![]() ![]() add_module(name, module) -> add_module(module, name=None) because some modules don't have proper names and, in the future people might want "private" modules.Since GNN operators take in multiple input. self._modules = ODict -> self._modules = so that the consequences of moving children are made explicit (and less onerous). Module: rAn extension of the :class:torch.nn.Sequential container in order to define a sequential GNN model.model nn.Sequential( nn.Linear(in, out), nn.Sigmoid(), nn.Linear(in, out), nn.Sigmoid() ) For the implementation of the model which is not in a single sequence, we define a model by subclassing the nn.Module class. ![]() We’ll define the class and inherit all the methods and attributes from the nn.Module package. Regarding the PR, insert_module takes both an optional index and name since the OrderedDict constructor precludes a bijection between names and indices.Īdd_module doesn't break any invariants, but it is redundant with insert_module(mod, None 'name'). For creating a model with a single layer we can simply define it by using nn.Sequential(). This should work identically to the nn.Sequential model above. In the context of something like Parallel or Concat, modules added by the list constructor would not have meaningful names, either. The feature, motivation and pitch The feature Like Python Lists add operator, + can be used to: concatenate two torch.nn.Sequential into 1 The following Python code illustrating the idea, and possibly the implementation from dataclass. I think that the real problem is that all modules must have names. Unfortunately, the rest of the API is mutable and I think that users would be confused by the inconsistency.Īdd_module makes no sense in context of sequential The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that. Here’s one way to construct an RNN cell in PyTorch using tahn and softmax activations.The hidden state output can be used as an input to the next RNN. I agree with the sentiment and, indeed, the implementation is effectively so. Computation of the hidden state and output. ![]()
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5/3/2024 04:35:37 am
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