Python 3.x AssertionError：某些对象具有未还原的属性

我在官方的TensorFlow网站上培训了一个基本的LSTM文本预测。我已经设法在GTX 1050ti上训练了我的模型多达40个时代，并将检查点文件保存在一个单独的文件夹中。但是，当我现在尝试恢复模型时，会出现以下长错误：-

StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
WARNING:tensorflow:Entity <function standard_gru at 0x7f9e121324d0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function standard_gru at 0x7f9e121324d0>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function cudnn_gru at 0x7f9e120c1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function cudnn_gru at 0x7f9e120c1d40>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function standard_gru at 0x7f9e121324d0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function standard_gru at 0x7f9e121324d0>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:Entity <function cudnn_gru at 0x7f9e120c1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function cudnn_gru at 0x7f9e120c1d40>: AttributeError: module 'gast' has no attribute 'Num'
WARNING:tensorflow:From /home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py:1200: NameBasedSaverStatus.__init__ (from tensorflow.python.training.tracking.util) is deprecated and will be removed in a future version.
Instructions for updating:
Restoring a name-based tf.train.Saver checkpoint using the object-based restore API. This mode uses global names to match variables, and so is somewhat fragile. It also adds new restore ops to the graph each time it is called when graph building. Prefer re-encoding training checkpoints in the object-based format: run save() on the object-based saver (the same one this message is coming from) and use that checkpoint in the future.
Traceback (most recent call last):
  File "main.py", line 95, in <module>
    model.load_weights(checkpoint_dir)
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
    return super(Model, self).load_weights(filepath, by_name)
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/network.py", line 1398, in load_weights
    status.assert_nontrivial_match()
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py", line 917, in assert_nontrivial_match
    return self.assert_consumed()
  File "/home/awesome_ruler/.local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py", line 894, in assert_consumed
    (unused_attributes,))
AssertionError: Some objects had attributes which were not restored: {<tf.Variable 'embedding_1/embeddings:0' shape=(65, 256) dtype=float32, numpy=
array([[-0.00044268, -0.02351714, -0.01139065, ..., -0.00327835,
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       [-0.02313181,  0.04697707, -0.02350216, ...,  0.040385  ,
         0.03087702,  0.02765551],
       [ 0.0410727 ,  0.00130001,  0.0051438 , ...,  0.02899202,
         0.04258115, -0.03773504],
       ...,
       [-0.03134514,  0.01370119,  0.00993627, ..., -0.02257681,
         0.02617678,  0.03761976],
       [-0.02954974,  0.02407967,  0.02768463, ..., -0.0056519 ,
        -0.01507735,  0.04617763],
       [-0.04113789, -0.03544737,  0.01056757, ...,  0.01236727,
        -0.01791535, -0.01635399]], dtype=float32)>: ['embedding_1/embeddings'], <tf.Variable 'dense_1/kernel:0' shape=(1024, 65) dtype=float32, numpy=
array([[-6.7811467e-02, -2.5536597e-02,  5.1763237e-02, ...,
        -6.9665730e-02,  3.9457709e-02, -5.3290475e-02],
       [ 1.5835620e-02, -3.0763537e-02, -7.4058644e-02, ...,
         3.8087368e-05, -9.1508478e-03,  5.5485427e-02],
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       ...,
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      dtype=float32)>: ['dense_1/bias'], <tf.Variable 'gru_1/kernel:0' shape=(256, 3072) dtype=float32, numpy=
array([[ 0.00432818,  0.03131782,  0.00038544, ..., -0.00559966,
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       [ 0.00954719, -0.02883693, -0.03259185, ..., -0.02587803,
         0.02906795, -0.00559821]], dtype=float32)>: ['gru_1/kernel'], <tf.Variable 'gru_1/recurrent_kernel:0' shape=(1024, 3072) dtype=float32, numpy=
array([[ 9.11542401e-03,  1.50135346e-02,  2.96630897e-02, ...,
         2.25223936e-02,  2.31253020e-02, -2.96920985e-02],
       [-2.21075956e-02, -8.46013427e-06, -2.16848943e-02, ...,
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       ...,
       [ 3.39106433e-02,  6.54980540e-03, -1.27352085e-02, ...,
        -4.14674729e-03,  3.53236459e-02, -1.36333425e-02],
       [-3.50691415e-02, -1.76392253e-02,  1.67468414e-02, ...,
        -2.06982102e-02, -1.06042419e-02,  2.26641595e-02],
       [-1.14825107e-02, -3.46554294e-02, -1.83847174e-03, ...,
         2.25809850e-02,  2.45791934e-02, -2.70933360e-02]], dtype=float32)>: ['gru_1/recurrent_kernel'], <tf.Variable 'gru_1/bias:0' shape=(2, 3072) dtype=float32, numpy=
array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)>: ['gru_1/bias']}

我使用网站的

生成文本
功能来预测一些事情

我想类似的问题也被贴出来了，但没有得到回复。我正在使用Tf[GPU]2.0-beta1，这是GPU的最新Tf版本

我犯了一个非常愚蠢的错误，这个错误太小了，我怀疑是否有人能把它捡起来。在这方面：-

checkpoint_dir = 'CheckPoints/ckpt_40.index'

虽然文件名的前缀为“.index”，但由于某种原因，将该扩展名附加到变量/调用函数会导致文件因某种原因（可能是错误）而死机。更有用的是指出错误扩展名的错误

因此，对于其他有此问题的人，只需将检查点目录更改为this==>

checkpoint_dir = 'CheckPoints/ckpt_40  # .index has been removed'

请接受你自己的回答-
checkpoint_dir = 'CheckPoints/ckpt_40  # .index has been removed'