问Tensorflow中的迭代器在初始化后返回相同的输出

EN

img_tensor = get_images_tensor()
label_tensor = get_labels_tensor()
print img_tensor.get_shape()

def create_ds_from_tensor():
    #Creating Dataset from images and labels
    stl_ds = tf.data.Dataset.from_tensor_slices({"images":img_tensor,"labels":label_tensor})
    return stl_ds


def create_next_batch_iterator(dataset,seedin = 6,batch_size = 10):
    #Setting batch size
    batch_size = batch_size
    #Shuffling the dataset
    shuffled_ds = dataset.shuffle(100,seed = seedin,reshuffle_each_iteration = True)
    #Getting batch_size number of images
    batch = shuffled_ds.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))

    #Creating a iterator for the dataset
    #iterator = batch.make_one_shot_iterator()
    iterator = batch.make_initializable_iterator()
    #Getting the next batch
    #next_batch = iterator.get_next()
    return iterator

def get_next_batch(iterator):
    return iterator.get_next()

#Create session
sess = tf.Session()
dataset = create_ds_from_tensor()
iterator = create_next_batch_iterator(dataset)
next_batch = get_next_batch(iterator)['labels']
sess.run(iterator.initializer)
count = 0
init_count = 1
while True:
    try:
        print(sess.run(next_batch))
        count+=1
    except tf.errors.OutOfRangeError:
        print 'End of Dataset'
        #break
        sess.run(iterator.initializer)
        init_count+=1
        print init_count,' initialization'
        if init_count==10:
            break  

End of Dataset
2 initialization
[6 1 7 6 9 3 8 4 6 2]

End of Dataset
3 initialization
[6 1 7 6 9 3 8 4 6 2]