              var_g = tf.reshape(var_g, shape=[1, -1])  # For tf.nn.softmax().
              var_g = tf.nn.softmax(var_g)
              return tf.reshape(var_g, shape=[size, size, 1, 1])
  
          def _ssim_helper(var_x, var_y, max_val, kernel, compensation=1.0):
              """
              Helper function for computing SSIM.
              SSIM estimates covariances with weighted sums.  The default parameters
              use a biased estimate of the covariance:
              Suppose `reducer` is a weighted sum, then the mean estimators are
 
</s> remove             var_g = tf.square(coords)
            var_g *= -0.5 / tf.square(sigma)

            var_g = tf.reshape(var_g, shape=[1, -1]) + tf.reshape(var_g, shape=[-1, 1])
            var_g = tf.reshape(var_g, shape=[1, -1])  # For tf.nn.softmax().
            var_g = tf.nn.softmax(var_g)
            return tf.reshape(var_g, shape=[size, size, 1, 1])
 </s> add             gauss = tf.square(coords)
            gauss *= -0.5 / tf.square(sigma)
            gauss = tf.reshape(gauss, shape=[1, -1]) + tf.reshape(gauss, shape=[-1, 1])
            gauss = tf.reshape(gauss, shape=[1, -1])  # For tf.nn.softmax().
            gauss = tf.nn.softmax(gauss)
            return tf.reshape(gauss, shape=[size, size, 1, 1]) </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             luminance = (num0 + c_1) / (den0 + c_1)
 </s> add             luminance = (num0 + c_one) / (den0 + c_one) </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     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])
        def _ssim_helper(img1, img2, max_val, kernel, compensation=1.):
              var_g = tf.reshape(var_g, shape=[1, -1])  # For tf.nn.softmax().
              var_g = tf.nn.softmax(var_g)
              return tf.reshape(var_g, shape=[size, size, 1, 1])
  
        def _ssim_helper(img1, img2, max_val, kernel, compensation=1.):
              """
              Helper function for computing SSIM.
              SSIM estimates covariances with weighted sums.  The default parameters
              use a biased estimate of the covariance:
              Suppose `reducer` is a weighted sum, then the mean estimators are
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