title: Prevent network weights to be updated when performing backward pass
body: <p>I am trying to perform a backward pass through my network and I don't want to update my network weights of my network when, I do a backward pass.</p>
  
  <pre><code>output = net:forward(input)
  err = criterion:forward(output, label)
  df_do = criterion:backward(output, label)
  net:backward(input, df_do)
  </code></pre>
  
  <p>I'm assuming this can be done using by any of the two methods</p>
  
  <pre><code>accGradParameters(input, gradOutput, scale)
  accUpdateGradParameters(input, gradOutput, learningRate)
  </code></pre>
  
  <p>Can,I do this using the optim package in torch?</p>