diff --git a/tutorial-contents/401_CNN.py b/tutorial-contents/401_CNN.py
index 1f9daea..8016158 100644
--- a/tutorial-contents/401_CNN.py
+++ b/tutorial-contents/401_CNN.py
@@ -65,7 +65,7 @@ class CNN(nn.Module):
                 out_channels=16,            # n_filters
                 kernel_size=5,              # filter size
                 stride=1,                   # filter movement/step
-                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
+                padding=2,                  # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1
             ),                              # output shape (16, 28, 28)
             nn.ReLU(),                      # activation
             nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)
@@ -115,7 +115,7 @@ for epoch in range(EPOCH):
 
         if step % 50 == 0:
             test_output, last_layer = cnn(test_x)
-            pred_y = torch.max(test_output, 1)[1].data.squeeze().numpy()
+            pred_y = torch.max(test_output, 1)[1].data.numpy()
             accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
             if HAS_SK:
@@ -129,6 +129,6 @@ plt.ioff()
 
 # print 10 predictions from test data
 test_output, _ = cnn(test_x[:10])
-pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
+pred_y = torch.max(test_output, 1)[1].data.numpy()
 print(pred_y, 'prediction number')
 print(test_y[:10].numpy(), 'real number')
diff --git a/tutorial-contents/402_RNN_classifier.py b/tutorial-contents/402_RNN_classifier.py
index 9ef11f7..3bb8231 100644
--- a/tutorial-contents/402_RNN_classifier.py
+++ b/tutorial-contents/402_RNN_classifier.py
@@ -47,7 +47,7 @@ train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_
 # convert test data into Variable, pick 2000 samples to speed up testing
 test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
 test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255.   # shape (2000, 28, 28) value in range(0,1)
-test_y = test_data.test_labels.numpy().squeeze()[:2000]    # covert to numpy array
+test_y = test_data.test_labels.numpy()[:2000]    # covert to numpy array
 
 
 class RNN(nn.Module):
@@ -94,13 +94,13 @@ for epoch in range(EPOCH):
 
         if step % 50 == 0:
             test_output = rnn(test_x)                   # (samples, time_step, input_size)
-            pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
+            pred_y = torch.max(test_output, 1)[1].data.numpy()
             accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
 
 # print 10 predictions from test data
 test_output = rnn(test_x[:10].view(-1, 28, 28))
-pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
+pred_y = torch.max(test_output, 1)[1].data.numpy()
 print(pred_y, 'prediction number')
 print(test_y[:10], 'real number')
 
diff --git a/tutorial-contents/403_RNN_regressor.py b/tutorial-contents/403_RNN_regressor.py
index c8b19ca..e9d9bae 100644
--- a/tutorial-contents/403_RNN_regressor.py
+++ b/tutorial-contents/403_RNN_regressor.py
@@ -20,8 +20,8 @@ INPUT_SIZE = 1      # rnn input size
 LR = 0.02           # learning rate
 
 # show data
-steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)
-x_np = np.sin(steps)    # float32 for converting torch FloatTensor
+steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)  # float32 for converting torch FloatTensor
+x_np = np.sin(steps)
 y_np = np.cos(steps)
 plt.plot(steps, y_np, 'r-', label='target (cos)')
 plt.plot(steps, x_np, 'b-', label='input (sin)')
@@ -55,7 +55,13 @@ class RNN(nn.Module):
         # instead, for simplicity, you can replace above codes by follows
         # r_out = r_out.view(-1, 32)
         # outs = self.out(r_out)
+        # outs = outs.view(-1, TIME_STEP, 1)
         # return outs, h_state
+        
+        # or even simpler, since nn.Linear can accept inputs of any dimension 
+        # and returns outputs with same dimension except for the last
+        # outs = self.out(r_out)
+        # return outs
 
 rnn = RNN()
 print(rnn)
@@ -71,8 +77,8 @@ plt.ion()           # continuously plot
 for step in range(100):
     start, end = step * np.pi, (step+1)*np.pi   # time range
     # use sin predicts cos
-    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)
-    x_np = np.sin(steps)    # float32 for converting torch FloatTensor
+    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)  # float32 for converting torch FloatTensor
+    x_np = np.sin(steps)
     y_np = np.cos(steps)
 
     x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)
diff --git a/tutorial-contents/502_GPU.py b/tutorial-contents/502_GPU.py
index 58cd07b..383e5ba 100644
--- a/tutorial-contents/502_GPU.py
+++ b/tutorial-contents/502_GPU.py
@@ -68,7 +68,7 @@ for epoch in range(EPOCH):
             test_output = cnn(test_x)
 
             # !!!!!!!! Change in here !!!!!!!!! #
-            pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze()  # move the computation in GPU
+            pred_y = torch.max(test_output, 1)[1].cuda().data  # move the computation in GPU
 
             accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)
             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)
@@ -77,7 +77,7 @@ for epoch in range(EPOCH):
 test_output = cnn(test_x[:10])
 
 # !!!!!!!! Change in here !!!!!!!!! #
-pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU
+pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU
 
 print(pred_y, 'prediction number')
 print(test_y[:10], 'real number')