You can create a new module/class as below and use it in the sequential as you are using other modules (call Flatten() ). ... <看更多>

module = nn.Linear(10, 5) -- 10 inputs, 5 outputs. Usually this would be added to a network of some kind, e.g.: mlp = nn.Sequential() mlp:add(module). ... <看更多>

__init__() self.inplanes = inplanes self.squeeze = nn. ... Sequential(*layers) def _init_params(self): for m in self.modules(): if isinstance(m, nn. ... <看更多>

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Sequential ( nn.Linear(env.observation_space.shape[0], num_hidden), nn. ... state_action_qvalues = state_qvalues.gather(1, actions.unsqueeze(-1)).squeeze(-1) ... <看更多>

An implementation of SENet, proposed in Squeeze-and-Excitation Networks by Jie Hu, Li Shen and Gang Sun, ... Segmentation model is just a PyTorch nn. ... <看更多>