(self, layer, I, O, denom, alpha=None, beta=None, ascale=1, bpower=1)
Forward propagate pooling layer.

        Arguments:
            layer (PoolLayer): The pool layer object, different backends have
                               different pool layers.
            I (Tensor): Input tensor.
            O (Tensor): output tensor.
            denom (Tensor): denominator tensor, stores the result of the squared pooling/contrast
            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)
Forward propagate pooling layer.
Forward propagate pooling layer .
def fprop_lrn(self, layer, I, O, denom, alpha=None, beta=None, ascale=1, bpower=1):
        """
        Forward propagate pooling layer.

        Arguments:
            layer (PoolLayer): The pool layer object, different backends have
                               different pool layers.
            I (Tensor): Input tensor.
            O (Tensor): output tensor.
            denom (Tensor): denominator tensor, stores the result of the squared pooling/contrast
            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 == O.size

        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

        array_I = I._tensor.reshape(layer.dimI)
        array_O = O._tensor.reshape(layer.dimO)  # _tensor to write to
        # although we can calculate directly into O, keeping denom around is useful for bprop
        array_d = denom._tensor.reshape(layer.dimO)  # _tensor to write to

        for k in range(K):
            sliceC, _ = layer.kSlice[k]
            _ascale = ascale / J
            for m in range(M):
                sliceD, _ = layer.mSlice[m]
                for p in range(P):
                    sliceH, _ = layer.pSlice[p]
                    for q in range(Q):
                        sliceW, _ = layer.qSlice[q]
                        sliceI = array_I[sliceC, sliceD, sliceH, sliceW, :].reshape(-1, N)
                        array_d[k, m, p, q, :] = 1 + _ascale * np.sum(np.square(sliceI), axis=0)

        array_O[:] = array_I * np.power(array_d, -bpower)
def fprop_lrn ( self , layer , I , O , denom , alpha = None , beta = None , ascale = 1 , bpower = 1 ) : assert layer . sizeI == I . size assert layer . sizeO == O . size 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 array_I = I . _tensor . reshape ( layer . dimI ) array_O = O . _tensor . reshape ( layer . dimO ) # _tensor to write to # although we can calculate directly into O, keeping denom around is useful for bprop array_d = denom . _tensor . reshape ( layer . dimO ) # _tensor to write to for k in range ( K ) : sliceC , _ = layer . kSlice [ k ] _ascale = ascale / J for m in range ( M ) : sliceD , _ = layer . mSlice [ m ] for p in range ( P ) : sliceH , _ = layer . pSlice [ p ] for q in range ( Q ) : sliceW , _ = layer . qSlice [ q ] sliceI = array_I [ sliceC , sliceD , sliceH , sliceW , : ] . reshape ( - 1 , N ) array_d [ k , m , p , q , : ] = 1 + _ascale * np . sum ( np . square ( sliceI ) , axis = 0 ) array_O [ : ] = array_I * np . power ( array_d , - bpower )