# cifar10_train.py
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

# cifar10.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import tensorflow as tf
import cifar10_input
import os
import sys
import urllib
import tarfile

# cifar10_input.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow_datasets as tfds

# cifar10_train.py
# 定義全局變量
FLAGS = tf.app.flags.FLAGS # 初始化

# 定義參數(shù)：tf.app.flags.DEFINE_xxx(參數(shù)1，參數(shù)2,，參數(shù)3),，xxx為參數(shù)類型

參數(shù)1為變量名，如train_dir,，可通過FLAGS.train_dir取得該變量的值
參數(shù)2為默認(rèn)值
參數(shù)3為說明內(nèi)容,，當(dāng)不設(shè)置該變量的值時(shí)，通過FLAGS.train_dir取到的是其默認(rèn)值(/tmp/cifar10_train),，若要設(shè)置該變量的值,，可通過運(yùn)行時(shí)寫參數(shù)--train_dir '路徑'來設(shè)置(python cifar10_train.py --train_dir '路徑'） - 鍵入-h/--help，則打印說明內(nèi)容

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.""")

# cifar10.py'
# 基本模型參數(shù)
tf.app.flags.DEFINE_integer('batch_size', 128, """Number of images to process in a batch.""")
tf.app.flags.DEFINE_boolean('use_fp16', True, """Train the model using fp16.""")
tf.app.flags.DEFINE_string('data_dir', './tmp/cifar10_data', """Path to the CIFAR-10 data directory.""")
tf.app.flags.DEFINE_integer('log_frequency', 10, """How often to log results to the console.""")
DATA_URL = 'http://www.cs./~kriz/cifar-10-binary.tar.gz'

# cifar10.py
# 檢測(cè)本地是否有數(shù)據(jù)集
def maybe_download_and_extract():
    """Download and extract the tarball from Alex's website."""
    dest_directory = FLAGS.data_dir # /tmp/cifar10_data
    # 判斷文件夾是否存在,，不存在則創(chuàng)建
    if not os.path.exists(dest_directory):
        os.makedirs(dest_directory)
    # 從URL中獲得文件名：DATA_URL定義為cifar10數(shù)據(jù)集下載地址,，這里將URL最后一個(gè)斜杠后面的內(nèi)容作為文件名
    filename = DATA_URL.split('/')[-1]
    # 合并文件路徑：將文件名與數(shù)據(jù)文件夾結(jié)合得到下載文件存放的路徑
    filepath = os.path.join(dest_directory, filename)
    # 判斷文件是否存在，如果存在,，表明數(shù)據(jù)集已經(jīng)下載,，就無需再下載，如果還沒下載,，則通過urllib.request.urlretrieve直接下載文件
    if not os.path.exists(filepath):
        # 定義下載過程中打印日志的回調(diào)函數(shù)：回調(diào)函數(shù)用于顯示下載進(jìn)度,，下載進(jìn)度為當(dāng)前下載量除以總下載量
        def _progress(count, block_size, total_size):
            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, float(count * block_size) / float(total_size) * 100.0))
            sys.stdout.flush()
        # 下載數(shù)據(jù)集    
        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath,reporthook=_progress)
        print()
        # 獲得文件信息
        statinfo = os.stat(filepath)
        print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
# 定義解壓路徑
extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
# 解壓縮：判斷解壓文件夾是否存在，若存在表明數(shù)據(jù)集已經(jīng)下載并解壓了,，就不需要操作
if not os.path.exists(extracted_dir_path):
        tarfile.open(filepath, 'r:gz').extractall(dest_directory)

# cifar10_train.py
def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    # 定義記錄訓(xùn)練步數(shù)的變量
    global_step = tf.train.get_or_create_global_step()

    # Get images and labels for CIFAR-10.
    # Force input pipeline to CPU:0 to avoid operations sometimes ending up on
    # GPU and resulting in a slow down.
    with tf.device('/cpu:0'):
      # 從CIFAR-10中導(dǎo)入數(shù)據(jù)和標(biāo)簽
      images, labels = cifar10.distorted_inputs()

# cifar10.py
def distorted_inputs():
  """Construct distorted input for CIFAR training using the Reader ops.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.
  """
  images, labels = cifar10_input.distorted_inputs(batch_size=FLAGS.batch_size)

  if FLAGS.use_fp16:
    images = tf.cast(images, tf.float16)
    labels = tf.cast(labels, tf.float16)
  return images, labels

# cifar10_input.py
# Process images of this size. Note that this differs from the original CIFAR
# image size of 32 x 32. If one alters this number, then the entire model
# architecture will change and any model would need to be retrained.
IMAGE_SIZE = 24

# Global constants describing the CIFAR-10 data set.
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000

def distorted_inputs(batch_size):
  """Construct distorted input for CIFAR training using the Reader ops.

