10类RGB图片：airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
图片尺寸32x32,50000张训练图片，10000张测试图片
项目源码：https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10
官方示例代码文件
cifar10.py 建立预测模型
cifar10_input.py tensorflow中读取训练图片
cifar10_input_test.py 测试用例文件
cifar10_train.py 使用单个cpu或gpu训练模型
cifar10_train_multi_gpu.py 使用多个GPU训练模型
cifar10_eval.py 在测试集上测试模型的性能
数据集数据文件
batches.meta.txt 存储每个类别的英文名称
data_batch_1.bin 训练数据1，每个文件以二进制存储了10000张32x32的彩色图片和对应的标签，1000个样本中每个样本由3073个字节组成，第一个字节为标签，剩下3072个字节为图像数据
data_batch_2.bin 训练数据2
data_batch_3.bin 训练数据3
data_batch_4.bin 训练数据4
data_batch_5.bin 训练数据5
test_batch.bin 测试图像和测试图像的标签
代码解析
cifar10_input.py
1 from __future__ import absolute_import 2 from __future__ import division 3 from __future__ import print_function 4 import os 5 from six.moves import xrange # pylint: disable=redefined-builtin 6 import tensorflow as tf 7 8 # 原图像的尺度为32*32,但根据常识，信息部分通常位于图像的中央， 9 # 这里定义了以中心裁剪后图像的尺寸24x24 10 IMAGE_SIZE = 24 11 NUM_CLASSES = 10 12 NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000 13 NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000 14 15 #读取数据函数 16 def read_cifar10(filename_queue): 17 """Reads and parses examples from CIFAR10 data files. 18 Recommendation: if you want N-way read parallelism, call this function 19 N times. This will give you N independent Readers reading different 20 files & positions within those files, which will give better mixing of 21 examples. 22 Args: 23 filename_queue: A queue of strings with the filenames to read from. 24 Returns: 25 An object representing a single example, with the following fields: 26 height: number of rows in the result (32) 27 width: number of columns in the result (32) 28 depth: number of color channels in the result (3) 29 key: a scalar string Tensor describing the filename & record number 30 for this example. 31 label: an int32 Tensor with the label in the range 0..9. 32 uint8image: a [height, width, depth] uint8 Tensor with the image data 33 """ 34 35 # 定义一个空的类对象，类似于c语言里面的结构体定义 36 class CIFAR10Record(object): 37 pass 38 result = CIFAR10Record() 39 label_bytes = 1 # 2 for CIFAR-100 40 result.height = 32 41 result.width = 32 42 result.depth = 3 43 #一张图像占用空间空间 44 image_bytes = result.height * result.width * result.depth 45 #数据集中一条记录占的字节数 46 record_bytes = label_bytes + image_bytes 47 # 定义一个Reader，它每次能从文件中读取固定字节数 48 reader = tf.FixedLengthRecordReader(record_bytes=record_bytes) 49 # 返回从filename_queue中读取的(key, value)对，key和value都是字符串类型的tensor，并且当队列中的某一个文件读完成时，该文件名会dequeue 50 result.key, value = reader.read(filename_queue) 51 # 解码操作可以看作读二进制文件，把字符串中的字节转换为数值向量,每一个数值占用一个字节,在[0, 255]区间内，因此out_type要取uint8类型 52 record_bytes = tf.decode_raw(value, tf.uint8)#将字符串Tensor转化成uint8类型 53 # 从一维tensor对象中截取一个slice,类似于从一维向量中筛选子向量，因为record_bytes中包含了label和feature，故要对向量类型tensor进行'parse'（语法分析）操作 54 #分别表示待截取片段的起点和长度，并且把标签由之前的uint8转变成int32数据类型 55 result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32) 56 #提取每条记录中的图像数据为result.depth, result.height, result.width 57 depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes],[label_bytes + image_bytes]),[result.depth, result.height, result.width]) 58 #改变为height, width, depth 59 result.uint8image = tf.transpose(depth_major, [1, 2, 0])#转置 60 return result 61 62 # 构建一个排列后的一组图片和分类 63 def _generate_image_and_label_batch(image, label, min_queue_examples, 64 batch_size, shuffle): 65 """Construct a queued batch of images and labels. 66 Args: 67 image: 3-D Tensor of [height, width, 3] of type.float32. 68 label: 1-D Tensor of type.int32 69 min_queue_examples: int32, minimum number of samples to retain 70 in the queue that provides of batches of examples. 71 batch_size: Number of images per batch. 72 shuffle: boolean indicating whether to use a shuffling queue. 73 Returns: 74 images: Images. 4D tensor of [batch_size, height, width, 3] size. 75 labels: Labels. 1D tensor of [batch_size] size. 76 """ 77 78 #线程数 79 num_preprocess_threads = 16 80 #布尔指示是否使用一个shuffling队列 81 if shuffle: #随即填充张量创建批次 82 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) 83 else: 84 # tf.train.batch(tensors, batch_size, num_threads=1, capacity=32,enqueue_many=False, shapes=None, dynamic_pad=False,allow_smaller_final_batch=False, shared_name=None, name=None) 85 #这里是用队列实现，已经默认使用enqueue_runner将enqueue_runner加入到Graph'senqueue_runner集合中 86 #其默认enqueue_many=False时，输入的tensor为一个样本【x,y,z】,输出为Tensor的一批样本 87 images, label_batch = tf.train.batch([image, label],batch_size=batch_size,num_threads=num_preprocess_threads,capacity=min_queue_examples + 3 * batch_size) 88 #将训练图片可视化，可拱直接检查图片正误 89 tf.summary.image('images', images) 90 return images, tf.reshape(label_batch, [batch_size]) 91 92 # 为CIFAR评价构建输入 93 # data_dir路径 94 # batch_size一个组的大小 95 def distorted_inputs(data_dir, batch_size): 96 """ 97 Construct distorted input for CIFAR training using the Reader ops. 98 Args: 99 data_dir: Path to the CIFAR-10 data directory. 