How to add skip connections to the model? about pytorch-deep-learning HOT 1 OPEN

Hey @SixtyTrees

Sequential does what it basically says, creates a model that is a sequence of layers.
And that is just syntactic sugar for feeding the output of a layer as input to the next one.

That being said, you cannot implement skip connections inside the nn.Sequential.
For that you have to subclass nn.Module and define the forward pass yourself.

Note that in order to create a skip connection the output dimension must match the dimensions the layer expects as input. There are 2 ways you can implement skip connections (that I know of), the first one is through concatenation, and the second one (with an example below) is using addition.

In a ResNet-like (which uses addition) you'd need your linear2 and linear3 output dims to match the input dim of linear4. This means:

nn.Linear(10, **10**),  # linear2
...
nn.Linear(**10**, **10**), # linear3 -- note that you have to change the in dim of linear3 as well
...
nn.Linear(**10**, 20),   # linear4

In a UNet-like architecture (which uses concatenation) you'd need the sum of the out dims of linear2 and linear3 to match the input dim of linear4:

nn.Linear(10, **15**),  # linear2
...
nn.Linear(15, **10**), # linear3
...
nn.Linear(**25**, 20),   # linear4

Here's a simple example of how to create a skip connection using addition:

class MySimpleSkipModel(nn.Module):
  def __init__(self):
    self.linear1 = nn.Linear(30, 10)
    self.linear2 = nn.Linear(10, 10)
    self.linear3 = nn.Linear(10, 5)
  
  def forward(self, inputs):
    l1_out = self.linear1(inputs)
    l2_out = self.linear2(l1_out)

    l3_out = self.linear3(l1_out + l2_out) # using the outputs of both linear1 and linear2, use torch.cat for concat
    return l3_out

from pytorch-deep-learning.