텍스트 데이터 전처리

text_dataset_from_directory function

tf.keras.preprocessing.text_dataset_from_directory(
    directory,
    labels="inferred",
    label_mode="int",
    class_names=None,
    batch_size=32,
    max_length=None,
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    follow_links=False,
)

Generates a tf.data.Dataset from text files in a directory.

If your directory structure is:

main_directory/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt

Then calling text_dataset_from_directory(main_directory, labels='inferred') will return a tf.data.Dataset that yields batches of texts from the subdirectories class_a and class_b, together with labels 0 and 1 (0 corresponding to class_a and 1 corresponding to class_b).

Only .txt files are supported at this time.

Arguments

• directory: Directory where the data is located. If labels is "inferred", it should contain subdirectories, each containing text files for a class. Otherwise, the directory structure is ignored.
• labels: Either "inferred" (labels are generated from the directory structure), or a list/tuple of integer labels of the same size as the number of text files found in the directory. Labels should be sorted according to the alphanumeric order of the text file paths (obtained via os.walk(directory) in Python).
• label_mode: - 'int': means that the labels are encoded as integers (e.g. for sparse_categorical_crossentropy loss). - 'categorical' means that the labels are encoded as a categorical vector (e.g. for categorical_crossentropy loss). - 'binary' means that the labels (there can be only 2) are encoded as float32 scalars with values 0 or 1 (e.g. for binary_crossentropy). - None (no labels).
• class_names: Only valid if "labels" is "inferred". This is the explict list of class names (must match names of subdirectories). Used to control the order of the classes (otherwise alphanumerical order is used).
• batch_size: Size of the batches of data. Default: 32.
• max_length: Maximum size of a text string. Texts longer than this will be truncated to max_length.
• shuffle: Whether to shuffle the data. Default: True. If set to False, sorts the data in alphanumeric order.
• seed: Optional random seed for shuffling and transformations.
• validation_split: Optional float between 0 and 1, fraction of data to reserve for validation.
• subset: One of "training" or "validation". Only used if validation_split is set.
• follow_links: Whether to visits subdirectories pointed to by symlinks. Defaults to False.

Returns

A tf.data.Dataset object. - If label_mode is None, it yields string tensors of shape (batch_size,), containing the contents of a batch of text files. - Otherwise, it yields a tuple (texts, labels), where texts has shape (batch_size,) and labels follows the format described below.

Rules regarding labels format: - if label_mode is int, the labels are an int32 tensor of shape (batch_size,). - if label_mode is binary, the labels are a float32 tensor of 1s and 0s of shape (batch_size, 1). - if label_mode is categorial, the labels are a float32 tensor of shape (batch_size, num_classes), representing a one-hot encoding of the class index.

Tokenizer class

tf.keras.preprocessing.text.Tokenizer(
    num_words=None,
    filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',
    lower=True,
    split=" ",
    char_level=False,
    oov_token=None,
    document_count=0,
    **kwargs
)

Text tokenization utility class.

This class allows to vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a vector where the coefficient for each token could be binary, based on word count, based on tf-idf...

Arguments

• num_words: the maximum number of words to keep, based on word frequency. Only the most common num_words-1 words will be kept.
• filters: a string where each element is a character that will be filtered from the texts. The default is all punctuation, plus tabs and line breaks, minus the ' character.
• lower: boolean. Whether to convert the texts to lowercase.
• split: str. Separator for word splitting.
• char_level: if True, every character will be treated as a token.
• oov_token: if given, it will be added to word_index and used to replace out-of-vocabulary words during text_to_sequence calls

By default, all punctuation is removed, turning the texts into space-separated sequences of words (words maybe include the ' character). These sequences are then split into lists of tokens. They will then be indexed or vectorized.

0 is a reserved index that won't be assigned to any word.