layers.py 文件源码

def backward(self, T, mode='X'):

        if 'X' in mode and 'G' in mode:

            D = T
            X = np.squeeze  (self.X, 1)
            G = np.tensordot(D, X, ([0,2,3],[0,1,2]))
            self.accumulate(G)

        if 'X' in mode: T = uncollapse(T, self.W)
        else          : T = np.sum(T, 1)[:,None]

        O = np.zeros((T.shape[0], 1) + (self.sh[2]-self.w[2]+1, self.sh[3]-self.w[3]+1) + tuple(self.w[1:]) + T.shape[7:], dtype='float32')

        _ = ne.evaluate('T', out=O[self.S]) #O[self.S] = T
        O = unexpand(O)

        return O