transformer_21.py

def unicode_to_ascii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn')


def normalize_string(s):
    s = unicode_to_ascii(s)
    s = re.sub(r'([!.?])', r' \1', s)
    s = re.sub(r'[^a-zA-Z.!?]+', r' ', s)
    s = re.sub(r'\s+', r' ', s)
    return s


raw_data_en, raw_data_fr = list(zip(*raw_data))
raw_data_en, raw_data_fr = list(raw_data_en), list(raw_data_fr)
raw_data_en = [normalize_string(data) for data in raw_data_en]
raw_data_fr_in = ['<start> ' + normalize_string(data) for data in raw_data_fr]
raw_data_fr_out = [normalize_string(data) + ' <end>' for data in raw_data_fr]


en_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='')
en_tokenizer.fit_on_texts(raw_data_en)
data_en = en_tokenizer.texts_to_sequences(raw_data_en)
data_en = tf.keras.preprocessing.sequence.pad_sequences(data_en,
                                                        padding='post')

fr_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='')
fr_tokenizer.fit_on_texts(raw_data_fr_in)
fr_tokenizer.fit_on_texts(raw_data_fr_out)
data_fr_in = fr_tokenizer.texts_to_sequences(raw_data_fr_in)
data_fr_in = tf.keras.preprocessing.sequence.pad_sequences(data_fr_in,
                                                           padding='post')

data_fr_out = fr_tokenizer.texts_to_sequences(raw_data_fr_out)
data_fr_out = tf.keras.preprocessing.sequence.pad_sequences(data_fr_out,
                                                            padding='post')


BATCH_SIZE = 5
dataset = tf.data.Dataset.from_tensor_slices(
    (data_en, data_fr_in, data_fr_out))
dataset = dataset.shuffle(20).batch(BATCH_SIZE)