问读入tfrecords文件时出现警告，程序一直运行不能停，print标记后发现未打印数据，如何修改？

"""这个是运行之后的警告提示：求助大神该怎么解决，我的标签内容是[1,0]或[0,1],图片数据是128*128*1，以下是我生成读取tfrecords文件的代码，以及神经网络反向传播中的代码，可能错误的地方我在其中进行了标注，求大神指出错误，多谢
2019-02-18 18:20:19.672703: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:From C:/srtp/CNN/backward.py:46: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.
WARNING:tensorflow:From C:\srtp\CNN\tfrecords_operate.py:40: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From C:\Python\lib\site-packages\tensorflow\python\training\input.py:276: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From C:\Python\lib\site-packages\tensorflow\python\training\input.py:188: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From C:\Python\lib\site-packages\tensorflow\python\training\input.py:197: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From C:\Python\lib\site-packages\tensorflow\python\training\input.py:197: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From C:\srtp\CNN\tfrecords_operate.py:41: TFRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.TFRecordDataset`.
WARNING:tensorflow:From C:\srtp\CNN\tfrecords_operate.py:63: shuffle_batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.
eee

Process finished with exit code -1"""
    
#tfrecords_operate.py

import tensorflow as tf
import csv
from PIL import Image

def write_tfRecord(tfRecordName,img_path,label_path,top_content):
    writer=tf.python_io.TFRecordWriter(tfRecordName)
    num_pic=0
    lab=[]
    with open(label_path) as lab_file:
        reader_label=lab_file.read()
        lab_initial = reader_label.split("\n")
        length = len(lab_initial)
        for i in range(length):
            lab_initial[i] = lab_initial[i].split(",")
    with open(img_path) as img_file:
        reader_img=csv.reader(img_file)
        for line in reader_img:
            s=top_content+line[0]
            img=Image.open(s)
            img=img.resize((128,128))
            img_raw=img.tobytes()
            j=0
            for j in range(length) :
                if lab_initial[j][0] in line[0]:
                     lab.append(int(lab_initial[j][1]))
                     break
            labb=[lab[num_pic],1-lab[num_pic]]
            print(s,labb)
            example=tf.train.Example(features=tf.train.Features(feature={'img_raw':tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),'lab':tf.train.Feature(int64_list=tf.train.Int64List(value=labb))}))
            writer.write(example.SerializeToString())
            num_pic += 1
    writer.close()


def read_tfRecord(tfRecord_path):
    filename_queue=tf.train.string_input_producer([tfRecord_path])
    reader=tf.TFRecordReader()
    _,serialized_example=reader.read(filename_queue)
    features=tf.parse_single_example(serialized_example,features={ 'img_raw':tf.FixedLenFeature([],tf.string),'lab':tf.FixedLenFeature([2],tf.int64)})
    img=tf.decode_raw(features['img_raw'],tf.uint8)
 
    img=tf.cast(img,tf.float32)*(1./255)
    #img=tf.cast(img,tf.uint8)
    label=tf.cast(features['lab'],tf.float32)
    return img,label

def get_tfrecord(num,is_train):
    if is_train==True:
        tfRecord_path = 'train.tfrecords'
    else:
        tfRecord_path = 'test.tfrecords'
    img,label=read_tfRecord(tfRecord_path)
    img_batch,label_batch=tf.train.shuffle_batch([img,label],batch_size=num,num_threads=2,capacity=1000,min_after_dequeue=700,seed=None,enqueue_many=True)
    #提示说这句话有警告
    return img_batch,label_batch

def main():
    write_tfRecord('train.tfrecords','C:/srtp/MURA-v1.1/train_img.csv','C:/srtp/MURA-v1.1/train_label.csv','C:/srtp/')
    write_tfRecord('test.tfrecords','C:/srtp/MURA-v1.1/test_img.csv','C:/srtp/MURA-v1.1/test_label.csv','C:/srtp/')

if __name__ == '__main__':
    main()
    
    
#backward.py

import tensorflow as tf
import forward
import tfrecords_operate as data

BATCH_SIZE=100
LEARNING_RATE_BASE=0.005
LEARNING_RATE_DECAY=0.99
LEARNING_RATE_STEP=1
REGULARIZER=0.0001
STEPS=10000
MOVING_AVERAGE_DECAY=0.99
MODEL_SAVE_PATH="model/"
MODEL_NAME='cnn'
train_num_examples=3457

def backward():
    x=tf.placeholder(tf.float32,[BATCH_SIZE,forward.IMAGE_SIZE,forward.IMAGE_SIZE,forward.NUM_CHANNELS])
    y_=tf.placeholder(tf.float32,[None,forward.OUTPUT_NODE])
    y=forward.forward(x,True,REGULARIZER)
    global_step=tf.Variable(0,trainable=False)

    ce=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
    cem=tf.reduce_mean(ce)
    loss=cem+tf.add_n(tf.get_collection('losses'))
    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, LEARNING_RATE_STEP, LEARNING_RATE_DECAY,
                                               staircase=True)
    train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)

    ema=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    ema_op=ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step,ema_op]):
        train_op=tf.no_op(name='train')

    saver=tf.train.Saver()

    with tf.Session() as sess:
        init_op=tf.global_variables_initializer()
        sess.run(init_op)

        ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess,ckpt.model_checkpoint_path)

        coord=tf.train.Coordinator()
        threads=tf.train.start_queue_runners(sess=sess,coord=coord)
        img_batch, label_batch = data.get_tfrecord(BATCH_SIZE, True)
        for i in range(STEPS):
            print('eee')#这个eee可以输出
            [xs,ys] = sess.run([img_batch,label_batch])
            reshaped_xs=tf.reshape(xs,(BATCH_SIZE,forward.IMAGE_SIZE,forward.IMAGE_SIZE,forward.NUM_CHANNELS))
            _,loss_value,step=sess.run([train_op,loss,global_step],feed_dict={x:reshaped_xs,y_:ys})
            print('888')#就是这个888一直输不出来
            if i%100==0:
                print('After %d training step(s),loss on training batch is %g.'%(step,loss_value))
                saver.save(sess,'model/cnn.ckpt',global_step=global_step)

        coord.request_stop()
        coord.join(threads)
def main():
    backward()

if __name__ == '__main__':
    main()