『TensorFlow』读书笔记_进阶卷积神经网络_分类cifar10_下

数据读取部分实现

文中采用了tensorflow的从文件直接读取数据的方式，逻辑流程如下，

实现如下，

# Author : Hellcat
# Time   : 2017/12/9

import os
import tensorflow as tf

IMAGE_SIZE = 24
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000

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
      """

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

      # Dimensions of the images in the CIFAR-10 dataset.
      label_bytes = 1  # 2 for CIFAR-100
      result.height = 32
      result.width = 32
      result.depth = 3
      image_bytes = result.height * result.width * result.depth
      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.
      # 初始化阅读器
      reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
      # 指定被阅读文件
      result.key, value = reader.read(filename_queue)

      # Convert from a string to a vector of uint8 that is record_bytes long.
      # read出来的是一个二进制的string，将它解码依照uint8格式解码
      record_bytes = tf.decode_raw(value, tf.uint8)

      # The first bytes represent the label, which we convert from uint8->int32.
      # tf.strided_slice(record_bytes, begin, end):
      # Extracts a strided slice of a tensor
      result.label = tf.cast(
          tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
      # print(result.label.get_shape()) # (?,)

      # The remaining bytes after the label represent the image, which we reshape
      # from [depth * height * width] to [depth, height, width].
      depth_major = tf.reshape(
          tf.strided_slice(record_bytes, [label_bytes],
                           [label_bytes + image_bytes]),
          [result.depth, result.height, result.width])
      # print(depth_major.get_shape()) # (3, 32, 32)

      # Convert from [depth, height, width] to [height, width, depth].
      result.uint8image = tf.transpose(depth_major, [1, 2, 0])

      return result

def distorted_inputs(data_dir, batch_size):
    '''
    读入&预处理图片
    :param data_dir: bin文件位置
    :param batch_size: 单批输出大小
    :return:
    '''
    # 读取文件名
    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
               for i in range(1, 6)]
    # 检查文件名对应的文件是否存在
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)
    # 建立文件名队列
    filename_queue = tf.train.string_input_producer(filenames)

    # 读取文件得到图片，转为tf.float32
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)

    height = IMAGE_SIZE
    width = IMAGE_SIZE
    # 随机裁剪
    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
    # 随机翻转
    distorted_image = tf.image.random_flip_left_right(distorted_image)
    # 随机亮度
    distorted_image = tf.image.random_brightness(distorted_image,max_delta=63)
    # 随机对比度
    distorted_image = tf.image.random_contrast(distorted_image,lower=0.2, upper=1.8)
    # 标准化
    float_image = tf.image.per_image_standardization(distorted_image)

    '''
    tf.Tensor.set_shape() 方法(method)会更新(updates)一个 Tensor 对象的静态 shape ，
    当静态 shape 信息不能够直接推导得出的时候，此方法常用来提供额外的 shape 信息。
    它不改变此 tensor 动态 shape 的信息。
    tf.reshape() 操作(operation)会以不同的动态 shape 创建一个新的 tensor。
    tf.strided_slice（）由于不会显示的计算tensor形状，所以其返回shape是？的，所以label
    需要使用set_shape，而image在skice之后已经reshape了，所以其tensor是有静态shape的。
    '''
    # Set the shapes of tensors.
    # float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    # Ensure that the random shuffling has good mixing properties.
    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)

    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label,
                                         min_queue_examples, batch_size,
                                         shuffle=True)
def _generate_image_and_label_batch(image, label, min_queue_examples,
                                    batch_size, shuffle):
    '''
    单batch数据生成
    :param image: reader读取的值经过处理后的tensor
    :param label: reader读取的值经过处理后的tensor
    :param min_queue_examples: 最短队列长度
    :param batch_size: batch尺寸
    :param shuffle: 是否随机化
    :return: batch的图片和标签
    '''
    num_preprocess_threads = 16
    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)

    # Display the training images in the visualizer.
    tf.summary.image('images', images)

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

def inputs(eval_data, data_dir, batch_size):
    """Construct input for CIFAR evaluation using the Reader ops.

    Args:
    eval_data: bool, indicating if one should use the train or eval data set.
    data_dir: Path to the CIFAR-10 data directory.
    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.
    """
    # 建立文件名队列
    if not eval_data:
        filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
                     for i in range(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

    # 确认文件是否存在
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)

    # 读取文件名队列
    filename_queue = tf.train.string_input_producer(filenames)

    # 读取文件
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)

    height = IMAGE_SIZE
    width = IMAGE_SIZE

    # 重置图片大小，简单裁剪或填充
    resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
                                                         height, width)

    # 标准化
    float_image = tf.image.per_image_standardization(resized_image)

    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])

    # Ensure that the random shuffling has good mixing properties.
    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(num_examples_per_epoch *
                           min_fraction_of_examples_in_queue)

    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label,
                                         min_queue_examples, batch_size,
                                         shuffle=False)

TensorFlow使用总结

tensorflow直接从文件读取数据流程

1.建立文件名队列

filename_queue = tf.train.string_input_producer(filenames)

2.阅读器初始化 & 单次读取规则设定

# 初始化阅读器
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
# 指定被阅读文件
result.key, value = reader.read(filename_queue)

3.对单次读取的数据tensor进行处理

# Convert from a string to a vector of uint8 that is record_bytes long.
# read出来的是一个二进制的string，将它解码依照uint8格式解码
record_bytes = tf.decode_raw(value, tf.uint8)
…… ……

　　由于读取来的tensor不具有静态shape，需要使用tensor.set_shape()指定shape(或者在处理中显示的赋予shape如使用reshape等函数)，否则无法建立图

read_input.label.set_shape([1])

4.将最后的规则tensor传入batch生成池节点中，输出的张量可以直接feed进网络

images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir,
                                                            batch_size=batch_size)

…… ……

image_batch, label_batch = sess.run([images_train, labels_train])
_, loss_value = sess.run(
　　　　　　　　　　　[train_op, loss],
                   feed_dict={image_holder:image_batch, label_holder:label_batch})

5.初始化队列(相关的线程控制器组件添加也在这里)

# 启动数据增强队列
tf.train.start_queue_runners()

　　附上线程控制组件使用示意，

import tensorflow as tf

sess = tf.Session()
coord = tf.train.coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)

# 训练过程

coord.request_stop()
coord.join(threads)