diff --git a/docs/autogen.py b/docs/autogen.py index 2a226c2674b..e89b95cc01b 100644 --- a/docs/autogen.py +++ b/docs/autogen.py @@ -259,6 +259,20 @@ preprocessing.sequence.TimeseriesGenerator, ] }, + { + 'page': 'preprocessing/image.md', + 'functions': [ + preprocessing.image.random_rotation, + preprocessing.image.random_shift, + preprocessing.image.random_shear, + preprocessing.image.random_zoom, + preprocessing.image.random_channel_shift, + preprocessing.image.random_brightness, + ], + 'classes': [ + preprocessing.image.ImageDataGenerator, + ] + }, { 'page': 'layers/wrappers.md', 'all_module_classes': [wrappers], diff --git a/docs/templates/preprocessing/image.md b/docs/templates/preprocessing/image.md index 43f71b34787..be65609fe99 100644 --- a/docs/templates/preprocessing/image.md +++ b/docs/templates/preprocessing/image.md @@ -1,213 +1,4 @@ -### ImageDataGenerator +### Image Preprocessing -```python -keras.preprocessing.image.ImageDataGenerator(featurewise_center=False, - samplewise_center=False, - featurewise_std_normalization=False, - samplewise_std_normalization=False, - zca_whitening=False, - zca_epsilon=1e-6, - rotation_range=0., - width_shift_range=0., - height_shift_range=0., - shear_range=0., - zoom_range=0., - channel_shift_range=0., - fill_mode='nearest', - cval=0., - horizontal_flip=False, - vertical_flip=False, - rescale=None, - preprocessing_function=None, - data_format=K.image_data_format()) -``` - -Generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches) indefinitely. - -- __Arguments__: - - __featurewise_center__: Boolean. Set input mean to 0 over the dataset, feature-wise. - - __samplewise_center__: Boolean. Set each sample mean to 0. - - __featurewise_std_normalization__: Boolean. Divide inputs by std of the dataset, feature-wise. - - __samplewise_std_normalization__: Boolean. Divide each input by its std. - - __zca_epsilon__: epsilon for ZCA whitening. Default is 1e-6. - - __zca_whitening__: Boolean. Apply ZCA whitening. - - __rotation_range__: Int. Degree range for random rotations. - - __width_shift_range__: Float (fraction of total width). Range for random horizontal shifts. - - __height_shift_range__: Float (fraction of total height). Range for random vertical shifts. - - __shear_range__: Float. Shear Intensity (Shear angle in counter-clockwise direction as radians) - - __zoom_range__: Float or [lower, upper]. Range for random zoom. If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`. - - __channel_shift_range__: Float. Range for random channel shifts. - - __fill_mode__: One of {"constant", "nearest", "reflect" or "wrap"}. Points outside the boundaries of the input are filled according to the given mode: - * "constant": `kkkkkkkk|abcd|kkkkkkkk` (`cval=k`) - * "nearest": `aaaaaaaa|abcd|dddddddd` - * "reflect": `abcddcba|abcd|dcbaabcd` - * "wrap": `abcdabcd|abcd|abcdabcd` - - __cval__: Float or Int. Value used for points outside the boundaries when `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 is applied, - otherwise we multiply the data by the value provided (before applying - any other transformation). - - __preprocessing_function__: function that will be implied on each input. - The function will run before any other modification on it. - The function should take one argument: - one image (Numpy tensor with rank 3), - and should output a Numpy tensor with the same shape. - - __data_format__: One of {"channels_first", "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". - -- __Methods__: - - __fit(x)__: Compute the internal data stats related to the data-dependent transformations, based on an array of sample data. - Only required if featurewise_center or featurewise_std_normalization or zca_whitening. - - __Arguments__: - - __x__: sample data. Should have rank 4. - In case of grayscale data, - the channels axis should have value 1, and in case - of RGB data, it should have value 3. - - __augment__: Boolean (default: False). Whether to fit on randomly augmented samples. - - __rounds__: int (default: 1). If augment, how many augmentation passes over the data to use. - - __seed__: int (default: None). Random seed. - - __flow(x, y)__: Takes numpy data & label arrays, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop. - - __Arguments__: - - __x__: data. Should have rank 4. - In case of grayscale data, - the channels axis should have value 