code: 
  import keras
  
  import numpy as np
  import math
  
  
  class PriorProbability(keras.initializers.Initializer):
      """
      Initializer applies a prior probability.
      """
  
      def __init__(self, probability=0.01):
          self.probability = probability
  
      def get_config(self):
          return {
              'probability': self.probability
          }
  
      def __call__(self, shape, dtype=None):
          # set bias to -log((1 - p)/p) for foregound
          result = np.ones(shape, dtype=dtype) * -math.log((1 - self.probability) / self.probability)
  
          return result
  
apis: numpy.ones