softmax_cross_entropy.py 文件源码

def forward_gpu(self, inputs):
        cupy = cuda.cupy
        x, t = inputs
        if chainer.is_debug():
            self._check_input_values(x, t)

        log_y = log_softmax._log_softmax(x, self.use_cudnn)
        if self.cache_score:
            self.y = cupy.exp(log_y)
        if self.class_weight is not None:
            shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)]
            log_y *= cupy.broadcast_to(
                self.class_weight.reshape(shape), x.shape)
        if self.normalize:
            coeff = cupy.maximum(1, (t != self.ignore_label).sum())
        else:
            coeff = max(1, len(t))
        self._coeff = cupy.divide(1.0, coeff, dtype=x.dtype)

        log_y = cupy.rollaxis(log_y, 1, log_y.ndim)
        ret = cuda.reduce(
            'S t, raw T log_y, int32 n_channel, raw T coeff', 'T out',
            't == -1 ? T(0) : log_y[_j * n_channel + t]',
            'a + b', 'out = a * -coeff[0]', '0', 'crossent_fwd'
        )(t, log_y.reduced_view(), log_y.shape[-1], self._coeff)
        return ret,