ml q2

machinelearning/models.py

@@ -1,6 +1,7 @@
 from torch import no_grad, stack
 from torch.utils.data import DataLoader
 from torch.nn import Module
+import torch
 
 
 """
@@ -111,6 +112,12 @@ class RegressionModel(Module):
         # Initialize your model parameters here
         "*** YOUR CODE HERE ***"
         super().__init__()
+        hidden_size1=400
+        hidden_size2=400
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.fc1 = Linear(1, hidden_size1).to(self.device)
+        self.fc2 = Linear(hidden_size1, hidden_size2).to(self.device)
+        self.fc_out = Linear(hidden_size2, 1,bias=False).to(self.device)
 
 
 
@@ -123,7 +130,11 @@ class RegressionModel(Module):
         Returns:
             A node with shape (batch_size x 1) containing predicted y-values
         """
-        "*** YOUR CODE HERE ***"
+        x=x.to(self.device)
+        x = relu(self.fc1(x))
+        x = relu(self.fc2(x))
+        x = self.fc_out(x)
+        return x
 
     
     def get_loss(self, x, y):
@@ -136,7 +147,18 @@ class RegressionModel(Module):
                 to be used for training
         Returns: a tensor of size 1 containing the loss
         """
-        "*** YOUR CODE HERE ***"
+        return mse_loss(self.forward(x.to(self.device)), y.to(self.device))
+    
+    def run(self, x):
+        """
+        Runs the model for a batch of examples.
+
+        Inputs:
+            x: a node with shape (batch_size x 1)
+        Returns:
+            A node with shape (batch_size x 1) containing predicted y-values
+        """
+        return self.forward(x)
  
   
 
@@ -154,7 +176,25 @@ class RegressionModel(Module):
             dataset: a PyTorch dataset object containing data to be trained on
             
         """
-        "*** YOUR CODE HERE ***"
+        optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
+        dataloader = DataLoader(dataset, batch_size=10, shuffle=True)
+        max_round=10000
+        allow_loss=1e-4
+        for round_cnt in range(max_round):
+            for sample in dataloader:
+                x = sample['x'].to(self.device)
+                y = sample['label'].to(self.device)
+                loss = self.get_loss(x, y)
+                if loss < allow_loss:
+                    break
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+            if loss < allow_loss:
+                break
+            if round_cnt%100==0:
+                print(f"round: {round_cnt}, loss: {loss.item()}")
+        print(f"round: {round_cnt}, loss: {loss.item()}")