Change kernel to 5x5 in 1st Conv2d layer in model init

Signed-off-by: Kiersten Stokes <kierstenstokes@gmail.com>

Author: kiersten-stokes

beginner_source/introyt/introyt1_tutorial.py

@@ -176,9 +176,9 @@ class LeNet(nn.Module):
 
     def __init__(self):
         super(LeNet, self).__init__()
-        # 1 input image channel (black & white), 6 output channels, 3x3 square convolution
+        # 1 input image channel (black & white), 6 output channels, 5x5 square convolution
         # kernel
-        self.conv1 = nn.Conv2d(1, 6, 3)
+        self.conv1 = nn.Conv2d(1, 6, 5)
         self.conv2 = nn.Conv2d(6, 16, 3)
         # an affine operation: y = Wx + b
         self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension