title: how to implement mix loss function of ms-ssim and l1 in tensorflow
body: <p>Based on the below paper : <a href="https://arxiv.org/abs/1511.08861" rel="nofollow noreferrer">https://arxiv.org/abs/1511.08861</a></p>
  
  <p>I want to implement a mix loss of ms-ssim and l1 in tensorflow. 
  Based on the nvidia implementation for Caffe at <a href="https://github.com/NVlabs/PL4NN/blob/master/src/loss.py" rel="nofollow noreferrer">https://github.com/NVlabs/PL4NN/blob/master/src/loss.py</a></p>
  
  <p>The loss is mathematically represented as :
  Loss= α*(MS-ssim)+(1-α)*(Gσ) *(l1_norm)</p>
  
  <p>where α is a value emperically set and <strong>G</strong> stands for Gaussian filter with standard deviation 'σ'.</p>
  
  <p>According to the caffe implementation it calculates forward and backward computation but as tensorflow handles the backpropagation how can this be implemented in tensorflow.</p>
  
  <p>Is there any open source work on the mix loss fucntion for image processing?</p>