add MNIST examples thanks to @PhoenixDai and links for image completi…

…on code of @bamos

README.md

@@ -1,44 +1,44 @@
-DCGAN in Tensorflow
-====================
+# DCGAN in Tensorflow
 
 Tensorflow implementation of [Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434) which is a stabilize Generative Adversarial Networks. The referenced torch code can be found [here](https://github.com/soumith/dcgan.torch).
 
 ![alt tag](DCGAN.png)
 
-*To avoid the fast convergence of D (discriminator) network, G (generatior) network is updatesd twice for each D network update which is a different from original paper.*
+* [Brandon Amos](http://bamos.github.io/) wrote an excellent [blog post](http://bamos.github.io/2016/08/09/deep-completion/) and [image completion code](https://github.com/bamos/dcgan-completion.tensorflow) based on this repo.
+* *To avoid the fast convergence of D (discriminator) network, G (generatior) network is updatesd twice for each D network update which is a different from original paper.*
 
 
-Online Demo
------------
+## Online Demo
 
 [<img src="https://raw.githubusercontent.com/carpedm20/blog/master/content/images/face.png">](http://carpedm20.github.io/faces/)
 
 [link](http://carpedm20.github.io/faces/)
 
 
-Prerequisites
--------------
+## Prerequisites
 
 - Python 2.7 or Python 3.3+
 - [Tensorflow](https://www.tensorflow.org/)
 - [SciPy](http://www.scipy.org/install.html)
 - (Optional) [Align&Cropped Images.zip](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) : Large-scale CelebFaces Dataset
 
 
-Usage
------
+## Usage
 
 First, download dataset with:
 
     $ mkdir data
+    $ python download.py --datasets mnist
     $ python download.py --datasets celebA
 
-To train a model with celebA dataset:
+To train a model with downloaded dataset:
 
+    $ python main.py --dataset mnist --is_train True
     $ python main.py --dataset celebA --is_train True --is_crop True
 
 To test with an existing model:
 
+    $ python main.py --dataset mnist
     $ python main.py --dataset celebA --is_crop True
 
 Or, you can use your own dataset (without central crop) by:
@@ -49,10 +49,11 @@ Or, you can use your own dataset (without central crop) by:
     $ python main.py --dataset DATASET_NAME
 
 
-Results
--------
+## Results
 
-![result](https://media.giphy.com/media/l3nW2iYprSsXtagYo/giphy.gif)
+![result](assets/training.gif)
+
+### celebA
 
 After 6th epoch:
 
@@ -62,19 +63,28 @@ After 10th epoch:
 
 ![result4](assets/test_2016-01-27 15:08:54.png)
 
-With asian face dataset (with high noises):
+### Asian face dataset
 
 ![custom_result1](web/img/change5.png)
 
 ![custom_result1](web/img/change2.png)
 
 ![custom_result2](web/img/change4.png)
 
+### MNIST
+
+MNIST codes are written by [@PhoenixDai](https://github.com/PhoenixDai).
+
+![mnist_result1](assets/mnist1.png)
+
+![mnist_result2](assets/mnist2.png)
+
+![mnist_result3](assets/mnist3.png)
+
 More results can be found [here](./assets/) and [here](./web/img/).
 
 
-Training details
-----------------
+## Training details
 
 Details of the loss of Discriminator and Generator (with custom dataset not celebA).
 
@@ -89,7 +99,6 @@ Details of the histogram of true and fake result of discriminator (with custom d
 ![d__hist](assets/d__hist.png)
 
 
-Author
-------
+## Author
 
 Taehoon Kim / [@carpedm20](http://carpedm20.github.io/)