Generates a tf.data.Dataset
from text files in a directory.
tf.keras.utils.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
)
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.
Args |
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),
None (no labels),
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
|
String describing the encoding of labels . Options are:
- '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 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.
If None , the data will not be batched
(the dataset will yield individual samples).
|
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
|
Subset of the data to return.
One of "training", "validation" or "both".
Only used if validation_split is set.
When subset="both" , the utility returns a tuple of two datasets
(the training and validation datasets respectively).
|
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 `categorical`, the labels are a `float32` tensor
of shape `(batch_size, num_classes)`, representing a one-hot
encoding of the class index.