torch.nn.functional. . num_classes ) #. add the list of modules to current module.The PyTorch scheme of defining everything as subclasses of nn.Module, initializing all the layers/operations/etc. in the constructor and then connecting them together in the forward method can be messy. This is especially true if you have lots of shortcut connections and want to code your model with loops for arbitrary depth.
Modules allow each channel to independently configured and upgraded as test plans change. N6700 series modular power supply is our most popular programmable power supply. Enable browser cookies for improved site capabilities and performance. modules ( iterable, optional) – an iterable of modules to add. Example: class MyModule (nn.Module): def __init__ (self): super (MyModule, self).__init__ () self.linears = nn.ModuleList ( [nn.Linear (10, 10) for i in range (10)]) def forward (self, x): # ModuleList can act as an iterable, or be indexed using ints for i, l in enumerate (self.linears): x = self.linears [i // 2] (x) + l (x) return x.
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