根据Tensorflow的ConvNet教程，有些点对我来说不是很明显：

• 被扭曲的图像是否实际上被添加到原始图像池中？
• 还是扭曲后的图像代替了原始图像？
• 产生了多少扭曲图像？（即定义了什么样的增强因子？）

教程中的函数流程似乎如下：

cifar_10_train.py

def train    """Train CIFAR-10 for a number of steps."""    with tf.Graph().as_default():        [...]        # Get images and labels for CIFAR-10.        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.    Raises:      ValueError: If no data_dir    """    if not FLAGS.data_dir:        raise ValueError('Please supply a data_dir')    data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')    return cifar10_input.distorted_inputs(data_dir=data_dir,                                          batch_size=FLAGS.batch_size)

最后是cifar10_input.py

def distorted_inputs(data_dir, batch_size):    """Construct distorted input for CIFAR training using the Reader ops.    Args:    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.    """    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]    for f in filenames:        if not tf.gfile.Exists(f):            raise ValueError('Failed to find file: ' + f)    # Create a queue that produces the filenames to read.    filename_queue = tf.train.string_input_producer(filenames)    # Read examples from files in the filename queue.    read_input = read_cifar10(filename_queue)    reshaped_image = tf.cast(read_input.uint8image, tf.float32)    height = IMAGE_SIZE    width = IMAGE_SIZE    # Image processing for training the network. Note the many random    # distortions applied to the image.    # Randomly crop a [height, width] section of the image.    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])    # Randomly flip the image horizontally.    distorted_image = tf.image.random_flip_left_right(distorted_image)    # Because these operations are not commutative, consider randomizing    # the order their operation.    distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)    distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)    # Subtract off the mean and divide by the variance of the pixels.    float_image = tf.image.per_image_whitening(distorted_image)    # 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)

回答：

被扭曲的图像是否实际上被添加到原始图像池中？

这取决于池的定义。在Tensorflow中，你有ops，它们是你网络图中的基本对象。在这里，数据生成本身就是一个操作。因此，你没有一个有限的训练样本集，而是有一个可能无限的从训练集中生成的样本集。

还是扭曲后的图像代替了原始图像？

正如你从所包含的源代码中看到的——样本从训练批次中取出，然后随机变换，因此使用未经修改的图像的概率非常小（特别是使用了裁剪，这总是会进行修改）。

产生了多少扭曲图像？（即定义了什么样的增强因子？）

没有这样的东西，这是一个永无止境的过程。可以将其视为对可能无限的数据源的随机访问，因为这就是这里有效发生的情况。每个批次都可能与前一个不同。