class_docstr: The negative of the mean cosine proximity between predictions and targets.
  The cosine proximity is defined as below:
      x'(i) = x(i) / sqrt(max(sum(x(i)^2), 1e-12))
      y'(i) = y(i) / sqrt(max(sum(x(i)^2), 1e-12))
      cosine_proximity(x, y) = mean(-1 * x'(i) * y'(i))
  
  >>> loss = CosineProximity()
  creating: createZooKerasCosineProximity
class_docstr_tok: ["The","negative","of","the","mean","cosine","proximity","between","predictions","and","targets",".","The","cosine","proximity","is","defined","as","below",":","x","'","(","i",")","=","x","(","i",")","\/","sqrt","(","max","(","sum","(","x","(","i",")","^2",")",",","1e-12",")",")","y","'","(","i",")","=","y","(","i",")","\/","sqrt","(","max","(","sum","(","x","(","i",")","^2",")",",","1e-12",")",")","cosine_proximity","(","x",",","y",")","=","mean","(","-1","*","x","'","(","i",")","*","y","'","(","i",")",")",">",">",">","loss","=","CosineProximity","(",")","creating",":","createZooKerasCosineProximity"]
code: def __init__(self, bigdl_type="float"):
      super(CosineProximity, self).__init__(None, bigdl_type)
  
imports: import sys
  from zoo.pipeline.api.keras.base import ZooKerasCreator
  from bigdl.nn.criterion import Criterion
  from bigdl.util.common import JTensor
label_desc: Use image node_id 1 to create a new CosineProximity object from inherited base classes: LossFunction with example: obj = CosineProximity(bigdl_type)