Incorrect dimensions in layers

The dimensions in the tutorial don't match with the image.
The first and second convolution layers have incorrect dimensions.
The tutorial code has the correct thing.

Author: codewithkaranjeswani

beginner_source/blitz/neural_networks_tutorial.py

@@ -48,10 +48,10 @@ def __init__(self):
         super(Net, self).__init__()
         # 1 input image channel, 6 output channels, 3x3 square convolution
         # kernel
-        self.conv1 = nn.Conv2d(1, 6, 3)
-        self.conv2 = nn.Conv2d(6, 16, 3)
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
         # an affine operation: y = Wx + b
-        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension 
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 6*6 from image dimension 
         self.fc2 = nn.Linear(120, 84)
         self.fc3 = nn.Linear(84, 10)
 
@@ -99,7 +99,7 @@ def num_flat_features(self, x):
 print(out)
 
 ########################################################################
-# Zero the gradient buffers of all parameters and backprops with random
+# Zero the gradient buffers of all parameters and backprop with random
 # gradients:
 net.zero_grad()
 out.backward(torch.randn(1, 10))