adding example program 'example.py'

example.py

@@ -0,0 +1,66 @@
+from sklearn import datasets
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+import numpy as np
+from numpyneuron import (
+    NN,
+    Relu,
+    Sigmoid,
+    CrossEntropyWithLogits,
+)
+
+
+RANDOM_SEED = 2
+
+
+def _preprocess_digits(
+    seed: int,
+) -> tuple[np.ndarray, ...]:
+    digits = datasets.load_digits(as_frame=False)
+    n_samples = len(digits.images)
+    data = digits.images.reshape((n_samples, -1))
+    y = OneHotEncoder().fit_transform(digits.target.reshape(-1, 1)).toarray()
+    X_train, X_test, y_train, y_test = train_test_split(
+        data,
+        y,
+        test_size=0.2,
+        random_state=seed,
+    )
+    return X_train, X_test, y_train, y_test
+
+
+def train_nn_classifier() -> None:
+    X_train, X_test, y_train, y_test = _preprocess_digits(seed=RANDOM_SEED)
+
+    nn_classifier = NN(
+        epochs=2_000,
+        hidden_size=16,
+        batch_size=1,
+        learning_rate=0.01,
+        loss_fn=CrossEntropyWithLogits(),
+        hidden_activation_fn=Relu(),
+        output_activation_fn=Sigmoid(),
+        input_size=64,  # 8x8 pixel grid images
+        output_size=10,  # digits 0-9
+        seed=2,
+    )
+
+    nn_classifier.train(
+        X_train=X_train,
+        y_train=y_train,
+    )
+
+    pred = nn_classifier.predict(X_test=X_test)
+
+    pred = np.argmax(pred, axis=1)
+    y_test = np.argmax(y_test, axis=1)
+
+    accuracy = accuracy_score(y_true=y_test, y_pred=pred)
+
+    print(f"accuracy on validation set: {accuracy:.4f}")
+
+
+if __name__ == "__main__":
+    train_nn_classifier()
+