<h3 dir="auto">What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?</h3>
<p dir="auto">Similar issue here:</p>
<ol dir="auto">
<li><a href="http://stackoverflow.com/questions/39249704/tensorflow-valueerror-duplicate-feature-column-key-found-for-column" rel="nofollow">http://stackoverflow.com/questions/39249704/tensorflow-valueerror-duplicate-feature-column-key-found-for-column</a></li>
<li>have tried example at<br>
<a href="https://www.tensorflow.org/versions/master/tutorials/monitors/index.html" rel="nofollow">https://www.tensorflow.org/versions/master/tutorials/monitors/index.html</a><br>
still exhibits same issue</li>
<li>I will look at github deltas in 0.10.0rc0 between 1 above head and see if same fix applied to tf.contrib.learn</li>
</ol>
<h3 dir="auto">Environment info</h3>
<p dir="auto">Operating System:<br>
OS-X 10.11.6 Python 3.5 vi Homebrew<br>
Installed version of CUDA and cuDNN:<br>
N/A</p>
<h2 dir="auto">installed from binary pip package:</h2>
<ol dir="auto">
<li>link to the pip package you installed:<br>
<a href="https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0rc0-py3-none-any.whl" rel="nofollow">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.10.0rc0-py3-none-any.whl</a></li>
<li>The output from <code class="notranslate">python -c "import tensorflow; print(tensorflow.__version__)"</code>.<br>
0.10.0rc0</li>
</ol>
<h3 dir="auto">minimal reproducible example (</h3>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="
import tensorflow as tf
import numpy as np

# Data sets
IRIS_TRAINING = &quot;iris_training.csv&quot;
IRIS_TEST = &quot;iris_test.csv&quot;

# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
                                                       target_dtype=np.int)
test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
                                                   target_dtype=np.int)

# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column(&quot;&quot;, dimension=4)]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10, 20, 10],
                                            n_classes=3,
                                            model_dir=&quot;/tmp/iris_model&quot;)

# Fit model.
classifier.fit(x=training_set.data,
               y=training_set.target,
               steps=2000)

# Evaluate accuracy.
accuracy_score = classifier.evaluate(x=test_set.data,
                                     y=test_set.target)[&quot;accuracy&quot;]
print('Accuracy: {0:f}'.format(accuracy_score))

# Classify two new flower samples.
new_samples = np.array(
    [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = classifier.predict(new_samples)
print('Predictions: {}'.format(str(y)))"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span>
<span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span>

<span class="pl-c"># Data sets</span>
<span class="pl-v">IRIS_TRAINING</span> <span class="pl-c1">=</span> <span class="pl-s">"iris_training.csv"</span>
<span class="pl-v">IRIS_TEST</span> <span class="pl-c1">=</span> <span class="pl-s">"iris_test.csv"</span>

<span class="pl-c"># Load datasets.</span>
<span class="pl-s1">training_set</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">contrib</span>.<span class="pl-s1">learn</span>.<span class="pl-s1">datasets</span>.<span class="pl-s1">base</span>.<span class="pl-en">load_csv</span>(<span class="pl-s1">filename</span><span class="pl-c1">=</span><span class="pl-v">IRIS_TRAINING</span>,
                                                       <span class="pl-s1">target_dtype</span><span class="pl-c1">=</span><span class="pl-s1">np</span>.<span class="pl-s1">int</span>)
<span class="pl-s1">test_set</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">contrib</span>.<span class="pl-s1">learn</span>.<span class="pl-s1">datasets</span>.<span class="pl-s1">base</span>.<span class="pl-en">load_csv</span>(<span class="pl-s1">filename</span><span class="pl-c1">=</span><span class="pl-v">IRIS_TEST</span>,
                                                   <span class="pl-s1">target_dtype</span><span class="pl-c1">=</span><span class="pl-s1">np</span>.<span class="pl-s1">int</span>)

