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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  MacOS Sierra 10.12.5
• TensorFlow installed from (source or binary):
  created environment in conda, then installed tf via pip
• TensorFlow version (use command below):
  1.2.0
• Python version:
  2.7
• Bazel version (if compiling from source):
  Not installed
• CUDA/cuDNN version:
• GPU model and memory:
• Exact command to reproduce:

You can collect some of this information using our environment capture script:

https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh

You can obtain the TensorFlow version with

python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the problem

Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
Hi, I am very new in tensorflow, and trying to check if attention_ocr works on my data.
I followed the instructions from How to use a pre-trained model but somehow failed in restoring the checkpoints.

The following is checked:

1. checkpoint is complete
2. right path to the checkpoint
3. right tf version working with V2 checkpoints
4. Nothing changes after run saver.train.restore() (predictions remained the same)

The checkpoint of attention_ocr could not be restored correctly by tf.train.saver
1.
The checkpoint was downloaded as suggested:

wget http://download.tensorflow.org/models/attention_ocr_2017_05_17.tar.gz
tar xf attention_ocr_2017_05_17.tar.gz
ls -lh

total 64216
-rw-r-----  1 liuhuichuan  staff    14M  5 18 04:07 model.ckpt-399731.data-00000-of-00001
-rw-r-----  1 liuhuichuan  staff   8.2K  5 18 04:07 model.ckpt-399731.index
-rw-r-----  1 liuhuichuan  staff    17M  5 18 04:07 model.ckpt-399731.meta

But somehow:

print os.path.exists('../attention_ocr_2017_05_17/model.ckpt-399731.data-00000-of-00001')
print os.path.exists('../attention_ocr_2017_05_17/model.ckpt-399731.index')
print tf.train.get_checkpoint_state('../attention_ocr_2017_05_17/model.ckpt-399731')

returns:

Ture
Ture
None

A very simple test is attempted:

saver = tf.train.import_meta_graph('../attention_ocr_2017_05_17/model.ckpt-399731.meta')
with tf.Session() as sess:
    print os.path.exists('./attention_ocr_2017_05_17/model.ckpt-399731.meta')
    print tf.train.get_checkpoint_state('../attention_ocr_2017_05_17/model.ckpt-399731')
    saver.restore(sess,'../attention_ocr_2017_05_17/model.ckpt-399731')

returns no error, but still not restored:

2017-08-06 16:24:41.346086: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
True
2017-08-06 16:24:41.346124: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-06 16:24:41.346129: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-06 16:24:41.346133: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
None
INFO:tensorflow:Restoring parameters from ../attention_ocr_2017_05_17/model.ckpt-399731
INFO 2017-08-06 16:24:41.000354: tf_logging.py: 82 Restoring parameters from ../attention_ocr_2017_05_17/model.ckpt-399731

Process finished with exit code 0

Source code / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.