Welcome to BlueBird's documentation!
====================================

.. image:: _static/bluebird.png
   :width: 60px
   :height: 60px
   :alt: logo
   :align: left

BlueBird is a small deep learning library.
It was developed to help me (the author) learn better about deep learning.

I thought that it was a cool project so I decided to share it with others.

Down bellow you will find installation instruction and a basic tutorial.

Note: This documentation was generated automatically so there may be some bugs and it's not easy to navigate.

Install Guide
=============

BlueBird can be downloaded with pip (make sure you set up virtual environment).
If you want to use the latest version, get it from `github <https://github.com/Stoick01/bluebird>`_.

``pip install bluebird-stoick01``


Quick Start
===========

Simple module looks like this:

.. code-block:: python

   from bluebird.nn import NeuralNet
   from bluebird.activations import Relu
   from bluebird.layers import Input, Linear

   # create the module
   net = NeuralNet([
      Input(10), # input layer
      Linear(50),  # hidden layer
      Relu(), # activation function
      Linear(10) # output layer
   ])

   # build the module
   net.build()

   # train your model
   net.fit(X_train, y_train, num_epochs=20)


Let's explain this a bit:
   #. Imports: From nn you need to import your model. We also import activation functions (Relu in this example)
      and layers (Input and Linear)
   #. Then we create the model. The model accepts a list of layers where the first layer has to be of the input type.
      After that you can put layers in any order. You have to define how many neurons each layer has, except the input,
      where you define number of inputs.
   #. After you have created your model, you can build, during this step you can define optimizer and loss functions,
      which you can find in ``bluebird.optimizer`` and ``bluebird.loss``
   #. Final step is to train your model. Where you pass it your data and define number of epochs.

Layers and Activations
----------------------

You can find all the layers and activations in ``bluebird.layers`` and ``bluebird.activations``.

Every layer and activation is explained in the mode detail on it's own page, but in general:
   * All models begin with input type layer
   * All other layers must have defined number of neurons, aka the output dimension, (input in handled during build faze of the module)
   * Activations only need to be initialized

Building the model
------------------

After creation each model must be built. This is a crucial step, where all the weights in layers are initialized and each layer is built.

You can also pass the loss function and optimizer which can be found in ``bluebird.loss`` and ``bluebird.optimizer``


Contribution Guide
==================

I don't expect anyone to contribute but just in case you want to, contact me, or open an issue on github before committing anything.

.. autosummary::
   :toctree: _autosummary
   :template: custom-module-template.rst
   :recursive:

   bluebird
   .. image:: _static/bluebird.png
   :width: 60px
   :height: 60px
   :alt: logo
   :align: left


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`