gan design new version

doc/design/gan_api.md

@@ -6,6 +6,11 @@ It contains several important machine learning concepts, including building and
 
 In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN as an example due to its good performance on image generation.
 
+<p align="center">
+<img src="./dcgan.png" width = "90%" align="center"/><br/>
+Borrow this photo from the original DC-GAN paper.
+</p>
+
 ## The Conditional-GAN might be a class. 
 This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure:
 
@@ -26,11 +31,6 @@ Returns a 0/1 binary label.
 ### build_model(self):
 build the whole GAN model, define training loss for both generator and discrimator.
 
-<p align="center">
-<img src="./dcgan.png" width = "90%" align="center"/><br/>
-Borrow this photo from the original DC-GAN paper.
-</p>
-
 ## Discussion on Engine Functions required to build GAN
 - Trace the ternsor and variable dependency in the engine executor. (Very critical, otherwise GAN can'be be trained correctly)
 - Different optimizers responsible for optimizing different loss.
@@ -151,6 +151,10 @@ def build_model(self):
 ```
 
 ## Main function for the demo:
+Generally, the user of GAN just need to the following things:
+- Define an object as DCGAN class;
+- Build the DCGAN model;
+- Specify two optimizers for two different losses with respect to different parameters.
 ```python
 # pd for short, should be more concise.
 from paddle.v2 as pd
@@ -158,7 +162,6 @@ import numpy as np
 import logging
 
 if __name__ == "__main__":
-
     # dcgan
     dcgan = DCGAN()
     dcgan.build_model()
@@ -167,8 +170,8 @@ if __name__ == "__main__":
     data_X, data_y = self.load_mnist()
     
     # Two subgraphs required!!!
-    d_optim = pd.train.Adam(lr = .001, beta= .1).minimize(self.d_loss, )
-    g_optim = pd.train.Adam(lr = .001, beta= .1).minimize(self.g_loss)
+    d_optim = pd.train.Adam(lr = .001, beta= .1).minimize(dcgan.d_loss, dcgan.theta_D)
+    g_optim = pd.train.Adam(lr = .001, beta= .1).minimize(dcgan.g_loss, dcgan.theta_G)
 
     # executor
     sess = pd.executor()
@@ -183,11 +186,11 @@ if __name__ == "__main__":
         batch_z = np.random.uniform(-1., 1., [batch_size, z_dim])
 
         if batch_id % 2 == 0:
-          sess.eval(d_optim, 
+          sess.run(d_optim, 
                    feed_dict = {dcgan.images: batch_im,
                                 dcgan.y: batch_label,
                                 dcgan.z: batch_z})
         else:
-          sess.eval(g_optim,
+          sess.run(g_optim,
                    feed_dict = {dcgan.z: batch_z})
 ```