added "Usage" section to README.md

README.md

@@ -25,7 +25,52 @@ Experiment tracking: https://wandb.ai/sadhaklal/mlp-california-housing
 ## Usage
 
 ```
+from sklearn.datasets import fetch_california_housing
 
+housing = fetch_california_housing(as_frame=True)
+
+from sklearn.model_selection import train_test_split
+
+X_train_full, X_test, y_train_full, y_test = train_test_split(housing['data'], housing['target'], test_size=0.25, random_state=42)
+X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, test_size=0.25, random_state=42)
+
+X_means, X_stds = X_train.mean(axis=0), X_train.std(axis=0)
+X_train = (X_train - X_means) / X_stds
+X_valid = (X_valid - X_means) / X_stds
+X_test = (X_test - X_means) / X_stds
+
+import torch
+
+device = torch.device("cpu")
+
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+class MLP(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(8, 50)
+        self.fc2 = nn.Linear(50, 50)
+        self.fc3 = nn.Linear(50, 50)
+        self.fc4 = nn.Linear(50, 1)
+
+    def forward(self, x):
+        act = torch.relu(self.fc1(x))
+        act = torch.relu(self.fc2(act))
+        act = torch.relu(self.fc3(act))
+        return self.fc4(act)
+
+model = MLP.from_pretrained("sadhaklal/mlp-california-housing")
+model.to(device)
+model.eval()
+
+# Let's predict on 3 unseen examples from the test set:
+print(f"Ground truth housing prices: {y_test.values[:3]}")
+x_new = torch.tensor(X_test.values[:3], dtype=torch.float32)
+x_new = x_new.to(device)
+with torch.no_grad():
+    preds = model(x_new)
+print(f"Predicted housing prices: {preds.squeeze()}")
 ```
 
 ## Metric