title: Why isn't this Conv2d_Transpose / deconv2d returning the original input in tensorflow?
body: <pre><code>weights = tf.placeholder("float",[5,5,1,1])
  imagein = tf.placeholder("float",[1,32,32,1])
  conv = tf.nn.conv2d(imagein,weights,strides=[1,1,1,1],padding="SAME")
  deconv = tf.nn.conv2d_transpose(conv, weights, [1,32,32,1], [1,1,1,1],padding="SAME")
  
  
  dw = np.random.rand(5,5,1,1)
  noise = np.random.rand(1,32,32,1)
  
  sess = tf.InteractiveSession()
  
  convolved = conv.eval(feed_dict={imagein: noise, weights: dw})
  deconvolved = deconv.eval(feed_dict={imagein: noise, weights: dw})
  </code></pre>
  
  <p>I've been trying to figure out conv2d_transpose in order to reverse a convolution in Tensorflow. My understanding is that "deconvolved" should contain the same data as "noise" after applying a normal convolution and then its transpose, but "deconvolved" just contains some completely different image. Is there something wrong with my code, or is the theory incorrect?</p>