lrn2d.py 文件源码

def call(self, x, mask=None):
        X = x
        half_n = self.n // 2
        input_sqr = K.square(X)
        if K._BACKEND == 'theano':
            b, ch, r, c = X.shape
            extra_channels = T.alloc(0., b, ch + 2*half_n, r, c)
            input_sqr = T.set_subtensor(
                extra_channels[:, half_n:half_n+ch, :, :], input_sqr)
        elif K._BACKEND == 'tensorflow':
            b, ch, r, c = K.int_shape(X)
            up_dims = tf.pack([tf.shape(X)[0], half_n, r, c])
            up = tf.fill(up_dims, 0.0)
            middle = input_sqr
            down_dims = tf.pack([tf.shape(X)[0], half_n, r, c])
            down = tf.fill(down_dims, 0.0)
            input_sqr = K.concatenate([up, middle, down], axis=1)
        scale = self.k
        norm_alpha = self.alpha / self.n
        for i in range(self.n):
            scale += norm_alpha * input_sqr[:, i:i+ch, :, :]
        scale = scale ** self.beta
        result = X / scale
        return result