  Args:
    batch_size: Number of images per batch.

  Returns:
    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
    labels: Labels. 1D tensor of [batch_size] size.
  """
  # 要讀入的數(shù)據(jù)文件
  filenames = [os.path.join('./tmp/cifar10_data/cifar-10-batches-bin/', 'data_batch_%d.bin' % i)
               for i in range(1, 6)]
  # 如果有數(shù)據(jù)文件缺失,，拋出異常
  for f in filenames:
    # print(f)
    if not tf.gfile.Exists(f):
      raise ValueError('Failed to find file: ' + f)
  # 把要讀取的全部文件打包為一個(gè)tf內(nèi)部的queue類型，之后tf開文件就從這個(gè)queue中取目錄
  filename_queue = tf.train.string_input_producer(filenames)
  with tf.name_scope('data_augmentation'):
    # Read examples from files in the filename queue.
    # 讀取文件隊(duì)列中文件的樣本
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)

    height = IMAGE_SIZE
    width = IMAGE_SIZE

    # 用于訓(xùn)練網(wǎng)絡(luò)的圖像處理,，請(qǐng)注意應(yīng)用于圖像的許多隨機(jī)失真
    # 隨機(jī)裁剪圖像的[height, width]部分
    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])

    # 隨機(jī)水平翻轉(zhuǎn)圖像
    distorted_image = tf.image.random_flip_left_right(distorted_image)

    # 由于這些操作是不可交換的,，因此可以考慮隨機(jī)化和調(diào)整操作的順序
    # 在某范圍隨機(jī)調(diào)整圖片亮度
    distorted_image = tf.image.random_brightness(distorted_image,
                                                 max_delta=63)
    # 在某范圍隨機(jī)調(diào)整圖片對(duì)比度
    distorted_image = tf.image.random_contrast(distorted_image,
                                               lower=0.2, upper=1.8)

    # 減去平均值并除以像素的方差，白化操作：均值變?yōu)?,，方差變?yōu)?
    float_image = tf.image.per_image_standardization(distorted_image)

    # 設(shè)置張量的形狀.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    # 確保隨機(jī)shuffling有好的混合性質(zhì)
    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue)

    print('Filling queue with %d CIFAR images before starting to train. '
          'This will take a few minutes.' % min_queue_examples)

    # 通過建立一個(gè)樣本隊(duì)列來生成一批image和label
    return _generate_image_and_label_batch(float_image, read_input.label,min_queue_examples, batch_size)

def read_cifar10(filename_queue):
  """
      讀取和解析來自CIFAR10數(shù)據(jù)文件的樣本
      建議：如果您想要N路并行讀取,，請(qǐng)調(diào)用此函數(shù)N次
      這會(huì)給你N個(gè)獨(dú)立的Readers，閱讀那些文件中不同的文件和位置,，這將提供更好的混合例子

      ARGS：
      filename_queue：具有要讀取的文件名的字符串隊(duì)列,。

      返回：
      表示單個(gè)樣本的對(duì)象，包含以下字段：
      height：結(jié)果中的行數(shù)（32）
      width：結(jié)果中的列數(shù)（32）
      depth：結(jié)果中的顏色通道數(shù)量（3）
      key：描述這個(gè)例子文件名和記錄號(hào)的標(biāo)量字符串張量
      label：一個(gè)int32張量,，帶有范圍為0..9的標(biāo)簽
      uint8image：一個(gè)圖像數(shù)據(jù)的[height, width, depth] uint8 張量
      """

