class_docstr: A loss that measures the Binary Cross Entropy between the target and the output
  
  # Arguments
  size_average: Boolean. Whether losses are averaged over observations for each
              mini-batch. Default is True. If False, the losses are instead
              summed for each mini-batch.
  weights: weights over the input dimension
  
  >>> loss = BinaryCrossEntropy()
  creating: createZooKerasBinaryCrossEntropy
class_docstr_tok: ["A","loss","that","measures","the","Binary","Cross","Entropy","between","the","target","and","the","output","#","Arguments","size_average",":","Boolean",".","Whether","losses","are","averaged","over","observations","for","each","mini-batch",".","Default","is","True",".","If","False",",","the","losses","are","instead","summed","for","each","mini-batch",".","weights",":","weights","over","the","input","dimension",">",">",">","loss","=","BinaryCrossEntropy","(",")","creating",":","createZooKerasBinaryCrossEntropy"]
code: def __init__(
      self, weights=None, size_average=True, bigdl_type="float"
  ):
      super(BinaryCrossEntropy, self).__init__(
          None, bigdl_type, JTensor.from_ndarray(weights), size_average
      )
  
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 BinaryCrossEntropy object from inherited base classes: LossFunction with example: obj = BinaryCrossEntropy(weights, size_average, bigdl_type)