1, and in case - of RGB data, it should have value 3. - - __y__: labels. - - __batch_size__: int (default: 32). - - __shuffle__: boolean (default: True). - - __seed__: int (default: None). - - __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 (default: `''`). Prefix to use for filenames of saved pictures (only relevant if `save_to_dir` is set). - - __save_format__: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "png". - - __yields__: Tuples of `(x, y)` where `x` is a numpy array of image data and `y` is a numpy array of corresponding labels. - The generator loops indefinitely. - - __flow_from_directory(directory)__: Takes the path to a directory, and generates batches of augmented/normalized data. Yields batches indefinitely, in an infinite loop. - - __Arguments__: - - __directory__: 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)`, default: `(256, 256)`. - The dimensions to which all images found will be resized. - - __color_mode__: one of "grayscale", "rbg". Default: "rgb". Whether the images will be converted to have 1 or 3 color 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 `model.predict_generator()`, `model.evaluate_generator()`, etc.). 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) - - __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" (only relevant if `save_to_dir` is set). Default: "png". - - __follow_links__: whether to follow symlinks inside class subdirectories (default: False). - - -- __Examples__: - -Example of using `.flow(x, y)`: - -```python -(x_train, y_train), (x_test, y_test) = cifar10.load_data() -y_train = np_utils.to_categorical(y_train, num_classes) -y_test = np_utils.to_categorical(y_test, num_classes) - -datagen = ImageDataGenerator( - featurewise_center=True, - featurewise_std_normalization=True, - rotation_range=20, - width_shift_range=0.2, - height_shift_range=0.2, - horizontal_flip=True) - -# compute quantities required for featurewise normalization -# (std, mean, and principal components if ZCA whitening is applied) -datagen.fit(x_train) - -# fits the model on batches with real-time data augmentation: -model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), - steps_per_epoch=len(x_train) / 32, epochs=epochs) - -# here's a more "manual" example -for e in range(epochs): - print('Epoch', e) - batches = 0 - for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32): - model.fit(x_batch, y_batch) - batches += 1 - if batches >= len(x_train) / 32: - # we need to break the loop by hand because - # the generator loops indefinitely - break -``` - -Example of using `.flow_from_directory(directory)`: - -```python -train_datagen = ImageDataGenerator( - rescale=1./255, - shear_range=0.2, - zoom_range=0.2, - horizontal_flip=True) - -test_datagen = ImageDataGenerator(rescale=1./255) - -train_generator = train_datagen.flow_from_directory( - 'data/train', - target_size=(150, 150), - batch_size=32, - class_mode='binary') - -validation_generator = test_datagen.flow_from_directory( - 'data/validation', - target_size=(150, 150), - batch_size=32, - class_mode='binary') - -model.fit_generator( - train_generator, - steps_per_epoch=2000, - epochs=50, - validation_data=validation_generator, - validation_steps=800) -``` - -Example of transforming images and masks together. - -```python -# we create two instances with the same arguments -data_gen_args = dict(featurewise_center=True, - featurewise_std_normalization=True, - rotation_range=90., - width_shift_range=0.1, - height_shift_range=0.1, - zoom_range=0.2) -image_datagen = ImageDataGenerator(**data_gen_args) -mask_datagen = ImageDataGenerator(**data_gen_args) - -# Provide the same seed and keyword arguments to the fit and flow methods -seed = 1 -image_datagen.fit(images, augment=True, seed=seed) -mask_datagen.fit(masks, augment=True, seed=seed) - -image_generator = image_datagen.flow_from_directory( - 'data/images', - class_mode=None, - seed=seed) - -mask_generator = mask_datagen.flow_from_directory( - 'data/masks', - class_mode=None, - seed=seed) - -# combine generators into one which yields image and masks -train_generator = zip(image_generator, mask_generator) - -model.fit_generator( - train_generator, - steps_per_epoch=2000, - epochs=50) -``` +{{autogenerated}} diff --git a/keras/preprocessing/__init__.py b/keras/preprocessing/__init__.py index e69de29bb2d..9127e40f777 100644 --- a/keras/preprocessing/__init__.py +++ b/keras/preprocessing/__init__.py @@ -0,0 +1,3 @@ +from . import image +from . import sequence +from . import text diff --git a/keras/preprocessing/image.py b/keras/preprocessing/image.py index 97d97792d42..eb35000d9f3 100644 --- a/keras/preprocessing/image.py +++ b/keras/preprocessing/image.py @@ -176,6 +176,17 @@ def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0, def random_channel_shift(x, intensity, channel_axis=0): + """Perform a random channel shift. + + # Arguments + x: Input tensor. Must be 3D. + intensity: Transformation intensity. + channel_axis: Index of axis for channels in the input tensor. + + # Returns + Numpy image tensor. + + """ x = np.rollaxis(x, channel_axis, 0) min_x, max_x = np.min(x), np.max(x) channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x) @@ -186,6 +197,20 @@ def random_channel_shift(x, intensity, channel_axis=0): def random_brightness(x, brightness_range): + """Perform a random brightness shift. + + # Arguments + x: Input tensor. Must be 3D. + brightness_range: Tuple of floats; brightness range. + channel_axis: Index of axis for channels in the input tensor. + + # Returns + Numpy image tensor. + + # Raises + ValueError if `brightness_range` isn't a tuple. + + """ if len(brightness_range) != 2: raise ValueError('`brightness_range should be tuple or list of two floats. ' 'Received arg: ', brightness_range) @@ -387,51 +412,150 @@ def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'): class ImageDataGenerator(object): - """Generate minibatches of image data with real-time data augmentation. + """Generate batches of tensor image data with real-time data augmentation. + The data will be looped over (in batches). # Arguments - featurewise_center: set input mean to 0 over the dataset. - samplewise_center: set each sample mean to 0. - featurewise_std_normalization: divide inputs by std of the dataset. - samplewise_std_normalization: divide each input by its std. - zca_whitening: apply ZCA whitening. + featurewise_center: Boolean. Set input mean to 0 over the dataset, feature-wise. + samplewise_center: Boolean. Set each sample mean to 0. + featurewise_std_normalization: Boolean. Divide inputs by std of the dataset, feature-wise. + samplewise_std_normalization: Boolean. Divide each input by its std. zca_epsilon: epsilon for ZCA whitening. Default is 1e-6. - rotation_range: degrees (0 to 180). - width_shift_range: fraction of total width, if < 1, or pixels if >= 1. - height_shift_range: fraction of total height, if < 1, or pixels if >= 1. - brightness_range: the range of brightness to apply - shear_range: shear intensity (shear angle in degrees). - zoom_range: amount of zoom. if scalar z, zoom will be randomly picked - in the range [1-z, 1+z]. A sequence of two can be passed instead - to select this range. - channel_shift_range: shift range for each channel. - fill_mode: points outside the boundaries are filled according to the - given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default - is 'nearest'. - Points outside the boundaries of the input are filled according to the given mode: - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k) - 'nearest': aaaaaaaa|abcd|dddddddd - 'reflect': abcddcba|abcd|dcbaabcd - 'wrap': abcdabcd|abcd|abcdabcd - cval: value used for points outside the boundaries when fill_mode is - 'constant'. Default is 0. - horizontal_flip: whether to randomly flip images horizontally. - vertical_flip: whether to randomly flip images vertically. - rescale: rescaling factor. If None or 0, no rescaling is applied, - otherwise we multiply the data by the value provided. This is - applied after the `preprocessing_function` (if any provided) - but before any other transformation. + zca_whitening: Boolean. Apply ZCA whitening. + rotation_range: Int. Degree range for random rotations. + width_shift_range: Float (fraction of total width). Range for random horizontal shifts. + height_shift_range: Float (fraction of total height). Range for random vertical shifts. + shear_range: Float. Shear Intensity (Shear angle in counter-clockwise direction as radians) + zoom_range: Float or [lower, upper]. Range for random zoom. If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`. + channel_shift_range: Float. Range for random channel shifts. + fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}. Default is 