Question

我的系统中有3500张图片。我想在每张图片上附上标签以便学习所以我做了答案清单

local_name = {'grassland': '[1,0,0,0,0,0,0]', 'canyon':'[0,1,0,0,0,0,0]', 'forest':'[0,0,1,0,0,0,0]',
'king\'s load':'[0,0,0,1,0,0,0]', 'lake':'[0,0,0,0,1,0,0]', 'desert':'[0,0,0,0,0,1,0]', 'ruin':'[0,0,0,0,0,0,1]'}
answer = []

def make_answer():
    for i in range (0, 3500):
        if i<500 :
            answer.append(local_name['grassland'])
        elif i<1000 :
            answer.append(local_name['canyon'])
        elif i<1500 :
            answer.append(local_name['forest'])
        elif i<2000 :
            answer.append(local_name['king\'s load'])
        elif i<2500 :
            answer.append(local_name['lake'])
        elif i<3000 :
            answer.append(local_name['desert'])
        elif i<3500 :
            answer.append(local_name['ruin'])
        else :
            print("not exist")

我进入队列

def input_pipeline(batch_size,train_logical = True):
    images = ['img/background/back_{}.png'.format(i) for i in range(0,3500)]
    image_queue = tf.train.string_input_producer([images,answer],shuffle=False) #error point
    image,label = read_image_files(image_queue) 

    min_after_dequeue = 5000
    capacity = min_after_dequeue + 3*batch_size
    example_batch, label_batch = tf.train.shuffle_batch([image,label],batch_size=batch_size,capacity=capacity,min_after_dequeue=min_after_dequeue)
    print(example_batch.shape,label_batch.shape)
    return (example_batch, label_batch)

ValueError：形状（3500，）和（）不兼容

答案的大小，图像都是3500 我该如何解决？

如何将标签附加到输入管道

0 个答案: