先在這裡種個草，開篇後慢慢補完

引入各種庫，並定義引數

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import time

import tensorflow as tf

import cifar10

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',
                           """Directory where to write event logs """
                           """and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,
                            """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            """Whether to log device placement.""")
tf.app.flags.DEFINE_integer('log_frequency', 10,
                            """How often to log results to the console.""")
 

下面是訓練函式主體

def train():
  """Train CIFAR-10 for a number of steps."""

#定義一個圖，關於Graph的用法查連結  
with tf.Graph().as_default():
 
    #獲取global_step，至於為什麼這麼用有待考證。
    tf.contrib.framework.get_or_create_global_step(Graph)
    #若無輸入圖，則為預設圖

    global_step = tf.contrib.framework.get_or_create_global_step()

    # Get images and labels for CIFAR-10.
    images, labels = cifar10.distorted_inputs()

    # Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

    # Calculate loss.
    loss = cifar10.loss(logits, labels)

    # Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = cifar10.train(loss, global_step)

    #log部分以後再補充？？？？
    class _LoggerHook(tf.train.SessionRunHook):
      """Logs loss and runtime."""

      def begin(self):
        self._step = -1
        self._start_time = time.time()

      def before_run(self, run_context):
        self._step += 1
        return tf.train.SessionRunArgs(loss)  # Asks for loss value.

      def after_run(self, run_context, run_values):
        if self._step % FLAGS.log_frequency == 0:
          current_time = time.time()
          duration = current_time - self._start_time
          self._start_time = current_time

          loss_value = run_values.results
          examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
          sec_per_batch = float(duration / FLAGS.log_frequency)

          format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                        'sec/batch)')
          print (format_str % (datetime.now(), self._step, loss_value,
                               examples_per_sec, sec_per_batch))
    
    #這裡要找到stop criterion？？？？
    with tf.train.MonitoredTrainingSession(
        checkpoint_dir=FLAGS.train_dir,
        hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
               tf.train.NanTensorHook(loss),
               _LoggerHook()],
        config=tf.ConfigProto(
            log_device_placement=FLAGS.log_device_placement)) as mon_sess:
      while not mon_sess.should_stop():
        mon_sess.run(train_op)


def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  train()


if __name__ == '__main__':
  tf.app.run()
 

該部分程式碼比較簡單，在主體函式 train() 中先通過 cifar10.distorted_input() 讀取影象和標籤，然後通過cifar10.inference() 進行 logits 的估計，通過cifar10.loss() 來計算損失，再建立一個 train_op=cifar10.train() 來進行模型訓練引數更新，直到滿足 stop criterion。呼叫的函式參見相應的文章。