(self, layer, I, O, E, delta, denom,
                  alpha=1., beta=0., ascale=1., bpower=1., repeat=1)
Backward propagate pooling layer.

        Arguments:
            layer (PoolLayer): The pool layer object. Different backends have
                               different pool layers.
            I (Tensor): Input tensor.
            E (Tensor): Error tensor.
            delta (Tensor): Gradient tensor (delta)
            denom (Tensor): denominator tensor computed during bprop
            ascale (float): scaling parameter (alpha) to multiply the pooled sum (1.25e-5 in AK)
            bpower (float): exponential parameter (beta) to raise denominator by (0.75 in AK)
Backward propagate pooling layer.
Backward propagate pooling layer .
def bprop_lrn(self, layer, I, O, E, delta, denom,
                  alpha=1., beta=0., ascale=1., bpower=1., repeat=1):
        """
        Backward propagate pooling layer.

        Arguments:
            layer (PoolLayer): The pool layer object. Different backends have
                               different pool layers.
            I (Tensor): Input tensor.
            E (Tensor): Error tensor.
            delta (Tensor): Gradient tensor (delta)
            denom (Tensor): denominator tensor computed during bprop
            ascale (float): scaling parameter (alpha) to multiply the pooled sum (1.25e-5 in AK)
            bpower (float): exponential parameter (beta) to raise denominator by (0.75 in AK)
        """

        assert layer.sizeI == I.size
        assert layer.sizeO == E.size
        assert layer.sizeI == delta.size
        op = layer.op

        J, T, R, S = layer.JTRS
        C, D, H, W, N = layer.dimI
        K, M, P, Q, N = layer.dimO
        pad_c, pad_d, pad_h, pad_w = layer.padding
        str_c, str_d, str_h, str_w = layer.strides
        WH = W * H
        DWH = D * W * H

        kernel_args = layer.bprop_kernel
        shared = layer.bprop_lut_size

        self._execute_lrn(layer, I, O, E, delta, denom,
                          alpha, beta, ascale, bpower, kernel_args, shared, repeat)
def bprop_lrn ( self , layer , I , O , E , delta , denom , alpha = 1. , beta = 0. , ascale = 1. , bpower = 1. , repeat = 1 ) : assert layer . sizeI == I . size assert layer . sizeO == E . size assert layer . sizeI == delta . size op = layer . op J , T , R , S = layer . JTRS C , D , H , W , N = layer . dimI K , M , P , Q , N = layer . dimO pad_c , pad_d , pad_h , pad_w = layer . padding str_c , str_d , str_h , str_w = layer . strides WH = W * H DWH = D * W * H kernel_args = layer . bprop_kernel shared = layer . bprop_lut_size self . _execute_lrn ( layer , I , O , E , delta , denom , alpha , beta , ascale , bpower , kernel_args , shared , repeat )