googlenet_LRN_def.py

class LRN(Layer):
    def __init__(self, alpha=0.0001,k=1,beta=0.75,n=5, **kwargs):
      self.alpha = alpha
      self.k = k
      self.beta = beta
      self.n = n
      super(LRN, self).__init__(**kwargs)
    
    def call(self, x, mask=None):
      b, ch, r, c = x.shape
      half_n = self.n // 2 # half the local region
      input_sqr = T.sqr(x) # square the input
      extra_channels = T.alloc(0., b, ch + 2*half_n, r, c) # make an empty tensor with zero pads along channel dimension
      input_sqr = T.set_subtensor(extra_channels[:, half_n:half_n+ch, :, :],input_sqr) # set the center to be the squared input
      scale = self.k # offset for the scale
      norm_alpha = self.alpha / self.n # normalized alpha
      for i in range(self.n):
          scale += norm_alpha * input_sqr[:, i:i+ch, :, :]
      scale = scale ** self.beta
      x = x / scale
      return x
    
    def get_config(self):
      config = {"alpha": self.alpha,
          "k": self.k,
          "beta": self.beta,
          "n": self.n}
      base_config = super(LRN, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))