100 batch_size: Number of images per batch. 101 Returns: 102 images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. 103 labels: Labels. 1D tensor of [batch_size] size. 104 """ 105 # 那5个数据文件名1-5 106 filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] 107 for f in filenames: 108 if not tf.gfile.Exists(f): 109 raise ValueError('Failed to find file: ' + f) 110 111 filename_queue = tf.train.string_input_producer(filenames) 112 read_input = read_cifar10(filename_queue) 113 reshaped_image = tf.cast(read_input.uint8image, tf.float32) 114 height = IMAGE_SIZE 115 width = IMAGE_SIZE 116 #对图片随机裁剪成24x24 117 distorted_image = tf.random_crop(reshaped_image, [height, width, 3]) 118 # 水平（从左向右）随机翻转图形，0.5的概率 119 distorted_image = tf.image.random_flip_left_right(distorted_image) 120 # 通过随即因子调整图像亮度 121 distorted_image = tf.image.random_brightness(distorted_image,max_delta=63) 122 # 通过随机因子调整图像对比度 123 distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8) 124 float_image = tf.image.per_image_standardization(distorted_image) 125 # 设置张量的shape 126 float_image.set_shape([height, width, 3]) 127 read_input.label.set_shape([1]) 128 # 确保洗牌的随机性 129 min_fraction_of_examples_in_queue = 0.4 130 min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue) 131 print ('Filling queue with %d CIFAR images before starting to train. This will take a few minutes.' % min_queue_examples) 132 # Generate a batch of images and labels by building up a queue of examples. 133 return _generate_image_and_label_batch(float_image, read_input.label,min_queue_examples, batch_size,shuffle=True) 134 135 # 为CIFAR评价构建输入 136 # eval_data使用训练还是评价数据集 137 # data_dir路径 138 # batch_size一个组的大小 139 def inputs(eval_data, data_dir, batch_size): 140 """ 141 Construct input for CIFAR evaluation using the Reader ops. 142 Args: 143 eval_data: bool, indicating if one should use the train or eval data set. 144 data_dir: Path to the CIFAR-10 data directory. 145 batch_size: Number of images per batch. 146 Returns: 147 images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. 148 labels: Labels. 1D tensor of [batch_size] size. 149 """ 150 if not eval_data: 151 filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)] 152 num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN 153 else: 154 filenames = [os.path.join(data_dir, 'test_batch.bin')] 155 num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL 156 157 for f in filenames: 158 if not tf.gfile.Exists(f): 159 raise ValueError('Failed to find file: ' + f) 160 161 # Create a queue that produces the filenames to read. 162 # 文件名队列 163 #def string_input_producer(string_tensor, 164 # num_epochs=None, 165 # shuffle=True, 166 # seed=None, 167 # capacity=32, 168 # shared_name=None, 169 # name=None, 170 # cancel_op=None): 171 #根据上面的函数可以看出下面的这个默认对输入队列进行shuffle，string_input_producer返回的是字符串队列， 172 #使用enqueue_runner将enqueue_runner加入到Graph'senqueue_runner集合中 173 filename_queue = tf.train.string_input_producer(filenames) 174 # 从文件队列中读取解析出的图片队列 175 #read_cifar10从输入文件名队列中读取一条图像记录 176 read_input = read_cifar10(filename_queue) 177 # 将记录中的图像记录转换为float32 178 reshaped_image = tf.cast(read_input.uint8image, tf.float32) 179 height = IMAGE_SIZE 180 width = IMAGE_SIZE 181 # Crop the central [height, width] of the image. 24x24 182 resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,height, width) 183 184 # Subtract off the mean and divide by the variance of the pixels. 185 # 对图像数据进行归一化 186 float_image = tf.image.per_image_standardization(resized_image) 187 188 # Set the shapes of tensors. 189 float_image.set_shape([height, width, 3]) 190 read_input.label.set_shape([1]) 191 192 # Ensure that the random shuffling has good mixing properties. 193 min_fraction_of_examples_in_queue = 0.4 194 min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue) 195 196 # Generate a batch of images and labels by building up a queue of examples. 197 #根据当前记录中第一条记录的值，采用多线程的方法，批量读取一个batch中的数据 198 return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)