  # 定義返回的結(jié)果對(duì)象類
  class CIFAR10Record(object):
    pass

  result = CIFAR10Record()
  # 只有10個(gè)類別
  label_bytes = 1  # 2 for CIFAR-100
  # 32x32 RGB 的圖像
  result.height = 32
  result.width = 32
  result.depth = 3

  image_bytes = result.height * result.width * result.depth
  # 每條記錄的格式固定：label+image,，因此長(zhǎng)度固定
  record_bytes = label_bytes + image_bytes

  # 采用固定長(zhǎng)度的閱讀器，CIFAR-10格式?jīng)]有文件頭或文件尾，將header_bytes和footer_bytes保留為默認(rèn)值0
  reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)

  # 打開filename_queue中的文件,，讀取一條記錄
  result.key, value = reader.read(filename_queue)
  # 閱讀器的read方法會(huì)輸出一個(gè)key來表征輸入的文件和其中的紀(jì)錄(對(duì)于調(diào)試非常有用)
  # 同時(shí)得到一個(gè)字符串標(biāo)量,，這個(gè)字符串標(biāo)量可以被一個(gè)或多個(gè)解析器，或者轉(zhuǎn)換操作將其解碼為張量并且構(gòu)造成為樣本,。

  # 將字符串標(biāo)量轉(zhuǎn)換為長(zhǎng)度為record_bytes的uint8張量
  record_bytes = tf.decode_raw(value, tf.uint8)

  # 第一個(gè)字節(jié)代表了label,，類型轉(zhuǎn)換uint8->int32，與一般的切片操作不同,，tf.slice的第三個(gè)參數(shù)是切片的長(zhǎng)度
  result.label = tf.cast(tf.slice(record_bytes, [0], [label_bytes]), tf.int32)

  # 標(biāo)簽之后的剩余字節(jié)表示圖像,，reshape [depth * height * width] => [depth，height,，width]
  depth_major = tf.reshape(tf.slice(record_bytes, [label_bytes], [image_bytes]),
                           [result.depth, result.height, result.width])
  # 交換輸入張量的不同維度 [depth, height, width] => [height, width, depth].
  result.uint8image = tf.transpose(depth_major, [1, 2, 0])

  return result

def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size):
  """
      構(gòu)建排隊(duì)的一批圖像和標(biāo)簽
      ARGS：
       image：type.float32的[height,，width，3]的3-D張量
       label：type.int32的1-D張量
       min_queue_examples：int32,，在隊(duì)列中保留的最小樣本數(shù)量,，可提供多批樣本
       batch_size：每批次的圖像數(shù)量

       返回：
       images: Images. 4D張量 [batch_size，height,，width,，3]
       labels: Labels. 1D張量 [batch_size]
      """
  # 創(chuàng)建一個(gè)混合樣本的隊(duì)列，然后從樣本隊(duì)列中讀取batch_size的圖像+標(biāo)簽
  num_preprocess_threads = 16
  images, label_batch = tf.train.shuffle_batch([image, label], batch_size=batch_size,
                                               num_threads=num_preprocess_threads,
                                               capacity=min_queue_examples + 3 * batch_size,
                                               min_after_dequeue=min_queue_examples)
  # 在數(shù)據(jù)輸入管線的末端,我們需要有另一個(gè)隊(duì)列來執(zhí)行輸入樣本的訓(xùn)練(train),，評(píng)價(jià)(loss)和推理(inference)
  # 因此我們使用tf.train.shuffle_batch函數(shù)來對(duì)隊(duì)列中的樣本進(jìn)行亂序處理

  # 在可視化器中顯示訓(xùn)練圖像
  tf.summary.image('images', images)

  return images, tf.reshape(label_batch, [batch_size])

# cifar10_train.py
# Build a Graph that computes the logits predictions from the
    # inference model.
    logits = cifar10.inference(images)

# cifar10.py
# 盡可能地構(gòu)建好圖表，滿足促使神經(jīng)網(wǎng)絡(luò)向前反饋并做出預(yù)測(cè)的要求
TOWER_NAME = 'tower'

def inference(images):
    """
    構(gòu)建CIFAR-10模型
    ARGS：
     images：從distorted_inputs（）或inputs（）返回的圖像
    返回：
     Logits
    """
    # 我們使用tf.get_variable（）而不是tf.Variable（）來實(shí)例化所有變量,，以便跨多個(gè)GPU訓(xùn)練時(shí)能共享變量
    # 如果我們只在單個(gè)GPU上運(yùn)行此模型,，我們可以通過用tf.Variable（）替換tf.get_variable（）的所有實(shí)例來簡(jiǎn)化此功能
    
