diff --git a/mongoObserver.py b/mongoObserver.py
index dd5f52e..d30a343 100644
--- a/mongoObserver.py
+++ b/mongoObserver.py
@@ -45,8 +45,8 @@ def readAndtrain(epochs, batch_size, _run):
     input_dim = 11
     output_dim = 1
 
-    _run.info("Batch", str(batch_size))
-    _run.info("epoch",  str(epochs))
+    _run.log_scalar("Batch", str(batch_size))
+    _run.log_scalar("epoch",  str(epochs))
 
     model = LogisticRegressionModel(input_dim, output_dim)
     model.load_state_dict(torch.load('DEATH_EVENT.pth'))
@@ -67,16 +67,16 @@ def readAndtrain(epochs, batch_size, _run):
         loss.backward()
         optimizer.step()
 
-    _run.info("Lost", str(loss.item()))
+    _run.log_scalar("Lost", str(loss.item()))
 
 
     torch.save(model.state_dict(), 'DEATH_EVENT.pth')
     prediction= model(xTest)
-    _run.info("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
-    _run.info("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))
+    _run.log_scalar("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
+    # _run.log_scalar("F1", str(f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None)))
 
     print("accuracy_score", accuracy_score(yTest, np.argmax(prediction.detach().numpy(), axis=1)))
-    print("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))
+    # print("F1", f1_score(yTest, np.argmax(prediction.detach().numpy(), axis=1), average=None))
 
 @ex.automain
 def my_main(epochs, batch_size, _run):