TensorFlow错误:还原检查点文件

Question

我构建了自己的卷积神经网络，其中我跟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)

此工作正常，保存检查点文件（Saver版本V2）。然后，我尝试在Nother脚本中恢复检查点以评估模型。我有这个代码

# 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)

在哪里得到错误" NotFoundError（请参见上文，请参阅上面的回音）：key conv1/variable/endentialMovingaverage在检查点中找不到" conv1/varible/是变量范围。

>。

此错误甚至在我尝试恢复变量之前。您可以帮忙解决吗？

预先感谢

thejude

Answer 1

我以这种方式解决了它：
在图中创建第二个depudentialMovingAverage（...）之前，请致电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)

我花了2个小时...