    # conv1-第一層卷積
    with tf.variable_scope('conv1') as scope: #每一層都創(chuàng)建于一個(gè)唯一的tf.name_scope之下，創(chuàng)建于該作用域之下的所有元素都將帶有其前綴
        # 5*5 的卷積核,，64個(gè)
        kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64],
                                         stddev=1e-4, wd=0.0)
        # 卷積操作,，步長(zhǎng)為1，0padding SAME,，不改變寬高,，通道數(shù)變?yōu)?4
        conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
        # 在CPU上創(chuàng)建第一層卷積操作的偏置變量
        biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
        # 加上偏置
        bias = tf.nn.bias_add(conv, biases)
        # relu非線性激活
        conv1 = tf.nn.relu(bias, name=scope.name)
        # 創(chuàng)建激活顯示圖的summary
        _activation_summary(conv1)
        
    # pool1-第一層pooling
    # 3*3 最大池化，步長(zhǎng)為2
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                         padding='SAME', name='pool1')
    # norm1-局部響應(yīng)歸一化
    # LRN層,，對(duì)局部神經(jīng)元的活動(dòng)創(chuàng)建競(jìng)爭(zhēng)機(jī)制,，使得其中響應(yīng)比較大的值變得相對(duì)更大，并抑制其他反饋較小的神經(jīng)元,，增強(qiáng)了模型的泛化能力
    norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                    name='norm1')

    # conv2-第二層卷積
    with tf.variable_scope('conv2') as scope:
        # 卷積核：5*5 ,64個(gè)
        kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64],
                                         stddev=1e-4, wd=0.0)
        conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
        biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
        bias = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(bias, name=scope.name)
        _activation_summary(conv2)

    # norm2-局部響應(yīng)歸一化
    norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                    name='norm2')
    # pool2-第二層最大池化
    pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],
                         strides=[1, 2, 2, 1], padding='SAME', name='pool2')

    # local3-全連接層,，384個(gè)節(jié)點(diǎn)
    with tf.variable_scope('local3') as scope:
        # 把單個(gè)樣本的特征拼成一個(gè)大的列向量，以便我們可以執(zhí)行單個(gè)矩陣乘法
        dim = 1
        for d in pool2.get_shape()[1:].as_list():
             dim *= d
        reshape = tf.reshape(pool2, [FLAGS.batch_size, dim])
        
        # 權(quán)重
        weights = _variable_with_weight_decay('weights', shape=[dim, 384],
                                          stddev=0.04, wd=0.004)
        # 偏置
        biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
        # relu激活
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
        #生成summary
        _activation_summary(local3)

    # local4-全連接層,，192個(gè)節(jié)點(diǎn)
    with tf.variable_scope('local4') as scope:
        weights = _variable_with_weight_decay('weights', shape=[384, 192],
                                          stddev=0.04, wd=0.004)
        biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
        _activation_summary(local4)

    # softmax, i.e. softmax(WX + b)
    # 輸出層
    with tf.variable_scope('softmax_linear') as scope:
        # 權(quán)重
        weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],
                                          stddev=1/192.0, wd=0.0)
        # 偏置
        biases = _variable_on_cpu('biases', [NUM_CLASSES],
                              tf.constant_initializer(0.0))
        # 輸出層的線性操作
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
        # 生成summary
        _activation_summary(softmax_linear)
    return softmax_linear

def _variable_with_weight_decay(name, shape, stddev, wd):
    '''
    幫助創(chuàng)建一個(gè)權(quán)重衰減的初始化變量
    
    請(qǐng)注意,，變量是用截?cái)嗟恼龖B(tài)分布初始化的
    只有在指定了權(quán)重衰減時(shí)才會(huì)添加權(quán)重衰減
    
    Args:
    name: 變量的名稱
    shape: 整數(shù)列表
    stddev: 截?cái)喔咚沟臉?biāo)準(zhǔn)差
    wd: 加L2Loss權(quán)重衰減乘以這個(gè)浮點(diǎn)數(shù).如果沒有，此變量不會(huì)添加權(quán)重衰減.
    
