diff --git a/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.AaLearner.rst b/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.AaLearner.rst
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+:mod:`autoaug.autoaugment_learners`.AaLearner
+==============================================
+
+.. currentmodule:: autoaug.autoaugment_learners
+
+.. autoclass:: AaLearner
+ :members:
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diff --git a/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.EvoLearner.rst b/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.EvoLearner.rst
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+:mod:`autoaug.autoaugment_learners`.EvoLearner
+==============================================
+
+.. currentmodule:: autoaug.autoaugment_learners
+
+.. autoclass:: EvoLearner
+ :members:
\ No newline at end of file
diff --git a/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.GruLearner.rst b/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.GruLearner.rst
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+:mod:`autoaug.autoaugment_learners`.GruLearner
+==============================================
+
+.. currentmodule:: autoaug.autoaugment_learners
+
+.. autoclass:: GruLearner
+ :members:
\ No newline at end of file
diff --git a/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.RsLearner.rst b/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.RsLearner.rst
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+:mod:`autoaug.autoaugment_learners`.RsLearner
+==============================================
+
+.. currentmodule:: autoaug.autoaugment_learners
+
+.. autoclass:: RsLearner
+ :members:
\ No newline at end of file
diff --git a/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.UcbLearner.rst b/docs/source/explanations/aa_learners/autoaug.autoaugment_learners.UcbLearner.rst
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+:mod:`autoaug.autoaugment_learners`.UcbLearner
+==============================================
+
+.. currentmodule:: autoaug.autoaugment_learners
+
+.. autoclass:: UcbLearner
+ :members:
\ No newline at end of file
diff --git a/docs/source/explanations/autoaugment_learners.rst b/docs/source/explanations/autoaugment_learners.rst
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+AutoAugment learners
+--------------------
+
+.. toctree::
+ :maxdepth: 3
+ :caption: autoaugment_learners
+
+ aa_learners/autoaug.autoaugment_learners.AaLearner
+ aa_learners/autoaug.autoaugment_learners.EvoLearner
+ aa_learners/autoaug.autoaugment_learners.GruLearner
+ aa_learners/autoaug.autoaugment_learners.RsLearner
+ aa_learners/autoaug.autoaugment_learners.UcbLearner
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diff --git a/docs/source/howto/AaLearner.rst b/docs/source/howto/AaLearner.rst
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+AaLearner object and its children
+------------------------------------------------------------------------------------------------
+
+This is a page dedicated to demonstrating functionalities of :class:`AaLearner`.
+
+This is a how-to guide (in the sense describe in https://documentation.divio.com/structure/).
+
+######################################################################################################
+How to use the ``AaLearner`` class to find an optimal policy for a dataset-child_network pair
+######################################################################################################
+
+This section can also be read as a ``.py`` file in ``./tutorials/how_use_aalearner.py``.
+
+
+.. code-block::
+
+ import autoaug.autoaugment_learners as aal
+ import autoaug.child_networks as cn
+
+ import torchvision.datasets as datasets
+ import torchvision
+
+
+
+Defining the problem setting:
+
+.. code-block::
+
+ train_dataset = datasets.MNIST(root='./autoaug/datasets/mnist/train',
+ train=True, download=True, transform=None)
+ test_dataset = datasets.MNIST(root='./autoaug/datasets/mnist/test',
+ train=False, download=True, transform=torchvision.transforms.ToTensor())
+ child_network_architecture = cn.lenet
+
+
+.. warning::
+
+ In earlier versions, we had to write ``child_network_architecture=cn.LeNet``
+ and not ``child_network_architecture=cn.LeNet()``. But now we can do both.
+ Both types of objects can be input into ``AaLearner.learn()``.
+
+ More precisely, the ``child_network_architecture`` parameter has to be either
+ as ``nn.Module``, a ``function`` which returns a ``nn.Module``, or a ``type``
+ which inherits ``nn.Module``.
+
+ A downside (or maybe the upside??) of doing one of the latter two is that
+ the same randomly initialized weights are used for every policy.
