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

text_dataset_from_directory(
directory,
labels = "inferred",
label_mode = "int",
class_names = NULL,
batch_size = 32L,
max_length = NULL,
shuffle = TRUE,
seed = NULL,
validation_split = NULL,
subset = NULL,
...
)

## 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), NULL (no labels), or a list 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).

• NULL (no labels).

class_names

Only valid if labels is "inferred". This is the explicit 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.

Whether to visits subdirectories pointed to by symlinks. Defaults to FALSE.

...

For future compatibility (unused presently).

## Details

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.