1. 安装并导入Tensorflow和依赖项：

from __future__ import absolute_import, division, print_function, unicode_literals

import os

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
print(tf.version.VERSION)

2. 使用 MNIST 数据集

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

# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

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)

3. 定义模型
   使用 Keras 模型子类化（model subclassing） API 构建 tf.keras 模型：

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)

model = MyModel()

为训练选择优化器与损失函数：

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()

optimizer = tf.keras.optimizers.Adam()

选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）。

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

使用 tf.GradientTape 来训练模型：

@tf.function
def train_step(images, labels):
  with tf.GradientTape() as tape:
    predictions = model(images)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

测试模型：

@tf.function
def test_step(images, labels):
  predictions = model(images)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)

保存模型

model_save_path = r"./model"
tf.saved_model.save(model, model_save_path)

加载模型

imported = tf.saved_model.load(model_save_path)

4. 训练

ckpt= tf.train.Checkpoint(optimizer=optimizer, model=model)
manager = tf.train.CheckpointManager(ckpt, './tf_ckpts', max_to_keep=3)

EPOCHS = 20

ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
  print("Restored from {}".format(manager.latest_checkpoint))
else:
  print("Initializing from scratch.")
  
for epoch in range(EPOCHS):
  train_loss.reset_states()
  train_accuracy.reset_states()
  test_loss.reset_states()
  test_accuracy.reset_states()
  for images, labels in train_ds:
    train_step(images, labels)

  if epoch % 10 == 0:
    save_path = manager.save()
	
  for test_images, test_labels in test_ds:
	  test_step(test_images, test_labels)
	
  template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
  print (template.format(epoch+1,
                        train_loss.result(),
                        train_accuracy.result()*100,
                        test_loss.result(),
	                      test_accuracy.result()*100))

5. 根据测试数据集评估模型

   见3中测试模型