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载入并准备好 MNIST 数据集。将样本从整数转换为浮点数：

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

将模型的各层堆叠起来，以搭建 tf.keras.Sequential 模型。为训练选择优化器和损失函数：

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, activation=´softmax´)

])

model.compile(optimizer=´adam´,

loss=´sparse_categorical_crossentropy´,

metrics=[´accuracy´])

训练并验证模型：

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test, y_test, verbose=2)