保存和恢复tensorflow模型（LSTM）的问题

我正在开发一个生成文本的LSTM，在重用以前训练过的模型时遇到了问题。在将作为资源使用时，我在下面分解了我的代码

在这里，我建立了我的所有变量：

graph = tf.Graph()

with graph.as_default():
    global_step = tf.Variable(0)

    data = tf.placeholder(tf.float32, [batch_size, len_section, char_size])
    labels = tf.placeholder(tf.float32, [batch_size, char_size])

    .....

    #Reset at the beginning of each test
    reset_test_state = tf.group(test_output.assign(tf.zeros([1, hidden_nodes])), 
                                test_state.assign(tf.zeros([1, hidden_nodes])))

    #LSTM
    test_output, test_state = lstm(test_data, test_output, test_state)
    test_prediction = tf.nn.softmax(tf.matmul(test_output, w) + b)

    saver = tf.train.Saver()

在这里，我正在训练我的模型，并在30次迭代中保存一个检查点

with tf.Session(graph = graph) as sess:
    tf.global_variables_initializer().run()
    offset = 0

    for step in range(10000):

        offset = offset % len(X)

        if offset <= (len(X) - batch_size):

            batch_data = X[offset: offset + batch_size]
            batch_labels = y[offset:offset+batch_size]
            offset += batch_size

        else: 
            to_add = batch_size - (len(X) - offset)
            batch_data = np.concatenate((X[offset: len(X)], X[0: to_add]))
            batch_labels = np.concatenate((y[offset: len(X)], y[0: to_add]))
            offset = to_add

        _, training_loss = sess.run([optimizer, loss], feed_dict = {data : batch_data, labels : batch_labels})

        if step % 10 == 0:
            print('training loss at step %d: %.2f (%s)' % (step, training_loss, datetime.datetime.now()))

        if step % save_every == 0:
            saver.save(sess, checkpoint_directory + '/model.ckpt', global_step=step)

        if step == 30:
            break

执行此操作后，我得到以下错误：

DataLossError (see above for traceback): Unable to open table file /ckpt/model.ckpt-30.meta: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?

---

我不太确定我做错了什么。教程看起来很简单，但我显然遗漏了一些东西。任何类型的反馈都将非常感谢。

首先，请注意，如果您在从检查点恢复后初始化所有变量，您将获得它们的随机初始值，而不是经过训练的值

其次，如果您使用

tf.estimator.estimator

而不是自己实现它，那么保存/恢复正确就容易多了

第三，我不明白您是如何通过

model-150.meta

进行恢复的，但看到了关于

model-30.meta的错误。不过，我认为您应该只通过
model-30
（不带.meta后缀）

DataLossError (see above for traceback): Unable to open table file /ckpt/model.ckpt-30.meta: Data loss: not an sstable (bad magic number): perhaps your file is in a different file format and you need to use a different restore operator?