diff --git a/ml_pytroch_sacred.py b/ml_pytroch_sacred.py
index a61dcef..2373faa 100644
--- a/ml_pytroch_sacred.py
+++ b/ml_pytroch_sacred.py
@@ -100,21 +100,22 @@ def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
         history.append(result)
     return history
 
-@ex.automain
-def my_main(epochs):
-    input_size = len(input_cols)
-    output_size = len(output_cols)
-    model=Model_xPosition()
-    epochs = 2000
-    lr = 1e-5
-    learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
-    def predict_single(input, target, model):
-        inputs = input.unsqueeze(0)
-        predictions = model(inputs)
-        prediction = predictions[0].detach()
 
-        return "Target: "+str(target)+"          Predicted: "+str(prediction)+"\n"
+input_size = len(input_cols)
+output_size = len(output_cols)
+model=Model_xPosition()
+epochs = 1000
+lr = 1e-5
+learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
+def predict_single(input, target, model):
+    inputs = input.unsqueeze(0)
+    predictions = model(inputs)
+    prediction = predictions[0].detach()
+
+    return "Target: "+str(target)+"          Predicted: "+str(prediction)+"\n"
     
+@ex.automain
+def my_main(epochs):   
     for i in random.sample(range(0, len(val_ds)), 10):
         input_, target = val_ds[i]
         print(predict_single(input_, target, model),end="")