def get_net():
net = nn.Sequential(nn.Linear(4, 10), nn.ReLU(), nn.Linear(10, 1))
net.apply(init_weights)
return net
Is it correct to call this Multi-Layer Perceptron an RNN? Or does calling something an RNN only depend on the having a sliding window training & label set?
tau is 4 in this case correct? What do both 10s mean contextually?
A few about the max steps section
Are you predicting a sequence of length step size, or are you shifting each window by the step size?
Iām confused about this code in Chapter 9.1. If I understand correctly, our FEATURE should be a T-tau fragment of length tua; why is the FEATURE here actually a tau fragment of length T-tau
def get_dataloader(self, train):
features = [self.x[i : self.T-self.tau+i] for i in range(self.tau)]
self.features = torch.stack(features, 1)
self.labels = self.x[self.tau:].reshape((-1, 1))
i = slice(0, self.num_train) if train else slice(self.num_train, None)
return self.get_tensorloader([self.features, self.labels], train, i)