    Returns:
    變量張量
    '''
    var = _variable_on_cpu(name, shape,
                         tf.truncated_normal_initializer(stddev=stddev))
    if wd is not None:
        weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
        tf.add_to_collection('losses', weight_decay)
    return var


def _variable_on_cpu(name, shape, initializer):
    '''
    幫助創(chuàng)建存儲(chǔ)在CPU內(nèi)存上的變量
    ARGS：
     name：變量的名稱
     shape：整數(shù)列表
     initializer：變量的初始化操作
    返回：
     變量張量
    '''
    with tf.device('/cpu:0'): #用 with tf.device 創(chuàng)建一個(gè)設(shè)備環(huán)境, 這個(gè)環(huán)境下的 operation 都統(tǒng)一運(yùn)行在環(huán)境指定的設(shè)備上.
        var = tf.get_variable(name, shape, initializer=initializer)
    return var

def _activation_summary(x):
    '''
    為激活創(chuàng)建summary
    
    添加一個(gè)激活直方圖的summary
    添加一個(gè)測(cè)量激活稀疏度的summary
    
    ARGS：
     x：張量
    返回：
     沒有
    '''
    # 如果這是多GPU訓(xùn)練，請(qǐng)從名稱中刪除'tower_ [0-9] /'.這有助于張量板上顯示的清晰度.
    tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
    tf.summary.histogram(tensor_name + '/activations', x)
    tf.summary.scalar(tensor_name + '/sparsity',tf.nn.zero_fraction(x))

# cifar10_train.py
# Calculate loss.
    loss = cifar10.loss(logits, labels)

# cifar10.py
# 描述損失函數(shù),，往inference圖中添加生成損失（loss）所需要的操作（ops）
def loss(logits, labels):
    '''
    將L2Loss添加到所有可訓(xùn)練變量
    添加"Loss" and "Loss/avg"的summary
    ARGS：
    logits：來自inference（）的Logits
    labels：來自distorted_inputs或輸入（）的標(biāo)簽.一維張量形狀[batch_size]
    
    返回：
    float類型的損失張量
    '''
    
    labels = tf.cast(labels, tf.int64)
    # 計(jì)算這個(gè)batch的平均交叉熵?fù)p失
    # 添加一個(gè)tf.nn.softmax_cross_entropy_with_logits操作,，用來比較inference()函數(shù)所輸出的logits Tensor與labels
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
    tf.add_to_collection('losses', cross_entropy_mean)
    
    # 總損失定義為交叉熵?fù)p失加上所有的權(quán)重衰減項(xiàng)（L2損失）
    return tf.add_n(tf.get_collection('losses'), name='total_loss')

# cifar10_train.py
# Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = cifar10.train(loss, global_step)

# cifar10.py
# Constants describing the training process.
# 描述模型的訓(xùn)練
MOVING_AVERAGE_DECAY = 0.9999     # The decay to use for the moving average.
NUM_EPOCHS_PER_DECAY = 350.0      # Epochs after which learning rate decays.
LEARNING_RATE_DECAY_FACTOR = 0.1  # Learning rate decay factor.
INITIAL_LEARNING_RATE = 0.1       # Initial learning rate.

def train(total_loss, global_step):
    '''
    訓(xùn)練 CIFAR-10模型
    
    創(chuàng)建一個(gè)optimizer并應(yīng)用于所有可訓(xùn)練變量. 為所有可訓(xùn)練變量添加移動(dòng)平均值.
    ARGS：
     total_loss：loss()的全部損失
     global_step：記錄訓(xùn)練步數(shù)的整數(shù)變量
    返回：
     train_op：訓(xùn)練的op
    '''
    # 影響學(xué)習(xí)率的變量
    num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size
    decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
    
    # 根據(jù)步驟數(shù)以指數(shù)方式衰減學(xué)習(xí)率
    lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,
                                  global_step,
                                  decay_steps,
                                  LEARNING_RATE_DECAY_FACTOR,
                                  staircase=True)
    # Summary是對(duì)網(wǎng)絡(luò)中Tensor取值進(jìn)行監(jiān)測(cè)的一種Operation.這些操作在圖中是“外圍”操作，不影響數(shù)據(jù)流本身.
    # 把lr添加到觀測(cè)中
    tf.summary.scalar('learning_rate', lr)

