For instance, take a CNN classifier, you could define a nn.Sequential for the CNN part, then define another nn.Sequential for the fully connected classifier section of the model. In a more complicated module though, you might need to use multiple sequential submodules. nn as nn nn.Module nn.Sequential nn.Module Module: the main building block The Module is the main building block, it defines the base class for all neural network and you MUST subclass it. The objective of nn.Sequential is to quickly implement sequential modules such that you are not required to write the forward definition, it being implicitly known because the layers are sequentially called on the outputs. def mergeReLURecur(m): mout nn.Sequential () for i, (nodeName, node) in enumerate (m.namedchildren ()): handle nn.Sequential containers through recursion if type (node) in nn.Sequential: mout.addmodule (nodeName, mergeReLURecur (node)) continue enable built-in ReLU of CBconv elif type (node) in CBConv2d: chldrn list (m.children. Or a simpler way of putting it is: NN = Sequential( The equivalent here is: class NN(nn.Sequential): As I explained earlier, nn.Sequential is a special kind of nn.Module made for this particular widespread type of neural network. Then, you can simply use a nn.Sequential. the layers are called sequentially on the input, one by one. If the model you are defining is sequential, i.e. Here is an example of a module: class NN(nn.Module): PyTorch will handle backward pass with Autograd. When creating a new neural network, you would usually go about creating a new class and inheriting from nn.Module, and defining two methods: _init_ (the initializer, where you define your layers) and forward (the inference code of your module, where you use your layers). As such nn.Sequential is actually a direct subclass of nn.Module, you can look for yourself on this line. I should start by mentioning that nn.Module is the base class for all neural network modules in PyTorch. If the layers are sequentially used ( self.layer3(self.layer2(self.layer1(x))), you can leverage nn.Sequential to not have to define the forward function of the model.
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