<p dir="auto">The first tf.contrib.learn sample at<br>
<a href="https://www.tensorflow.org/get_started/get_started" rel="nofollow">https://www.tensorflow.org/get_started/get_started</a><br>
fails to run.</p>
<p dir="auto">The  code is right under<br>
"Basic usage<br>
Notice how much simpler the linear regression program becomes with tf.contrib.learn:"</p>
<p dir="auto">Output:</p>
<p dir="auto">WARNING:tensorflow:Using temporary folder as model directory: C:\Users\John\AppData\Local\Temp\tmpahtnt89p<br>
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.<br>
WARNING:tensorflow:From C:\Users\John\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.<br>
Instructions for updating:<br>
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.<br>
2017-02-19 11:53:57.997760: 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.<br>
2017-02-19 11:53:58.007657: 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.<br>
2017-02-19 11:53:58.008177: 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.<br>
2017-02-19 11:53:58.008726: 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.<br>
2017-02-19 11:53:58.009199: 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.<br>
2017-02-19 11:53:58.009591: 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.<br>
2017-02-19 11:53:58.010795: 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.<br>
2017-02-19 11:53:58.011384: 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.<br>
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.<br>
WARNING:tensorflow:From C:\Users\John\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.<br>
Instructions for updating:<br>
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.<br>
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.</p>
<hr>
<p dir="auto">(program exited with code: 0)</p>
<h3 dir="auto">What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?</h3>
<p dir="auto">None</p>
<h3 dir="auto">Environment info</h3>
<p dir="auto">Operating System: Windows 10<br>
Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)] on win32</p>
<p dir="auto">Installed version of CUDA and cuDNN:<br>
???</p>
<p dir="auto">If installed from binary pip package, provide:</p>
<ol dir="auto">
<li>
<p dir="auto">A link to the pip package you installed:<br>
pip install --upgrade <a href="https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0rc2-cp35-cp35m-win_amd64.whl" rel="nofollow">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0rc2-cp35-cp35m-win_amd64.whl</a> (to fix the BestSplits" error)</p>
</li>
<li>
<p dir="auto">The output from <code class="notranslate">python -c "import tensorflow; print(tensorflow.__version__)"</code>.<br>
1.0.0-rc2</p>
</li>
</ol>
<p dir="auto">Please go to Stack Overflow for help and support:</p>
<p dir="auto"><a href="https://stackoverflow.com/questions/tagged/tensorflow" rel="nofollow">https://stackoverflow.com/questions/tagged/tensorflow</a></p>
<p dir="auto">If you open a GitHub issue, here is our policy:</p>
<ol dir="auto">
<li>It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead).</li>
<li>The form below must be filled out.</li>
<li>It shouldn't be a TensorBoard issue. Those go <a href="https://github.com/tensorflow/tensorboard/issues">here</a>.</li>
</ol>
<p dir="auto"><strong>Here's why we have that policy</strong>: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.</p>
<hr>
<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>:</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>:</li>
<li><strong>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device</strong>:</li>
<li><strong>TensorFlow installed from (source or binary)</strong>:</li>
<li><strong>TensorFlow version (use command below)</strong>:</li>
<li><strong>Python version</strong>:</li>
<li><strong>Bazel version (if compiling from source)</strong>:</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>:</li>
<li><strong>CUDA/cuDNN version</strong>:</li>
<li><strong>GPU model and memory</strong>:</li>
<li><strong>Exact command to reproduce</strong>:</li>
</ul>
<p dir="auto">You can collect some of this information using our environment capture script:</p>
<p dir="auto"><a href="https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh">https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh</a></p>
<p dir="auto">You can obtain the TensorFlow version with</p>
<p dir="auto">python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"</p>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">On imp</p>
<h3 dir="auto">Source code / logs</h3>
<p dir="auto">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.</p>