Issue 1:
I am using exporter from tensorflow.contrib.session_bundle to save out my model:

x = tf.placeholder(tf.float32, (None,) + (100, 200) + (1,))
....
saver = tf_saver.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(
sess.graph.as_graph_def(),
named_graph_signatures={
'inputs': exporter.generic_signature({'images': x}),
'outputs': exporter.generic_signature({'classes': y})})

and then I load the model back in (session_bundle from tensorflow.contrib.session_bundle):

sess, meta_graph_def = session_bundle.load_session_bundle_from_path(input)

However when I inspect the Placeholder tensor corresponding to the input x, I see no shape information:

> sess.graph.get_tensor_by_name(input_name)
<tf.Tensor 'Placeholder:0' shape=<unknown> dtype=float32>

Is this by design or is there some bug causing the shape to be lost?

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?

Report from SO: http://stackoverflow.com/questions/40733752/exported-tensorflow-model-not-preserving-placeholder-shape

Environment info

Operating System:

Using gcr.io/tensorflow/tensorflow:latest-devel-gpu docker iamge

Installed version of CUDA and cuDNN:

(please attach the output of ls -l /path/to/cuda/lib/libcud*):

# ls -al /usr/local/cuda/lib64/libcud*
-rw-r--r-- 1 root root 558720 Sep 14 23:02 /usr/local/cuda/lib64/libcudadevrt.a
lrwxrwxrwx 1 root root 16 Sep 14 23:05 /usr/local/cuda/lib64/libcudart.so -> libcudart.so.8.0
lrwxrwxrwx 1 root root 19 Sep 14 23:05 /usr/local/cuda/lib64/libcudart.so.8.0 -> libcudart.so.8.0.44
-rw-r--r-- 1 root root 415432 Sep 14 23:02 /usr/local/cuda/lib64/libcudart.so.8.0.44
-rw-r--r-- 1 root root 775162 Sep 14 23:02 /usr/local/cuda/lib64/libcudart_static.a
lrwxrwxrwx 1 1000 users 13 Jul 27 05:55 /usr/local/cuda/lib64/libcudnn.so -> libcudnn.so.5
lrwxrwxrwx 1 1000 users 17 Jul 27 05:55 /usr/local/cuda/lib64/libcudnn.so.5 -> libcudnn.so.5.1.5
-rwxrwxr-x 1 1000 users 79337624 Jul 27 05:53 /usr/local/cuda/lib64/libcudnn.so.5.1.5
-rw-rw-r-- 1 1000 users 69756172 Jul 27 05:53 /usr/local/cuda/lib64/libcudnn_static.a

If installed from source, provide

The commit hash (git rev-parse HEAD)

4d924e796368163eff11a8151e8505715345f58d

The output of bazel version

# bazel version
INFO: Reading 'startup' options from /root/.bazelrc: --batch
Extracting Bazel installation...
Build label: 0.3.2
Build target: bazel-out/local-fastbuild/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar
Build time: Fri Oct 7 17:25:10 2016 (1475861110)
Build timestamp: 1475861110
Build timestamp as int: 1475861110

Issue 2:
I am following along from this point: https://www.tensorflow.org/get_started/get_started#basic_usage

It is a linear regression example.

I have approached this execution from two angles:

Angle 1: Running python and copying one line at a time.

When run this line: estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

I get this issue: WARNING:tensorflow:Using temporary folder as model directory: C:\Users\elavi\AppData\Local\Temp\tmprirdvmnn INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_tf_config': gpu_options { per_process_gpu_memory_fraction: 1 } , '_task_type': None, '_save_checkpoints_steps': None, '_master': '', '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x0000021D2049C390>, '_task_id': 0, '_save_checkpoints_secs': 600, '_save_summary_steps': 100, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_evaluation_master': '', '_tf_random_seed': None, '_environment': 'local', '_num_ps_replicas': 0}

Angle 2: It may be more helpful to see how I run it as a script as a whole. The output is:

WARNING:tensorflow:Using temporary folder as model directory: C:\Users\elavi\AppData\Local\Temp\tmp41huenz5 WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change. WARNING:tensorflow:From C:\Users\elavi\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\head.py:521: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30. Instructions for updating: Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported. 2017-02-24 00:02:36.386704: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 2017-02-24 00:02:36.388071: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 2017-02-24 00:02:36.389235: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 2017-02-24 00:02:36.389290: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-02-24 00:02:36.390010: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\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. 2017-02-24 00:02:36.390894: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\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. 2017-02-24 00:02:36.391659: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\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. 2017-02-24 00:02:36.392248: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\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. WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change. WARNING:tensorflow:From C:\Users\elavi\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\contrib\learn\python\learn\estimators\head.py:521: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30. Instructions for updating: Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported. WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.

I am interested in what is going on but I really need things simplified. A list of steps to fix this would be great. Also, if there are any better ways I could pose my problems in the future, please let me know.

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?

Many. The issue is that I am very new. My ability to understand solutions is very small. I am not well versed with the required terminology. So I have seen this problem posed and closed but I just can't follow along.

Environment info

Operating System: Windows 10

Installed version of CUDA and cuDNN: I have the latest version but I probably shouldn't because I do not have a dedicated graphics card. (I am doing initial development on Microsoft Surface Pro 4)

TF Version

1.0.0-rc2