added "Usage" section to README.md

README.md

@@ -19,6 +19,65 @@ Code: https://github.com/sambitmukherjee/dlwpt-exercises/blob/main/chapter_8/exe
 
 Experiment tracking: https://wandb.ai/sadhaklal/custom-cnn-cifar2
 
+## Usage
+
+```
+!pip install -q datasets
+
+from datasets import load_dataset
+
+cifar10 = load_dataset("cifar10")
+label_map = {0: 0, 2: 1}
+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
+
+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 = tfms(img)
+batch = img.unsqueeze(0)
+
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
+
+class Net(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, stride=1)
+        self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1, stride=1)
+        self.fc1 = nn.Linear(8 * 8 * 8, 32)
+        self.fc2 = nn.Linear(32, 2)
+
+    def forward(self, x):
+        out = F.max_pool2d(torch.tanh(self.conv1(x)), kernel_size=2, stride=2) # Output shape: (batch_size, 16, 16, 16)
+        out = F.max_pool2d(torch.tanh(self.conv2(out)), kernel_size=2, stride=2) # Output shape: (batch_size, 8, 8, 8)
+        out = out.view(-1, 8 * 8 * 8) # Output shape: (batch_size, 512)
+        out = torch.tanh(self.fc1(out)) # Output shape: (batch_size, 32)
+        out = self.fc2(out) # Output shape: (batch_size, 2)
+        return out
+
+model = Net.from_pretrained("sadhaklal/custom-cnn-cifar2")
+model.eval()
+
+with torch.no_grad():
+    logits = model(batch)
+    pred = logits[0].argmax().item()
+    proba = torch.softmax(logits, dim=1)
+
+print(f"Predicted class: {class_names[pred]}")
+print(f"Predicted class probabilities ('airplane' vs. 'bird'): {proba[0].tolist()}")
+```
+
 ## Metric
 
 Accuracy on `cifar2_val`: 0.8995