code: import tensorflow as tf
  mnist = tf.keras.datasets.mnist
  
  (x_train, y_train),(x_test, y_test) = mnist.load_data()
  x_train, x_test = x_train / 255.0, x_test / 255.0
  
  model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
  ])
  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])
  
  model.fit(x_train, y_train, epochs=5)
  model.evaluate(x_test, y_test)
apis: tensorflow.keras.layers.Flatten tensorflow.keras.layers.Dropout tensorflow.keras.layers.Dense