def train()

in china/2020_GCR_Kubeflow_Workshop/resources/mnist-tensorflow-jupyter.py [0:0]

• 20 lines of code
• 3 McCabe index (conditional complexity)

def train(train_images, train_labels, epochs, model_summary_path):
  if model_summary_path:
    logdir=model_summary_path # + datetime.now().strftime("%Y%m%d-%H%M%S")
    tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

  model = keras.Sequential([
    keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3,
                        strides=2, activation='relu', name='Conv1'),
    keras.layers.Flatten(),
    keras.layers.Dense(10, activation=tf.nn.softmax, name='Softmax')
  ])
  model.summary()

  model.compile(optimizer=tf.train.AdamOptimizer(),
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy']
                )
  if model_summary_path:
    model.fit(train_images, train_labels, epochs=epochs, callbacks=[tensorboard_callback])
  else:
    model.fit(train_images, train_labels, epochs=epochs)

  return model