CIFAR10 代码分析详解——cifar10_input.py_cifar10传统机器学习代码-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_19918373/article/details/68918302

本文详细分析了CIFAR10数据集的输入代码cifar10_input.py，重点讲解了如何构建队列以及从队列中有效读取和处理机器学习训练数据的过程。

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

import os

from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

# 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

先看函数定义及说明，输入的是 filename_queue ，输出的是单个图像及其标签。注释还提示要想有 N 路并行读取，可以调用该函数 N 次，从而使读取更为乱序。

def read_cifar10(filename_queue):
  """Reads and parses examples from CIFAR10 data files.
  Recommendation: if you want N-way read parallelism, call this function
  N times.  This will give you N independent Readers reading different
  files & positions within those files, which will give better mixing of
  examples.
  Args:
    filename_queue: A queue of strings with the filenames to read from.
  Returns:
    An object representing a single example, with the following fields:
      height: number of rows in the result (32)
      width: number of columns in the result (32)
      depth: number of color channels in the result (3)
      key: a scalar string Tensor describing the filename & record number
        for this example.
      label: an int32 Tensor with the label in the range 0..9.
      uint8image: a [height, width, depth] uint8 Tensor with the image data
  """

result 记录存储要输出的信息。

  class CIFAR10Record(object):
    pass
  result = CIFAR10Record()

  # Dimensions of the images in the CIFAR-10 dataset.
  # See http://www.cs.toronto.edu/~kriz/cifar.html for a descripti