'nearest'. + Points outside the boundaries of the input are filled according to the given mode: + 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k) + 'nearest': aaaaaaaa|abcd|dddddddd + 'reflect': abcddcba|abcd|dcbaabcd + 'wrap': abcdabcd|abcd|abcdabcd + cval: Float or Int. Value used for points outside the boundaries when `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 is applied, + otherwise we multiply the data by the value provided (before applying + any other transformation). preprocessing_function: function that will be implied on each input. - The function will run before any other modification on it. - The function should take one argument: - one image (Numpy tensor with rank 3), - and should output a Numpy tensor with the same shape. - data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension - (the depth) is at index 1, in 'channels_last' mode it is at index 3. + The function will run after the image is resized and augmented. + The function should take one argument: + one image (Numpy tensor with rank 3), + and should output a Numpy tensor with the same shape. + data_format: One of {"channels_first", "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". - validation_split: fraction of images reserved for validation (strictly between 0 and 1). + validation_split: Float. Fraction of images reserved for validation (strictly between 0 and 1). + + # Examples + Example of using `.flow(x, y)`: + + ```python + (x_train, y_train), (x_test, y_test) = cifar10.load_data() + y_train = np_utils.to_categorical(y_train, num_classes) + y_test = np_utils.to_categorical(y_test, num_classes) + + datagen = ImageDataGenerator( + featurewise_center=True, + featurewise_std_normalization=True, + rotation_range=20, + width_shift_range=0.2, + height_shift_range=0.2, + horizontal_flip=True) + + # compute quantities required for featurewise normalization + # (std, mean, and principal components if ZCA whitening is applied) + datagen.fit(x_train) + + # fits the model on batches with real-time data augmentation: + model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), + steps_per_epoch=len(x_train) / 32, epochs=epochs) + + # here's a more "manual" example + for e in range(epochs): + print('Epoch', e) + batches = 0 + for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32): + model.fit(x_batch, y_batch) + batches += 1 + if batches >= len(x_train) / 32: + # we need to break the loop by hand because + # the generator loops indefinitely + break + ``` + Example of using `.flow_from_directory(directory)`: + + ```python + train_datagen = ImageDataGenerator( + rescale=1./255, + shear_range=0.2, + zoom_range=0.2, + horizontal_flip=True) + + test_datagen = ImageDataGenerator(rescale=1./255) + + train_generator = train_datagen.flow_from_directory( + 'data/train', + target_size=(150, 150), + batch_size=32, + class_mode='binary') + + validation_generator = test_datagen.flow_from_directory( + 'data/validation', + target_size=(150, 150), + batch_size=32, + class_mode='binary') + + model.fit_generator( + train_generator, + steps_per_epoch=2000, + epochs=50, + validation_data=validation_generator, + validation_steps=800) + ``` + + Example of transforming images and masks together. + + ```python + # we create two instances with the same arguments + data_gen_args = dict(featurewise_center=True, + featurewise_std_normalization=True, + rotation_range=90., + width_shift_range=0.1, + height_shift_range=0.1, + zoom_range=0.2) + image_datagen = ImageDataGenerator(**data_gen_args) + mask_datagen = ImageDataGenerator(**data_gen_args) + + # Provide the same seed and keyword arguments to the fit and flow methods + seed = 1 + image_datagen.fit(images, augment=True, seed=seed) + mask_datagen.fit(masks, augment=True, seed=seed) + + image_generator = image_datagen.flow_from_directory( + 'data/images', + class_mode=None, + seed=seed) + + mask_generator = mask_datagen.flow_from_directory( + 'data/masks', + class_mode=None, + seed=seed) + + # combine generators into one which yields image and masks + train_generator = zip(image_generator, mask_generator) + + model.fit_generator( + train_generator, + steps_per_epoch=2000, + epochs=50) + ``` """ def __init__(self, @@ -537,6 +661,29 @@ def __init__(self, def flow(self, x, y=None, batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None): + """Takes numpy data & label