在Tensorflow中读取大型训练/验证/测试数据集

将多个大数据集加载到张量流中的正确方法是什么？

我有三个大数据集（文件），分别用于训练，验证和测试。我可以通过tf.train.string_input_producer成功加载训练集，并将其输入到tf.train.shuffle_batch对象中。然后，我可以迭代地获取一批数据以优化模型。

但是，当我尝试以相同的方式加载我的验证集时，我陷入了困境，即使我没有在string_input_producer中设置num_epochs，该程序仍会说“ OutOfRange Error”。

谁能给它一些照明？除此之外，我还在思考在张量流中进行训练/验证的正确方法是什么？实际上，我没有看到任何对大型数据集进行训练和测试的示例（我进行了大量搜索）。我真奇怪...

下面的代码段。

def extract_validationset(filename, batch_size):
  with tf.device("/cpu:0"):
    queue = tf.train.string_input_producer([filename])
    reader = tf.TextLineReader()
    _, line = reader.read(queue)

    line = tf.decode_csv(...)
    label = line[0]
    feature = tf.pack(list(line[1:]))

    l, f = tf.train.batch([label, feature], batch_size=batch_size, num_threads=8)
    return l, f

def extract_trainset(train, batch_size):
  with tf.device("/cpu:0"):
    train_files = tf.train.string_input_producer([train])
    reader = tf.TextLineReader()
    _, train_line = reader.read(train_files)

    train_line = tf.decode_csv(...)

    l, f = tf.train.shuffle_batch(...,
  batch_size=batch_size, capacity=50000, min_after_dequeue=10000,  num_threads=8)
  return l, f

....

label_batch, feature_batch = extract_trainset("train", batch_size)
label_eval, feature_eval = extract_validationset("test", batch_size)

with tf.Session() as sess:
  tf.initialize_all_variables().run()
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  # Loop through training steps.
  for step in xrange(int(num_epochs * train_size) // batch_size):
    feature, label = sess.run([feature_batch, label_batch])
    feed_dict = {train_data_node: feature, train_labels_node: label}

    _, l, predictions = sess.run([optimizer, loss, evaluation], feed_dict=feed_dict)

    # after EVAL_FREQUENCY steps, do evaluation on whole test set
    if step % EVAL_FREQUENCY == 0:
      for step in xrange(steps_per_epoch):
      f, l = sess.run([feature_eval, label_eval])
      true_count += sess.run(evaluation, feed_dict={train_data_node: f, train_labels_node: l})

    print('Precision @ 1: %0.04f' % true_count / num_examples)

<!---- ERROR ---->
tensorflow.python.framework.errors.OutOfRangeError: FIFOQueue '_5_batch/fifo_queue' is closed and has insufficient elements (requested 334, current size 0)
 [[Node: batch = QueueDequeueMany[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/fifo_queue, batch/n)]]

由op u'batch'引起，定义为：