前言

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在tensorflow的官方文档中得卷积神经网络一章，有一个使用cifar-10图片数据集的实验，搭建卷积神经网络倒不难，但是那个cifar10_input文件着实让我费了一番心思。配合着官方文档也算看的七七八八，但是中间还是有一些不太明白，不明白的mark一下，这次记下一些已经明白的。

研究

cifar10_input.py文件的read操作，主要的就是下面的代码：

if not eval_data:
  filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
         for i in xrange(1, 6)]
  num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
 else:
  filenames = [os.path.join(data_dir, 'test_batch.bin')]
  num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
...
filename_queue = tf.train.string_input_producer(filenames)

...

label_bytes = 1 # 2 for CIFAR-100
 result.height = 32
 result.width = 32
 result.depth = 3
 image_bytes = result.height * result.width * result.depth
 # Every record consists of a label followed by the image, with a
 # fixed number of bytes for each.
 record_bytes = label_bytes + image_bytes

 # Read a record, getting filenames from the filename_queue. No
 # header or footer in the CIFAR-10 format, so we leave header_bytes
 # and footer_bytes at their default of 0.
 reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
 result.key, value = reader.read(filename_queue)

 ...

 if shuffle:
  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)
 else:
  images, label_batch = tf.train.batch(
    [image, label],
    batch_size=batch_size,
    num_threads=num_preprocess_threads,
    capacity=min_queue_examples + 3 * batch_size)

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