      TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty for Image Restoration,
      Chengwu Lu & Hua Huang, 2014 (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
  
      Parameters:
          y_true: The predicted frames at each scale.
          y_true: The ground truth frames at each scale
  
      Return:
          The GD loss.
      """
 
</s> remove     TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty for Image Restoration,
    Chengwu Lu & Hua Huang, 2014 (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
 </s> add     TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty
    for Image Restoration, Chengwu Lu & Hua Huang, 2014
    (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf) </s> remove         The GD loss.
 </s> add         The GD loss </s> remove     Calculates the first and second order gradient difference between pixels of an image in the
    x and y dimensions. These gradients are then compared between the ground truth and the
    predicted image and the difference is taken. The difference used is a smooth L1 norm
    (approximate to MAE but differable at zero) When used as a loss, its minimization will result
    in predicted images approaching the samelevel of sharpness / blurriness as the ground truth.
 </s> add     Calculates the first and second order gradient difference between pixels of
    an image in the x and y dimensions. These gradients are then compared between
    the ground truth and the predicted image and the difference is taken. When
    used as a loss, its minimization will result in predicted images approaching
    the same level of sharpness / blurriness as the ground truth. </s> remove     mc_s_and_ssim = tf.stack(mc_s + [tf.nn.relu(ssim_per_channel)], axis=-1)
 </s> add     mcs_and_ssim = tf.stack(mc_s + [tf.nn.relu(ssim_per_channel)], axis=-1) </s> remove     ms_ssim = tf.reduce_prod(tf.pow(mc_s_and_ssim, power_factors), [-1])
 </s> add     ms_ssim = tf.reduce_prod(tf.pow(mcs_and_ssim, power_factors), [-1])
        y_true: The predicted frames at each scale
      TV+TV2 Regularization with Nonconvex Sparseness-Inducing Penalty for Image Restoration,
      Chengwu Lu & Hua Huang, 2014 (http://downloads.hindawi.com/journals/mpe/2014/790547.pdf)
  
      Parameters:
        y_true: The predicted frames at each scale
          y_true: The ground truth frames at each scale
  
      Return:
          The GD loss.
      """
keep keep keep keep replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
lib/model/losses.py