+
+Using the random search learner to evaluate randomly generated policies: (You
+can use any other learner in place of random search learner as well)
+
+.. code-block::
+
+ # aa_agent = aal.GruLearner()
+ # aa_agent = aal.EvoLearner()
+ # aa_agent = aal.UcbLearner()
+ # aa_agent = aal.ac_learner()
+ aa_agent = aal.RsLearner(
+ sp_num=7,
+ toy_size=0.01,
+ batch_size=4,
+ learning_rate=0.05,
+ max_epochs=float('inf'),
+ early_stop_num=35,
+ )
+ aa_agent.learn(train_dataset,
+ test_dataset,
+ child_network_architecture=child_network_architecture,
+ iterations=15000)
+
+You can set further hyperparameters when defining a AaLearner.
+
+Also, depending on what learner you are using, there might be unique hyperparameters.
+For example, in the GRU learner you can tune the exploration parameter ``alpha``.
+
+Viewing the results:
+
+``.history`` is a list containing all the policies tested and the respective
+accuracies obtained when trained using them.
+
+.. code-block::
+
+ print(aa_agent.history)
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diff --git a/docs/source/howto/AutoAugment.rst b/docs/source/howto/AutoAugment.rst
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+++ b/docs/source/howto/AutoAugment.rst
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+AutoAugment object
+------------------
+
+######################################################################################################
+How to use a ``AutoAugment`` object to apply AutoAugment policies to ``Datasets`` objects
+######################################################################################################
+
+This is a page dedicated to demonstrating functionalities of :class:`AutoAugment`, which
+we use as a helper class to help us apply AutoAugment policies to datasets.
+
+This is a tutorial (in the sense describe in https://documentation.divio.com/structure/).
+
+For an example of how the material is used in our library, see the source code of
+:meth:`AaLearner._test_autoaugment_policy <autoaug.autoaugment_learners.AaLearner>`.
+
+Let's say we have a policy within the search space specified by the original
+AutoAugment paper:
+
+.. code-block::
+
+ my_policy = [
+ (("Invert", 0.8, None), ("Contrast", 0.2, 6)),
+ (("Rotate", 0.7, 2), ("Invert", 0.8, None)),
+ (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
+ (("ShearY", 0.5, 8), ("Invert", 0.7, None)),
+ (("AutoContrast", 0.5, None), ("Equalize", 0.9, None))
+ ]
+
+And that we also have a dataset that we want to apply this policy to:
+
+.. code-block::
+
+ train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True)
+ test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False,
+ transform=torchvision.transforms.ToTensor())
+
+The ``train_dataset`` object will have an attribute ``.transform`` with the
+default value ``None``.
+The ``.transform`` attribute takes a function which takes an image as an input
+and returns a transformed image.
+
+We need a function which will apply the ``my_policy`` and we use
+an ``AutoAugment`` for this job.
+
+.. code-block::
+ :caption: Creating an ``AutoAugment`` object and imbueing it with ``my_policy``.
+
+ aa_transform = AutoAugment()
+ aa_transform.subpolicies = my_policy
+ train_transform = transforms.Compose([
+ aa_transform,
+ transforms.ToTensor()
+ ])
+
+We can use ``train_transform`` as an image function:
+
+.. code-block::
+ :caption: This function call will return an augmented image
+
+ augmented_image = train_transform(original_image)
+
+We usually apply an image function to a ``Dataset`` like this:
+
+.. code-block::
+
+ train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, transform=my_function)
+
+However, in our library we often have to apply a image function *after* the ``Dataset``
+object was already created. (For example, a ``Dataset`` object is created and trained on
+multiple times using different policies).
+In this case, we alter the ``.transform`` attribute:
+
+.. code-block::
+
+ train_dataset.transform = train_transform
+
+Now if we can create a ``DataLoader`` object from ``train_dataset``, it will automatically
+apply ``my_policy``.
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diff --git a/docs/source/howto/howto_main.rst b/docs/source/howto/howto_main.rst
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+How-to Guides for accomplishing common goals
+--------------------------------------------
+
+.. toctree::
+ :maxdepth: 2
+ :caption: How-to Guides:
+
+ installation
+ AaLearner
+ AutoAugment
diff --git a/docs/source/howto/installation.rst b/docs/source/howto/installation.rst
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+Installing ``autoaug``
+----------------------
+
+As simple as
+
+.. code-block::
+
+ pip install auto-augmentation
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