# How do you calculate the channel or input to the first linear layer?

Here is a network and if you could please explain to me how the `128 * 1 * 1` shape is calculated I will appreciate it very much.

I am aware of this formula `(W + F + 2P / S) + 1` but I am having trouble calculating`128 * 1 * 1`.

In this formula:
W = Input Width
F = Kernel size
S = Stride

The size of the input is `(1,28,28)` ie the MNIST dataset from torchvision.

So as you can see I have looked into this problem but I cannot calculate the `128 * 1 * 1` input to
`self.f1 = nn.Linear(128 * 1 * 1, 1000)` in the network below So, if you could answer this question using some formula I will appreciate it very much.

``````class Net(nn.Module):
"""A representation of a convolutional neural network comprised of VGG blocks."""
def __init__(self, n_channels):
super(Net, self).__init__()
# VGG block 1
self.conv1 = nn.Conv2d(n_channels, 64, (3,3))
self.act1 = nn.ReLU()
self.pool1 = nn.MaxPool2d((2,2), stride=(2,2))
# VGG block 2
self.conv2 = nn.Conv2d(64, 64, (3,3))
self.act2 = nn.ReLU()
self.pool2 = nn.MaxPool2d((2,2), stride=(2,2))
# VGG block 3
self.conv3 = nn.Conv2d(64, 128, (3,3))
self.act3 = nn.ReLU()
self.pool3 = nn.MaxPool2d((2,2), stride=(2,2))
# Fully connected layer
self.f1 = nn.Linear(128 * 1 * 1, 1000)
self.act4 = nn.ReLU()
# Output layer
self.f2 = nn.Linear(1000, 10)
self.act5 = nn.Softmax(dim=1)

def forward(self, X):
"""This function forward propagates the input."""
# VGG block 1
X = self.conv1(X)
X = self.act1(X)
X = self.pool1(X)
# VGG block 2
X = self.conv2(X)
X = self.act2(X)
X = self.pool2(X)
# VGG block 3
X = self.conv3(X)
X = self.act3(X)
X = self.pool3(X)
# Flatten
X = X.view(-1, 128)
# Fully connected layer
X = self.f1(X)
X = self.act4(X)
# Output layer
X = self.f2(X)
X = self.act5(X)

return X

``````

thanks!

you need to provide the size of the input for that

i edited my question. the size is `(1, 28, 28)`.

so (excluding the batch dimension):
input: 1X28X28
after conv2d: 64X28X28
after maxpool: 64X13X13
after conv2d: 64X11X11
after maxpool: 64X5X5
after conv2d: 128X3X3
after maxpool: 128X1X1

now you have tensor of size (N, 128X1X1) which you view as (N, 128) by flattening it.
Thatâ€™s why the number of input features to the first linear layer is 128

thank you so much!

Just to make sure i would like to ask this. after the first pool you actually get 13.5, right? but you rounded off to 13? this is correct, right?

yes
look here:
https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html
you need to round down

thank you so much!

I appreciate it!