import tensorflow as tf
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation="relu", input_shape=(X_train.shape[1],)),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Dense(32, activation="relu"),
    tf.keras.layers.Dense(1, activation="sigmoid"),
])

model.compile(
    optimizer="adam",
    loss="binary_crossentropy",
    metrics=["accuracy"],
)

history = model.fit(
    X_train, y_train,
    epochs=30,
    batch_size=32,
    validation_split=0.2,
    verbose=0,
)

loss, acc = model.evaluate(X_test, y_test, verbose=0)
print(f"Test accuracy: {acc:.3f}")