              E[cov_{xy}] = (1 - sum_i w_i ^ 2) Cov(X, Y).
              For SSIM measure with unbiased covariance estimators, pass as `compensation`
              argument (1 - sum_i w_i ^ 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(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
 
</s> remove               For non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]),
 </s> add               For non-covolutional version, this is usually tf.reduce_mean(img1, [1, 2]), </s> remove             #   (2 * cov_{xy} + c_2) / (cov_{xx} + cov_{yy} + c_2).
 </s> add             #   (2 * cov_{xy} + c_two) / (cov_{xx} + cov_{yy} + c_two). </s> remove             num1 = reducer(var_x * var_y, kernel) * 2.0
            den1 = reducer(tf.square(var_x) + tf.square(var_y), kernel)
            c_2 *= compensation
            c_s = (num1 - num0 + c_2) / (den1 - den0 + c_2)
 </s> add             num1 = reducer(img1 * img2, kernel) * 2.0
            den1 = reducer(tf.square(img1) + tf.square(img2), kernel)
            c_s = (num1 - num0 + c_two) / (den1 - den0 + c_two) </s> remove             luminance = (num0 + c_1) / (den0 + c_1)
 </s> add             luminance = (num0 + c_one) / (den0 + c_one) </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.
            img1: First set of images.
            img2: Second set of images.
              E[cov_{xy}] = (1 - sum_i w_i ^ 2) Cov(X, Y).
              For SSIM measure with unbiased covariance estimators, pass as `compensation`
              argument (1 - sum_i w_i ^ 2).
              Arguments:
            img1: First set of images.
            img2: Second set of images.
            img1: First set of images.
            img2: 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
keep keep keep keep replace 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