计算整个训练集的准确性

我正在对具有张量流的卷积神经网络进行首次测试。我使用了编程指南中推荐的队列运行器方法（请参见下面的会话定义）。输出是cnn的最后结果（此处仅给出最后一步）。label_batch_vector是训练标签批次。

output = tf.matmul(h_pool2_flat, W_fc1) + b_fc1
label_batch_vector = tf.one_hot(label_batch, 33)

correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(label_batch_vector, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

print_accuracy = tf.Print(accuracy, [accuracy])

# Create a session for running operations in the Graph.
sess = tf.Session()

# Initialize the variables (like the epoch counter).
sess.run(init_op)

# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)

try:
    while not coord.should_stop():
        # Run training steps or whatever
        sess.run(train_step)
        sess.run(print_accuracy)

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')
finally:
    # When done, ask the threads to stop.
    coord.request_stop()

# Wait for threads to finish.
coord.join(threads)
sess.close()

我的问题是准确性是针对每个批次计算的，我希望针对每个纪元进行计算。我将需要执行以下操作：初始化一个epoch_accuracy张量，对于每个在epoch中计算的批次精度，将其添加到epoch_accuracy中。在纪元末显示计算出的训练集精度。但是我没有在我实现的this队列线程中找到任何这样的示例（实际上是TensorFlow推荐的方法）。有人可以帮忙吗？