class_docstr: Strongly Convex BinaryCrossentropy loss using l2 weight regularization.
class_docstr_tok: ["Strongly","Convex","BinaryCrossentropy","loss","using","l2","weight","regularization","."]
docstr: Return l2 loss using 0.5*reg_lambda as the l2 term (as desired).
  
  L2 regularization is required for this loss function to be strongly convex.
  
  Returns:
    The L2 regularizer layer for this loss function, with regularizer constant
    set to half the 0.5 * reg_lambda.
docstr_tok: ["Return","l2","loss","using","0.5","*","reg_lambda","as","the","l2","term","(","as","desired",")",".","L2","regularization","is","required","for","this","loss","function","to","be","strongly","convex",".","Returns",":","The","L2","regularizer","layer","for","this","loss","function",",","with","regularizer","constant","set","to","half","the","0.5","*","reg_lambda","."]
code: def kernel_regularizer(self):
      """Return l2 loss using 0.5*reg_lambda as the l2 term (as desired).
  
      L2 regularization is required for this loss function to be strongly convex.
  
      Returns:
        The L2 regularizer layer for this loss function, with regularizer constant
        set to half the 0.5 * reg_lambda.
      """
      return L1L2(l2=self.reg_lambda / 2)
  
imports: import tensorflow.compat.v1
  from tensorflow.compat.v1.python.framework import ops
  from tensorflow.compat.v1.python.keras import losses
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow.compat.v1.python.keras.utils import losses_utils
  from tensorflow.compat.v1.python.platform import tf_logging
label_desc: Use image node_id 7 for calling the StrongConvexBinaryCrossentropy obj's underlying member method code with example usage: obj.kernel_regularizer() and returns: L1L2