Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[keras/preprocessing] Standardise docstring usage of "Default to" #17977

Merged
merged 3 commits into from
May 16, 2023
Merged

[keras/preprocessing] Standardise docstring usage of "Default to" #17977

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
d7486bf
[keras/preprocessing/image.py,keras/preprocessing/text.py] Standardis…
SamuelMarks Apr 13, 2023
af5d96a
[keras/preprocessing/text.py] Docstring grammar improvements
SamuelMarks Apr 23, 2023
44c0b83
[keras/preprocessing/image.py] Consistent indent of 2 for `flow_from_…
SamuelMarks May 4, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
135 changes: 68 additions & 67 deletions keras/preprocessing/image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1225,9 +1225,9 @@ class ImageDataGenerator:
`fill_mode = "constant"`.
horizontal_flip: Boolean. Randomly flip inputs horizontally.
vertical_flip: Boolean. Randomly flip inputs vertically.
rescale: rescaling factor. Defaults to None. If None or 0, no rescaling
rescale: rescaling factor. If None or 0, no rescaling
is applied, otherwise we multiply the data by the value provided
(after applying all other transformations).
(after applying all other transformations). Defaults to `None`.
preprocessing_function: function that will be applied on each input. The
function will run after the image is resized and augmented.
The function should take one argument: one image (Numpy tensor with
Expand All @@ -1236,9 +1236,9 @@ class ImageDataGenerator:
"channels_last". "channels_last" mode means that the images should
have shape `(samples, height, width, channels)`, "channels_first" mode
means that the images should have shape `(samples, channels, height,
width)`. It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`. If you never set it, then
it will be "channels_last".
width)`. When unspecified, uses `image_data_format` value found in
your Keras config file at `~/.keras/keras.json` (if exists) else
'channels_last'. Defaults to "channels_last".
validation_split: Float. Fraction of images reserved for validation
(strictly between 0 and 1).
dtype: Dtype to use for the generated arrays.
Expand Down Expand Up @@ -1580,70 +1580,71 @@ def flow_from_directory(
"""Takes the path to a directory & generates batches of augmented data.

