new_contents: import tensorflow.contrib.keras as keras
  
  # 1. Import data
  (train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()
  
  # 2. Preprocess data
  train_images = train_images.reshape((60000, 28 * 28))
  train_images = train_images.astype('float32') / 255
  
  test_images  = test_images.reshape((10000, 28 * 28))
  test_images  = test_images.astype('float32') / 255
  
  train_labels = keras.utils.to_categorical(train_labels)
  test_labels  = keras.utils.to_categorical(test_labels)
  
  # 3. Build model
  model = keras.models.Sequential([
      keras.layers.Dense(512, input_shape=(28 * 28,), activation='relu'),
      keras.layers.Dense(10, activation='softmax')
  ])
  
  model.compile(loss='categorical_crossentropy',
                optimizer='rmsprop',
                metrics=['accuracy'])
  
  viz = keras.callbacks.TensorBoard(log_dir='logs',
                                    write_graph=True)
  
  # 4. Train network
  model.fit(train_images,
            train_labels,
            epochs=5,
            batch_size=128,
            callbacks=[viz])
  
  # 5. Test model
  test_loss, test_acc = model.evaluate(test_images, test_labels)
  
  print 'Test accuracy:', test_acc
  print 'Complete.'
  
subject: Add mnist example script.