TensorFlow CIFAR

我正在通过TensorFlow入门指南CNN的CIFAR-10示例

现在在cifar10_train.py中的火车功能中，我们获得了图像

images,labels = cifar10.distorted_inputs()
 
 在distorted_inputs()函数中，我们在队列中生成文件名，然后读取一条记录 
 
 # 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 = cifar10_input.read_cifar10(filename_queue)
 reshaped_image = tf.cast(read_input.uint8image, tf.float32)
 
 当我添加调试代码时， read_input变量仅包含一个带有图像及其高度，宽度和标签名称的记录。 
 
 然后该示例对读取的图像/记录应用一些变形，然后将其传递给_generate_image_and_label_batch()函数。 
 
 这个函数然后返回形状为[batch_size, 32, 32, 3] batch_size = 128 [batch_size, 32, 32, 3]的4D张量，其中batch_size = 128 。 
 
 上述函数在返回批处理时使用tf.train.shuffle_batch()函数。 
 
 我的问题是额外的记录来自tf.train.shuffle_batch()函数的位置？ 我们没有传递任何文件名或读者对象。 
 
 有人可以从我们如何从1条记录转到128条记录？ 我看了看文档，但不明白。 
I was going through the CIFAR-10 example at TensorFlow getting started guide for CNN
 
Now in the train function in cifar10_train.py we get images as 
 
images,labels = cifar10.distorted_inputs()
 
In the distorted_inputs() function we generate the filenames in a queue and then read a single record as 
 
 # 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 = cifar10_input.read_cifar10(filename_queue)
 reshaped_image = tf.cast(read_input.uint8image, tf.float32)
 
When I add debugging code, the read_input variable contains only 1 record with an image and its height, width, and label name.
 
The example then applies some distortion to the read image/record and then passes it to the _generate_image_and_label_batch() function.
 
This function then returns a 4D Tensor of shape [batch_size, 32, 32, 3] where batch_size = 128. 
 
The above function utilizes the tf.train.shuffle_batch() function when returns the batch. 
 
My question is where do the extra records come from in the tf.train.shuffle_batch() function? We are not passing it any filename or reader object. 
 
Can someone shed some light on how we go from 1 record to 128 records? I looked into the documentation but didn't understand.
                
最满意答案
                
                    
                        
 tf.train.shuffle_batch()函数可用于生成包含一批输入的（一个或多个）张量。 在内部， tf.train.shuffle_batch()创建一个tf.RandomShuffleQueue ，它在图像和标签张量上调用q.enqueue()来排队单个元素（图像 - 标签对）。 然后它返回q.dequeue_many(batch_size)的结果，它将batch_size随机选择的元素（图像标签对）连接成一批图像和一批标签。 
 
 请注意，尽管它看起来像read_input和filename_queue这样的代码有函数关系，但还有一个额外的read_input 。 简单评估tf.train.shuffle_batch()的结果将永远阻塞，因为没有元素被添加到内部队列中。 为了简化这一点，当您调用tf.train.shuffle_batch() ，TensorFlow会将QueueRunner添加到图中的内部集合中。 稍后调用tf.train.start_queue_runners() （例如，在cifar10_train.py ）将启动一个向队列中添加元素的线程，并允许继续进行训练。 线程和队列HOWTO有更多关于它如何工作的信息。 
The tf.train.shuffle_batch() function can be used to produce (one or more) tensors containing a batch of inputs. Internally, tf.train.shuffle_batch() creates a tf.RandomShuffleQueue, on which it calls q.enqueue() with the image and label tensors to enqueue a single element (image-label pair). It then returns the result of q.dequeue_many(batch_size), which concatenates batch_size randomly selected elements (image-label pairs) into a batch of images and a batch of labels.
 
Note that, although it looks from the code like read_input and filename_queue have a functional relationship, there is an additional wrinkle. Simply evaluating the result of tf.train.shuffle_batch() will block forever, because no elements have been added to the internal queue. To simplify this, when you call tf.train.shuffle_batch(), TensorFlow will add a QueueRunner to an internal collection in the graph. A later call to tf.train.start_queue_runners() (e.g. here in cifar10_train.py) will start a thread that adds elements to the queue, and enables training to proceed. The Threading and Queues HOWTO has more information on how this works.