also prints out chk on 99, data_dim added to check filename

src/train/train.py

@@ -27,6 +27,7 @@ def train_DER(
     path_to_model="models/",
     data_prescription="linear_homoskedastic",
     inject_type="predictive",
+    data_dim="0D",
     noise_level="low",
     save_all_checkpoints=False,
     save_final_checkpoint=False,
@@ -95,7 +96,8 @@ def train_DER(
     best_loss = np.inf  # init to infinity
     model, lossFn = models.model_setup_DER(loss_type,
                                            DEVICE,
-                                           n_hidden=n_hidden)
+                                           n_hidden=n_hidden,
+                                           data_type=data_dim)
     if verbose:
         print("model is", model, "lossfn", lossFn)
     opt = torch.optim.Adam(model.parameters(), lr=INIT_LR)
@@ -127,7 +129,6 @@ def train_DER(
             # send the input to the device
             # (x, y) = (x.to(device), y.to(device))
             # perform a forward pass and calculate the training loss
-
             pred = model(x)
             loss = lossFn(pred, y, COEFF)
             if plot or savefig:
@@ -259,6 +260,8 @@ def train_DER(
                     + str(data_prescription)
                     + "_"
                     + str(inject_type)
+                    + "_"
+                    + str(data_dim)
                     + "_loss_"
                     + str(loss_type)
                     + "_COEFF_"
@@ -280,6 +283,8 @@ def train_DER(
                 + str(data_prescription)
                 + "_"
                 + str(inject_type)
+                + "_"
+                + str(data_dim)
                 + "_noise_"
                 + str(noise_level)
                 + "_loss_"
@@ -313,6 +318,8 @@ def train_DER(
                 },
                 filename
             )
+            if epoch == 99:
+                print('checkpoint saved here', filename)
 
         if save_final_checkpoint and (e % (EPOCHS - 1) == 0) and (e != 0):
             filename = (
@@ -323,6 +330,8 @@ def train_DER(
                 + str(data_prescription)
                 + "_"
                 + str(inject_type)
+                + "_"
+                + str(data_dim)
                 + "_noise_"
                 + str(noise_level)
                 + "_loss_"