Building the Keras projects docs.
Project description
keras-autodoc
keras-autodoc will fetch the docstrings from the functions you wish to document and will insert them in the markdown files.
Take a look at the documentation!
Install
pip install keras-autodoc
We recommend pinning the version (eg: pip install keras-autodoc==0.3.2
). We may break compatibility without any warning.
Example
Let's suppose that you have a docs
directory:
./docs
|-- autogen.py
|-- mkdocs.yml
The API is quite simple:
# content of docs/autogen.py from keras_autodoc import DocumentationGenerator pages = {'layers/core.md': ['keras.layers.Dense', 'keras.layers.Flatten'], 'callbacks.md': ['keras.callbacks.TensorBoard']} doc_generator = DocumentationGenerator(pages) doc_generator.generate('./sources')
# content of docs/mkdocs.yml site_name: My_site docs_dir: sources site_description: 'My pretty site.' nav: - Core: layers/core.md - Callbacks: - Some callbacks: callbacks.md
Then you just have to run:
python autogen.py mkdocs serve
and you'll be able to see your website at localhost:8000/callbacks.
Docstring format:
The docstrings used should use the The docstrings follow the Google Python Style Guide with markdown, or just plain markdown.
For example, let's take this class:
class ImageDataGenerator: """Generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches). # Arguments featurewise_center: Boolean. Set input mean to 0 over the dataset, feature-wise. zca_whitening: Boolean. Apply ZCA whitening. width_shift_range: Float, 1-D array-like or int - float: fraction of total width, if < 1, or pixels if >= 1. - 1-D array-like: random elements from the array. - int: integer number of pixels from interval `(-width_shift_range, +width_shift_range)` - With `width_shift_range=2` possible values are integers `[-1, 0, +1]`, same as with `width_shift_range=[-1, 0, +1]`, while with `width_shift_range=1.0` possible values are floats in the interval `[-1.0, +1.0)`. # Examples Example of using `.flow(x, y)`: ```python datagen = ImageDataGenerator( featurewise_center=True, zca_whitening=True, width_shift_range=0.2) # 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) ``` """ def __init__(self,featurewise_center, zca_whitening, width_shift_range): pass
will be rendered as:
ImageDataGenerator class:
dummy_module.ImageDataGenerator(featurewise_center, zca_whitening, width_shift_range=0.0)
Generate batches of tensor image data with real-time data augmentation.
The data will be looped over (in batches).
Arguments
- featurewise_center: Boolean. Set input mean to 0 over the dataset, feature-wise.
- zca_whitening: Boolean. Apply ZCA whitening.
- width_shift_range: Float, 1-D array-like or int
- float: fraction of total width, if < 1, or pixels if >= 1.
- 1-D array-like: random elements from the array.
- int: integer number of pixels from interval
(-width_shift_range, +width_shift_range)
- With
width_shift_range=2
possible values are integers[-1, 0, +1]
, same as withwidth_shift_range=[-1, 0, +1]
, while withwidth_shift_range=1.0
possible values are floats in the interval[-1.0, +1.0)
.
Examples
Example of using .flow(x, y)
:
datagen = ImageDataGenerator( featurewise_center=True, zca_whitening=True, width_shift_range=0.2) # 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)
Take a look at our docs
If you want examples, you can take a look at the docs directory of autokeras as well as the generated docs.
You can also look at the docs directory of keras-tuner.
Project details
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