tensorflow-协调器

修改了代码跑不出来，可能版本变了？

–
即内存队列前,有一个文件队列，文件队列存放的参与训练的文件名，设为N个epoch，则文件名队列中有N个批次的所有文件名。

tf.train.slice_input_producer

# -*- coding:utf-8 -*-  
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准备放入文件名称队列  
    input_queue = tf.train.slice_input_producer([images, label], num_epochs=epoch_num, shuffle=False)  

    #从文件名称队列中读取文件准备放入文件队列  
    image_batch, label_batch = tf.train.batch(input_queue, batch_size=batch_size, num_threads=2, capacity=64, allow_smaller_final_batch=False)  
    return image_batch, label_batch  

image_batch, label_batch = get_batch_data(batch_size=batch_size)  


with tf.Session() as sess:  

    # 先执行初始化工作  
    sess.run(tf.global_variables_initializer())  
    ## ?
    sess.run(tf.local_variables_initializer())  

    # 开启一个协调器  
    coord = tf.train.Coordinator()  
    # 使用start_queue_runners 启动队列填充  
    threads = tf.train.start_queue_runners(sess, coord)  

    try:  
        while not coord.should_stop():  
            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])  
            print(image_batch_v.shape, label_batch_v)  
    except tf.errors.OutOfRangeError:  #如果读取到文件队列末尾会抛出此异常  
        print("done! now lets kill all the threads……")  
    finally:  
        # 协调器coord发出所有线程终止信号  
        coord.request_stop()  
        print('all threads are asked to stop!')  
    coord.join(threads) #把开启的线程加入主线程，等待threads结束  
    print('all threads are stopped!')

image size (5, 224, 224, 3),label size :(5,)
************
(3, 224, 224, 3) [0 1 2]
************
(3, 224, 224, 3) [3 4 0]
************
(3, 224, 224, 3) [1 2 3]
************
done! now lets kill all the threads……
all threads are asked to stop!
all threads are stopped!

import tensorflow as tf  

images = ['img1', 'img2', 'img3', 'img4', 'img5']  
labels= [1,2,3,4,5]  

epoch_num= 10
#tf.train.slice_input_producer是一个tensor生成器，
#作用是按照设定，每次从一个tensor列表中按顺序或者随机抽取出一个tensor放入文件名队列。 
f = tf.train.slice_input_producer([images, labels],num_epochs=2,shuffle=True)  

with tf.Session() as sess:  
    sess.run(tf.global_variables_initializer())  
    #这个必须有局部变量f属于
    sess.run(tf.local_variables_initializer())
    # 开启一个协调器  
    coord = tf.train.Coordinator()  
    #还需要调用tf.train.start_queue_runners 函数来启动执行文件名队列填充的线程
    # 使用start_queue_runners 启动队列填充  
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)  
    for i in range(epoch_num):  
        k = sess.run(f)  
        print ('************************')  
        print(i,k)
    # 协调器coord发出所有线程终止信号 
    coord.request_stop()
    #把开启的线程加入主线程，等待threads结束  
    coord.join(threads)
    print("Done Well !!!")

************************
0 [b'img1', 1]
************************
1 [b'img4', 4]
************************
2 [b'img3', 3]
************************
3 [b'img2', 2]
************************
4 [b'img5', 5]
************************
5 [b'img3', 3]
************************
6 [b'img1', 1]
************************
7 [b'img4', 4]
************************
8 [b'img5', 5]
************************
9 [b'img2', 2]
Done Well !!!

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

tf.train.slice_input_producer

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