              Arguments:
              x: First set of images.
              y: Second set of images.
              reducer: Function that computes 'local' averages from set of images.
                For non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]),
                and for convolutional version, this is usually tf.nn.avg_pool or
                tf.nn.conv2d with weighted-sum kernel.
              max_val: The dynamic range (i.e., the difference between the maximum
                possible allowed value and the minimum allowed value).
              compensation: Compensation factor. See above.
 
</s> remove             x: First set of images.
            y: Second set of images.
 </s> add             img1: First set of images.
            img2: Second set of images. </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         a: penalty factor. larger number give larger weight to large deviations
        c: scale factor used to adjust to the input scale (i.e. inputs of mean 1e-4 or 256)

 </s> add         alpha: penalty factor. larger number give larger weight to large deviations
        beta: scale factor used to adjust to the input scale (i.e. inputs of mean 1e-4 or 256 ) </s> remove         argument_list.append({"opts": ("-m", "--model-dir"),
 </s> add         argument_list.append({"opts": ("-tib", "--timelapse-input-B"), </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)
              For non-covolutional version, this is usually tf.reduce_mean(img1, [1, 2]),
              Arguments:
              x: First set of images.
              y: Second set of images.
              reducer: Function that computes 'local' averages from set of images.
              For non-covolutional version, this is usually tf.reduce_mean(img1, [1, 2]),
                and for convolutional version, this is usually tf.nn.avg_pool or
                tf.nn.conv2d with weighted-sum kernel.
              max_val: The dynamic range (i.e., the difference between the maximum
                possible allowed value and the minimum allowed value).
              compensation: Compensation factor. See above.
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