bugfixes

Author: robin-janssen

codes/surrogates/LatentPolynomial/latent_poly.py

@@ -399,7 +399,7 @@ def total_loss(
         traj_loss = criterion(x_pred, x_true)
 
         # identity loss (reconstruct x0)
-        identity = self.identity_loss(x_true, params.to(self.device))
+        identity = self.identity_loss(x_true, params)
 
         # derivative losses: compute once
         d_pred = self.first_derivative(x_pred)
@@ -455,6 +455,7 @@ def identity_loss(self, x_true: Tensor, params: Tensor = None):
         # only reconstruct the initial state
         x0 = x_true[:, 0, :]
         if not self.config.coeff_network and params is not None:
+            params = params.to(self.device)
             enc_input = torch.cat([x0, params], dim=1)
         else:
             enc_input = x0

codes/utils/utils.py

@@ -62,7 +62,7 @@ def create_model_dir(
 
     # Check if the directory exists, and create it if it doesn't
     if not os.path.exists(full_path):
-        os.makedirs(full_path)
+        os.makedirs(full_path, exist_ok=True)
 
     return full_path
 

datasets/primordial/surrogates_config.py

@@ -42,37 +42,38 @@ class LatentNeuralODEConfig:
 
 
 @dataclass
-class FullyConnectedConfig:
-    """Model config for FullyConnected for the primordial dataset"""
+class LatentPolyConfig:
+    """Model config for LatentPoly for the primordial dataset"""
 
-    # primordial_final, trial 174
-    scheduler: str = "poly"
-    optimizer: str = "adam"
+    # primordial_final_latentpoly, trial 31
+    scheduler: str = "schedulefree"
+    optimizer: str = "SGD"
     loss_function: nn.Module = nn.MSELoss()
-    momentum: float = 0.0132
-    degree: int = 4
-    latent_features: int = 8
+    activation: nn.Module = nn.ReLU()
     coder_layers: int = 2
     coder_width: int = 470
-    learning_rate: float = 1.77e-04
-    regularization_factor: float = 9.20e-03
-    activation: nn.Module = nn.ReLU()
+    degree: int = 4
+    latent_features: int = 8
+    learning_rate: float = 0.000177
+    momentum: float = 0.0132
+    regularization_factor: float = 0.0092
 
 
 @dataclass
-class LatentPolyConfig:
-    """Model config for LatentPoly for the primordial dataset"""
+class FullyConnectedConfig:
+    """Model config for FullyConnected for the primordial dataset"""
 
-    # primordial_final, trial 31
-    scheduler: str = "schedulefree"
-    optimizer: str = "SGD"
+    # primordial_final_fullyconnected, trial 174
+    scheduler: str = "poly"
+    optimizer: str = "AdamW"
     loss_function: nn.Module = nn.SmoothL1Loss()
+    activation: nn.Module = nn.ELU()
     beta: float = 3.73
     hidden_size: int = 470
+    learning_rate: float = 0.00127
     num_hidden_layers: int = 5
-    learning_rate: float = 1.27e-03
-    regularization_factor: float = 3.23e-05
-    activation: nn.Module = nn.ELU()
+    poly_power: float = 1.48
+    regularization_factor: float = 3.3e-05
 
 
 # @dataclass