张量图与会话图不同

Question

我是tensorflow的新手。尝试训练语言模型，但发生错误。我怎么解决这个问题。下面是我的代码。

num_iterations = input_li_size // batch_size
print("number of iterations for each epoch :", num_iterations)
epochs = args.epochs
num_steps = num_iterations * epochs + 1

with tf.Session(graph=graph) as session:
    init.run()
    print("Initialized - Tensorflow")

    average_loss = 0
    for step in range(num_steps):
        batch_inputs, batch_labels = generate_batch(step, batch_size)

        word_list = []
        for word in batch_inputs:
            word_list.append(word_to_pos_dict[word])

        feed_dict = {}
        for i in range(batch_size):
            feed_dict[words_matrix[i]] = word_list[i]
        feed_dict[train_inputs] = batch_inputs
        feed_dict[train_labels] = batch_labels

        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0

        if step % 20000 == 0:
            pos_embed = pos_embeddings.eval()

            # Print nearest words
            sim = similarity.eval()
            for i in range(valid_size):
                valid_pos = pos_reverse_dict[valid_examples[i]]
                top_k = 8
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = 'Nearest to %s:' % str(valid_pos)
                for k in range(top_k):
                    close_word = pos_reverse_dict[nearest[k]]
                    log_str = '%s %s,' % (log_str, str(close_word))
                print(log_str)

 # Save vectors
save_model(pos_li, pos_embeddings.eval(), "pos.vec")

这是实际结果。

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-64-d994e55dced8> in <module>()
      1 # Save vectors
----> 2 save_model(pos_li, pos_embeddings.eval(), "pos.vec")

~\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py in eval(self, session)
   1649       A numpy `ndarray` with a copy of the value of this variable.
   1650     """
-> 1651     return self._variable.eval(session=session)
   1652 
   1653   def initialized_value(self):

~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in eval(self, feed_dict, session)
    711 
    712     """
--> 713     return _eval_using_default_session(self, feed_dict, self.graph, session)
    714 
    715 

~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
   5146                        "`eval(session=sess)`")
   5147     if session.graph is not graph:
-> 5148       raise ValueError("Cannot use the default session to evaluate tensor: "
   5149                        "the tensor's graph is different from the session's "
   5150                        "graph. Pass an explicit session to "

ValueError: Cannot use the default session to evaluate tensor: the tensor's graph is different from the session's graph. Pass an explicit session to `eval(session=sess)`.