arrays, and generates batches of + augmented/normalized data. + + # Arguments + x: data. Should have rank 4. + In case of grayscale data, + the channels axis should have value 1, and in case + of RGB data, it should have value 3. + y: labels. + batch_size: int (default: 32). + shuffle: boolean (default: True). + seed: int (default: None). + 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 (default: `''`). Prefix to use for filenames of saved pictures + (only relevant if `save_to_dir` is set). + save_format: one of "png", "jpeg" (only relevant if `save_to_dir` is set). Default: "png". + + # Returns + An Iterator yielding tuples of `(x, y)` where `x` is a numpy array of image data and + `y` is a numpy array of corresponding labels.""" return NumpyArrayIterator( x, y, self, batch_size=batch_size, @@ -558,6 +705,45 @@ def flow_from_directory(self, directory, follow_links=False, subset=None, interpolation='nearest'): + """Takes the path to a directory, and generates batches of augmented/normalized data. + + # Arguments + directory: 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)`, default: `(256, 256)`. + The dimensions to which all images found will be resized. + color_mode: one of "grayscale", "rbg". Default: "rgb". + Whether the images will be converted to have 1 or 3 color 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 + `model.predict_generator()`, `model.evaluate_generator()`, etc.). + 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) + 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" (only relevant if `save_to_dir` is set). Default: "png". + follow_links: whether to follow symlinks inside class subdirectories (default: False). + + # Returns + A DirectoryIterator yielding tuples of `(x, y)` where `x` is a numpy array of image data and + `y` is a numpy array of corresponding labels. + """ return DirectoryIterator( directory, self, target_size=target_size, color_mode=color_mode, @@ -718,24 +904,18 @@ def fit(self, x, augment=False, rounds=1, seed=None): - """Fits internal statistics to some sample data. - - Required for featurewise_center, featurewise_std_normalization - and zca_whitening. + """Compute the internal data stats related to the data-dependent transformations, based on an array of sample data. + Only required if featurewise_center or featurewise_std_normalization or zca_whitening. # Arguments - x: Numpy array, the data to fit on. Should have rank 4. - In case of grayscale data, - the channels axis should have value 1, and in case - of RGB data, it should have value 3. - augment: Whether to fit on randomly augmented samples - rounds: If `augment`, - how many augmentation passes to do over the data - seed: random seed. - - # Raises - ValueError: in case of invalid input `x`. - """ + x: sample data. Should have rank 4. + In case of grayscale data, + the channels axis should have value 1, and in case + of RGB data, it should have value 3. + augment: Boolean (default: False). Whether to fit on randomly augmented samples. + rounds: int (default: 1). If augment, how many augmentation passes over the data to use. + seed: int (default: None). Random seed. + """ x = np.asarray(x, dtype=K.floatx()) if x.ndim != 4: raise ValueError('Input to `.fit()` should have rank 4. ' diff --git a/tests/test_documentation.py b/tests/test_documentation.py index d5894a56809..1e1a56d46f3 100644 --- a/tests/test_documentation.py +++ b/tests/test_documentation.py @@ -6,7 +6,7 @@ import pytest -modules = ['keras.layers', 'keras.models', 'keras', 'keras.backend.tensorflow_backend'] +modules = ['keras.layers', 'keras.models', 'keras', 'keras.backend.tensorflow_backend', 'keras.preprocessing.image'] accepted_name = ['from_config'] accepted_module = ['keras.legacy.layers', 'keras.utils.generic_utils'] @@ -27,12 +27,14 @@ def handle_class(name, member): def handle_function(name, member): - if is_accepted(name, member): + if is_accepted(name, member) or member_too_small(member): + # We don't need to check this one. return doc = member.__doc__ - if doc is None and not member_too_small(member): + if doc is None: raise ValueError("{} function doesn't have any documentation".format(name), member.__module__, inspect.getmodule(member).__file__) + args = list(inspect.signature(member).parameters.keys()) assert_args_presence(args, doc, member, name) assert_function_style(name, member, doc, args)