title: How to mix categorical, discrete and continuous data as input in tensorflow?
body: <p>I am new to tensorflow. I have a dataset that has continuous, discrete and categorical values. The sample data is as follows:</p>
  
  <pre><code>     col1    col2    col3  col4  col5  col6  Class
  0    22    23.40   45.60  11    1.0   0.0    0.0
  1   346    67.40  235.60  23    1.0   1.0    0.0
  2    22    67.34  364.66  17    0.0   0.0    1.0
  3  1231   124.44  213.89  14    1.0   0.0    1.0
  </code></pre>
  
  <p>col1 and col4 are discrete variables.
  col2 and col3 are continuous variables.
  col5 and col6 are categorical variables. 
  Class is the target variable. </p>
  
  <p>I was wondering if I can pass along the above data directly as input to the placeholder <code>X</code>.</p>
  
  <pre><code>X = tf.placeholder(tf.float32, [None, numFeatures])
  </code></pre>
  
  <p>I do not have to apply <code>tf.one_hot</code>, correct? Since my categorical variables are binary.</p>
  
  <p>How does tensorflow detect that col5 and col6 are categorical variables?</p>
  
  <p>Any help would be appreciated. Thank you!</p>