Module keras.preprocessing.text_dataset
Keras text dataset generation utilities.
Expand source code
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras text dataset generation utilities."""
import tensorflow.compat.v2 as tf
import numpy as np
from keras.preprocessing import dataset_utils
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.utils.text_dataset_from_directory',
'keras.preprocessing.text_dataset_from_directory',
v1=[])
def 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.
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:
- '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.
"""
if labels not in ('inferred', None):
if not isinstance(labels, (list, tuple)):
raise ValueError(
'`labels` argument should be a list/tuple of integer labels, of '
'the same size as the number of text files in the target '
'directory. If you wish to infer the labels from the subdirectory '
'names in the target directory, pass `labels="inferred"`. '
'If you wish to get a dataset that only contains text samples '
'(no labels), pass `labels=None`.')
if class_names:
raise ValueError('You can only pass `class_names` if the labels are '
'inferred from the subdirectory names in the target '
'directory (`labels="inferred"`).')
if label_mode not in {'int', 'categorical', 'binary', None}:
raise ValueError(
'`label_mode` argument must be one of "int", "categorical", "binary", '
'or None. Received: %s' % (label_mode,))
if labels is None or label_mode is None:
labels = None
label_mode = None
dataset_utils.check_validation_split_arg(
validation_split, subset, shuffle, seed)
if seed is None:
seed = np.random.randint(1e6)
file_paths, labels, class_names = dataset_utils.index_directory(
directory,
labels,
formats=('.txt',),
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links)
if label_mode == 'binary' and len(class_names) != 2:
raise ValueError(
'When passing `label_mode="binary", there must exactly 2 classes. '
'Found the following classes: %s' % (class_names,))
file_paths, labels = dataset_utils.get_training_or_validation_split(
file_paths, labels, validation_split, subset)
if not file_paths:
raise ValueError('No text files found.')
dataset = paths_and_labels_to_dataset(
file_paths=file_paths,
labels=labels,
label_mode=label_mode,
num_classes=len(class_names),
max_length=max_length)
if shuffle:
# Shuffle locally at each iteration
dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
dataset = dataset.prefetch(tf.data.AUTOTUNE).batch(batch_size)
# Users may need to reference `class_names`.
dataset.class_names = class_names
return dataset
def paths_and_labels_to_dataset(file_paths,
labels,
label_mode,
num_classes,
max_length):
"""Constructs a dataset of text strings and labels."""
path_ds = tf.data.Dataset.from_tensor_slices(file_paths)
string_ds = path_ds.map(
lambda x: path_to_string_content(x, max_length))
if label_mode:
label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes)
string_ds = tf.data.Dataset.zip((string_ds, label_ds))
return string_ds
def path_to_string_content(path, max_length):
txt = tf.io.read_file(path)
if max_length is not None:
txt = tf.compat.v1.strings.substr(txt, 0, max_length)
return txt
Functions
def path_to_string_content(path, max_length)
-
Expand source code
def path_to_string_content(path, max_length): txt = tf.io.read_file(path) if max_length is not None: txt = tf.compat.v1.strings.substr(txt, 0, max_length) return txt
def paths_and_labels_to_dataset(file_paths, labels, label_mode, num_classes, max_length)
-
Constructs a dataset of text strings and labels.
Expand source code
def paths_and_labels_to_dataset(file_paths, labels, label_mode, num_classes, max_length): """Constructs a dataset of text strings and labels.""" path_ds = tf.data.Dataset.from_tensor_slices(file_paths) string_ds = path_ds.map( lambda x: path_to_string_content(x, max_length)) if label_mode: label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes) string_ds = tf.data.Dataset.zip((string_ds, label_ds)) return string_ds
def 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 atf.data.Dataset
that yields batches of texts from the subdirectoriesclass_a
andclass_b
, together with labels 0 and 1 (0 corresponding toclass_a
and 1 corresponding toclass_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:
- - '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. - Iflabel_mode
is None, it yieldsstring
tensors of shape(batch_size,)
, containing the contents of a batch of text files. - Otherwise, it yields a tuple(texts, labels)
, wheretexts
has shape(batch_size,)
andlabels
follows the format described below. Rules regarding labels format: - iflabel_mode
isint
, the labels are anint32
tensor of shape(batch_size,)
. - iflabel_mode
isbinary
, the labels are afloat32
tensor of 1s and 0s of shape(batch_size, 1)
. - iflabel_mode
iscategorial
, the labels are afloat32
tensor of shape(batch_size, num_classes)
, representing a one-hot encoding of the class index.Expand source code
@keras_export('keras.utils.text_dataset_from_directory', 'keras.preprocessing.text_dataset_from_directory', v1=[]) def 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. 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: - '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. """ if labels not in ('inferred', None): if not isinstance(labels, (list, tuple)): raise ValueError( '`labels` argument should be a list/tuple of integer labels, of ' 'the same size as the number of text files in the target ' 'directory. If you wish to infer the labels from the subdirectory ' 'names in the target directory, pass `labels="inferred"`. ' 'If you wish to get a dataset that only contains text samples ' '(no labels), pass `labels=None`.') if class_names: raise ValueError('You can only pass `class_names` if the labels are ' 'inferred from the subdirectory names in the target ' 'directory (`labels="inferred"`).') if label_mode not in {'int', 'categorical', 'binary', None}: raise ValueError( '`label_mode` argument must be one of "int", "categorical", "binary", ' 'or None. Received: %s' % (label_mode,)) if labels is None or label_mode is None: labels = None label_mode = None dataset_utils.check_validation_split_arg( validation_split, subset, shuffle, seed) if seed is None: seed = np.random.randint(1e6) file_paths, labels, class_names = dataset_utils.index_directory( directory, labels, formats=('.txt',), class_names=class_names, shuffle=shuffle, seed=seed, follow_links=follow_links) if label_mode == 'binary' and len(class_names) != 2: raise ValueError( 'When passing `label_mode="binary", there must exactly 2 classes. ' 'Found the following classes: %s' % (class_names,)) file_paths, labels = dataset_utils.get_training_or_validation_split( file_paths, labels, validation_split, subset) if not file_paths: raise ValueError('No text files found.') dataset = paths_and_labels_to_dataset( file_paths=file_paths, labels=labels, label_mode=label_mode, num_classes=len(class_names), max_length=max_length) if shuffle: # Shuffle locally at each iteration dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed) dataset = dataset.prefetch(tf.data.AUTOTUNE).batch(batch_size) # Users may need to reference `class_names`. dataset.class_names = class_names return dataset