diff --git a/demo/gan/.gitignore b/demo/gan/.gitignore
index e9a33070cf40d81ec4391d63660973da040097c9..93a6f5080a16a601cffb0bff51af9aef3ba3bae7 100644
--- a/demo/gan/.gitignore
+++ b/demo/gan/.gitignore
@@ -1,4 +1,7 @@
 output/
+uniform_params/
+cifar_params/
+mnist_params/
 *.png
 .pydevproject
 .project
diff --git a/demo/gan/gan_conf.py b/demo/gan/gan_conf.py
index 6bd68727ba5321c0ac6fe896dc1f48e138632d69..4f57c80b77921c6d2df7bb50a90024f9356ad623 100644
--- a/demo/gan/gan_conf.py
+++ b/demo/gan/gan_conf.py
@@ -24,6 +24,9 @@ is_discriminator_training = mode == "discriminator_training"
 is_generator = mode == "generator"
 is_discriminator = mode == "discriminator"
 
+# The network structure below follows the ref https://arxiv.org/abs/1406.2661
+# Here we used two hidden layers and batch_norm
+
 print('mode=%s' % mode)
 # the dim of the noise (z) as the input of the generator network
 noise_dim = 10 
diff --git a/demo/gan/gan_trainer.py b/demo/gan/gan_trainer.py
index 063a98acdcf1ae9f31d5b1c8202dbc66fcf09540..09d223fba8c70beb5309ed6c7480d61f904e8bcb 100644
--- a/demo/gan/gan_trainer.py
+++ b/demo/gan/gan_trainer.py
@@ -90,10 +90,8 @@ def load_mnist_data(imageFile):
     data = numpy.zeros((n, 28*28), dtype = "float32")
     
     for i in range(n):
-        pixels = []
-        for j in range(28 * 28):
-            pixels.append(float(ord(f.read(1))) / 255.0 * 2.0 - 1.0)
-        data[i, :] = pixels
+        pixels = numpy.fromfile(f, 'ubyte', count=28*28)
+        data[i, :] = pixels / 255.0 * 2.0 - 1.0
 
     f.close()
     return data
@@ -129,7 +127,7 @@ def merge(images, size):
           ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
     return img.astype('uint8')
 
-def saveImages(images, path):
+def save_images(images, path):
     merged_img = merge(images, [8, 8])
     if merged_img.shape[2] == 1:
         im = Image.fromarray(numpy.squeeze(merged_img)).convert('RGB')
@@ -207,9 +205,15 @@ def main():
     useGpu = args.useGpu
     assert dataSource in ["mnist", "cifar", "uniform"]
     assert useGpu in ["0", "1"]
-            
+
+    if not os.path.exists("./%s_samples/" % dataSource):
+        os.makedirs("./%s_samples/" % dataSource)
+
+    if not os.path.exists("./%s_params/" % dataSource):
+        os.makedirs("./%s_params/" % dataSource)
+        
     api.initPaddle('--use_gpu=' + useGpu, '--dot_period=10', '--log_period=100', 
-                   '--gpu_id=' + args.gpuId)
+                   '--gpu_id=' + args.gpuId, '--save_dir=' + "./%s_params/" % dataSource)
     
     if dataSource == "uniform":
         conf = "gan_conf.py"
@@ -231,9 +235,6 @@ def main():
     else:
         data_np = load_uniform_data()
     
-    if not os.path.exists("./%s_samples/" % dataSource):
-        os.makedirs("./%s_samples/" % dataSource)
-    
     # this create a gradient machine for discriminator
     dis_training_machine = api.GradientMachine.createFromConfigProto(
         dis_conf.model_config)
@@ -321,7 +322,7 @@ def main():
         if dataSource == "uniform":
             plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (dataSource, train_pass))
         else:
-            saveImages(fake_samples, "./%s_samples/train_pass%s.png" % (dataSource, train_pass))
+            save_images(fake_samples, "./%s_samples/train_pass%s.png" % (dataSource, train_pass))
     dis_trainer.finishTrain()
     gen_trainer.finishTrain()