Update README.md

README.md

@@ -11,6 +11,34 @@ A simple CNN for handwritten digit classification, trained on the MNIST dataset.
 - Accuracy: 99.4% on test set
 - Pytorch
 
+# Usage
+```python
+import torch
+from torch import nn
+
+#Define the architecture
+class CNN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(64 * 7 * 7, 128)
+        self.fc2 = nn.Linear(128, 10)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 7 * 7)
+        x = self.relu(self.fc1(x))
+        return self.fc2(x)
+
+#Load model
+model = CNN()
+model.load_state_dict(torch.load("mnist_cnn.pth"))
+model.eval()
+```
 
 # Showcase
 ![image](https://cdn-uploads.huggingface.co/production/uploads/6945d6be622680b0eee91373/vNpvjnEHBU8KGbA3Wr7RB.png)