title: Tensorflow Logistic Regression
body: <p>I am trying to implement: <a href="https://www.tensorflow.org/versions/r0.11/tutorials/wide/index.html" rel="nofollow">https://www.tensorflow.org/versions/r0.11/tutorials/wide/index.html</a>
  on my dataset.</p>
  
  <p>I am basically trying to do a binary classification (0 or 1) based on some continuous and categorical features.</p>
  
  <p>NaNs were removed, new features created:</p>
  
  <pre><code>square_feet = tf.contrib.layers.real_valued_column("square_feet")
  guests_included = tf.contrib.layers.real_valued_column("guests_included")
  security_deposit = tf.contrib.layers.real_valued_column("security_deposit")
  cleaning_fee = tf.contrib.layers.real_valued_column("cleaning_fee")
  extra_people = tf.contrib.layers.real_valued_column("extra_people")
  </code></pre>
  
  <p>and</p>
  
  <pre><code>neighbourhood_group_cleansed = tf.contrib.layers.sparse_column_with_keys(column_name="neighbourhood_group_cleansed", keys=['Bronx', 'Queens', 'Staten Island', 'Brooklyn', 'Manhattan'])
  
  host_response_time = tf.contrib.layers.sparse_column_with_keys(column_name="host_response_time", keys=['within an hour', 'within a few hours', 'within a day', 'a few days or more'])
  </code></pre>
  
  <p>-- I got more a lot more features, but I think this conveys the gist.</p>
  
  <p>I copied those functions:</p>
  
  <pre><code>def input_fn(df):
      #  Creates a dictionary mapping from each continuous feature column name (k) to
      #  the values of that column stored in a constant Tensor.
      continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
      #  Creates a dictionary mapping from each categorical feature column name (k)
      #  to the values of that column stored in a tf.SparseTensor.
      categorical_cols = {k: tf.SparseTensor(indices=[[i, 0] for i in range(df[k].size)], values=df[k].values, shape=[df[k].size, 1]) for k in CATEGORICAL_COLUMNS}
      #  Merges the two dictionaries into one.
      feature_cols = dict(continuous_cols.items() + categorical_cols.items())
      #  Converts the label column into a constant Tensor.
      label = tf.constant(df[LABEL_COLUMN].values)
      #  Returns the feature columns and the label.
      return feature_cols, label
  
  
  def train_input_fn():
      return input_fn(df_train)
  
  
  def eval_input_fn():
      return input_fn(df_test)
  </code></pre>
  
  <p>Which will be later used by </p>
  
  <pre><code>model_dir = tempfile.mkdtemp()
  m = tf.contrib.learn.LinearClassifier(feature_columns=FEATURE_COLUMNS, model_dir=model_dir)
  m.fit(input_fn=train_input_fn, steps=200)
  </code></pre>
  
  <p>If I do a bit of debugging does the function <strong>input_fn()</strong> return a valid <strong>dict</strong> and the <strong>classification</strong>.
  However, I get this error:</p>
  
  <pre><code>/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.pyc in check_feature_columns(feature_columns)
      510   seen_keys = set()
      511   for f in feature_columns:
  --&gt; 512     key = f.key
      513     if key in seen_keys:
      514       raise ValueError('Duplicate feature column key found for column: {}. '
  AttributeError: 'str' object has no attribute 'key'
  </code></pre>
  
  <p>Debugging output of <strong>feature_cols</strong> (only an excerpt):</p>
  
  <pre><code>'square_feet': &lt;tf.Tensor 'Const_15:0' shape=(10000,) dtype=float64&gt;,
  'guests_included': &lt;tf.Tensor 'Const_16:0' shape=(10000,) dtype=float64&gt;,
  'security_deposit': &lt;tf.Tensor 'Const_17:0' shape=(10000,) dtype=float64&gt;,
  'cleaning_fee': &lt;tf.Tensor 'Const_18:0' shape=(10000,) dtype=float64&gt;,
  ..
  </code></pre>
  
  <p>and for <strong>label</strong></p>
  
  <pre><code>&lt;tf.Tensor 'Const_27:0' shape=(10000,) dtype=int64&gt;
  </code></pre>
  
  <p>and for <strong>df[LABEL_COLUMN].values</strong>:</p>
  
  <pre><code>array([1, 1, 1, ..., 1, 1, 1])
  </code></pre>
  
  <p>Help, hints, tips much appreciated. Those are my first steps with Tensorflow and I don't how to proceed or to further troubleshoot the error.</p>
  
  <p>Thank you!</p>
  
  <p>--- Update ---</p>
  
  <p>I tried to use</p>
  
  <pre><code>import tensorflow.contrib.learn.python.learn as learn
  </code></pre>
  
  <p>and now on a DNN classifier, just on the continuous columns </p>
  
  <pre><code>classifier = learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=2, feature_columns=CONTINUOUS_COLUMNS)
  
  classifier.fit(df_train[CONTINUOUS_COLUMNS], df_train['classification'], steps=200, batch_size=32)
  </code></pre>
  
  <p>and get the same error</p>
  
  <pre><code>/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.pyc in check_feature_columns(feature_columns)
      510   seen_keys = set()
      511   for f in feature_columns:
  --&gt; 512     key = f.key
      513     if key in seen_keys:
      514       raise ValueError('Duplicate feature column key found for column: {}. '
  
  AttributeError: 'str' object has no attribute 'key'
  </code></pre>