Viewing vanishing gradients

losses.py

@@ -7,4 +7,5 @@ class RMSLELoss(nn.Module):
         self.mse = nn.MSELoss()
         
     def forward(self, pred, actual):
-        return torch.sqrt(self.mse(torch.log(pred + 1), torch.log(actual + 1)))
+        resized_actual =  actual.view([actual.shape[0], actual.shape[1],1])
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+        return torch.sqrt(self.mse(torch.log(torch.add(pred,1)), torch.log(torch.add(resized_actual, 1))))
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main.py

@@ -8,6 +8,7 @@ import optimizers
 import torch
 import logging
 import torch.optim
+import torch.nn as nn
 
 def optimizer(cfg, model):
     result = {"Adam" : torch.optim.Adam(model.parameters())}
@@ -35,7 +36,7 @@ if __name__ == "__main__":
         use_cuda,
         valid_ratio,
         overwrite_index=True,
-        max_num_samples=max_num_samples,
+        max_num_samples=max_num_samples
     )
 
     if use_cuda :
@@ -43,17 +44,29 @@ if __name__ == "__main__":
     else :
         device = toch.device('cpu')
 
-    model = model.build_model(cfg, 18)
+    #model = model.build_model(cfg, 18)
+
+    model = nn.Sequential(
+        nn.Linear(18,1,False),
+        nn.ReLU()
+    )
     model = model.to(device)
 
+    for param in list(model.parameters()):
+        param = 1
+
     f_loss = losses.RMSLELoss()
 
     optimizer = optimizer(cfg, model)
+    #optimizer = torch.optim.Adam((model.parameters()), lr = 10000)
   
+
     for t in range(cfg["Training"]["Epochs"]):
         print("Epoch {}".format(t))
         train(model, train_loader, f_loss, optimizer, device)
 
+
+        print(list(model.parameters())[0].grad)
         val_loss = test.test(model, valid_loader, f_loss, device)
         print(" Validation : Loss : {:.4f}".format(val_loss))
 

train.py

@@ -27,10 +27,20 @@ def train(model, loader, f_loss, optimizer, device):
         outputs = model(inputs)
         loss = f_loss(outputs, targets)
 
+        print("Loss")
+        print(loss)
+
+        print("outputs")
+        print(outputs)
+
+        print("targets")
+        print(targets)
+
         # Backward and optimize
         optimizer.zero_grad()
         loss.backward()
-        optimizer.step()
 
-        print(model.regressor.weight)
-        print(model.regressor.bias)
+        print("GRads")
+        print(list(model.parameters())[0].grad)
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+
+        optimizer.step()
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