The transformation of data

We're going to look at transformations on the training and test image data. For x_train and x_test, we need to do the following:

1. Add a fourth dimension going from (60000, 28, 28) to (60000, 28, 28, 1),
2. Change it to the Float32 data type.
3. Normalize it between 0 and 1 (by dividing by 255).

In the following code block, we will perform normalization on the data:

x_train /=255
x_test /=255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

The shape of the data doesn't change after normalization:

x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples

In the next section, we will perform one-hot encoding on our target variables.