test_huber_loss.py 文件源码

def check_forward(self, x_data, t_data):
        x = chainer.Variable(x_data)
        t = chainer.Variable(t_data)
        loss = functions.huber_loss(x, t, delta=1)
        self.assertEqual(loss.data.dtype, numpy.float32)
        loss_value = cuda.to_cpu(loss.data)

        diff_data = cuda.to_cpu(x_data) - cuda.to_cpu(t_data)
        expected_result = numpy.zeros(self.shape)
        mask = numpy.abs(diff_data) < 1
        expected_result[mask] = 0.5 * diff_data[mask]**2
        expected_result[~mask] = numpy.abs(diff_data[~mask]) - 0.5
        loss_expect = numpy.sum(expected_result, axis=1)
        gradient_check.assert_allclose(loss_value, loss_expect)