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ABCDofDataScience t1_isi9ke4 wrote

Question: What exactly does Pytorch super(My_Neural_Network,self).__init__() do such that we need to include it in all Neural networks init() method?
After looking up online, all I found is: It initializes some special properties that are required for Neural Network but couldn't find any solid answer that describes in detail.

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itsyourboiirow t1_iskqchc wrote

Yeah I’m not sure about the details. But I would guess it’s so you can use back propagation and loss functions on your NN.

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ThrowThisShitAway10 t1_iso6km8 wrote

This is a feature of Python, not just PyTorch. We use the super function because we want our class to inherit the attributes of it's parent. For your PyTorch module to work, you have to inherit from the nn.Module class. It's not a big deal

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seiqooq t1_it40uw6 wrote

It’s a bit of a rabbit hole, but this is required for autograd to create the reverse computation graph (enables backpropagation). PyTorch has great videos on YouTube if you want to dig in, just search PyTorch autograd.

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