warn ( "dropout option adds dropout after all but last " "recurrent layer, so non-zero dropout expects " "num_layers greater than 1, but got dropout= ' ] weights = if self. Number ) or not 0 0 and num_layers = 1 : warnings. Basically, I need to declare an instance of torch::nn::Sequential() and copy an existing network to that. bidirectional = bidirectional num_directions = 2 if bidirectional else 1 if not isinstance ( dropout, numbers. , bidirectional : bool = False ) -> None : super ( RNNBase, self ). ![]() Linear Module The bread and butter of modules is the Linear module which does a linear transformation with a bias. The actual neural network architecture is then constructed on Lines 7-11 by first initializing a nn.Sequential object (very similar to Keras/TensorFlow’s Sequential class). ![]() ![]() Class RNNBase ( Module ): _constants_ = _jit_unused_properties_ = mode : str input_size : int hidden_size : int num_layers : int bias : bool batch_first : bool dropout : float bidirectional : bool def _init_ ( self, mode : str, input_size : int, hidden_size : int, num_layers : int = 1, bias : bool = True, batch_first : bool = False, dropout : float = 0. Note: most of the functionality implemented for modules can be accessed in a functional form via torch.nn.functional, but these require you to create and manage the weight tensors yourself.
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