Merge pull request #1 from ThomasDelteil/patch-33

Update info_gan.md

docs/tutorials/gluon/info_gan.md

@@ -88,10 +88,10 @@ split             = int(len(images)*0.8)
 test_images       = images[split:]
 test_filenames    = filenames[split:]
 train_images      = images[:split]
-train_filenames    = filenames[:split]
+train_filenames   = filenames[:split]
 
-train_data        = mx.gluon.data.ArrayDataset(nd.concatenate(train_images))
-train_dataloader  = mx.gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=4)
+train_data        = gluon.data.ArrayDataset(nd.concatenate(train_images))
+train_dataloader  = gluon.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, last_batch='rollover', num_workers=4)
 ```
 
 ## Generator
@@ -239,13 +239,13 @@ This function samples `c`, `z`, and concatenates them to create the generator in
 def create_generator_input():
 
     #create random noise
-    z      = mx.nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
+    z      = nd.random_normal(0, 1, shape=(batch_size, z_dim), ctx=ctx)
     label  = nd.array(np.random.randint(n_categories, size=batch_size)).as_in_context(ctx)
     c1     = nd.one_hot(label, depth=n_categories).as_in_context(ctx)
     c2     = nd.random.uniform(-1, 1, shape=(batch_size, n_continuous)).as_in_context(ctx)
 
     # concatenate random noise with c which will be the input of the generator
-    return mx.nd.concat(z, c1, c2, dim=1)
+    return nd.concat(z, c1, c2, dim=1)
 ```
 
 Define the training loop. 
@@ -265,8 +265,8 @@ with SummaryWriter(logdir='./logs/') as sw:
         print("Epoch", epoch)
         starttime = time.time()
 
-        d_error_epoch = mx.nd.zeros((1,), ctx=ctx)
-        g_error_epoch = mx.nd.zeros((1,), ctx=ctx)
+        d_error_epoch = nd.zeros((1,), ctx=ctx)
+        g_error_epoch = nd.zeros((1,), ctx=ctx)
 
         for idx, data in enumerate(train_dataloader):
             i = i + 1
@@ -372,11 +372,11 @@ Take some images from the test data, obtain its feature vector from `discriminat
 ```python
 feature_size = 8192 
 
-features = mx.nd.zeros((len(test_images), feature_size), ctx=ctx)
+features = nd.zeros((len(test_images), feature_size), ctx=ctx)
 
 for idx, image in enumerate(test_images):
 
-    feature = discriminator(mx.nd.array(image))
+    feature = discriminator(nd.array(image))
     feature = feature.reshape(feature_size,)
     features[idx,:] = feature.copyto(ctx)
 
@@ -434,3 +434,5 @@ with open("imagetsne.json", 'w') as outfile:
 Load the file with TSNEViewer. You can now inspect whether similiar looking images are grouped nearby or not. 
 
 <img src="https://raw.githubusercontent.com/NRauschmayr/web-data/master/mxnet/doc/tutorials/info_gan/tsne.png" style="width:800px;height:600px;">
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->