<span class="pl-c"># Specify that all features have real-value data</span>
<span class="pl-s1">feature_columns</span> <span class="pl-c1">=</span> [<span class="pl-s1">tf</span>.<span class="pl-s1">contrib</span>.<span class="pl-s1">layers</span>.<span class="pl-en">real_valued_column</span>(<span class="pl-s">""</span>, <span class="pl-s1">dimension</span><span class="pl-c1">=</span><span class="pl-c1">4</span>)]

<span class="pl-c"># Build 3 layer DNN with 10, 20, 10 units respectively.</span>
<span class="pl-s1">classifier</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">contrib</span>.<span class="pl-s1">learn</span>.<span class="pl-v">DNNClassifier</span>(<span class="pl-s1">feature_columns</span><span class="pl-c1">=</span><span class="pl-s1">feature_columns</span>,
                                            <span class="pl-s1">hidden_units</span><span class="pl-c1">=</span>[<span class="pl-c1">10</span>, <span class="pl-c1">20</span>, <span class="pl-c1">10</span>],
                                            <span class="pl-s1">n_classes</span><span class="pl-c1">=</span><span class="pl-c1">3</span>,
                                            <span class="pl-s1">model_dir</span><span class="pl-c1">=</span><span class="pl-s">"/tmp/iris_model"</span>)

<span class="pl-c"># Fit model.</span>
<span class="pl-s1">classifier</span>.<span class="pl-en">fit</span>(<span class="pl-s1">x</span><span class="pl-c1">=</span><span class="pl-s1">training_set</span>.<span class="pl-s1">data</span>,
               <span class="pl-s1">y</span><span class="pl-c1">=</span><span class="pl-s1">training_set</span>.<span class="pl-s1">target</span>,
               <span class="pl-s1">steps</span><span class="pl-c1">=</span><span class="pl-c1">2000</span>)

<span class="pl-c"># Evaluate accuracy.</span>
<span class="pl-s1">accuracy_score</span> <span class="pl-c1">=</span> <span class="pl-s1">classifier</span>.<span class="pl-en">evaluate</span>(<span class="pl-s1">x</span><span class="pl-c1">=</span><span class="pl-s1">test_set</span>.<span class="pl-s1">data</span>,
                                     <span class="pl-s1">y</span><span class="pl-c1">=</span><span class="pl-s1">test_set</span>.<span class="pl-s1">target</span>)[<span class="pl-s">"accuracy"</span>]
<span class="pl-en">print</span>(<span class="pl-s">'Accuracy: {0:f}'</span>.<span class="pl-en">format</span>(<span class="pl-s1">accuracy_score</span>))

<span class="pl-c"># Classify two new flower samples.</span>
<span class="pl-s1">new_samples</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-en">array</span>(
    [[<span class="pl-c1">6.4</span>, <span class="pl-c1">3.2</span>, <span class="pl-c1">4.5</span>, <span class="pl-c1">1.5</span>], [<span class="pl-c1">5.8</span>, <span class="pl-c1">3.1</span>, <span class="pl-c1">5.0</span>, <span class="pl-c1">1.7</span>]], <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">float</span>)
<span class="pl-s1">y</span> <span class="pl-c1">=</span> <span class="pl-s1">classifier</span>.<span class="pl-en">predict</span>(<span class="pl-s1">new_samples</span>)
<span class="pl-en">print</span>(<span class="pl-s">'Predictions: {}'</span>.<span class="pl-en">format</span>(<span class="pl-en">str</span>(<span class="pl-s1">y</span>)))</pre></div>
<h3 dir="auto">What other attempted solutions have you tried?</h3>
<p dir="auto">Referenced head example</p>
<h3 dir="auto">Logs or other output that would be helpful</h3>
<p dir="auto">WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False)<br>
WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)<br>
WARNING:tensorflow:Given features: Tensor("input:0", shape=(?, 4), dtype=float32), required signatures: TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False).<br>
WARNING:tensorflow:Given targets: Tensor("output:0", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).</p>
<p dir="auto">Consider allowing tensorflow.select to accept scalar conditions.  I.e., <code class="notranslate">tf.select(condition, t, e, name=None)</code> with <code class="notranslate">condition</code> a scalar, and <code class="notranslate">t</code>, <code class="notranslate">e</code> having the same shape.</p>