Let's view some examples.

Normalize, output = (input - mean )/ std:

normalize = vision.Normalize(mean=(0.14,), std= (0.44))
normalized_image = normalize(rescaled_image)
print(normalized_image)

the data changed from:to :

anyway, it is useful ,maybe related to kaiming normalized.

Tokenize:

def my_tokenizer(content):
    return content.split()
test_dataset = test_dataset.map(text.PythonTokenizer(my_tokenizer))
print(next(test_dataset.create_tuple_iterator()))

for example, if I have texts ['Welcome to Beijing']

I would split into three words(tokens), get ['Welcome' ,'to','Beijing'], which is saved as a tensor, a common type in transformer.

more concretely, we can def a vocab, for example:

vocab = text.Vocab.from_dataset(test_dataset)
print(vocab.vocab())

and we get:

and we can transform the tokens to index:

test_dataset = test_dataset.map(text.lookup(vocab))
print(next(test_dataset.create_tuple_iterator()))