import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Генерация данных
X, y = make_classification(
    n_samples=10000,
    n_features=50,
    n_informative=30,
    n_redundant=20,
    random_state=42
)

# Разделение и масштабирование
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Построение модели
model = Sequential([
    Dense(128, activation='relu', input_shape=(50,)),
    Dropout(0.3),
    Dense(64, activation='relu'),
    Dropout(0.3),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

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

# Обучение модели
model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=64,
    validation_split=0.2,
    verbose=1
)

# Оценка качества
loss, accuracy = model.evaluate(X_test, y_test)