    # 生成所有損失和相關(guān)和的移動(dòng)平均值的summary
    loss_averages_op = _add_loss_summaries(total_loss)

    # 計(jì)算梯度
    with tf.control_dependencies([loss_averages_op]):
        opt = tf.train.GradientDescentOptimizer(lr)
        grads = opt.compute_gradients(total_loss)

    # 應(yīng)用梯度.
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    # 為可訓(xùn)練變量添加直方圖summary.
    for var in tf.trainable_variables():
        tf.summary.histogram(var.op.name, var)

    # 為梯度添加直方圖summary
    for grad, var in grads:
        if grad is not None: 
            tf.summary.histogram(var.op.name + '/gradients', grad)

    # 跟蹤所有可訓(xùn)練變量的移動(dòng)平均值
    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    with tf.control_dependencies([apply_gradient_op]):
        variables_averages_op = variable_averages.apply(tf.trainable_variables())

    return variables_averages_op

def _add_loss_summaries(total_loss):
    
    '''
    往CIFAR-10模型中添加損失summary
    為所有損失和相關(guān)summary生成移動(dòng)平均值,，以便可視化網(wǎng)絡(luò)的性能
    
    ARGS：
     total_loss：loss()的全部損失
    返回：
     loss_averages_op：用于生成移動(dòng)平均的損失
    '''
    # 計(jì)算所有單個(gè)損失和總損失的移動(dòng)平均
    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
    losses = tf.get_collection('losses')
    loss_averages_op = loss_averages.apply(losses + [total_loss])

    # 把所有的單個(gè)損失和總損失添加到summary觀測(cè)中,，平均損失也添加觀測(cè)
    for l in losses + [total_loss]:
        # 將每個(gè)損失命名為損失的原始名稱+“（raw）”，并將損失的移動(dòng)平均版本命名為損失的原始名稱
        # 這一行代碼應(yīng)該已經(jīng)過時(shí)了,，執(zhí)行時(shí)提醒：
        # INFO:tensorflow:Summary name conv1/weight_loss (raw) is illegal; using conv1/weight_loss__raw_ instead.
        tf.summary.scalar(l.op.name + ' (raw)', l)
        tf.summary.scalar(l.op.name, loss_averages.average(l))

    return loss_averages_op

# cifar10_train.py
def main(argv=None):  # pylint: disable=unused-argument
    # 執(zhí)行下載和解壓數(shù)據(jù)集
    cifar10.maybe_download_and_extract()
    # 刪除之前訓(xùn)練過程中產(chǎn)生的一些臨時(shí)文件,，并重新生成目錄
    if tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.DeleteRecursively(FLAGS.train_dir)
    tf.gfile.MakeDirs(FLAGS.train_dir)
    # 執(zhí)行訓(xùn)練
    train()

if __name__ == '__main__':
    # tf.app.run()若未傳入?yún)?shù),，則執(zhí)行main(argv=...)函數(shù)，該函數(shù)同時(shí)會(huì)解析FLAGS,，若不執(zhí)行tf.app.run(),，F(xiàn)LAGS不能正常使用
    tf.app.run()

# cifar10_train.py
def train():
    ……
    class _LoggerHook(tf.train.SessionRunHook):
      """Logs loss and runtime."""

      # begin方法初始化訓(xùn)練步數(shù)和起始時(shí)間
      def begin(self):
        self._step = -1
        self._start_time = time.time()

      # before_run方法用于運(yùn)行之前返回loss的值，同時(shí)計(jì)數(shù)訓(xùn)練步數(shù)
      def before_run(self, run_context):
        self._step += 1
        return tf.train.SessionRunArgs(loss)  # Asks for loss value.

      # after_run方法用于打印相關(guān)信息
      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))

    # tf.train.MonitoredTrainingSession 為監(jiān)督訓(xùn)練的會(huì)話
    with tf.train.MonitoredTrainingSession(
        # checkpoint_dir 恢復(fù)checkpoint的文件夾
        checkpoint_dir=FLAGS.train_dir,
        # tf.train.StopAtStepHook 到達(dá)last_step時(shí)發(fā)起停止的信號(hào)
        hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
               # tf.train.NanTensorHook 用于監(jiān)督loss是否為nan,，如果沒有收到停止信息就訓(xùn)練
               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)