Network defined in the tutorial does not match the picture

[Mutefish0]

In the Define the network section of the Neural Network tutorial, in the picture, the size of S2 is 6x14x14, and the size of S4 is 16x5x5. But actully in the code it was 6x15x15 and 16x6x6.

[Mutefish0]

I added some code to print the size of S2 and S4:

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
  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)
    # an affine operation: y = Wx + b
    self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84, 10)

  def forward(self, x):
    # Max pooling over a (2, 2) window
    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))

    print(x.size())

    # If the size is a square you can only specify a single number
    x = F.max_pool2d(F.relu(self.conv2(x)), 2)

    print(x.size())

    x = x.view(-1, self.num_flat_features(x))

    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x

  def num_flat_features(self, x):
    size = x.size()[1:]  # all dimensions except the batch dimension
    num_features = 1
    for s in size:
        num_features *= s
    return num_features

net = Net()

net(torch.rand(1, 1, 32, 32))

The result is

torch.Size([1, 6, 15, 15])
torch.Size([1, 16, 6, 6])

[Silverster98]

Yes, I have noticed that too! And it have been bothering me for a long time. I think it will also trouble other beginners. Hope the maintainers of PyTorch can correct it.

[holly1238]

This issue is fixed and merged in #1257