class MLP(torch.nn.Module): def __init__(self,D_in,H, D_out): """ In the constructor we instantiate two nn.Linear modules and assign them as member variables. """ super(MLPModel,self).__init__() self.hidden1 = torch.nn.Linear(D_in,H) self.hidden2 = torch.nn.Linear(H,D_out) self.sig = torch.nn.Sigmoid() def forward(self,x): """ In the forward function we accept a Tensor of input data and we must return a Tensor of output data. We can use Modules defined in the constructor as well as arbitrary operators on Tensors. """ out = self.sig(self.hidden1(x)) out = self.hidden2(out) return out
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