Update README.md

README.md

@@ -26,7 +26,7 @@ The main highlight of this model is the integration of a rare **bilinear layer (
 
 * **Loss Function:** Smooth L1 Loss
 * **Optimizer:** Adam (with StepLR scheduler)
-* **Error Rate:** ~0.0569% (Accuracy ~99.94%)
+* **Error Rate:** ~0.185%
 * **Extreme Test Case:** 
   * Input: `[[-6.0, 70.0, 4.0, -196.0]]`
   * Expected Mathematical Answer: `-13744.0000`
@@ -46,6 +46,11 @@ The main highlight of this model is the integration of a rare **bilinear layer (
 You can download the architecture file and the model weights directly from this repository:
 
 ```python
+import torch as t
+import torch.nn as nn
+import torch.optim as opt
+from torch.utils.data import DataLoader, Dataset
+
 class WebAISC(nn.Module):
 
     def __init__(self):
@@ -68,6 +73,8 @@ class WebAISC(nn.Module):
         x = self.x4(x)
         return x
 
+model = WebAISC
+
 test_input = t.tensor([[-6.0, 70.0, 4.0, -196.0]], dtype=t.float32)
 with t.no_grad():
     prediction = model(test_input)