softmax_cross_entropy.py 文件源码

def backward_gpu(self, inputs, grad_outputs):
        cupy = cuda.cupy
        x, t = inputs
        if hasattr(self, 'y'):
            y = self.y
        else:
            y = log_softmax._log_softmax(x, self.use_cudnn)
            cupy.exp(y, out=y)
        gloss = grad_outputs[0]
        n_unit = t.size // len(t)
        coeff = gloss * self._coeff
        if self.class_weight is None:
            gx = cuda.elementwise(
                'T y, S t, raw T coeff, S n_channel, S n_unit',
                'T gx',
                '''
                    const int c = (i / n_unit % n_channel);
                    gx = (t == -1) ? 0 : (coeff[0] * (y - (c == t)));
                ''',
                'softmax_crossent_bwd')(
                    y, cupy.expand_dims(t, 1), coeff, x.shape[1], n_unit)
        else:
            gx = cuda.elementwise(
                'T y, raw T w, S t, raw T coeff, S n_channel, S n_unit',
                'T gx',
                '''
                    const int c = (i / n_unit % n_channel);
                    gx = t == -1 ? 0 : coeff[0] * (y - (c == t)) * w[t];
                ''',
                'softmax_crossent_bwd')(
                    y, self.class_weight, cupy.expand_dims(t, 1), coeff,
                    x.shape[1], n_unit)
        return gx, None