我是深度学习和NLP的新手，现在正尝试开始使用经过预先训练的Google BERT模型。由于我打算使用BERT构建质量检查系统，因此我决定从SQuAD相关的微调入手。
我按照the official Google BERT GitHub repository中README.md的说明进行操作。
我输入的代码如下：
export BERT_BASE_DIR=/home/bert/Dev/venv/uncased_L-12_H-768_A-12/
export SQUAD_DIR=/home/bert/Dev/venv/squad
python run_squad.py \
--vocab_file=$BERT_BASE_DIR/vocab.txt \
--bert_config_file=$BERT_BASE_DIR/bert_config.json \
--init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \
--do_train=True \
--train_file=$SQUAD_DIR/train-v1.1.json \
--do_predict=True \
--predict_file=$SQUAD_DIR/dev-v1.1.json \
--train_batch_size=12 \
--learning_rate=3e-5 \
--num_train_epochs=2.0 \
--max_seq_length=384 \
--doc_stride=128 \
--output_dir=/tmp/squad_base/
几分钟后（培训开始时），我得到了：
a lot of output omitted
INFO:tensorflow:start_position: 53
INFO:tensorflow:end_position: 54
INFO:tensorflow:answer: february 1848
INFO:tensorflow:***** Running training *****
INFO:tensorflow: Num orig examples = 87599
INFO:tensorflow: Num split examples = 88641
INFO:tensorflow: Batch size = 12
INFO:tensorflow: Num steps = 14599
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Running train on CPU
INFO:tensorflow:*** Features ***
INFO:tensorflow: name = end_positions, shape = (12,)
INFO:tensorflow: name = input_ids, shape = (12, 384)
INFO:tensorflow: name = input_mask, shape = (12, 384)
INFO:tensorflow: name = segment_ids, shape = (12, 384)
INFO:tensorflow: name = start_positions, shape = (12,)
INFO:tensorflow: name = unique_ids, shape = (12,)
INFO:tensorflow:Error recorded from training_loop: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for /home/bert/Dev/venv/uncased_L-12_H-768_A-12//bert_model.ckpt
INFO:tensorflow:training_loop marked as finished
WARNING:tensorflow:Reraising captured error
Traceback (most recent call last):
File "run_squad.py", line 1283, in <module>
tf.app.run()
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "run_squad.py", line 1215, in main
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2400, in train
rendezvous.raise_errors()
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/error_handling.py", line 128, in raise_errors
six.reraise(typ, value, traceback)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/six.py", line 693, in reraise
raise value
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2394, in train
saving_listeners=saving_listeners
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 356, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 1181, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 1211, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2186, in _call_model_fn
features, labels, mode, config)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py", line 1169, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2470, in _model_fn
features, labels, is_export_mode=is_export_mode)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1250, in call_without_tpu
return self._call_model_fn(features, labels, is_export_mode=is_export_mode)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1524, in _call_model_fn
estimator_spec = self._model_fn(features=features, **kwargs)
File "run_squad.py", line 623, in model_fn
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
File "/home/bert/Dev/venv/bert/modeling.py", line 330, in get_assignment_map_from_checkpoint
init_vars = tf.train.list_variables(init_checkpoint)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/training/checkpoint_utils.py", line 95, in list_variables
reader = load_checkpoint(ckpt_dir_or_file)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/training/checkpoint_utils.py", line 64, in load_checkpoint
return pywrap_tensorflow.NewCheckpointReader(filename)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 314, in NewCheckpointReader
return CheckpointReader(compat.as_bytes(filepattern), status)
File "/home/bert/Dev/venv/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 526, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for /home/bert/Dev/venv/uncased_L-12_H-768_A-12//bert_model.ckpt
看来tensorflow无法找到检查点文件，但是据我所知，一个tensorflow检查点“文件”实际上是三个文件，这是调用它的正确方法（带有路径和前缀）。
我相信我会将文件放在正确的位置：
(venv) bert@bert-System-Product-Name:~/Dev/venv/uncased_L-12_H-768_A-12$ pwd
/home/bert/Dev/venv/uncased_L-12_H-768_A-12
(venv) bert@bert-System-Product-Name:~/Dev/venv/uncased_L-12_H-768_A-12$ ls
bert_config.json bert_model.ckpt.data-00000-of-00001 bert_model.ckpt.index bert_model.ckpt.meta vocab.txt
我在Ubuntu 16.04 LTS上运行
，以及NVIDIA GTX 1080 Ti（CUDA 9.0）
，带有Anaconda python 3.5发行版
在虚拟环境中使用tensorflow-gpu 1.11.0。
我希望代码能够平稳运行并开始培训（微调），因为它是官方代码，并且我已按照说明放置了文件。
最佳答案
我在回答我自己的问题。
我只是通过简单地删除/中的斜杠（$BERT_BASE_DIR）解决了该问题，因此变量从'/home/bert/Dev/venv/uncased_L-12_H-768_A-12/'更改为'/home/bert/Dev/venv/uncased_L-12_H-768_A-12'。
因此，前缀"/home/bert/Dev/venv/uncased_L-12_H-768_A-12//bert_model.ckpt"中不再有双斜杠。
tensorflow中的检查点恢复功能似乎将单斜杠或双斜杠视为不同，因为我相信bash会将它们解释为相同。