Self.add_module(str(len(self) + 1 ), module)Īfter doing this, you can add a torch.nn.Module to a Sequential like you posted in the question. Or a simpler way of putting it is: NN Sequential ( nn.Linear (10, 4), nn.ReLU (), nn.Linear (4, 2), nn.Linear ()) 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. These examples are extracted from open source projects. I think maybe the codes in which you found the using of add could have lines that modified the torch.nn.Module.add to a function like this: def add_module(self,module): p y p y x dx g y x p x dx g y x f x x p x dx. The following are 30 code examples of torch.nn.Sequential(). The forward () method of Sequential accepts any input and forwards it to the first module it contains. Alternatively, an OrderedDict of modules can be passed in. Modules will be added to it in the order they are passed in the constructor. (2): Linear(in_features=4, out_features=1, bias=True)Īs McLawrence said nn.Sequential doesn't have the add method. Sequential class torch.nn.Sequential(args) source A sequential container. (0): Linear(in_features=3, out_features=4, bias=True) This will result in a similar structure of your code, as adding directly.Īs described by the correct answer, this is what it would look as a sequence of arguments: device = vice('cpu') If you have a model with lots of layers, you can create a list first and then use the * operator to expand the list into positional arguments, like this: layers = This is why it is faster to flatten tensors inside forward. of 7 runs, 100000 loops each) This result shows creating a class would be slower approach. Sequential does not have an add method at the moment, though there is some debate about adding this functionality.Īs you can read in the documentation nn.Sequential takes as argument the layers separeted as sequence of arguments or an OrderedDict. flatten Flatten () t torch.Tensor (3,2,2).random (0, 10) timeit fflatten (t) 5.16 s 122 ns per loop (mean std.
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