  import keras.backend as K
  from keras.layers import Lambda, concatenate
  import numpy as np
  import tensorflow as tf
  from tensorflow.contrib.distributions import Beta
  
  from .normalization import InstanceNormalization
  if K.backend() == "plaidml.keras.backend":
      from plaidml.op import extract_image_patches
  else:
 
</s> remove from keras.layers import ZeroPadding2D
 </s> add from keras.layers.pooling import _GlobalPooling2D </s> remove from lib.model.losses import DSSIMObjective, PenalizedLoss
 </s> add from lib.model.losses import DSSIMObjective, PenalizedLoss, gradient_loss
from lib.model.losses import generalized_loss, l_inf_norm, gmsd_loss </s> remove         a=1.999999 (lim as a->2), c=1.0/255.0 will give L2 / RMSE loss
 </s> add         a=1.999999 (lim as a->2), beta=1.0/255.0 will give L2 / RMSE loss </s> remove         coverage = self.get_coverage(image)
 </s> add         coverage = self.get_coverage(image) // 2
from tensorflow.distributions import Beta
  import keras.backend as K
  from keras.layers import Lambda, concatenate
  import numpy as np
  import tensorflow as tf
from tensorflow.distributions import Beta
  
  from .normalization import InstanceNormalization
  if K.backend() == "plaidml.keras.backend":
      from plaidml.op import extract_image_patches
  else:
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