weighted_cross_entropy.py 文件源码

def backward_gpu(self, inputs, grad_outputs):
        cupy = cuda.cupy
        x, t = inputs
        gloss = grad_outputs[0]
        n_unit = t.size // len(t)
        coeff = gloss * self._coeff
        gx = cuda.elementwise(
            'T y, S t, raw T coeff, S n_channel, S n_unit, raw T weights',
            'T gx',
            '''
               const int c = (i / n_unit % n_channel);
               gx = ((t == -1) || (c != t)) ? 0 : ((weights[t]*coeff[0]) / max(y, 1e-5));
            ''',
            'crossent_bwd')(self.y, cupy.expand_dims(t, 1), -coeff, x.shape[1], n_unit, self.weights.reduced_view())
        return gx, None