text: import  tensorflow as tf
  from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
  
  
  def preprocess(x, y):
      """
      x is a simple image, not a batch
      """
      x = tf.cast(x, dtype=tf.float32) / 255.
      x = tf.reshape(x, [28*28])
      y = tf.cast(y, dtype=tf.int32)
      y = tf.one_hot(y, depth=10)
      return x,y
  
  
  batchsz = 128
  (x, y), (x_val, y_val) = datasets.mnist.load_data()
  print('datasets:', x.shape, y.shape, x.min(), x.max())
  
  
  
  db = tf.data.Dataset.from_tensor_slices((x,y))
  db = db.map(preprocess).shuffle(60000).batch(batchsz)
  ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
  ds_val = ds_val.map(preprocess).batch(batchsz) 
  
  sample = next(iter(db))
  print(sample[0].shape, sample[1].shape)
  
  
  network = Sequential([layers.Dense(256, activation='relu'),
                       layers.Dense(128, activation='relu'),
                       layers.Dense(64, activation='relu'),
                       layers.Dense(32, activation='relu'),
                       layers.Dense(10)])
  network.build(input_shape=(None, 28*28))
  network.summary()
  
  
  
  
  network.compile(optimizer=optimizers.Adam(lr=0.01),
  		loss=tf.losses.CategoricalCrossentropy(from_logits=True),
  		metrics=['accuracy']
  	)
  
  network.fit(db, epochs=5, validation_data=ds_val, validation_freq=2)
   
  network.evaluate(ds_val)
  
  sample = next(iter(ds_val))
  x = sample[0]
  y = sample[1] # one-hot
  pred = network.predict(x) # [b, 10]
  # convert back to number 
  y = tf.argmax(y, axis=1)
  pred = tf.argmax(pred, axis=1)
  
  print(pred)
  print(y)
  