Remove LRN2D layer.

Author: fchollet

keras/layers/normalization.py

@@ -80,44 +80,3 @@ def get_config(self):
                   "momentum": self.momentum}
         base_config = super(BatchNormalization, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
-
-
-class LRN2D(Layer):
-    """
-    This code is adapted from pylearn2.
-    License at: https://github.com/lisa-lab/pylearn2/blob/master/LICENSE.txt
-    """
-
-    def __init__(self, alpha=1e-4, k=2, beta=0.75, n=5, **kwargs):
-        if n % 2 == 0:
-            raise NotImplementedError("LRN2D only works with odd n. n provided: " + str(n))
-        super(LRN2D, self).__init__(**kwargs)
-        self.alpha = alpha
-        self.k = k
-        self.beta = beta
-        self.n = n
-
-    def get_output(self, train):
-        X = self.get_input(train)
-        b, ch, r, c = K.shape(X)
-        half_n = self.n // 2
-        input_sqr = K.square(X)
-        extra_channels = K.zeros((b, ch + 2 * half_n, r, c))
-        input_sqr = K.concatenate([extra_channels[:, :half_n, :, :],
-                                   input_sqr,
-                                   extra_channels[:, half_n + ch:, :, :]],
-                                  axis=1)
-        scale = self.k
-        for i in range(self.n):
-            scale += self.alpha * input_sqr[:, i:i + ch, :, :]
-        scale = scale ** self.beta
-        return X / scale
-
-    def get_config(self):
-        config = {"name": self.__class__.__name__,
-                  "alpha": self.alpha,
-                  "k": self.k,
-                  "beta": self.beta,
-                  "n": self.n}
-        base_config = super(LRN2D, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))