values ()) # NB: We can't really type check this function as the type of input # may change dynamically (as is tested in # TestScript.test_sequential_intermediary_types). See installation for further installation options, especially if you want to use a GPU. _dir_ () keys = return keys def _iter_ ( self ) -> Iterator : return iter ( self. We recommend Python 3.6 or higher, and at least PyTorch 1.6.0. args Sub-modules of torch.nn.Module that will be added to the container in. In the second case, the number of graphs equals the number of modules inside this container. DGL supports two modes: sequentially apply GNN modules on 1) the same graph or 2) a list of given graphs. _modules ) def _dir_ ( self ): keys = super ( Sequential, self ). A sequential container for stacking graph neural network modules. keys (), idx ) delattr ( self, key ) def _len_ ( self ) -> int : return len ( self. keys ()): delattr ( self, key ) else : key = self. keys (), idx ) return setattr ( self, key, module ) def _delitem_ ( self, idx : Union ) -> None : if isinstance ( idx, slice ): for key in list ( self. values (), idx ) def _setitem_ ( self, idx : int, module : Module ) -> None : key : str = self. index ( idx ) if not - size Union : if isinstance ( idx, slice ): return self. add_module ( str ( idx ), module ) def _get_item_by_idx ( self, iterator, idx ) -> T : """Get the idx-th item of the iterator""" size = len ( self ) idx = operator. add_module ( key, module ) else : for idx, module in enumerate ( args ): self. class Sequential: public torch:: nn:: ModuleHolder < SequentialImpl > ΒΆ A ModuleHolder subclass for SequentialImpl._init_ () if len ( args ) = 1 and isinstance ( args, OrderedDict ): for key, module in args. Now talking about the code by using Sequential module you are telling the PyTorch that you are developing an architecture that will work in a sequential manner and by specifying ReLU you are bringing the concept of Non-Linearity in the picture (ReLU is one of the widely used activation functions in the Deep learning framework). def _init_ ( self, * args ): super ( Sequential, self ). def _init_ ( self, arg : 'OrderedDict' ) -> None. Sequential torch.nn.Sequential 1Sequential import torch from torch import nn from torch.nn import Conv2d, MaxPool2d, Flatten, Linear class Model (nn.Module): def init ( self ): super (Model, self). Class Sequential ( Module ): def _init_ ( self, * args : Module ) -> None.
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