[doc] finished the doc for the DCGAN

doc/guide.rst → doc/guide_conditionalgan.rst

 .. py:currentmodule:: bob.learn.pytorch
 
-User's guide
-============
-
-DCGAN
------
-
 Conditional GAN
----------------
+===============
 
-DR-GAN
-------

doc/guide_dcgan.rst

+.. py:currentmodule:: bob.learn.pytorch
+
+DCGAN
+=====
+
+DCGAN stands for deeply convolutional GAN, and is described in the following paper [dcgan]_::
+
+
+  @InProceedings{radford-iclr-2016,
+    Author         = {Radford, A. and Metz, L. and Chintala, S.},
+    Title          = {Unsupervised {R}epresentation {L}earning with {D}eep
+                   {C}onvolutional {G}enerative {A}dversarial {N}etworks},
+    BookTitle      = {Intl {C}onf. on {L}earning {R}epresentation},
+    seq-number     = {50},
+    year           = 2016
+  }
+
+Also, most of the code (architecture and training) has been borrowed `here <https://github.com/pytorch/examples/tree/master/dcgan>`_.
+
+Here, the goal is to generate images of frontal faces. You can train a model 
+using the MULTI-PIE database just that by using the dedicated script::
+
+  $ ./bin/train_dcgan_multipie.py 
+
+If you have access to a GPU, you should run the script using this option::
+
+  $ ./bin/train_dcgan_multipie.py --use-gpu
+
+
+Note that this script will consider frontal faces only (but all illumination conditions).
+Generated samples will be saved after each training epoch. You can specify where to write
+images using the ``--output-dir`` option (default to ``./dcgan-multipie``).
+
+Here are some examples of saved images.
+
+.. figure:: img/dcgan-multipie-epoch-1.png
+   :align: center
+    
+   After 1 epoch
+
+.. figure:: img/dcgan-multipie-epoch-5.png
+   :align: center
+    
+   After 5 epoch
+
+.. figure:: img/dcgan-multipie-epoch-20.png
+   :align: center
+    
+   After 20 epoch

doc/guide_drgan.rst

+.. py:currentmodule:: bob.learn.pytorch
+
+DR-GAN
+======
+

doc/index.rst

@@ -43,15 +43,27 @@ This package is basically organized as follows (some files are omitted for clari
 + ``scripts`` contains the various scripts to perform training. 
 + ``trainers`` contains files implementing the different training procedure 
 
+
+In order to build the package, activate your bob environment, and do::
+
+  $ buildout 
+
 In the user's guide you will find more detailed instructions on how to run the various examples.
 
 .. toctree::
    :maxdepth: 3
 
-   guide
-
+   guide_dcgan
+   guide_conditionalgan
+   guide_drgan
    py_api
 
+
+References
+----------
+
+.. [dcgan]  *A.Radford, L. Metz, S. Chintala*. **Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks** Intl Conf. on Learning Representation, 2016. `arXiv <https://arxiv.org/abs/1511.06434>`__
+
 Licensing
 ---------