转https://blog.csdn.net/dcrmg/article/details/79776876

#import tensorflow as tf
#
#images = ['img1', 'img2', 'img3', 'img4', 'img5']
#labels= [1,2,3,4,5]
#
#epoch_num=8
#
#f = tf.train.slice_input_producer([images, labels],num_epochs=None,shuffle=False)
#
#with tf.Session() as sess:
#    sess.run(tf.global_variables_initializer())
#    coord = tf.train.Coordinator()
#    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#    for i in range(epoch_num):
#        k = sess.run(f)
#        print ('************************')
#        print (i,k)
#
#    coord.request_stop()
#    coord.join(threads)

import tensorflow as tf
import numpy as np

# 样本个数
sample_num=5
# 设置迭代次数
epoch_num = 2
# 设置一个批次中包含样本个数
batch_size = 3
# 计算每一轮epoch中含有的batch个数
batch_total = int(sample_num/batch_size)+1

# 生成4个数据和标签
def generate_data(sample_num=sample_num):
    labels = np.asarray(range(0, sample_num))
    images = np.random.random([sample_num, 224, 224, 3])
    print('image size {},label size :{}'.format(images.shape, labels.shape))

    return images,labels

def get_batch_data(batch_size=batch_size):
    images, label = generate_data()
    # 数据类型转换为tf.float32
    images = tf.cast(images, tf.float32)
    label = tf.cast(label, tf.int32)

    #从tensor列表中按顺序或随机抽取一个tensor
    input_queue = tf.train.slice_input_producer([images, label], shuffle=False)

    image_batch, label_batch = tf.train.batch(input_queue, batch_size=batch_size, num_threads=1, capacity=64)
    return image_batch, label_batch

image_batch, label_batch = get_batch_data(batch_size=batch_size)

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess, coord)
    try:
        for i in range(epoch_num):  # 每一轮迭代
            print ('************')
            for j in range(batch_total): #每一个batch
                print ('--------')
                # 获取每一个batch中batch_size个样本和标签
                image_batch_v, label_batch_v = sess.run([image_batch, label_batch])
                # for k in
                print(image_batch_v.shape, label_batch_v)
    except tf.errors.OutOfRangeError:
        print("done")
    finally:
        coord.request_stop()
    coord.join(threads)