TensorFlow错误：恢复检查点文件。

Question

我建立了自己的卷积神经网络，在其中追踪所有可训练变量的移动平均值（tensorflow 1.0）：

variable_averages = tf.train.ExponentialMovingAverage(
        0.9999, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
train_op = tf.group(apply_gradient_op, variables_averages_op)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=10)
summary_op = tf.summary.merge(summaries)
init = tf.global_variables_initializer()
sess = tf.Session(config=tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=False))
sess.run(init)
# start queue runners
tf.train.start_queue_runners(sess=sess)

summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

# training loop
start_time = time.time()
for step in range(FLAGS.max_steps):
        _, loss_value = sess.run([train_op, loss])
        duration = time.time() - start_time
        start_time = time.time()
        assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

        if step % 1 == 0:
            # print current model status
            num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
            examples_per_sec = num_examples_per_step/duration
            sec_per_batch = duration/FLAGS.num_gpus
            format_str = '{} step{}, loss {}, {} examples/sec, {} sec/batch'
            print(format_str.format(datetime.now(), step, loss_value, examples_per_sec, sec_per_batch))
        if step % 50 == 0:
            summary_str = sess.run(summary_op)
            summary_writer.add_summary(summary_str, step)
        if step % 10 == 0 or step == FLAGS.max_steps:
            print('save checkpoint')
            # save checkpoint file
            checkpoint_file = os.path.join(FLAGS.train_dir, 'model.ckpt')
            saver.save(sess, checkpoint_file, global_step=step)

这个可以正常工作，检查点文件被保存了（保存器版本V2）。然后我尝试在另一个脚本中恢复检查点以评估模型。在那里，我有这段代码。

# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
    MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)

我遇到了一个错误，错误信息为“NotFoundError（请参见上面的回溯）：在检查点中未找到键conv1/Variable/ExponentialMovingAverage”，其中conv1/variable/是一个变量作用域。这个错误甚至出现在我尝试恢复变量之前。你能帮忙解决一下吗？谢谢！TheJude

- TheJude

Answer 1

我是这样解决的：
在创建图中的第二个ExponentialMovingAverage(...)之前调用tf.reset_default_graph()。

# reset the graph before create a new ema
tf.reset_default_graph()
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)

花了我2个小时...

- Tao