[Second][FFN] Simple

main.py

@@ -9,6 +9,7 @@ import torch
 import logging
 import torch.optim
 import torch.nn as nn
+import create_submission
 
 def optimizer(cfg, model):
     result = {"Adam" : torch.optim.Adam(model.parameters())}
@@ -36,7 +37,9 @@ if __name__ == "__main__":
         use_cuda,
         valid_ratio,
         overwrite_index=True,
-        max_num_samples=max_num_samples
+        max_num_samples=max_num_samples,
+        train_transform=dataloader.transform_remove_space_time(),
+        valid_transform=dataloader.transform_remove_space_time()
     )
 
     if use_cuda :
@@ -47,8 +50,13 @@ if __name__ == "__main__":
     #model = model.build_model(cfg, 18)
 
     model = nn.Sequential(
-        nn.Linear(18,1,False),
-        nn.ReLU()
+        nn.Linear(14,8,False),
+        nn.ReLU(),
+        nn.Linear(8, 8, True),
+        nn.ReLU(),
+        nn.Linear(8,35,True),
+        nn.ReLU(),
+        nn.Linear(35,1, True)
     )
     model = model.to(device)
 
@@ -62,14 +70,16 @@ if __name__ == "__main__":
   
 
     for t in range(cfg["Training"]["Epochs"]):
+        torch.autograd.set_detect_anomaly(True)
         print("Epoch {}".format(t))
         train(model, train_loader, f_loss, optimizer, device)
 
 
-        print(list(model.parameters())[0].grad)
+        #print(list(model.parameters())[0].grad)
         val_loss = test.test(model, valid_loader, f_loss, device)
         print(" Validation : Loss : {:.4f}".format(val_loss))
 
+    create_submission.create_submission(model)
     """
     logdir = generate_unique_logpath(top_logdir, "linear")
     print("Logging to {}".format(logdir))