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  • Seach in google: "tensorflow:Error encountered when serializing"

    Seach in google:   "tensorflow:Error encountered when serializing"

    1. https://www.codetd.com/article/2304143

    不加上这句的话,运行时会报如下警告。不过,运行结果都没啥问题…

    WARNING:tensorflow:Error encountered when serializing global_step.
    Type is unsupported, or the types of the items don't match field type in CollectionDef.
    'Tensor' object has no attribute 'to_proto'



    2.  https://www.itread01.com/content/1545194888.html

    PS:執行過程中可能會有warning:

    ‘list’ object has no attribute ‘name’
    WARNING:tensorflow:Error encountered when serializing lstm_output_embeddings.
    Type is unsupported, or the types of the items don’t match field type in CollectionDef.

    應該不用擔心,還是能夠繼續執行的,後面也不受影響

    3. https://github.com/tflearn/tflearn/issues/190#issuecomment-231545279

    This warning shouldn't impact your training, this is just linked with variable serialization when saving the model, I will check what is wrong there.
    In general, LSTM performs better than simpleRNN as they can 'remember' information longer, that can explain the difference in accuracy you noticed.

    4 .http://www.linzehui.me/2018/08/12/%E7%A2%8E%E7%89%87%E7%9F%A5%E8%AF%86/%E5%A6%82%E4%BD%95%E5%B0%86ELMo%E8%AF%8D%E5%90%91%E9%87%8F%E7%94%A8%E4%BA%8E%E4%B8%AD%E6%96%87/

    PS:运行过程中可能会有warning:

    ‘list’ object has no attribute ‘name’
    WARNING:tensorflow:Error encountered when serializing lstm_output_embeddings.
    Type is unsupported, or the types of the items don’t match field type in CollectionDef.

    应该不用担心,还是能够继续运行的,后面也不受影响。

    5. https://github.com/tensorflow/tensorflow/issues/9939

    WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.
    Type is unsupported, or the types of the items don't match field type in CollectionDef.
    'dict' object has no attribute 'name'
    

    However, a metagraphdef does export and I am able to successfully use it to recreate the trained model. After playing around with simpler architectures, it looks like the problem comes from the average pooling I do at the end, which involves a call to tf.contrib.layers.avg_pool2d. For a trivial example that elicits this warning, please see the script at

    https://gist.github.com/nryant/1f69cda71fd6a468fa5641855199f843

     
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  • 原文地址:https://www.cnblogs.com/morganh/p/10288438.html
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