代码如下：
#读取文件数据
def read_data(file_queue):
# 读取的时候需要跳过第一行
reader = tf.TextLineReader(skip_header_lines=1)
key, value = reader.read(file_queue)
# 对于数据源中空的值设置默认值
record_defaults = [[''], [''], [''], [''], [0.], [0.], [0.], [0.], [''],[0], [''], [0.], [''], [''], [0]]
# 定义decoder，每次读取的执行都从文件中读取一行。然后，decode_csv 操作将结果解析为张量列表
province, city, address, postCode, longitude,latitude, price, buildingTypeId, buildingTypeName, tradeTypeId, tradeTypeName, expectedDealPrice, listingDate, delislingDate, daysOnMarket = tf.decode_csv(value, record_defaults)
return tf.stack([price,expectedDealPrice]),daysOnMarket
#批量获取
def create_pipeline(filename,batch_size,num_epochs=None):
file_queue = tf.train.string_input_producer([filename],num_epochs=num_epochs)
example,dayOnMarket = read_data(file_queue)#example,label 样本和样本标签,batch_size 返回一个样本batch样本集的样本个数
min_after_dequeue = 1000#出队后队列至少剩下的数据个数，小于capacity（队列的长度）否则会报错，
capacity = min_after_dequeue+batch_size#队列的长度
#example_batch,label_batch= tf.train.shuffle_batch([example,label],batch_size=batch_size,capacity=capacity,min_after_dequeue=min_after_dequeue)#把队列的数据打乱了读取
example_batch,daysOnMarket_batch= tf.train.batch([example,dayOnMarket],batch_size=batch_size,capacity=capacity)#顺序读取
return example_batch,daysOnMarket_batch