Add linear layer to time series prediction

As is the final network output is modulated with a tanh nonlinearity.
This is undesirable. As a simple / realistic fix we add a final
linear layer.

time_sequence_prediction/train.py

@@ -12,23 +12,26 @@ class Sequence(nn.Module):
     def __init__(self):
         super(Sequence, self).__init__()
         self.lstm1 = nn.LSTMCell(1, 51)
-        self.lstm2 = nn.LSTMCell(51, 1)
+        self.lstm2 = nn.LSTMCell(51, 51)
+        self.linear = nn.Linear(51, 1)
 
     def forward(self, input, future = 0):
         outputs = []
         h_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False)
         c_t = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False)
-        h_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False)
-        c_t2 = Variable(torch.zeros(input.size(0), 1).double(), requires_grad=False)
+        h_t2 = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False)
+        c_t2 = Variable(torch.zeros(input.size(0), 51).double(), requires_grad=False)
 
         for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):
             h_t, c_t = self.lstm1(input_t, (h_t, c_t))
             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
-            outputs += [h_t2]
+            output = self.linear(h_t2)
+            outputs += [output]
         for i in range(future):# if we should predict the future
-            h_t, c_t = self.lstm1(h_t2, (h_t, c_t))
+            h_t, c_t = self.lstm1(output, (h_t, c_t))
             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
-            outputs += [h_t2]
+            output = self.linear(h_t2)
+            outputs += [output]
         outputs = torch.stack(outputs, 1).squeeze(2)
         return outputs