set norm to mean & std of CIFAR10(#1818)

beginner_source/introyt/introyt1_tutorial.py

@@ -288,7 +288,7 @@ def num_flat_features(self, x):
 
 transform = transforms.Compose(
     [transforms.ToTensor(),
-     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+     transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))])
 
 
 ##########################################################################
@@ -297,9 +297,28 @@ def num_flat_features(self, x):
 # -  ``transforms.ToTensor()`` converts images loaded by Pillow into 
 #    PyTorch tensors.
 # -  ``transforms.Normalize()`` adjusts the values of the tensor so
-#    that their average is zero and their standard deviation is 0.5. Most
+#    that their average is zero and their standard deviation is 1.0. Most
 #    activation functions have their strongest gradients around x = 0, so
 #    centering our data there can speed learning.
+#    The values passed to the transform are the means (first tuple) and the
+#    standard deviations (second tuple) of the rgb values of the images in
+#    the dataset. You can calculate these values yourself by running these
+#    few lines of code:
+#          ```
+#           from torch.utils.data import ConcatDataset
+#           transform = transforms.Compose([transforms.ToTensor()])
+#           trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+#                                        download=True, transform=transform)
+#
+#           #stack all train images together into a tensor of shape 
+#           #(50000, 3, 32, 32)
+#           x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
+#           
+#           #get the mean of each channel            
+#           mean = torch.mean(x, dim=(0,2,3)) #tensor([0.4914, 0.4822, 0.4465])
+#           std = torch.std(x, dim=(0,2,3)) #tensor([0.2470, 0.2435, 0.2616])  
+# 
+#          ```   
 # 
 # There are many more transforms available, including cropping, centering,
 # rotation, and reflection.