import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Загружаем данные
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2)

# Нормализуем данные
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Создаем модель
model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(32, activation='relu'),
    Dense(16, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Компилируем модель
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Обучаем модель
model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)

# Оцениваем модель
loss, accuracy = model.evaluate(X_test_scaled, y_test)
print(f"Точность модели: {accuracy:.2f}")