gan api

doc/design/gan_api.md

@@ -222,6 +222,10 @@ import logging
 
 if __name__ == "__main__":
     # dcgan class in the default graph/block
+    # if we use dependency engine as tensorflow
+    # the codes, will be slightly different like:
+    # dcgan = DCGAN()
+    # dcgan.build_model()
     with pd.block() as def_block:
       dcgan = DCGAN()
       dcgan.build_model(def_block)
@@ -230,8 +234,12 @@ if __name__ == "__main__":
     data_X, data_y = self.load_mnist()
     
     # Two subgraphs required!!!
-    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)
+    with pd.block().d_block():
+      d_optim = pd.train.Adam(lr = .001, beta= .1)
+      d_step = d_optim.minimize(dcgan.d_loss, dcgan.theta_D)
+    with pd.block.g_block():
+      g_optim = pd.train.Adam(lr = .001, beta= .1)
+      g_step = pd.minimize(dcgan.g_loss, dcgan.theta_G)
 
     # executor
     sess = pd.executor()
@@ -246,11 +254,11 @@ if __name__ == "__main__":
         batch_z = np.random.uniform(-1., 1., [batch_size, z_dim])
 
         if batch_id % 2 == 0:
-          sess.run(d_optim, 
+          sess.run(d_step, 
                    feed_dict = {dcgan.images: batch_im,
                                 dcgan.y: batch_label,
                                 dcgan.z: batch_z})
         else:
-          sess.run(g_optim,
+          sess.run(g_step,
                    feed_dict = {dcgan.z: batch_z})
 ```