabels <- to_categorical(validy)
testLabels <- to_categorical(testy)
trainLabels
OUTPUT
[,1] [,2] [,3] [1,] 1 0 0 [2,] 1 0 0 [3,] 1 0 0 [4,] 0 1 0 [5,] 0 1 0 [6,] 0 1 0 [7,] 0 0 1 [8,] 0 0 1 [9,] 0 0 1
From the preceding code, we can see the following:
- We have stored target values for each image in trainy , validy, and testy, where 0, 1, and 2 indicate bicycle, car, and airplane images respectively.
- We carry out one-hot encoding of trainy , validy, and testy by using the to_categorical function. One-hot encoding here helps to convert a factor variable into a combination of zeros and ones.
Now we have the data in a format that can be used for developing a deep neural network classification model, and that is what we will do in the next section.