Как использовать готовые наборы данных в TensorFlow?

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import tensorflow as tf
from tensorflow.keras.datasets import mnist

# Загрузка набора данных MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Подготовка данных
x_train, x_test = x_train / 255.0, x_test / 255.0

# Создание датасета
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

# Определение модели
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

# Определение функции потерь и оптимизатора
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()

# Обучение модели
for epoch in range(5):
  for x_batch, y_batch in train_ds:
    with tf.GradientTape() as tape:
      logits = model(x_batch, training=True)
      loss_value = loss_fn(y_batch, logits)
    grads = tape.gradient(loss_value, model.trainable_weights)
    optimizer.apply_gradients(zip(grads, model.trainable_weights))
  # Оценка точности модели на тестовых данных
  for x_batch, y_batch in test_ds:
    logits = model(x_batch, training=False)
    test_loss = loss_fn(y_batch, logits)
    test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()(y_batch, logits)
  print(f'Epoch {epoch + 1}, '
        f'Loss: {loss_value:.4f}, '
        f'Accuracy: {test_accuracy:.4f}')