Metadata-Version: 2.1
Name: keras-autodoc
Version: 0.5.0
Summary: Building the Keras projects docs.
Home-page: https://github.com/keras-team/keras-autodoc
Author: The Keras team
Author-email: gabrieldemarmiesse@gmail.com
License: Apache License 2.0
Description: # keras-autodoc
        
        ![](https://https://github.com/keras-team/keras-autodoc/workflows/.github/workflows/dockerimage.yml/badge.svg?branch=master)
        
        
        [Autodoc](http://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html) for [mkdocs](https://www.mkdocs.org/).
        
        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](https://gabrieldemarmiesse.github.io/keras-autodoc/)!
        
        ### Install
        
        ```bash
        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:
        
        ```python
        # 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')
        ```
        
        ```yaml
        # 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:
        
        ```bash
        python autogen.py
        mkdocs serve
        ```
        
        and you'll be able to see your website at [localhost:8000/callbacks](http://localhost:8000/callbacks/).
        
        ### Docstring format:
        
        The docstrings used should use the The docstrings follow the [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#381-docstrings) with markdown, or just plain markdown.
        
        For example, let's take this class:
        
        ```python
        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:
        
        ```python
        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 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)
        ```
        
        ### Take a look at our docs
        
        If you want examples, you can take a look at [the docs directory of autokeras](https://github.com/keras-team/autokeras/tree/master/docs) as well as [the generated docs](https://autokeras.com/).
        
        You can also look at [the docs directory of keras-tuner](https://github.com/keras-team/keras-tuner/tree/master/docs).
        
Platform: UNKNOWN
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Utilities
Classifier: Topic :: Documentation
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
Provides-Extra: tests
