title: Is there a tensorflow equivalent to np.empty?
body: <p>Numpy has this helper function, <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.empty.html" rel="noreferrer">np.empty</a>, which will: </p>
  
  <blockquote>
    <p>Return a new array of given shape and type, without initializing entries.</p>
  </blockquote>
  
  <p>I find it pretty useful when I want to create a tensor using <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.empty.html" rel="noreferrer">tf.concat</a> since: </p>
  
  <blockquote>
    <p>The number of dimensions of the input tensors must match, and all dimensions except axis must be equal.</p>
  </blockquote>
  
  <p>So it comes in handy to start with an empty tensor of an expected shape. Is there any way to achieve this in tensorflow?</p>
  
  <p>[edit]</p>
  
  <p>A simplified example of why I want this</p>
  
  <pre><code>    netInput = np.empty([0, 4])
      netTarget = np.empty([0, 4])
      inputWidth = 2
  
      for step in range(data.shape.as_list()[-2]-frames_width-1):
          netInput = tf.concat([netInput, data[0, step:step + frames_width, :]], -2)
          target = tf.concat([target, data[0, step + frames_width + 1:step + frames_width + 2, :]], -2)
  </code></pre>
  
  <p>In this example, if netInput or netTarget are initialized, I'll be concatenating an extra example with that initialization. And to initialize them with the first value, I need to hack the loop. Nothing mayor, I just wondered if there is a 'tensorflow' way to solve this.</p>