Args:
directory: string, path to the target directory. It should contain
one subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images
inside each of the subdirectories directory tree will be included
in the generator. See [this script](
https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
for more details.
target_size: Tuple of integers `(height, width)`, defaults to `(256,
256)`. The dimensions to which all images found will be resized.
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
Whether the images will be converted to have 1, 3, or 4 channels.
classes: Optional list of class subdirectories (e.g. `['dogs',
'cats']`). Default: None. If not provided, the list of classes
will be automatically inferred from the subdirectory
names/structure under `directory`, where each subdirectory will be
treated as a different class (and the order of the classes, which
will map to the label indices, will be alphanumeric). The
dictionary containing the mapping from class names to class
indices can be obtained via the attribute `class_indices`.
class_mode: One of "categorical", "binary", "sparse",
"input", or None. Default: "categorical".
Determines the type of label arrays that are returned:
- "categorical" will be 2D one-hot encoded labels,
- "binary" will be 1D binary labels,
"sparse" will be 1D integer labels,
- "input" will be images identical
to input images (mainly used to work with autoencoders).
- If None, no labels are returned
(the generator will only yield batches of image data,
which is useful to use with `model.predict_generator()`).
Please note that in case of class_mode None,
the data still needs to reside in a subdirectory
of `directory` for it to work correctly.
batch_size: Size of the batches of data (default: 32).
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.
save_to_dir: None or str (default: None). This allows you to
optionally specify a directory to which to save the augmented
pictures being generated (useful for visualizing what you are
doing).
save_prefix: Str. Prefix to use for filenames of saved pictures
(only relevant if `save_to_dir` is set).
save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
"tif", "jpg" (only relevant if `save_to_dir` is set). Default:
"png".
follow_links: Whether to follow symlinks inside
class subdirectories (default: False).
subset: Subset of data (`"training"` or `"validation"`) if
`validation_split` is set in `ImageDataGenerator`.
interpolation: Interpolation method used to resample the image if
the target size is different from that of the loaded image.
Supported methods are `"nearest"`, `"bilinear"`, and `"bicubic"`.
If PIL version 1.1.3 or newer is installed, `"lanczos"` is also
supported. If PIL version 3.4.0 or newer is installed, `"box"` and
`"hamming"` are also supported. By default, `"nearest"` is used.
keep_aspect_ratio: Boolean, whether to resize images to a target
size without aspect ratio distortion. The image is cropped in
the center with target aspect ratio before resizing.
directory: string, path to the target directory. It should contain
one subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images
inside each of the subdirectories directory tree will be included
in the generator. See [this script](
https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
for more details.
target_size: Tuple of integers `(height, width)`. The dimensions to
which all images found will be resized. Defaults to `(256,256)`.
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
Whether the images will be converted to have 1, 3, or 4 channels.
classes: Optional list of class subdirectories (e.g. `['dogs',
'cats']`). Default: None. If not provided, the list of classes
will be automatically inferred from the subdirectory
names/structure under `directory`, where each subdirectory will be
treated as a different class (and the order of the classes, which
will map to the label indices, will be alphanumeric). The
dictionary containing the mapping from class names to class
indices can be obtained via the attribute `class_indices`.
class_mode: One of "categorical", "binary", "sparse",
"input", or None.
Determines the type of label arrays that are returned:
- "categorical" will be 2D one-hot encoded labels,
- "binary" will be 1D binary labels,
- "sparse" will be 1D integer labels,
- "input" will be images identical
to input images (mainly used to work with autoencoders).
- If None, no labels are returned
(the generator will only yield batches of image data,
which is useful to use with `model.predict_generator()`).
Please note that in case of class_mode None,
the data still needs to reside in a subdirectory
of `directory` for it to work correctly.
Defaults to "categorical".
batch_size: Size of the batches of data. Defaults to `32`.
shuffle: Whether to shuffle the data If `False`, sorts the
data in alphanumeric order. Defaults to `True`.
seed: Optional random seed for shuffling and transformations.
save_to_dir: None or str (default: None). This allows you to
optionally specify a directory to which to save the augmented
pictures being generated (useful for visualizing what you are
doing).
save_prefix: Str. Prefix to use for filenames of saved pictures
(only relevant if `save_to_dir` is set).
save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
"tif", "jpg" (only relevant if `save_to_dir` is set).
Defaults to "png".
follow_links: Whether to follow symlinks inside
class subdirectories. Defaults to `False`.
subset: Subset of data (`"training"` or `"validation"`) if
`validation_split` is set in `ImageDataGenerator`.
interpolation: Interpolation method used to resample the image if
the target size is different from that of the loaded image.
Supported methods are `"nearest"`, `"bilinear"`, and `"bicubic"`.
If PIL version 1.1.3 or newer is installed, `"lanczos"` is also
supported. If PIL version 3.4.0 or newer is installed, `"box"` and
`"hamming"` are also supported. Defaults to `"nearest"`.
keep_aspect_ratio: Boolean, whether to resize images to a target
size without aspect ratio distortion. The image is cropped in
the center with target aspect ratio before resizing.

Returns:
A `DirectoryIterator` yielding tuples of `(x, y)`
where `x` is a numpy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
and `y` is a numpy array of corresponding labels.
A `DirectoryIterator` yielding tuples of `(x, y)`
where `x` is a numpy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
and `y` is a numpy array of corresponding labels.
"""
return DirectoryIterator(
directory,
Expand Down
6 changes: 3 additions & 3 deletions keras/preprocessing/text.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -154,11 +154,11 @@ def hashing_trick(
Args:
text: Input text (string).
n: Dimension of the hashing space.
hash_function: defaults to python `hash` function, can be 'md5' or
any function that takes in input a string and returns a int.
hash_function: When `None` uses a python `hash` function. Can be 'md5'
or any function that takes in input a string and returns a int.
Note that 'hash' is not a stable hashing function, so
it is not consistent across different runs, while 'md5'
is a stable hashing function.
is a stable hashing function. Defaults to `None`.
filters: list (or concatenation) of characters to filter out, such as
punctuation. Default: ``!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\\t\\n``,
includes basic punctuation, tabs, and newlines.
Expand Down