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!
pip install keras-autodocWe recommend pinning the version (eg: pip install keras-autodoc==0.3.2). We may break compatibility without any warning.
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.mdThen you just have to run:
python autogen.py
mkdocs serveand you'll be able to see your website at localhost:8000/callbacks.
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):
passwill be rendered as:
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=2possible values are integers[-1, 0, +1], same as withwidth_shift_range=[-1, 0, +1], while withwidth_shift_range=1.0possible 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)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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent