added "Usage" section to README.md

README.md

@@ -20,14 +20,64 @@ Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10
 
 Experiment tracking: https://wandb.ai/sadhaklal/logistic-regression-iris
 
-## Metric
-
-The validation set contains 30% of the examples (selected at random using stratification on the target variable):
+## Usage
 
 ```
+!pip install -q datasets
+
+from datasets import load_dataset
+
+iris = load_dataset("scikit-learn/iris")
+iris.set_format("pandas")
+iris_df = iris['train'][:]
+X = iris_df[['PetalLengthCm', 'PetalWidthCm']]
+y = (iris_df['Species'] == "Iris-setosa").astype(int)
+
+class_names = ["Not Iris-setosa", "Iris-setosa"]
+
 from sklearn.model_selection import train_test_split
 
 X_train, X_val, y_train, y_val = train_test_split(X.values, y.values, test_size=0.3, stratify=y, random_state=42)
+X_means, X_stds = X_train.mean(axis=0), X_train.std(axis=0)
+
+import torch
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+device = torch.device("cpu")
+
+class LinearModel(nn.Module, PyTorchModelHubMixin):
+    def __init__(self):
+        super().__init__()
+        self.fc = nn.Linear(2, 1)
+
+    def forward(self, x):
+        out = self.fc(x)
+        return out
+
+model = LinearModel.from_pretrained("sadhaklal/logistic-regression-iris")
+model.to(device)
+
+# Inference on new data:
+import numpy as np
+
+X_new = np.array([[2.0, 0.5], [3.0, 1.0]]) # Contains data on 2 new flowers.
+X_new = ((X_new - X_means) / X_stds) # Normalize.
+X_new = torch.from_numpy(X_new).float()
+
+model.eval()
+X_new = X_new.to(device)
+with torch.no_grad():
+    logits = model(X_new)
+proba = torch.sigmoid(logits.squeeze())
+preds = (proba > 0.5).long()
+
+print(f"Predicted classes: {preds}")
+print(f"Predicted probabilities of being Iris-setosa: {proba}")
 ```
 
+## Metric
+
+As shown above, the validation set contains 30% of the examples (selected at random in a stratified fashion).
+
 Accuracy on the validation set: 1.0