运行seq2seq mod时发生tensorflow错误

Question

在运行RNN教程example时，我在读取数据行语句后出现以下错误：

reading data line 22500000

W tensorflow/core/common_runtime/executor.cc:1052] 0x3ef81ae60 Compute status: Not found: ./checkpoints_directory/translate.ckpt-200.tempstate15092134273276121938
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_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOA
T, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/save/tensor_names, save/save/shapes_and_slices, Variable, Variable_1, embedding_attention_seq2seq/RNN/EmbeddingWrappe
r/embedding, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2se
q/RNN/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Candidate/Line
ar/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/Mu
ltiRNNCell/Cell1/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/M
atrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/embedding_at
tention_decoder/attention_decoder/Attention_0/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/Attention_0/Linear/Matrix, embedding_attention_seq2seq/embedding_attenti
on_decoder/attention_decoder/AttnOutputProjection/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnOutputProjection/Linear/Matrix, embedding_attention_seq2seq/embe
dding_attention_decoder/attention_decoder/AttnV_0, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnW_0, embedding_attention_seq2seq/embedding_attention_decoder/attention_decod
er/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell0/GRUCell
/Candidate/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell0/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder
/attention_decoder/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Matrix, embedding_attentio
n_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell1/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell1/GRUCel
l/Candidate/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell1/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/at
tention_decoder/MultiRNNCell/Cell1/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/Bias, embedding_attenti
on_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell2/GRU
Cell/Gates/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/MultiRNNCell/Cell2/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/emb
edding, proj_b, proj_w)]]
global step 200 learning rate 0.5000 step-time 14.56 perplexity 2781.37
Traceback (most recent call last):
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/tran
slate/translate.py", line 264, in <module>
    tf.app.run()
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/platform
/default/_app.py", line 15, in run
    sys.exit(main(sys.argv))
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/tran
slate/translate.py", line 261, in main
    train()
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/tran
slate/translate.py", line 180, in train
    model.saver.save(sess, checkpoint_path, global_step=model.global_step)
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/training
/saver.py", line 847, in save
    self._save_tensor_name, {self._filename_tensor_name: checkpoint_file})
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/client/s
ession.py", line 401, in run
    results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
  File "/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel-out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/client/s
ession.py", line 477, in _do_run 
    e.code)
tensorflow.python.framework.errors.NotFoundError: ./checkpoints_directory/translate.ckpt-200.tempstate15092134273276121938
         [[Node: save/save = SaveSlices[T=[DT_FLOAT, DT_INT32, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/save/tensor_names, save/save/shapes_and_slices, Variable, Variable_1, embedding_attention_seq2seq/RNN/EmbeddingWrapper/embedding, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell0/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell1/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Candidate/Linear/Matrix, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Gates/Linear/Bias, embedding_attention_seq2seq/RNN/MultiRNNCell/Cell2/GRUCell/Gates/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/Attention_0/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/Attention_0/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnOutputProjection/Linear/Bias, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnOutputProjection/Linear/Matrix, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnV_0, embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/AttnW_0, embedding_attention_seq2seq/embedding_attention_decoder/attention_decod

默认/默认/_应用程序副本“，第15行 系统出口（主(系统argv)) File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/translate/translate.py“，第261行，主要 训练（） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/translate/translate.py“，130号线，列车内 模型=创建模型（sess，False） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/translate/translate.py”，第109行，在create_model中 仅前进=仅前进）
File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/models/rnn/translate/seq2seq_模型.py”，第153行，ininit 自我保护程序= 列车保护器(tf.all_变量()) File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/training/saver.py”，第693行，ininit restore_sequential=按顺序还原） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/training/saver.py“，第411行，内部版本
save_tensor=self._AddSaveOps（文件名_tensor，vars_to_save） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/training/saver.py“，第114行，in\u AddSaveOps 保存=自我保存操作（文件名_tensor，vars_to_save） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/training/saver.py“，第68行，在save_op
张量片=[vs.切片规格对于vars_to \u save中的vs） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/ops/io\u ops.py“，第149行，in\u save 张量，名称=名称） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/ops/gen_io_操作.py”，第343行，in\u save_slices 名称=名称） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/ops/op_def_库.py“，第646行，应用程序中 op_def=操作定义） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/framework/ops.py”，第1767行，create_op 原发_op=self.\u default_original_op，op_def=op_def） File“/home/temp_user/.cache/bazel/_bazel_temp_user/7cf40d683d56020fae2d5abbde7f9f05/tensorflow/bazel out/local_linux-opt/bin/tensorflow/models/rnn/translate/translate.runfiles/tensorflow/python/framework/ops.py”，第1008行，ininit self.\u traceback=\u extract_stack（）

错误：来自命令的非零返回代码“1”：进程已退出，状态为1。在

在那么这个问题的原因是什么，因为另一个语言模型示例正在工作，库也已经构建好了。根据注释，我创建了检查点目录，仍然抛出相同的错误： tensorflow/core/common\u运行时/执行人抄送：1052]0x400d2bbe0计算状态：未找到：./checkpoints_目录/翻译.ckpt-200.温度状态9246663217899500702

Answer 1

我也遇到了同样的问题。 在运行代码之前创建[checkpoint directory]可以解决这个问题！在

Answer 2

我认为这是前一个检查点没有正确保存时出现的问题之一。您可以通过以下步骤更正它。在

1.您可以删除所有检查点文件并重新开始培训：

rm checkpoint
rm translate-ckpt-*

现在，重新开始训练。在

或者，您可以删除最新的检查点并从上一个检查点启动它。在

1.转到目录，删除最新的检查点，本例为：

^{pr2}$

2.现在编辑检查点文件。你可能会看到

model_checkpoint_path: "data/translate.ckpt-200"
all_model_checkpoint_paths: "data/translate.ckpt-170"
all_model_checkpoint_paths: "data/translate.ckpt-180"
all_model_checkpoint_paths: "data/translate.ckpt-190"
all_model_checkpoint_paths: "data/translate.ckpt-200"

3.删除最后一行并将检查点设置为上一阶段。在

model_checkpoint_path: "data/translate.ckpt-190"
all_model_checkpoint_paths: "data/translate.ckpt-170"
all_model_checkpoint_paths: "data/translate.ckpt-180"
all_model_checkpoint_paths: "data/translate.ckpt-190"

4.重新开始训练。在