added "Usage" section in README.md

README.md

@@ -19,6 +19,60 @@ Code: https://github.com/sambitmukherjee/dlwpt-exercises/blob/main/chapter_7/exe
 
 Experiment tracking: https://wandb.ai/sadhaklal/mlp-cifar2-v2
 
+## Usage
+
+!pip install -q datasets
+
+from datasets import load_dataset
+
+cifar10 = load_dataset("cifar10")
+label_map = {0: 0.0, 2: 1.0}
+class_names = ['airplane', 'bird']
+cifar2_train = [(example['img'], label_map[example['label']]) for example in cifar10['train'] if example['label'] in [0, 2]]
+cifar2_val = [(example['img'], label_map[example['label']]) for example in cifar10['test'] if example['label'] in [0, 2]]
+
+example = cifar2_val[0]
+img, label = example
+
+import torch
+from torchvision.transforms import v2
+
+val_tfms = v2.Compose([
+    v2.ToImage(),
+    v2.ToDtype(torch.float32, scale=True),
+    v2.Normalize(mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616])
+])
+img = val_tfms(img)
+batch = img.reshape(-1).unsqueeze(0) # Flatten.
+
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+class MLPForCIFAR2(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(3072, 64), # Hidden layer.
+            nn.Tanh(),
+            nn.Linear(64, 1) # Output layer.
+        )
+
+    def forward(self, x):
+        return self.mlp(x)
+
+model = MLPForCIFAR2.from_pretrained("sadhaklal/mlp-cifar2-v2")
+model.eval()
+
+import torch.nn.functional as F
+
+with torch.no_grad():
+    logits = model(batch)
+    proba = F.sigmoid(logits.squeeze())
+    pred = int(proba.item() > 0.5)
+
+print(f"Predicted class: {class_names[pred]}")
+print(f"Predicted class probabilities ('airplane' vs. 'bird'): {[proba.item(), 1 - proba.item()]}")
+
 ## Metric
 
-Accuracy on cifar2_val: 0.829
+Accuracy on `cifar2_val`: 0.829