diff --git a/Code/python/src/aiRNN/losses.py b/Code/python/src/aiRNN/losses.py
index 23cf738..b211e39 100644
--- a/Code/python/src/aiRNN/losses.py
+++ b/Code/python/src/aiRNN/losses.py
@@ -6,7 +6,7 @@ import math
 
 
 class HaversineMSEAltitudeLoss(nn.Module):
-    def __init__(self, alt_const=1.0, earth_radius_km=6371.0):
+    def __init__(self, alt_const=1e-4, earth_radius_km=6371.0):
         super().__init__()
         self.alt_const = alt_const
         self.R = earth_radius_km
@@ -47,7 +47,7 @@ class HaversineMSEAltitudeLoss(nn.Module):
         return haversine_mse + alt_penalty
     
 class HaversineAltitudeLoss(nn.Module):
-    def __init__(self, alt_const=1.0, earth_radius_km=6371.0):
+    def __init__(self, alt_const=1e-4, earth_radius_km=6371.0):
         super().__init__()
         self.alt_const = alt_const
         self.R = earth_radius_km
diff --git a/Code/python/src/aiRNN/models.py b/Code/python/src/aiRNN/models.py
index 891ffbb..9dfc0d4 100644
--- a/Code/python/src/aiRNN/models.py
+++ b/Code/python/src/aiRNN/models.py
@@ -7,17 +7,24 @@ class BaseRNN(nn.Module):
                  rnn_size, out_size, rnn_type="RNN", device="cpu"):
         super().__init__()
 
-        self.time_proj = nn.Linear(time_in, hidden_size, device=device)
-        self.feat_proj = nn.Linear(feat_in, hidden_size, device=device)
+        base_unit = lambda in_size, out_size: nn.Sequential(
+            nn.Linear(in_size, out_size, device=device),
+            nn.ReLU(),
+            nn.Linear(out_size, out_size, device=device),
+            nn.ReLU(),
+        )
+
+        self.time_proj = base_unit(time_in, hidden_size)
+        self.feat_proj = base_unit(feat_in, hidden_size)
         self.context_proj = (
-            nn.Linear(context_in, hidden_size, device=device) if context_in is not None else None
+            base_unit(context_in, hidden_size) if context_in is not None else None
         )
 
         if rnn_type == "RNN":
             self.rnn = nn.RNN(
                 input_size=hidden_size*3,
                 hidden_size=rnn_size,
-                num_layers=2,
+                num_layers=3,
                 batch_first=True,
                 device=device
             )
@@ -25,7 +32,7 @@ class BaseRNN(nn.Module):
             self.rnn = nn.LSTM(
                 input_size=hidden_size*3,
                 hidden_size=rnn_size,
-                num_layers=2,
+                num_layers=3,
                 batch_first=True,
                 device=device
             )
@@ -33,8 +40,11 @@ class BaseRNN(nn.Module):
             raise ValueError("Unsupported rnn_type")
 
         readout_in = rnn_size
-        self.readout = nn.Linear(readout_in, out_size, device=device)
-
+        self.readout = nn.Sequential(
+            nn.Linear(readout_in, hidden_size, device=device),
+            nn.ReLU(),
+            nn.Linear(hidden_size, out_size, device=device),
+        )
         self.context_in = context_in
         self.rnn_size = rnn_size