test_softmax_cross_entropy.py 文件源码

def check_forward(self, x_data, t_data, use_cudnn=True):
        x = chainer.Variable(x_data)
        t = chainer.Variable(t_data)
        loss = functions.softmax_cross_entropy(
            x, t, use_cudnn=use_cudnn, normalize=self.normalize,
            cache_score=self.cache_score)
        self.assertEqual(loss.data.shape, ())
        self.assertEqual(loss.data.dtype, self.dtype)
        self.assertEqual(hasattr(loss.creator, 'y'), self.cache_score)
        loss_value = float(cuda.to_cpu(loss.data))

        # Compute expected value
        loss_expect = 0.0
        count = 0
        x = numpy.rollaxis(self.x, 1, self.x.ndim).reshape(
            (self.t.size, self.x.shape[1]))
        t = self.t.ravel()
        for xi, ti in six.moves.zip(x, t):
            if ti == -1:
                continue
            log_z = numpy.ufunc.reduce(numpy.logaddexp, xi)
            loss_expect -= (xi - log_z)[ti]
            count += 1

        if self.normalize:
            if count == 0:
                loss_expect = 0.0
            else:
                loss_expect /= count
        else:
            loss_expect /= len(t_data)

        gradient_check.assert_allclose(
            loss_expect, loss_value, **self.check_forward_options)