added mlflow logging

zad1.py

@@ -7,16 +7,11 @@ import numpy as np
 import logging
 from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
 
-# logging.basicConfig(level=logging.WARN)
-# logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.WARN)
+logger = logging.getLogger(__name__)
 
-# mlflow.set_tracking_uri("http://localhost:5000")
-# mlflow.set_experiment("s123456")
-# def eval_metrics(actual, pred):
-#     rmse = np.sqrt(mean_squared_error(actual, pred))
-#     mae = mean_absolute_error(actual, pred)
-#     r2 = r2_score(actual, pred)
-#     return rmse, mae, r2
+mlflow.set_tracking_uri("http://localhost:5000")
+mlflow.set_experiment("s487176")
 
 import requests
 
@@ -90,6 +85,12 @@ class TabularModel(nn.Module):
         out = self.fc2(out)
         out = self.softmax(out)
         return out
+    
+    def predict(self, x):
+        with torch.no_grad():
+            output = self.forward(x)
+            _, predicted = torch.max(output, 1)
+            return predicted
 
 input_dim = wine_train.shape[1] - 1
 hidden_dim = 32
@@ -98,27 +99,31 @@ model = TabularModel(input_dim, hidden_dim, output_dim)
 criterion = nn.CrossEntropyLoss()
 optimizer = torch.optim.Adam(model.parameters())
 
-
-model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=32, output_dim=2)
-criterion = nn.CrossEntropyLoss()
-optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
-
 num_epochs = 10
-for epoch in range(num_epochs):
-    running_loss = 0.0
-    for i, data in enumerate(train_dataloader, 0):
-        inputs, labels = data
-        labels = labels.type(torch.LongTensor)
-        optimizer.zero_grad()
-        outputs = model(inputs)
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-        running_loss += loss.item()
+lr = 0.01
+alpha = 0.01
+model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=hidden_dim, output_dim=output_dim)
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=alpha)
+with mlflow.start_run():
+    mlflow.log_params({"learning rate":lr,"alpha":alpha})
 
-    # Print the loss every 1000 mini-batches
-    if (epoch%2)  == 0:
-        print(f'Epoch {epoch + 1}, loss: {running_loss / len(train_dataloader):.4f}')
+
+    for epoch in range(num_epochs):
+        running_loss = 0.0
+        for i, data in enumerate(train_dataloader, 0):
+            inputs, labels = data
+            labels = labels.type(torch.LongTensor)
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            running_loss += loss.item()
+
+        # Print the loss every 1000 mini-batches
+        if (epoch%2)  == 0:
+            print(f'Epoch {epoch + 1}, loss: {running_loss / len(train_dataloader):.4f}')
 
 print('Finished Training')
 
@@ -128,9 +133,12 @@ total = 0
 with torch.no_grad():
     for data in test_dataloader:
         inputs, labels = data
-        outputs = model(inputs.float())
-        _, predicted = torch.max(outputs.data, 1)
+        predicted = model.predict(inputs.float())
         total += labels.size(0)
         correct += (predicted == labels).sum().item()
-print('Accuracy on test set: %d %%' % (100 * correct / total))
+        
+accuracy= 100 * correct / total
+print('Accuracy on test set: %d %%' % accuracy)
 
+mlflow.log_metric("test_accuracy", accuracy)
+mlflow.sklearn.log_model(model, "model")