Move to tanh activation and fix bug in altitude loss term

Code/python/src/aiRNN/losses.py

@@ -42,7 +42,8 @@ class HaversineMSEAltitudeLoss(nn.Module):
 
         # Altitude penalty
         delta_alt = alt2 - alt1
-        alt_penalty = self.alt_const * torch.mean(delta_alt ** 2)
+        delta_alt *= self.alt_const
+        alt_penalty = torch.mean(delta_alt ** 2)
 
         return haversine_mse + alt_penalty
     
@@ -83,7 +84,8 @@ class HaversineAltitudeLoss(nn.Module):
 
         # Altitude penalty
         delta_alt = alt2 - alt1
-        alt_penalty = self.alt_const * torch.mean(delta_alt ** 2)
+        delta_alt *= self.alt_const
+        alt_penalty = torch.mean(delta_alt ** 2)
 
         return haversine_loss + alt_penalty
     

Code/python/src/aiRNN/models.py

@@ -9,9 +9,9 @@ class BaseRNN(nn.Module):
 
         base_unit = lambda in_size, out_size: nn.Sequential(
             nn.Linear(in_size, out_size, device=device),
-            nn.ReLU(),
+            nn.Tanh(),
             nn.Linear(out_size, out_size, device=device),
-            nn.ReLU(),
+            nn.Tanh(),
         )
 
         self.time_proj = base_unit(time_in, hidden_size)
@@ -42,7 +42,7 @@ class BaseRNN(nn.Module):
         readout_in = rnn_size
         self.readout = nn.Sequential(
             nn.Linear(readout_in, hidden_size, device=device),
-            nn.ReLU(),
+            nn.Tanh(),
             nn.Linear(hidden_size, out_size, device=device),
         )
         self.context_in = context_in