How to implement pixel-wise classification for scene labeling in TensorFlow? : <p>I am working on a deep learning model using <strong>Google's TensorFlow</strong>. The model should be used to <strong>segment and label scenes</strong>. </p>

<ol>
<li>I am using the <strong>SiftFlow dataset</strong> which has <em>33 semantic
classes</em> and <em>images with 256x256 pixels</em>.  </li>
<li>As a result, at my final layer using convolution and deconvolution I arrive at the following tensor(array) <em>[256, 256, 33]</em>. </li>
<li>Next I would like to
apply <em>Softmax</em> and compare the results to a semantic label of size
<em>[256, 256]</em>.</li>
</ol>

<p><strong>Questions:</strong>
Should I apply mean averaging or argmax to my final layer so its shape becomes <em>[256,256,1]</em> and then loop through each pixel and classify as if I were classying <em>256x256</em> instances? If the answer is yes, how, if not, what other options?</p>
