tensorflow输入管道返回多个值

Question

我正在尝试在tensorflow中创建一个用于图像分类的输入管道，因此我希望创建图像批次和相应的标签。Tensorflow文档建议我们可以使用tf.train.batch进行批量输入：

train_batch, train_label_batch = tf.train.batch(
[train_image, train_image_label],
batch_size=batch_size,
num_threads=1,
capacity=10*batch_size,
enqueue_many=False,
shapes=[[224,224,3], [len(labels),]],
allow_smaller_final_batch=True
)

然而，我在想，如果我像这样输入图表，会不会有问题：

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label_batch, logits=Model(train_batch)))

问题是，成本函数中的操作是将图像及其对应的标签出队，还是单独返回它们？从而导致训练具有错误的图像和标签。

Answer 1

为了保持图像和标签的顺序，您需要考虑几件事。

假设我们需要一个函数来提供图像和标签。

def _get_test_images(_train=False):


"""
Gets the test images and labels as a batch

Inputs:
======
_train      : Boolean if images are from training set
random_crop     : Boolean if random cropping is allowed
random_flip         : Boolean if random horizontal flip is allowed
distortion      : Boolean if distortions are allowed

Outputs:
========
images_batch    : Batch of images containing BATCH_SIZE images at a time
label_batch     : Batch of labels corresponding to the images in images_batch
idx         : Batch of indexes of images
"""

#get images and labels
_,_img_names,_img_class,index= _get_list(_train = _train)

#total number of distinct images used for train will be equal to the images
#fed in tf.train.slice_input_producer as _img_names

img_path,label,idx = tf.train.slice_input_producer([_img_names,_img_class,index],shuffle=False)

img_path,label,idx = tf.convert_to_tensor(img_path),tf.convert_to_tensor(label),tf.convert_to_tensor(idx)
img_path = tf.cast(img_path,dtype=tf.string)

#read file 
image_file = tf.read_file(img_path)

#decode jpeg/png/bmp
#tf.image.decode_image won't give shape out. So it will give error while resizing
image = tf.image.decode_jpeg(image_file)

#image preprocessing
image = tf.image.resize_images(image, [IMG_DIM,IMG_DIM])

float_image = tf.cast(image,dtype=tf.float32)

#subtracting mean and divide by standard deviation
float_image = tf.image.per_image_standardization(float_image)

#set the shape
float_image.set_shape(IMG_SIZE)
labels_original = tf.cast(label,dtype=tf.int32)
img_index = tf.cast(idx,dtype=tf.int32)

#parameters for shuffle
batch_size = BATCH_SIZE
min_fraction_of_examples_in_queue = 0.3
num_preprocess_threads = 1
num_examples_per_epoch = MAX_TEST_EXAMPLE
min_queue_examples = int(num_examples_per_epoch *
                       min_fraction_of_examples_in_queue)

images_batch, label_batch,idx = tf.train.batch(
        [float_image,label,img_index],
        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_batch)

return images_batch, label_batch,idx

在这里，tf.train.slice_input_producer([_img_names,_img_class,index],shuffle=False)是一个有趣的东西，看看如果您将shuffle=True放在哪里，它将协调所有三个数组。

第二件事是，num_preprocess_threads。只要您使用单线程进行出队操作，批处理就会以确定性的方式出现。但是多个线程将随机地对数组进行混洗。例如，对于图像0001.jpg，如果True label为1，则可能得到2或4。一旦它退出队列，它就是张量形式。对于这样的张量，tf.nn.softmax_cross_entropy_with_logits应该不会有问题。