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 = cupy.log(x)
    if self.cache_score:
      self.y = x
    if getattr(self, 'normalize', True):
      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 ? 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,