在tensorflow会话中处理多个文件

import tensorflow as tf

# 定义文件列表
file_list = ['file1.csv', 'file2.csv', 'file3.csv']

# 创建文件名队列
filename_queue = tf.train.string_input_producer(file_list)

# 读取文件内容
reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# 解析文件内容
record_defaults = [[0.0], [0.0], [0.0], [0.0]]
col1, col2, col3, col4 = tf.decode_csv(value, record_defaults=record_defaults)

# 构建数据批次
batch_size = 32
col1_batch, col2_batch, col3_batch, col4_batch = tf.train.batch([col1, col2, col3, col4], batch_size=batch_size)

# 创建会话
with tf.Session() as sess:
    # 启动文件名队列
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    # 读取和处理数据
    try:
        while not coord.should_stop():
            # 获取数据批次
            col1_data, col2_data, col3_data, col4_data = sess.run([col1_batch, col2_batch, col3_batch, col4_batch])
            
            # 在这里进行数据处理和模型训练
            # ...
            
    except tf.errors.OutOfRangeError:
        print('Done training -- epoch limit reached')
    finally:
        # 关闭线程和会话
        coord.request_stop()

    coord.join(threads)